601 New Jersey Avenue, NW • Suite 9000 • Washington, DC 20001

(202) 220-3700 • Fax: (202) 220-3759 • www.medpac.gov
The Medicare Payment Advisory Commission (MedPAC) is an independent federal body 

established by the Balanced Budget Act of 1997 (P.L. 105–33) to advise the U.S. Congress 

on issues affecting the Medicare program. In addition to advising the Congress on payments 

to health plans participating in the Medicare Advantage program and providers in Medicare’s 

traditional fee-for-service program, MedPAC is also tasked with analyzing access to care, 

quality of care, and other issues affecting Medicare.

The Commission’s 17 members bring diverse expertise in the financing and delivery of 

health care services. Commissioners are appointed to three-year terms (subject to renewal) 

by the Comptroller General and serve part time. Appointments are staggered; the terms of 

five or six Commissioners expire each year. The Commission is supported by an executive 

director and a staff of analysts, who typically have backgrounds in economics, health policy, 

and public health.

MedPAC meets publicly to discuss policy issues and formulate its recommendations to 

the Congress. In the course of these meetings, Commissioners consider the results of staff 

research, presentations by policy experts, and comments from interested parties. (Meeting 

transcripts are available at www.medpac.gov.) Commission members and staff also seek input 

on Medicare issues through frequent meetings with individuals interested in the program, 

including staff from congressional committees and the Centers for Medicare & Medicaid 

Services (CMS), health care researchers, health care providers, and beneficiary advocates.

Two reports—issued in March and June each year—are the primary outlets for Commission 

recommendations. In addition to annual reports and occasional reports on subjects requested 

by the Congress, MedPAC advises the Congress through other avenues, including comments 

on reports and proposed regulations issued by the Secretary of the Department of Health and 

Human Services, testimony, and briefings for congressional staff. 
J U N E 2 0 0 7

Report to the Congress

Promoting Greater

Efficiency in Medicare

601 New Jersey Avenue, NW • Suite 9000 • Washington, DC 20001

(202) 220-3700 • Fax: (202) 220-3759 • www.medpac.gov

June 15, 2007

The Honorable Richard B. Cheney

President of the Senate

U.S. Capitol

Washington, DC 20510

Dear Mr. Vice President:

I am pleased to submit the Medicare Payment Advisory Commission’s June 2007 Report to the Congress: 

Promoting Greater Efficiency in Medicare. This report fulfills MedPAC’s legislative mandate to examine 

issues affecting the Medicare program and to make specific recommendations to the Congress.

The Commission’s goal is to recommend policies that promote efficiency in the Medicare program. 

In this report, we:

• describe the changing beneficiary profile in Medicare,

• recommend that an entity develop information on the comparative effectiveness of alternative therapies, 

• examine how to increase efficiency in the Medicare Advantage program,

• respond to our mandate to report on design features of a pay-for-performance system,

• discuss decreasing the number of avoidable hospital readmissions,

• respond to our mandate to recommend a new wage index system, 

• recommend a new approach for prescription drugs that can be paid for under both Part B and Part D,

• examine payment system refinements and adding quality measures for skilled nursing facilities, and

• discuss changes to physicians’ practice expense payments.

The report concludes by fulfilling our statutory obligation to analyze the Secretary of HHS’s estimate of the 

update for physician services (Appendix A of this report).

Sincerely,

Glenn Hackbarth, J.D.

Chairman

Enclosure

601 New Jersey Avenue, NW • Suite 9000

Washington, DC 20001

202-220-3700 • Fax: 202-220-3759

www.medpac.gov

Glenn M. Hackbarth, J.D., Chairman

Robert D. Reischauer, Ph.D., Vice Chairman

Mark E. Miller, Ph.D., Executive Director
 June 15, 2007

The Honorable Nancy Pelosi

Speaker of the House

U.S. House of Representatives

U.S. Capitol

Room H-232 

Washington, DC 20515

Dear Madam Speaker:

I am pleased to submit the Medicare Payment Advisory Commission’s June 2007 Report to the Congress: 

Promoting Greater Efficiency in Medicare. This report fulfills MedPAC’s legislative mandate to examine 

issues affecting the Medicare program and to make specific recommendations to the Congress.

The Commission’s goal is to recommend policies that promote efficiency in the Medicare program. 

In this report, we:

• describe the changing beneficiary profile in Medicare,

• recommend that an entity develop information on the comparative effectiveness of alternative therapies, 

• examine how to increase efficiency in the Medicare Advantage program,

• respond to our mandate to report on design features of a pay-for-performance system,

• discuss decreasing the number of avoidable hospital readmissions,

• respond to our mandate to recommend a new wage index system, 

• recommend a new approach for prescription drugs that can be paid for under both Part B and Part D,

• examine payment system refinements and adding quality measures for skilled nursing facilities, and

• discuss changes to physicians’ practice expense payments.

The report concludes by fulfilling our statutory obligation to analyze the Secretary of HHS’s estimate of the 

update for physician services (Appendix A of this report).

Sincerely,

Glenn Hackbarth, J.D.

Chairman

Enclosure

601 New Jersey Avenue, NW • Suite 9000

Washington, DC 20001

202-220-3700 • Fax: 202-220-3759

www.medpac.gov

Glenn M. Hackbarth, J.D., Chairman

Robert D. Reischauer, Ph.D., Vice Chairman

Mark E. Miller, Ph.D., Executive Director
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 

This report was prepared with the assistance of many 

people. Their support was key as the Commission 

considered policy issues and worked toward consensus on 

its recommendations.

Despite a heavy workload, staff members of the Centers 

for Medicare & Medicaid Services were particularly 

helpful during preparation of the report. We thank William 

Clark, Kent Clemens, Jim Coan, Philip Cotterill, Rick 

Ensor, Marc Hartstein, Anne Hornsby, Steve Jencks, Terry 

Kay, Jeff Kelman, Larry Kocot, Sheila Lambowitz, Tim 

Love, Karen Milgate, Penny Mohr, Ed Mortimore, Carolyn 

Mullen, Steve Phurrough, Elizabeth Richter, John Shatto, 

Donald Thompson, Thomas Valuck, and John Warren.

The Commission also received valuable insights and 

assistance from others in government, industry, and the 

research community who generously offered their time and 

knowledge. They include Joe Antos, Rich Averill, Brett 

Baker, Colin Baker, Jim Baumgardner, Mara Benner, Jim 

Bentley, Bob Berenson, Cindy Brown, Tom Clark, Joshua 

Cohen, Eric Coleman, James Cosgrove, John Coster, 

Kathleen Dalton, Bill Dombi, Brian Ellsworth, Kurt Gillis, 

Federico Girosi, Marsha Gold, Vicki Gottlich, Peter Gruhn, 

Kris Haltmeyer, Elizabeth Hargrave, Karen Heller, 

Lisa Hines, Sam Ho, Jack Hoadley, David Howard, 

Haiden Huskamp, Greg Johnson, Kathleen King, Andy 

Kramer, Teresa Lee, Keith Lind, Kathryn Linehan, Korbin 

Liu, Daniel Lloyd, Barbara Manard, Don May, Sharon 

McIlrath, Darrell McKigney, Katie Merrell, Marilyn 

Moon, Alan Morgan, Paulette Morgan, Curt Mueller, 

Lyle Nelson, Peter J. Neumann, Joe Newhouse, Karen 

Pace, Larry Patton, Greg Pope, Margaret Reagan, Laurie 

Salmon, Marissa Schlaifer, Mark Selna, Jean Slutsky, Elise 

Smith, Richard Smith, Sherry Smith, Dixie Sommers, 

Steve Speil, George Stamas, Bruce Steinwald, David 

Stevenson, Mary St. Pierre, Jen Thompson, Sean Tunis, 

Howard Weiss, and Valerie Wilbur.

Once again, the programmers at Social and Scientific 

Systems provided highly capable assistance to 

Commission staff. In particular, we appreciate the hard 

work of Valerie Aschenbach, Daksha Damera, Deborah 

Johnson, Christina Larson Chebili, John May, Shelly 

Mullins, Scott Roberts, Bryan Sayer, Mary Beth Spittel, 

Charles Thomson, Susan Tian, and Arlene Turner. 

Finally, the Commission wishes to thank Cay Butler 

and John Ulmer for their help in editing and producing 

this report. 

Acknowledgments

Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 vii

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

Executive summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

Chapters

1 Medicare in the 21st century: Changing beneficiary profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Changes in the characteristics of Medicare beneficiaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Modifying traditional Medicare to better serve future beneficiaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Summary and next steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2 Producing comparative-effectiveness information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

The United States needs more credible comparative information sponsored by an independent entity . . . . . . . . . . . . . . . . . . . . . . 33

Increasing the capacity to produce comparative-effectiveness information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3 Update on the Medicare Advantage program and implementing past recommendations . . . . . . . . . . 57

The Commission’s views on private plans in Medicare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Efficiency in Medicare Advantage and extra benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Options for moving to benchmarks at 100 percent of FFS expenditures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Equity between sectors and among plan types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Special needs plans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

Future work on Medicare Advantage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

4 Value-based purchasing: Pay for performance in home health care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

Pay for performance in Medicare: The Commission’s design principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

Pay for performance for home health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

Circumstances of the home health sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

Additional technical information on home health pay for performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

5 Payment policy for inpatient readmissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

Why focus on readmissions? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

How common are readmissions? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

How can hospitals reduce readmissions? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .111

How can Medicare policy encourage hospitals to adopt strategies to reduce readmissions? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

6 An alternative method to compute the wage index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

Current approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

New approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

Wage index differences across sectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

Table of contents
viii Table of contents

Caveats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

Additional technical information on constructing a compensation index from BLS data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

7 Issues in Medicare coverage of drugs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

Overlapping coverage between Part B and Part D drugs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

Delivering Part D benefits to residents of long-term care facilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167

Directions for future research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178

8 Skilled nursing facilities: The need for reform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187

Options for reforming the skilled nursing facility prospective payment system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191

Hospital-based SNFs: Analysis from the hospital perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203

Understanding the declines in SNF quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212

9 Analysis of changes to physicians’ practice expense payments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223

CMS’s methods and data changes redistributed practice expense payments across services . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227

Allocating indirect practice expenses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233

Adjusting for geographic differences in practice expenses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244

Appendix

A Review of CMS’s preliminary estimate of the physician update for 2008 . . . . . . . . . . . . . . . . . . . . . . . . . 251

How TRHCA affects 2007 and 2008 updates for physician services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252

Calculating the update . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253

Reviewing CMS’s estimate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255

B Commissioners’ voting on recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261

Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267

More about MedPAC

Commission members . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271

Commissioners’ biographies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273

Commission staff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277
Executive summary

Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 xi

The entrance of the baby-boom generation into the ranks 

of Medicare beneficiaries brings into even sharper focus 

the issues of increasing use of services, gaps in quality, 

and achieving the best value for Medicare spending. The 

concept of efficiency, using fewer inputs to get the same 

or better outcomes, becomes ever more important. In this 

report, after describing the changing beneficiary profile 

in Medicare and its implications for the program, we 

examine several approaches to promote greater efficiency 

in the Medicare program. 

The concept of efficiency should include not only getting 

more for a set amount of inputs, but getting more of the 

right care. One way we recommend to do so is to develop 

information on the comparative effectiveness of alternative 

therapies. Efficiency encompasses quality as well as 

quantity and cost, and we develop a design for a home 

health pay-for-performance (P4P) system that illustrates 

the issues and possible solutions in P4P programs in 

Medicare. Another aspect of efficiency is getting the 

right amount of care over an entire episode of care. One 

possibility we discuss in this report is to decrease the 

number of avoidable hospital readmissions through higher 

quality care, better care transition at discharge, and better 

care coordination. 

Traditionally, MedPAC has been concerned with payment 

accuracy, because if a payment system sends the wrong 

signals through its prices, providers will be encouraged to 

provide a less-than-optimal mix of services. This report 

considers several improvements to payment accuracy. In 

response to a congressional mandate, the Commission 

recommends a new approach for computing the hospital 

wage index that will increase its accuracy. The wage 

index is used to adjust payments for differences in labor 

costs across geographic areas; there are issues about the 

current system’s equity and accuracy. Another source 

of inefficiency in the program is the overlap between 

the new Part D program for prescription drugs and the 

previous limited drug coverage in the program under 

Part B. The report makes recommendations to sort out 

these overlaps and promote efficiency and convenience 

for the beneficiaries. The report also examines reforming 

the payment system for skilled nursing facilities (SNFs) 

and introducing new quality measures. Finally, the 

report discusses changes to physicians’ practice expense 

(PE) payments—an important part of the physician fee 

schedule.

Medicare in the 21st century: Changing 

beneficiary profile

The profile of Medicare beneficiaries will change as the 

baby-boom generation enters and ages into the program, 

and those changes—discussed in Chapter 1—prelude 

important implications for the Medicare program. Basic 

demographic changes include changes in beneficiaries’ 

age and ethnic mix as well as disparity in education and 

income. In addition, there are important trends in the 

characteristics of Medicare beneficiaries, such as an 

increasing proportion of beneficiaries being treated for 

multiple chronic conditions, a decreasing proportion of 

beneficiaries with disabilities and employer-sponsored 

health insurance, and changes in family structure that 

affect the availability of adult children to provide longterm care for their parents.

Changes in the characteristics of Medicare beneficiaries 

will affect program spending and the types of services 

beneficiaries will want and need in the future. Possible 

ways to change Medicare to address the needs of future 

beneficiaries include:

• facilitate care coordination in traditional Medicare; 

• expand the use of health information technology, 

which may improve efficiency and quality of care to 

all beneficiaries and facilitate care coordination; 

• increase the use of comparative-effectiveness analyses 

as a source of information and guidance for providers 

and beneficiaries (which we discuss in Chapter 2);

• implement public health efforts that promote healthy 

lifestyles; and

• modify the benefits and cost sharing of traditional 

Medicare.

Producing comparative-effectiveness 

information

Comparative-effectiveness analysis compares the clinical 

effectiveness of a service (drugs, devices, diagnostic 

and surgical procedures, diagnostic tests, and medical 

services) with its alternatives. In Chapter 2, we find 

that not enough credible, empirically based information 

is available for health care providers and patients to 

make informed decisions about alternative services for 

diagnosing and treating most common clinical conditions. 

Executive summary
xii Executive summar y

Many new services disseminate quickly into routine 

medical care with little or no basis for knowing whether 

they outperform existing treatments. Information about the 

value of alternative health strategies could improve quality 

and reduce variation in practice styles. 

Although several public agencies conduct comparativeeffectiveness research, it is not their main focus and their 

efforts are not conducted on a large enough scale. For 

private-sector groups, conducting this type of research 

is costly and, when it is made publicly available, the 

benefits accrue to all users, not just to those who pay for 

it. Because the information can benefit all users and is a 

public good, it is underproduced by the private sector; a 

federal role is necessary to produce unbiased information 

and make it publicly available.

Consequently, the Commission recommends that the 

Congress should charge an independent entity to sponsor 

credible research on comparative effectiveness of health 

care services and disseminate this information to patients, 

providers, and public and private payers. Such an entity 

would:

• be independent and have a secure and sufficient 

source of funding (the Commission prefers a public–

private option to reflect that all payers and patients 

will gain from this information);

• produce objective information and operate under a 

transparent process; 

• seek input on agenda items from patients, providers, 

and payers; 

• re-examine the comparative effectiveness of 

interventions over time; 

• disseminate information to providers, patients, and 

public and private health plans; 

• have no role in making or recommending coverage or 

payment decisions for payers; and

• have an independent board to oversee it.

The entity’s primary mission would be to sponsor studies 

that compare the clinical effectiveness of a service with 

its alternatives. Payers, including Medicare, could use this 

information to inform coverage and payment decisions. 

While cost effectiveness is not a primary mission, the 

Commission does not rule out the entity producing such 

analyses. In the simplest case, cost may be an important 

factor to consider for two services that are equally 

effective in a given population. But even when clinical 

effectiveness differs, it may be important for end users to 

be aware of costs. 

Update on the Medicare Advantage 

program and implementing past 

recommendations

Private plans have the potential to promote greater 

efficiency in the delivery of health care and improved 

outcomes for enrollees; hence, the Commission supports 

their participation. However, we report in Chapter 3 that 

for most Medicare Advantage (MA) private plans the 

current approach to payment does not promote efficiency, 

primarily because county benchmarks—which are the 

basis of payment for MA plans—exceed Medicare fee-forservice (FFS) expenditure levels. 

Benchmarks averaged 116 percent of expected FFS 

spending in 2006, and those high benchmarks enabled 

plans to offer extra benefits to attract enrollees, resulting in 

significant enrollment growth in MA. Enrollment growth 

has been greatest in private fee-for-service (PFFS) plans 

rather than in coordinated care plans. Yet, on average, 

PFFS plans provide the basic Medicare benefit package 

at a cost higher than the traditional FFS program, while 

HMOs do so for less. In other words, PFFS plans are 

providing extra benefits because of the higher payment 

rates, not because of greater efficiency. 

The continuing growth in enrollment in high-benchmark 

counties (where PFFS enrollment is concentrated) and the 

growth in types of plans that are less efficient heighten 

our concerns about the MA program. Current MA 

payment policy is inconsistent with MedPAC’s principle 

of payment equity between MA and the traditional FFS 

program. In the context of MA, equity would be achieved 

by setting benchmarks at 100 percent of FFS. However, 

the Commission recognizes that changing MA plan 

payment rates to achieve financial neutrality too quickly 

will cause disruptions for beneficiaries in some markets, 

and thus the Congress may want a transition period. The 

timing of a transition to a plan payment system that is 

financially neutral needs to take into account the effect on 

beneficiaries. We offer several options.

In addition to the variations in efficiency among plans, 

there are also wide differences in plan performance 

on quality measures. Such differences highlight the 

importance of the Commission’s recommendation to 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 xiii

institute a P4P system in MA and the importance of 

having all plans report on quality measures. PFFS plans, 

for example, are exempt from most quality measurement 

requirements, which is an example of the unlevel playing 

field that exists in MA with regard to plan standards and 

contracting requirements. The Commission is concerned 

that differing standards provide an advantage to one plan 

type over another. 

With respect to special needs plans (SNPs), we provide 

an update on plan availability and participation as of early 

2007. In 2007, the number of SNPs has again risen, to 476, 

from 276 in 2006 and 125 in 2005. SNP enrollment as 

of March 2007 was about 843,000, compared to 532,000 

enrollees in July 2006. We intend to continue studying 

what the proper role should be for SNPs in the MA 

program and what criteria might be established for these 

plans. 

Value-based purchasing: Pay for 

performance in home health care

In the Deficit Reduction Act of 2005, the Congress asked 

the Commission to discuss the design of a P4P system in 

home health care to improve the value of health care that 

Medicare purchases. In Chapter 4, we have applied general 

principles for P4P design specifically to the home health 

sector; however, the principles could be used in other 

settings as well. The key aspects of program design are:

• Funding the reward pool. P4P should be budget 

neutral; it should not add money to or remove money 

from the system. 

• Setting thresholds for performance. There are several 

ways to set thresholds; the most common one is to use 

a set percentage of providers. An alternative is to use 

a test of statistical significance: High performance is 

a score statistically significantly above the average, 

and poor performance is significantly below the 

average. Improvement could be regarded as a score 

significantly greater than the provider’s previous score.

• Balancing rewards for attainment and improvement. 

If the rewards are exclusive (a provider can receive 

either an attainment reward or an improvement reward 

but not both), then less weight could be placed on the 

improvement rewards since those providers are, by 

definition, providing lower quality care as measured 

by the P4P system.

• Determining the size of the reward. In a budgetneutral system, the size of the reward is constrained 

by the size of the penalty placed on poorly performing 

providers; when money is removed from the system 

to fund the pool, then the entire reward pool should 

be spent on rewards. The size of the reward should be 

proportional to the provider’s Medicare payments.

As we discuss each of these aspects of program design in 

Chapter 4, we offer a P4P model built from home health 

data to illustrate these points. However, the circumstances 

of home health care may pose particular challenges for 

P4P in that sector. Our analysis suggests that the current 

home health payment system overpays providers and pays 

inaccurately for some patients. Adding a quality incentive 

to a payment system that does not accurately pay providers 

for the costs of different patients could result in the quality 

incentive being overwhelmed by the current payment 

incentives. The Commission will continue to consider 

reforms to the payment system. P4P should be put in 

place at the same time as Medicare improves the payment 

system to create stronger incentives to improve quality. 

Payment policy for inpatient readmissions

Medicare’s hospital payment system provides no 

explicit encouragement or reward for hospitals that 

reduce readmissions, although readmissions indicate 

the possibility of poor care or missed opportunities to 

better coordinate care. Medicare pays for each admission 

based on the patient’s diagnosis regardless of whether 

it is an initial stay or a readmission for the same or 

a related condition; almost 18 percent of admissions 

result in readmissions within 30 days of discharge. Yet 

research shows that hospital-based initiatives to improve 

communication with beneficiaries and other caregivers, 

coordinate care after discharge, and improve the quality 

of care during the initial admission can avert many 

readmissions—to the benefit of beneficiaries and the 

program.

To encourage hospitals to adopt strategies to reduce 

readmissions, Chapter 5 explores a two-step policy option 

that starts with public reporting of hospital-specific 

readmission rates for a subset of conditions. The second 

step of the policy is an adjustment to the underlying 

payment method to financially encourage lower 

readmission rates. For example, one could create a penalty 

for hospitals with high readmission rates and hold all other 

hospitals harmless.
xiv Executive summar y

We focus on the hospital’s role but recognize that other 

providers can be instrumental in avoiding readmissions, 

including physicians and post-acute care providers. 

Similarly, beneficiaries have responsibility in the effort 

to avoid readmissions and should be encouraged to be 

engaged in their own care. Aligning incentives across all 

who can influence the patient’s outcome is essential to 

induce the needed collaboration among FFS providers 

to reduce readmissions and, more broadly, foster greater 

“systemness” and integration in the delivery of health care.

An alternative method to compute the 

wage index

In the Tax Relief and Health Care Act of 2006 (TRHCA), 

the Congress mandated that the Commission report on a 

revision of the wage index. The TRHCA also requires the 

Secretary to consider the Commission’s recommendations 

in the fiscal year 2009 inpatient prospective payment 

system proposed rule.

In Chapter 6, we explore an alternative method for 

calculating wage indexes for hospitals and other sectors. 

The wage index we develop addresses specific issues of 

concern to the Congress, including eliminating exceptions, 

minimizing variation in the wage index across county 

borders, and using the hospital wage index in other 

settings. It also addresses other issues in the current 

system, such as distinguishing between the effects of skill 

mix differences and wage differences. The MedPAC index 

is based on wage data from the Bureau of Labor Statistics 

and the Census Bureau, and benefits data are from the 

provider cost reports submitted to CMS. 

The Commission recommends first that the Congress 

should repeal the existing hospital wage index statute 

including reclassifications and exceptions, and give the 

Secretary authority to establish new wage index systems. 

Second, the Commission recommends that the Secretary 

should use this new authority to establish a hospital 

compensation index that:

• uses wage data representing all employers and 

industry-specific occupational weights,

• is adjusted for geographic differences in the ratio of 

benefits to wage,

• is adjusted at the county level and smooths large 

differences between counties, and 

• is implemented so that large changes in wage index 

values are phased in over a transition period.

Because it uses the same underlying data for all settings, 

the method can easily be tailored to SNFs and home 

health agencies. However, we find that the SNF, home 

health agency, and hospital wage indexes under the new 

approach are highly correlated. Therefore, the Commission 

also recommends that the Secretary should use that 

hospital compensation index for the home health and SNF 

prospective payment systems and evaluate its use in the 

other Medicare FFS prospective payment systems.

Issues in Medicare coverage of drugs

As Medicare’s Part D prescription drug benefit becomes 

established, two issues have arisen that we address in 

Chapter 7: instances when there is an overlap in coverage 

for certain drugs between Part B and Part D, and delivery 

of Part D benefits to Medicare beneficiaries who reside in 

long-term care facilities. 

We offer recommendations to address three issues with 

overlap drugs: 

• Drugs that can be prescribed for many indications. 

Currently a drug plan must determine whether a drug 

should be covered under Part B before it can approve 

a claim, so plans often require prior authorization 

before the pharmacist can dispense the drug. The 

Commission recommends that the Congress change 

the law to allow CMS to identify selected overlap 

drugs that are covered under Part D most of the time 

and are low cost and direct plans always to cover them 

under Part D. 

• For drugs that continue to be covered by Part B and 

Part D, permitting plans to cover a transitional supply 

of drugs under Part D. Until a plan determines whether 

a drug is covered under Part B or Part D, it is not 

allowed to provide emergency supplies to beneficiaries 

under Part D. We recommend that the Congress 

authorize prescription drug plans to approve transition 

supplies while coverage is being determined.

• New preventive vaccines that are covered under Part 

D instead of Part B. Because physicians administer 

the vaccines but cannot directly bill drug plans, 

patients might have to pay the physician and then 

seek repayment from their drug plan, which might 

discourage beneficiaries from getting vaccines. We 

recommend that the Congress should permit coverage 

for appropriate preventive vaccines under Part B 

instead of Part D.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 xv

About 5 percent of Medicare beneficiaries reside in 

long-term nursing facilities (NFs), and their drugs are 

often dispensed by long-term care pharmacies (LTCPs). 

Under Part D, LTCPs must negotiate with numerous plan 

sponsors over payments for services delivered to NF 

residents. Tensions have grown between some Part D plans 

and LTCPs over pharmacies’ desire for timely dispensing 

and plans’ desire to determine whether prescriptions are 

covered and appropriate before paying for them. Also, 

CMS is concerned that the separate rebates LTCPs receive 

directly from drug manufacturers could undercut the 

benefit management of the Part D plans and potentially 

raise program costs.

The Commission intends to monitor this issue and will 

look at data as they become available. The chapter does 

not make recommendations on this issue but does examine 

three potential options for providing Part D benefits in 

long-term care settings.

Skilled nursing facilities: The need for 

payment system reform

Chapter 8 discusses issues related to Medicare’s payment 

system for SNFs and the measures used to assess the 

quality of care provided in them. The current design of the 

prospective payment system results in impaired access for 

certain beneficiaries who require expensive nontherapy 

ancillary (NTA) services and encourages providers to 

furnish therapy even when the services are of little or no 

value. 

The chapter describes CMS’s extensive research to refine 

the payment system and concludes that options can be 

designed that better target payments for NTA and therapy 

services and for stays with unusually high costs. Many of 

the options will require trade-offs between their predictive 

abilities and the burdens they impose on CMS and 

providers. Better data on the use of NTA services during 

the SNF stay, patient diagnoses, nursing costs, and patient 

assessment information at admission and discharge would 

facilitate redesign efforts.

We then consider why some hospitals continue to operate 

their SNFs, despite the SNFs’ apparent poor financial 

performance, while other hospitals have closed their units. 

In site visits and interviews, hospital administrators told 

us their reasons—including nonfinancial factors—for 

keeping their SNFs open or for closing them. The 

administrators indicated that they consider how the SNF 

contributed to the combined financial performance of the 

hospital and the SNF. Our analyses found that hospital 

and SNF revenues together covered the combined direct 

costs (which do not include overhead and capital costs) 

of the patients. Losses on the SNF side can be offset by 

improved performance on the hospital inpatient side from 

shorter lengths of stay and alternative uses for scarce 

inpatient beds.

In our March 2007 report, we noted that two measures 

of SNF quality—risk-adjusted rates of discharge to 

the community and avoidable hospital readmissions—

indicated that quality had declined between 2000 and 

2004. Yet quality scores improved for the same facilities 

based on the publicly reported SNF quality measures. 

This difference in trend, combined with our previous 

concerns about the publicly reported measures, leads us 

to urge CMS to report community discharge rates and 

rehospitalization rates for Medicare patients. CMS should 

also reconsider our 2006 recommendation to change the 

timing of the patient assessment so that changes in health 

status are gathered for all patients. 

Analysis of changes to physicians’ practice 

expense payments 

In Chapter 9, the Commission examines how CMS 

determines PE payment rates in the physician fee 

schedule; PE payments account for close to half of the $58 

billion Medicare spent under the fee schedule in 2005. We 

describe the major changes that CMS has recently made 

to PE rates and their impacts, examine CMS’s method for 

allocating indirect costs to specific services, and explore 

how the agency adjusts PE payment rates to account for 

geographic differences in input prices. 

Beginning in 2007, CMS is using new methods to 

calculate direct and indirect PE relative value units 

(RVUs), using the same approach to calculate PE RVUs 

for services that both do and do not involve physician 

work, and using more current practice cost data to 

calculate indirect PE RVUs for eight specialty groups. In 

addition, CMS adopted significant changes to physician 

work RVUs, which affect both the physician work and the 

PE components of the fee schedule. Collectively, these 

changes represent the biggest revision to the methods and 

data used to calculate PE RVUs since 1999. CMS will 

phase in these changes over a four-year period.

The new PE methods and data redistribute PE payments 

across services. When CMS fully implements the changes 

in 2010, PE RVUs will increase by 7 percent for evaluation 

and management services and by 3 percent for other 
xvi Executive summar y

(nonmajor) procedures and tests. By contrast, PE RVUs 

will decrease by 8 percent for major procedures and by 9 

percent for imaging services. 

Because indirect costs represent about two-thirds of 

total practice costs, we examine CMS’s new method for 

calculating indirect PE RVUs and explore other methods to 

pay indirect practice costs. We also discuss the sensitivity 

of the PE RVUs to changes in the calculation method.

Finally, we examine how CMS adjusts PE payment rates 

to account for geographic differences in the price of inputs 

used in operating a physician practice. Payments would 

be more accurate if the payment system excluded costs 

that do not vary geographically, such as equipment and 

supplies, from the geographic adjustment. 

Review of CMS’s preliminary estimate of 

the physician update for 2008

Appendix A fulfills the Commission’s requirement to 

review CMS’s estimate of the 2008 update for physician 

services. CMS’s preliminary estimate of the 2008 payment 

update for physician services is –5.1 percent. However, 

when combined with the effect of the TRHCA, CMS 

estimates the net change to the conversion factor from 

2007 to 2008 to be –9.9 percent. Due to continued growth 

in expenditures on physician services and increased 

spending associated with legislative overrides to avert 

payment cuts for physician services, the sustainable 

growth rate (SGR) formula has called for negative updates 

since 2002. 

In reviewing the technical details involved in estimating 

the update under current law (in accordance with the 

SGR formula), we find that CMS used estimates in 

calculating the update that are consistent with recent 

trends. Moreover, the Commission anticipates that no 

alteration in the factors of CMS’s estimates would be 

large enough to eliminate the application of the statutory 

limit the SGR formula imposes. 
Medicare in the 21st century: 

Changing beneficiary profile

C h a p t er 1

Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007

Medicare in the 21st century:

Changing beneficiary profile

1

Chapter summary

The profile of Medicare beneficiaries is expected to change in ways 

that could have strong implications for the Medicare program. Some of 

these profile changes could include:

• A greater proportion of beneficiaries being treated for multiple 

chronic conditions, which puts upward pressure on Medicare 

costs (Thorpe and Howard 2006). This increase reflects growth 

in the prevalence of obese beneficiaries, advances in technology 

for diagnosing and treating conditions, and changes in disease 

definitions.

• Fewer beneficiaries with disabilities, who tend to be more costly 

than those without disabilities. This decrease suggests downward 

pressure on Medicare costs. However, the costliness of beneficiaries 

without disabilities has been increasing much faster than the 

costliness of the disabled (Chernew et al. 2005).

In this chapter

• Changes in the 

characteristics of Medicare 

beneficiaries

• Modifying traditional 

Medicare to better serve 

future beneficiaries

• Summary and next steps

C H A PT E R 
Medicare in the 21st centur y: Changing beneficiar y profile

• Fewer beneficiaries with employer-sponsored insurance (ESI) to 

supplement Medicare (KFF/Hewitt Associates 2005, 2004). ESI is 

relatively comprehensive supplemental coverage, so a decline in its 

prevalence could reduce beneficiaries’ service use and expose them to 

greater financial liability. 

• Key changes in family structure including people having fewer children, 

more women having children after age 35, and adult children living 

greater distances from their parents. These changes may affect the 

availability of adult children to provide long-term care for their parents. 

As beneficiaries are less able to rely on their children for unpaid care in 

the home, they may turn to institutions such as assisted living facilities 

and nursing homes or to paid custodial care in the home. Medicare 

generally does not cover the care provided by these sources, so increased 

reliance on them can substantially increase a beneficiary’s financial 

liabilities. 

• Demographic changes based on census data suggest:

• The race/ethnicity mix of Medicare beneficiaries will change, with 

a higher percentage of beneficiaries being Hispanic or Asian. This 

could affect the Medicare program if Hispanic or Asian beneficiaries 

have different health care profiles than other beneficiaries.

• The percentage of beneficiaries age 85 or older is likely to first 

decline as the baby-boom generation enters Medicare, and then 

increase as that group ages. Older beneficiaries cost the Medicare 

program 40 percent more than the average beneficiary, are more 

likely to have a living arrangement that includes formal assistance, 

and are more likely to have comorbidities, particularly Alzheimer’s 

disease.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007

• Years of formal education will increase among Medicare 

beneficiaries. More educated beneficiaries may be more involved in 

the clinical decisions regarding their health. In addition, higher levels 

of education have been shown to be correlated with later onset of 

Alzheimer’s disease.

• Per capita income typically grows more slowly than health care costs, 

especially in recent years. If growth in health care costs continues 

to outpace growth in per capita income, access to care could be 

adversely affected. Also, the distribution of income among the 

elderly may become more uneven, which may increase disparities in 

access to care between wealthy and poor beneficiaries.

Changes in the characteristics of Medicare beneficiaries will affect program 

spending and the types of services beneficiaries will want and need in 

the future. We convened a panel of experts who shared their thoughts on 

which changes in beneficiary characteristics will be most important and 

how the Medicare program could be changed to better serve beneficiaries. 

Combining the panel’s thoughts with previous MedPAC work on program 

changes, we developed the following list of possible ways to change 

Medicare to address the needs of future beneficiaries:

• Facilitate care coordination in traditional Medicare. This would 

especially help improve the care of those with chronic conditions.

• Expand the use of health information technology (IT), which may 

improve efficiency and quality of care for all beneficiaries and facilitate 

care coordination. Moreover, as beneficiaries’ level of formal education 

rises, their use and understanding of IT may expand as well. Therefore, 

increased use of health IT may help beneficiaries make more informed 

decisions about their health care.

• Increase the use of comparative-effectiveness analyses as a source of 

information and guidance for providers and beneficiaries.
Medicare in the 21st centur y: Changing beneficiar y profile

• Implement public health efforts that promote healthy lifestyles, such as 

programs that help reduce the prevalence of obesity through better diet 

and exercise. In addition to Medicare beneficiaries, such a program could 

also target younger populations so that they have already made beneficial 

lifestyle changes before becoming eligible for Medicare.

• Modify the benefits and cost sharing of traditional Medicare in the 

following ways:

• A single deductible for Part A and Part B. Currently, they have 

separate (and very different) deductibles.

• No cost sharing beyond the deductible for hospital inpatient care, 

but cost sharing beyond the deductible for most other services. The 

structure of this cost sharing should be carefully considered so that 

beneficiaries do not have incentives to forgo services that are highly 

beneficial.

• A stop loss that limits beneficiaries’ financial liabilities, which reduces 

their risk of becoming impoverished from a costly illness. Reducing 

this risk will have greater importance if ESI becomes less prevalent as 

a source of supplemental insurance or if beneficiaries’ incomes grow 

more slowly than their financial liabilities from health care.

The analysis presented in this chapter is the first in a two-step process. In the 

second step, we will develop estimates of the effects of changes in the profile 

of Medicare beneficiaries and modifications to the Medicare program that 

address those changes, with a focus on the design of the benefit package. 

We emphasize that the purpose of this work is not to address the long-run 

sustainability of the Medicare program. Other changes will be needed to 

address that issue. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007

The Medicare program is facing important changes in the 

coming decades. Well-known changes include substantial 

growth in the beneficiary population—as the babyboom generation becomes eligible for Medicare—and 

technological advancements in health care that extend 

lives. The impact of the baby-boom generation on the 

size of the beneficiary population will put strong upward 

pressure on Medicare spending and considerable strain on 

the federal budget. Advances in technology can take many 

forms but are frequently associated with upward pressure 

on health care use and spending because they are often 

costly and usually add to, rather than replace, existing 

technology.

A change that has not been as widely studied is the likely 

change in the profile of beneficiaries’ characteristics in 

the coming decades. We convened a panel of experts and 

reviewed the literature to identify the changes that are 

likely to be important to the Medicare program. The list of 

changing characteristics that we developed includes:

• Greater prevalence of being treated for chronic 

conditions, especially for multiple chronic conditions, 

which increases beneficiaries’ health care use (Thorpe 

and Howard 2006);

• Fewer beneficiaries with disabilities, which suggests 

downward pressure on health care use. But the 

difference in spending between disabled and 

nondisabled beneficiaries has declined, which will 

reduce or could even eliminate the downward pressure 

from fewer disabled beneficiaries (Chernew et al. 

2005);

• Fewer beneficiaries with employer-sponsored 

insurance (ESI) to supplement Medicare, which may 

reduce beneficiaries’ access to care and increase their 

risk of catastrophic loss from health care expenses 

(KFF/Hewitt Associates 2005, 2004);

• People having fewer children, more women having 

children after age 35, and adult children living greater 

distances from their parents, which may reduce 

the availability of adult children of beneficiaries to 

provide long-term care in the home; and

• Demographic changes suggest:

• The racial/ethnic mix may change, with an 

increase in the percentage of beneficiaries of 

Hispanic origin and, to a lesser extent, Asian 

origin, who may have different health care needs 

than other beneficiaries.

• The proportion of beneficiaries who are age 85 

or older may decrease and then increase. These 

beneficiaries are likely to have different health 

care needs than younger beneficiaries.

• Beneficiaries may have more formal education, 

which may increase their participation in clinical 

decisions and is correlated with later onset of 

Alzheimer’s disease.

• Income may grow more slowly than health care 

costs or may become less evenly distributed. 

These income issues may lead to access problems 

for at least some beneficiaries or may exacerbate 

differences in access to care between high-income 

and low-income beneficiaries.

In this chapter, we discuss the potential effects of these 

changing characteristics and how they may affect 

beneficiaries’ health care use and their interaction with the 

health care system. In addition, our expert panel discussed 

some of the ways the Medicare program could be changed 

to better serve future beneficiaries. We synthesized their 

views with previous MedPAC studies on how to improve 

Medicare to address the changing characteristics of 

Medicare beneficiaries.

Changes in the characteristics of 

Medicare beneficiaries

This section discusses the potential qualitative effects of 

the changes to the profile of beneficiary characteristics, 

with some supporting empirical results.

Increase in treatment of chronic conditions 

puts upward pressure on Medicare 

spending 

Chronic conditions are widespread among Medicare 

beneficiaries (Figure 1-1, p. 8). Research indicates that 

an increase in the treated prevalence (the percentage 

of the population receiving treatment) of many chronic 

conditions has fueled much of the increase in Medicare 

spending over the last two decades.1 Also, the proportion 

of beneficiaries treated for multiple chronic conditions has 

increased. In 1987, 31 percent of Medicare beneficiaries 

received treatment for five or more chronic conditions, 

accounting for about half of total health care spending 

on Medicare beneficiaries. Fifteen years later, more than 

half of all Medicare beneficiaries were treated for five 

or more chronic conditions, accounting for 76 percent of 
Medicare in the 21st centur y: Changing beneficiar y profile

health care spending on beneficiaries. Nearly all spending 

growth for Medicare beneficiaries from 1987 to 2002 can 

be attributed to those treated for three or more chronic 

conditions (Thorpe and Howard 2006).

The reasons underlying the increased treated prevalence of 

chronic conditions include:

• Higher rates of obesity—defined as a body mass 

index (BMI) of 30 or higher—likely have increased 

the prevalence of conditions such as diabetes, 

hyperlipidemia, and hypertension.2 Recent data 

suggest that the obesity rate among the elderly is 

at a historically high level (Figure 1-2). The impact 

of obesity on the prevalence of chronic conditions 

may become even stronger in the coming decades 

because the prevalence of obesity is higher among the 

population age 40 to 59 than among those age 60 or 

older (Ogden et al. 2006).

• Technology for identifying the presence of conditions 

has advanced, such as the dual-energy X-ray 

absorptiometry (DXA) scan for osteoporosis. These 

advances have resulted in patients being diagnosed for 

conditions that could not have been detected several 

years ago. 

• Technology for treating conditions has advanced, such 

as the development of selective serotonin reuptake 

inhibitors (SSRIs) for treating depression, discussed 

on p. 10. Looking forward, personalized medicine, 

which uses genetic information to tailor treatments 

to a patient, may become an important technological 

advance in the coming years.

• Clinical definitions of some diseases have changed. 

For example, the definition for metabolic syndrome—

which increases the risk of cardiovascular disease, 

stroke, and diabetes—includes abnormal fasting 

glucose levels.3 In 2004, the American Diabetes 

Association lowered the definition of abnormal fasting 

glucose levels from 110 milligrams per deciliter 

(mg/dL) to 100 mg/dL. This change increased the 

prevalence of metabolic syndrome among adults age 

20 or older by 20 percent (Ford et al. 2004).

Chronic diseases are prevalent among Medicare beneficiaries, 2003

Note:	 CHD (coronary heart disease), COPD (chronic obstructive pulmonary disease). Analytic sample consists of beneficiaries in traditional Medicare.

Source:	 MedPAC analysis of 2003 Medicare Current Beneficiary Survey Cost and Use file.

Chronic diseases are prevalent among Medicare beneficiaries, 2003 FIGURE

1-1

Note and Source in InDesign Percent of beneficiaries

70

50

60

40

30

20

10

0

Hypertension CHD Stroke Cancer Diabetes Alzheimer’s COPD

F i g ure

1 –1
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007

Obesity has increased prevalence of chronic 

conditions and Medicare spending 

Increased obesity rates among the Medicare population 

have not only increased the treated prevalence of chronic 

conditions, they have likely played a role in the spending 

increase over the last two decades because many obese 

people have multiple conditions such as hyperlipidemia, 

diabetes, and hypertension.4 Data from the Agency for 

Healthcare Research and Quality (AHRQ) indicate that 

the share of Medicare spending attributable to obese 

beneficiaries nearly tripled from 9.4 percent in 1987 to 

24.8 percent in 2002.5

Obesity is a particularly important risk factor because 

it has spread across all age groups and segments of 

society, and research indicates that it tends to reduce life 

expectancy. Over the last three decades, improvements 

in risk factors such as smoking, high blood pressure, 

and drinking have increased life expectancy. However, 

increased obesity rates have offset part of these gains. 

Moreover, continued increases in obesity rates would 

further erode the gains from improvements in other risk 

factors (Cutler et al. 2007).

However, research also suggests that the effect of obesity 

on life expectancy may decline with age and even may 

have no effect once people reach age 70 (Lakdawalla et al. 

2005, Olshanky et al. 2005, Fontaine et al. 2003, Stevens 

et al. 1998). This finding may reflect a complicated 

relationship in which obesity can have very different 

effects on longevity depending on an individual’s medical 

circumstances. For example, it is plausible that the age 

at which an individual becomes obese may affect life 

expectancy. More research on this issue may help clarify 

the effect of age on the association between obesity and 

longevity.

Irrespective of its effect on longevity, obesity increases 

disability rates. Obese beneficiaries spend a greater 

amount of their lifetimes with a limitation in one or more 

activities of daily living (ADLs) than beneficiaries who 

are the recommended weight (the list of ADLs includes 

eating, bathing, dressing, transferring from bed to chair, 

walking, and using a toilet). Obese 70-year-olds can expect 

to spend 40 percent more of their remaining years with 

a limitation in one or more ADLs than 70-year-olds of 

recommended weight (Lakdawalla et al. 2005). Moreover, 

obesity increases the likelihood of having several chronic 

conditions including diabetes, gallbladder disease, 

hypertension, and osteoarthritis; it also increases the 

likelihood of needing dialysis (Must et al. 1999).

The increased limitations in ADLs, presence of chronic 

conditions, and need for dialysis among the obese translate 

to higher annual spending on health care. To the extent that 

the effect of obesity on life expectancy declines as people 

age, research suggests that lifetime Medicare spending is 

much higher (34 percent) among the obese than among 

those of recommended weight (Lakdawalla et al. 2005).

Technology has increased treatment of chronic 

conditions 

Although obesity likely played a role in the growth of the 

proportion of beneficiaries treated for chronic conditions, 

increases in the share of nonobese beneficiaries treated 

for five or more chronic conditions indicate that other 

factors also matter. From 1987 to 2002, the share of 

beneficiaries who had the recommended weight and were 

treated for five or more chronic conditions increased 

from 11.5 percent to 16.0 percent, and the percentage of 

total Medicare spending these beneficiaries accounted for 

increased from 19.6 percent to 24.1 percent (Thorpe and 

Howard 2006).6

F i g ure

1 –2 Obesity rates have been increasing 

among older Americans

Source:	 Table 73 in Health, United States, 2005 from the National Center for

Health Statistics.

Obesity rates have been increasing

among older Americans

FIGURE

1–X

Note and Source are in InDesign. Percent obese

50

10

40

30

20

0

Age 65–74 Age 75+ Age 65–74 Age 75+

1988–1994

1999–2002

Men Women
10 Medicare in the 21st centur y: Changing beneficiar y profile

One explanation for the increase in the proportion of 

beneficiaries of recommended weight being treated for 

five or more chronic conditions is that physicians are more 

aggressively diagnosing and treating healthier beneficiaries. 

In 1987, 33 percent of beneficiaries treated for five or more 

chronic conditions reported good or excellent health. This 

proportion increased to 60 percent in 2002.

Another reason for this increase in the proportion of 

relatively healthy beneficiaries being treated for five 

or more chronic conditions may be the introduction 

of technologies for either treating or detecting chronic 

conditions at earlier or less severe stages. An example of 

a relatively new technology that treats a chronic condition 

is SSRIs for depression. Prior to SSRIs, the most common 

method for treating depression was psychotherapy, which 

often entailed large costs to patients, in both time and 

money (Howard et al. 2006). A technology that detects a 

chronic condition is the DXA scan for osteoporosis.

Reconciliation and summary of literature on 

chronic conditions 

In contrast to the results from the research we have cited 

thus far, other research suggests that chronic conditions 

only modestly affect Medicare spending. One team of 

researchers examined seven chronic conditions and found 

that cumulative Medicare spending beginning at age 65 

is only moderately higher among beneficiaries with a 

particular condition than among those without it. For 

example, a beneficiary with diabetes at age 65 has about 

$17,000 more in cumulative health care spending than a 

beneficiary without diabetes at age 65 (Joyce et al. 2005).

It appears that two bodies of research found very different 

effects of chronic conditions on Medicare spending. 

However, these seemingly inconsistent results can be 

reconciled. The research by Joyce and colleagues indicates 

that lifetime costs of beneficiaries who do not have a 

chronic condition at age 65 are only moderately lower 

than for those who do have a chronic condition at age 65. 

However, this research does not account for the fact that 

many beneficiaries without a chronic condition at age 

65 develop one at a later age, so it does not fully reflect 

the effect of chronic conditions on beneficiaries’ lifetime 

costs. Also, the research by Joyce and colleagues does 

not reflect the increase in the proportion of beneficiaries 

treated for chronic conditions. In contrast, Thorpe and 

Howard show that much of the increase in Medicare 

spending has been due to an increase in the prevalence of 

beneficiaries being treated for chronic conditions.

In summary, it appears that an increase in the proportion of 

beneficiaries being treated for several chronic conditions 

is increasing Medicare spending. It is plausible that high 

obesity rates, technological advances, and changing 

clinical definitions will continue to expand the treated 

prevalence of chronic conditions, which will raise 

Medicare spending in the future. 

These trends in the prevalence of treatment for chronic 

conditions and the prevalence of obesity suggest that it 

could be beneficial for Medicare to encourage systems 

of care coordination. However, most beneficiaries are 

in traditional Medicare, which complicates effective 

use of care coordination. Encouraging systems of 

care coordination would require changes in traditional 

Medicare that we discuss later in this chapter.

Disability rates have declined, but cost 

pressures have not 

Research indicates that the rate of disability among 

Medicare beneficiaries, usually measured by limitations 

in ADLs, has been decreasing. The average number of 

ADL limitations per noninstitutionalized beneficiary 

decreased from 0.68 in 1992 to 0.61 in 2000, and the 

percentage of beneficiaries with at least one ADL 

limitation fell from 30.4 percent to 27.8 percent over the 

same period. In general, a beneficiary’s annual cost to the 

Medicare program tends to increase as the number of ADL 

limitations increases (Chernew et al. 2005). Therefore, it 

would be reasonable to expect a decline in the prevalence 

of disability to result in lower Medicare expenditures.

However, downward pressure on Medicare spending from 

lower disability rates has been at least partially offset 

and possibly eliminated by nondisabled beneficiaries 

becoming more costly in relation to disabled beneficiaries. 

Spending for the beneficiaries with no ADL limitations 

increased more than 20 percent in inflation-adjusted terms 

from 1992 to 2000. In contrast, it increased 10 percent 

for those with one or two ADLs, increased 0.6 percent for 

those with three or four ADLs, and decreased 10 percent 

for the most disabled (five or more ADLs).

Because of the faster rate of cost growth among the least 

disabled, lower disability rates among beneficiaries may 

not slow total Medicare spending. This is especially true if 

the treated prevalence of chronic conditions among healthy 

beneficiaries continues to increase. Moreover, it is not 

clear whether the decline in disability rates will continue 

when the baby-boom generation begins to enter Medicare. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 11

In a recent study, researchers used results from a survey 

that interviewed a cohort of current Medicare beneficiaries 

when they were age 51 to 56 and later interviewed a 

cohort of baby boomers when they were age 51 to 56. The 

baby boomers reported more difficulty than the Medicare 

beneficiaries in activities such as walking, climbing stairs, 

getting up from chairs, and kneeling or crouching (Soldo 

et al. 2006). 

Percentage of beneficiaries with ESI has 

declined and is likely to decline further

The percentage of Medicare beneficiaries who have 

ESI—retiree health coverage through a former employer—

declined from 28.1 percent in 1997 to 25.5 percent in 2002 

(Fronstin 2005). This decline is likely to accelerate in the 

future. Large employers are much more likely to offer 

coverage than smaller employers, but the proportion of 

large employers that offer health benefits to future retirees 

has been declining.

Among the large firms offering subsidized retiree health 

benefits, 8 percent decided in 2004 to drop these benefits 

for future retirees, and 12 percent decided to do so in 

2005 (KFF/Hewitt Associates 2005, 2004). Some of the 

firms that terminated coverage for future retirees will 

offer affected employees “access only” coverage that 

requires the employee to pay the full premium. However, 

it is plausible that many employees will decide paying the 

full premium is “not worth it” and decline that coverage. 

Because employers are dropping coverage for future 

retirees rather than current retirees, these changes may not 

have a noticeable effect on trends in insurance coverage 

until at least a few years after the baby-boom generation 

starts to retire (Fronstin 2005).

Another factor that could reduce the prevalence of ESI 

is the accounting rules the Governmental Accounting 

Standards Board (GASB) has recently issued. These rules 

are similar to those the Financial Accounting Standards 

Board established in the early 1990s, which observers 

have credited with leading fewer businesses to provide 

health benefits to future retirees. The new rules from the 

GASB require public agencies such as state and local 

governments to fully disclose the future cost of health 

insurance benefits, something many had not been doing. 

When the new accounting rules begin in 2008, the full 

cost of future health benefits will become clear, and the 

magnitude of the liability will be large for many state and 

local governments. For example, the California Legislative 

Analyst’s Office estimates a liability of $40 billion to $70 

billion for retiree health care and related liabilities. As the 

magnitude of the liability becomes clear, state and local 

governments may reduce the generosity or availability of 

health benefits for future retirees (Porterfield 2006).

The decline in ESI coverage is likely to increase the use 

of three alternatives: medigap supplemental insurance, 

Medicare Advantage (MA) plans, and traditional Medicare 

without supplemental coverage (Medicare only). Two of 

these alternatives—medigap and Medicare only—are 

typically less comprehensive than traditional Medicare 

with ESI coverage, so they may make beneficiaries more 

aware of the costs of services. Therefore, the decline in 

ESI coverage can reduce beneficiaries’ service use and, 

consequently, Medicare spending. 

However, all three alternatives have features that make 

them generally less attractive to beneficiaries than most 

forms of ESI coverage. Not only is medigap generally less 

comprehensive than ESI, but beneficiaries with medigap 

typically pay more in premiums because employers often 

subsidize their employees’ ESI premiums. MA plans 

often have small or no premiums and often supplement 

standard Medicare coverage. However, most MA 

enrollees are in managed care plans that generally are 

more restrictive regarding provider choice than traditional 

Medicare combined with an ESI plan.7 Finally, going 

without supplemental coverage requires no additional 

premiums, but it exposes beneficiaries to full Medicare 

cost sharing, which increases their risk of becoming 

impoverished because of a costly illness. To the extent that 

more beneficiaries become impoverished, more will incur 

enough medical expenses to “spend down” their income so 

that they qualify for Medicaid.

In the absence of any changes to traditional Medicare and 

MA, the decline in the prevalence of ESI will likely result 

in increased medigap and MA enrollment. However, the 

members of our expert panel believe that the benefits and 

cost sharing in traditional Medicare could be restructured 

so that beneficiaries may be more satisfied with Medicare 

only. Also, employer coverage among the working 

population is becoming less comprehensive. Therefore, 

future beneficiaries may be more willing to accept a 

restructuring of traditional Medicare, because they may 

view a restructured Medicare program as better coverage 

than they had during their working years. We discuss 

potential changes to the benefits and cost sharing in 

traditional Medicare in more detail later in this chapter.
12 Medicare in the 21st centur y: Changing beneficiar y profile

Adult children may be less available to 

provide long-term care 

The discussion with our expert panel revealed concerns 

about sources of long-term care provided in the home for 

Medicare beneficiaries. Historically, family members, 

primarily women, provided much of this care (CDC/

Merck Institute of Aging and Health 2004). However, 

demographic changes are occurring that could diminish 

the extent to which adult children will be available to 

provide long-term care in the future:

• Baby boomers who are nearing Medicare eligibility 

had fewer children than their parents.

• More adult children live long distances from their 

parents, making it impractical for them to be sources 

of care.

• The prevalence of women having children after age 

35 has increased. Having children at older ages makes 

women less available to provide care for their aged 

parents.

• Increased life expectancy is making it more common 

for the children of beneficiaries to be Medicare 

beneficiaries themselves. Providing care to a very old 

Medicare beneficiary may be physically demanding 

for someone who is age 65 or older.

It is not clear whether these demographic changes will 

decrease the availability of adult children to provide longterm care. But, to the extent their availability decreases, 

more beneficiaries may have to rely on sources outside the 

home such as assisted living facilities and nursing homes.

Increased use of these other sources to provide long-term 

care could present both a problem and an opportunity 

for Medicare. It could be a problem because Medicare 

does not cover long-term care provided by these other 

sources, so use of these other sources can be quite costly 

to beneficiaries to the point they become impoverished. 

It could present an opportunity because providers could 

deliver some types of care more efficiently because the 

typical assisted living facility has many beneficiaries 

living near each other. For example, house calls and 

programs that encourage preventive services and care 

management in the home can be done more efficiently in 

assisted living facilities. Medicare does not cover those 

types of services, but the panel suggested that it could 

change its policies to encourage their use.

Racial/ethnic composition of Medicare 

beneficiaries will change

The Medicare program will likely see a change in the 

racial and ethnic composition of its beneficiaries. Current 

and projected demographics suggest growth in the 

percentage of beneficiaries of Hispanic origin and, to a 

lesser extent, the percentage that are of Asian origin. Data 

from the Census Bureau indicate that in 2005 about 6 

percent of the population age 65 or older was Hispanic and 

3 percent was Asian. At the same time, 9 percent of the 

population age 50 to 54 was Hispanic and 4 percent was 

Asian. In the extended future, the Census Bureau projects 

that the percentage of the U.S. population that is Hispanic 

will increase from 14 percent in 2005 to 20 percent in 

2030, and the percentage that is Asian will increase from 

4.3 percent in 2005 to 6.2 percent in 2030.

Changes in the racial and ethnic profiles of beneficiaries 

may present issues for Medicare because of differences 

in language and health profiles. Language barriers can 

make it difficult for beneficiaries to find providers of care 

with whom they are comfortable, can make it difficult 

for beneficiaries to understand the Medicare system 

(especially the complicated benefits and cost-sharing 

systems), and can result in medical errors when the patient 

and provider have a difficult time understanding each 

other.

T A B L E

1 –1 Hispanic and non-Hispanic 

beneficiaries have 

different disease profiles

Percent of beneficiaries

Condition Hispanic Non-Hispanic

Hypertension 57.6% 59.1%

CHD 13.1 12.8

Stroke 10.7 12.3

Cancer 10.9 17.4

Diabetes 31.5 19.8

Alzheimer’s disease 4.4 3.8

COPD 15.3 15.6

Limitations in three or more ADLs 19.1 12.6

Note:	 CHD (coronary heart disease), COPD (chronic obstructive pulmonary

disease), ADL (activity of daily living). ADLs include bathing, dressing,

eating, transferring from bed or chair, walking, and using a toilet.

Population includes only beneficiaries in traditional Medicare.

Source:	 MedPAC analysis of 2003 Medicare Current Beneficiary Survey Cost and

Use file.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 13

Also, Hispanic beneficiaries are a particular minority 

group that has some important differences from other 

beneficiaries in terms of their health profiles. Relative 

to other beneficiaries, Hispanics are more likely to have 

diabetes, less likely to have cancer, and more likely to 

have limitations in three or more ADLs (Table 1-1). In 

addition, data from the National Health and Nutrition 

Examination Survey indicate that 37 percent of Hispanics 

age 60 or older are obese, compared with 31 percent of all 

Americans age 60 or older (Ogden et al. 2006).

Whether we will continue to see these differences in 

health profiles between Hispanic and other beneficiaries 

may depend on why the differences exist in the first 

place. If they are due to underlying physical attributes, 

the differences are likely to persist. But, if they are due 

to cultural factors, they may dissipate with assimilation. 

Also, research indicates that racial and ethnic minorities—

especially Hispanics—are more likely to lack health 

insurance than non-Hispanic whites (NCHS 2006), 

and lack of health insurance can affect an individual’s 

health profile (Fowler-Brown et al. 2007). Therefore, if 

differences in health insurance coverage between nonHispanic whites and minorities continue, differences in 

health profiles may continue as well.

Proportion of beneficiaries who are age 

85 or older will fluctuate

The Census Bureau projects that the percentage of 

beneficiaries that is age 85 or older will initially increase 

from current levels, then decrease as the baby-boom 

generation becomes eligible for Medicare, and then 

increase at a fast rate as the baby boomers age. In 2005, 

13.9 percent of the U.S. population age 65 or older was 

also age 85 or older. The Census Bureau projects that the 

proportion will increase to 15.2 percent in 2010, decrease 

to 13.4 percent in 2030, and then increase to 19.2 percent 

in 2040.

Changes in the proportion of beneficiaries age 85 or older 

may be important because these very elderly beneficiaries 

are relatively costly. In 2003, per capita Medicare 

expenditures for beneficiaries age 85 or older were 40 

percent higher than for those of all beneficiaries (MedPAC 

2006a). In addition, these beneficiaries are more likely to 

have a living arrangement that involves formal assistance 

such as a nursing home or assisted living facility. Care 

in these facilities can be quite costly to beneficiaries or 

their families because it is often not covered by Medicare 

(Stone 2007).8 Finally, these beneficiaries have important 

differences in their health profiles compared with the 

overall Medicare population, including a higher probability 

of having Alzheimer’s disease, ever having a stroke, or 

having limitations in three or more ADLs (Table 1-2). As 

the population age 85 or older makes up a larger share of 

the Medicare population, the conditions that are relatively 

prevalent in that population—particularly Alzheimer’s 

disease and dementia—are issues that Medicare may need 

to address to better serve future beneficiaries.

Increase in formal education may affect 

how beneficiaries interact with providers

The amount of formal education among Medicare 

beneficiaries will increase in the coming decades, and 

our expert panel indicated this could be an important 

development. The proportion of beneficiaries who did not 

complete high school will decrease, and the proportion 

with an undergraduate degree or higher will increase. Data 

from the Census Bureau indicate that, in 2004, 27 percent 

of the U.S. population age 65 or older did not complete 

high school compared with only 14 percent of the 

population age 55 to 64. Also, 19 percent of the population 

age 65 or older has a bachelor’s degree or higher compared 

with 28 percent of the population age 55 to 64.

T A B L E

1 –2 Beneficiaries age 85 or older 

are more likely to have 

Alzheimer’s disease, a stroke, or 

functional limitations, 2003

Percent of beneficiaries

Condition Age 85 or older All

Hypertension 61.9% 59.0%

CHD 11.9 12.8

Stroke 17.8 12.2

Cancer 16.6 16.9

Diabetes 16.1 20.6

Alzheimer’s disease 12.3 3.9

COPD 10.9 15.6

Limitations in three or more ADLs 30.9 13.1

Note:	 CHD (coronary heart disease), COPD (chronic obstructive pulmonary

disease), ADL (activity of daily living). ADLs include bathing, dressing,

eating, transferring from bed or chair, walking, and using a toilet.

Population includes only beneficiaries in traditional Medicare.

Source:	 MedPAC analysis of 2003 Medicare Current Beneficiary Survey Cost and

Use file.
14 Medicare in the 21st centur y: Changing beneficiar y profile

More educated beneficiaries can affect the Medicare 

program by taking a more active role in the clinical 

decisions that affect their health. Some members of our 

expert panel suggested that more educated beneficiaries 

come to their encounters with providers more prepared 

in terms of understanding their medical options. 

Consequently, they may ask their providers more questions 

about treatment options and have a better understanding of 

the alternatives for treating a particular condition.

Also, more educated beneficiaries may be more willing to 

use—and more adept at using—information technology to 

improve their health care. This may include using personal 

health records so that they can easily share their medical 

history with their providers or using the Internet to become 

more informed consumers by accessing information on 

providers and health plans. 

Finally, a more educated population may result in a 

different health care profile among Medicare beneficiaries. 

For example, research indicates that higher levels of 

education are correlated with later onset of Alzheimer’s 

disease and with later onset of chronic conditions (Gatz et 

al. 2006, Smith 2005).

Patterns of income growth could affect 

access to care

Per capita income usually increases each year among 

Medicare beneficiaries, as it does among the rest of the 

U.S. population. Per capita income (adjusted for inflation) 

increased by an average of 1.3 percent per year from 

1993 to 2003 among Americans age 65 or older (Census 

Bureau 2005). The future rate of income growth and the 

distribution of income can have important implications 

for beneficiaries’ access to care. This will become an 

even more pressing issue if ESI continues to decline 

as a source of supplemental insurance, because more 

beneficiaries may to turn to medigap—which typically 

is less comprehensive than ESI and usually requires 

larger premium contributions from beneficiaries—or to 

traditional Medicare with no supplemental insurance.

Recent data on growth in beneficiaries’ incomes and 

health care costs suggest beneficiaries may have greater 

difficulty paying their health care expenses in the future. 

For example, from 1993 to 2003, the Part B premium 

increased at an inflation-adjusted rate of 2.5 percent per 

year, which is nearly twice the annual rate of increase in 

per capita income among the population age 65 or older, 

1.3 percent.9 Moreover, the monthly Part B premium has 

increased substantially in recent years, rising from $78.50 

in 2005 to $93.50 in 2007. 

Another issue regarding beneficiaries’ incomes is that 

changes in income equality could lead to increasing 

differences in access to care between wealthy and 

poor beneficiaries. Data from the Census Bureau are 

ambiguous about the trend in income equality. From 

1993 to 1999, there was little change in income equality 

as indicated by the Gini coefficient, a measure of the 

difference between perfect income equality and the actual 

distribution of income. However, income became slightly 

less evenly distributed in 2000 and 2001 (the most recent 

years of available data). Among low-income beneficiaries, 

this has implications for participation in Part B and 

supplemental insurance plans. For example, research 

indicates that participation in health plans declines 

as premiums become larger in proportion to income 

(Hudman and O’Malley 2003).

A final issue related to beneficiaries’ future income is 

whether members of the baby-boom generation have saved 

enough to help pay their future health care costs and other 

retirement expenses. If they are not adequately funding 

their retirement, there may be a large future increase in 

the percentage of beneficiaries who qualify for Medicaid. 

However, there is not consensus in the literature on this 

issue. Some studies argue that baby boomers are not 

well positioned to fund their retirement because of low 

retirement savings (Gist 2006, DeVaney and Chiremba 

2005, Goodman and Orszag 2005). In contrast, others 

argue that these studies do not accurately represent the 

ability of members of the baby-boom generation to fund 

their retirements because they exclude important sources 

of wealth such as capital gains. If one considers total 

wealth accumulation, it can be argued that the financial 

behavior of baby boomers is similar to that of previous 

generations (CBO 2003).

Modifying traditional Medicare to better 

serve future beneficiaries

We drew heavily on ideas discussed by our expert panel 

and from previous MedPAC work to identify some policy 

changes that would allow the Medicare program to better 

serve future beneficiaries. The possible policy changes 

discussed by our expert panel or previously analyzed by 

MedPAC include:
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 15

• facilitate care coordination in traditional Medicare for 

beneficiaries who have chronic conditions or who are 

complex cases;

• improve incentives to use health information 

technology (IT) such as electronic health records;

• expand use of comparative-effectiveness analyses and 

make results available to help guide providers’ and 

beneficiaries’ decisions about care;

• develop public health initiatives that promote healthy 

lifestyles; and

• change the structure of benefits and cost sharing in 

traditional Medicare, such as putting a catastrophic 

limit on beneficiaries’ cost sharing.

In the next several sections, we discuss how these changes 

can be implemented and how they are related to the 

changing profile of Medicare beneficiaries. Some of the 

changes are interconnected—such as facilitating care 

coordination and increasing use of IT—and we include 

this interconnectedness in our discussion. These changes 

are not intended to address the long-run sustainability 

of the Medicare program. Other changes are needed to 

address that issue. We discuss them in Chapter 1 of our 

March 2007 report to the Congress (MedPAC 2007).

Facilitating care coordination

In previous work, the Commission explored ways to 

expand care coordination by creating incentives for a 

patient’s providers to share clinical information among 

each other, monitor the patient’s status between visits, 

and fully communicate with the patient about how to care 

for his or her condition(s) (MedPAC 2006b). Patients 

who can benefit the most from care coordination have 

several chronic conditions and other complex needs. 

Therefore, the increase in the proportion of beneficiaries 

being treated for several chronic conditions indicates that 

facilitating care coordination could be quite beneficial to 

future beneficiaries.

Policymakers have shown an interest in advancing the 

role of care coordination in traditional Medicare. For 

example, the Medicare Prescription Drug, Improvement, 

and Modernization Act of 2003 (MMA) authorized the 

Medicare Health Support (MHS) program, a pilot program 

designed to develop and test coordinated care initiatives. 

In addition, the Medicare, Medicaid, and SCHIP Benefits 

Improvement and Protection Act of 2000 authorized 

a physician group practice demonstration intended to 

encourage care coordination among large physician 

groups. These programs are still in the early stages, so 

results on how well they reduce costs and improve quality 

are not yet available.

Why would care coordination be beneficial? 

Earlier in this chapter we discussed the substantial increase 

over the last two decades in the proportion of beneficiaries 

that are treated for chronic conditions. This proportion 

may continue to increase. In response, our expert panel 

suggested that greater use of care coordination in the 

Medicare program could improve the quality of care for 

beneficiaries with chronic conditions and has the potential 

to lower program costs.

In a previous analysis of care coordination, MedPAC 

interviewed experts and reviewed the literature. Our 

research indicated that care coordination can improve 

beneficiaries’ care by reducing hospitalizations—including 

readmissions—and use of emergency departments by 

improving adherence to evidence-based guidelines. 

Moreover, self-management programs for older adults 

have been found to improve care for hypertension and 

diabetes, and other interventions have been effective for 

coronary artery disease, diabetes, heart failure, and asthma 

(MedPAC 2006b).

Because there is some evidence that care coordination 

reduces hospitalizations, it is plausible that it could reduce 

upward pressure on Medicare spending and beneficiaries’ 

financial liability. However, care coordination also 

has the potential to increase Medicare costs unless the 

programs target the patients who would benefit the most 

and avoid those who would benefit little. Technological 

advancements have made it possible to identify conditions 

at very early stages of the disease. In some of these cases, 

the patient may be healthy enough that care coordination 

may provide little benefit. Using care coordination in those 

cases would do little more than increase program spending 

and, potentially, the patient’s cost sharing.

Obstacles to care coordination in traditional 

Medicare 

Our expert panel said that the structure of the fee-forservice payment system in traditional Medicare is an 

obstacle to effective care coordination. Traditional 

Medicare pays individual providers based on what they 

do in a visit or during an inpatient stay. Payment does 

not depend on how well a provider coordinates the care 
16 Medicare in the 21st centur y: Changing beneficiar y profile

provided in a visit or inpatient stay with the care the 

patient receives from other providers or in other settings. 

Moreover, many of the services required by individuals 

with chronic conditions or other complex needs, such as 

ongoing monitoring and education for self-management, 

are not performed within the typical face-to-face visit.

Early results from the MHS program suggest that 

successfully implementing care coordination into 

traditional Medicare may not be easy. Eight organizations 

contracted to participate in the MHS. After the first year, 

one dropped out of the program and another announced 

that it had missed its targets for cost reductions (Enrado 

2006).

Keys to facilitating effective coordinated care in 

traditional Medicare 

Fundamental changes to the structure of traditional 

Medicare are necessary to facilitate care coordination. 

These changes should include changes to the system of 

care delivery, the benefit system, and the systems for 

reimbursing providers.

In MedPAC’s previous work on care coordination, we 

discussed two models of care coordination that are 

currently being used in pilot or demonstration projects. 

We called one the provider group and the other the care 

management organization plus physician office. The 

two models are quite similar, with one key difference: 

the entity accountable for coordinating a patient’s care. 

In the provider group model, the accountable entity is a 

large provider group. In the other model, the accountable 

entity is a care management organization that works with 

a beneficiary’s providers. Our work on this issue revealed 

five factors that should be present for either of these 

models to be effective in traditional Medicare (MedPAC 

2006a):

• Care managers, usually nurses who act as the point 

person and oversee a patient’s care, must be available 

and have incentives to serve Medicare beneficiaries. 

The care manager develops a plan for tracking the 

patient’s status, helps the patient understand how 

to manage his or her condition, teaches the patient 

how to effectively navigate the health care system, 

and communicates the patient’s needs to his or her 

providers.

• Information systems should be available that allow 

care coordination programs to identify patients who 

would benefit most. This would hold down Medicare 

spending by avoiding care coordination for patients 

who would receive little benefit. In addition, providers 

could use information systems to track patients’ health 

status over time.

• The patient’s physician should be a part of the 

care coordination team. To encourage physicians’ 

participation, they should receive fees or a share of 

cost savings for the time they spend interacting with 

the care coordination team.

• Beneficiaries should be engaged in their care 

management, especially in regard to adhering to their 

care plan and properly monitoring their condition.

• The responsible organizations and the physicians 

interacting with the programs should be held 

accountable. The organizations responsible for a 

patient’s care should be accountable for cost savings 

and quality, which can be promoted through a payment 

system that ties payment to performance. Quality 

measures must be developed to indicate whether 

an organization is using the appropriate treatment 

methods for specific conditions, such as annual eye 

and foot exams for diabetics. Quality measures can 

also be used to hold physicians accountable through 

pay-for-performance programs.

Improving incentives to use health 

information technology

IT in the health care sector does not have a precise 

definition. It is perhaps best identified by how it is used: 

Providers use electronic mechanisms to collect, store, 

retrieve, and disseminate information. Health IT can be in 

many forms, but they can all be grouped into two broad 

categories, financial and clinical. In this section, we focus 

on clinical IT, which includes:

• electronic health records (EHRs), which typically 

have a record of a patient’s medical history that 

providers can access to help guide clinical decisions;

• computerized provider order entry (CPOE), which 

allows physicians and other providers to electronically 

order medications, lab tests, procedures, radiology 

studies, discharges, transfers, and referrals; and

• picture archiving and communications systems, 

which collect and store patients’ diagnostic and 

radiologic images in electronic files and allow for 

dissemination to health care sites when needed.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 17

Benefits of health information technology

MedPAC has previously reported on IT in the Medicare 

program, citing the potential to improve quality and 

efficiency as the primary benefits (MedPAC 2005, 2004). 

For example, IT could improve quality through reduced 

medication errors and adverse drug events in hospitals. 

In addition, IT could be used to efficiently collect quality 

data on providers. Providers could use these quality data 

to evaluate their performance, and payers and consumers 

could use the data to evaluate the quality of the care they 

receive and purchase. IT could improve efficiency by 

bringing cohesion to the fragmented delivery system of 

traditional Medicare. When treating a patient, providers 

often have to gather and evaluate data from a number 

of sources. These data are usually obtained via paper 

documents, telephone conversations, or fax machines. IT, 

especially EHRs, can streamline this process by putting all 

of a patient’s information in a single electronic file.

The improved efficiency and quality would be important 

to all beneficiaries, but IT can be especially helpful to 

those with chronic conditions. These beneficiaries often 

have several providers and many encounters with the 

health care system, which can make care coordination 

difficult. IT could facilitate coordination of their care by 

collecting their health care history in a single file that all 

their providers could access.

The Congress has shown interest in expanding the role of 

IT in the Medicare program. The MMA established the 

Medicare Care Management Performance Demonstration. 

The purpose of this three-year demonstration is to promote 

the adoption and use of health IT to improve the quality 

of care for chronically ill beneficiaries. Participating 

physicians who meet or exceed performance standards 

established by CMS in clinical delivery systems and 

patient outcomes receive bonus payments.

Although use of IT is usually discussed from the 

perspective of providers, greater use of IT among 

beneficiaries also may be advantageous. The amount 

of formal education among beneficiaries is expected 

to increase, which may result in future beneficiaries 

being more comfortable using IT. This could present an 

opportunity for greater use of personal health records 

(PHRs) among beneficiaries. As a concept, PHRs are 

files individuals maintain that contain information 

about their medical histories such as allergies, adverse 

drug reactions, illnesses, hospitalizations, surgeries, lab 

results, and family history.

PHRs allow patients to create a complete list of their 

medical history that they can easily reference and make 

it easier for them to share their medical history with 

their providers. This may reduce errors and eliminate 

duplicate procedures and processes. Because of these 

potential benefits of PHRs and because beneficiaries 

are becoming more comfortable with information 

technology, it may be advantageous for Medicare to 

encourage wider use of PHRs.

Obstacles to adopting health information 

technology

Use of IT by health care providers has been growing but 

remains low. A recent study estimates that in 2005, 

5 percent of hospitals were using CPOEs and 24 percent 

of physicians were using EHRs (Jha et al. 2006). Many 

factors appear to contribute to the slow uptake of IT. 

Providers, particularly physicians, cite the cost of IT and 

the lack of a clear return on investment. Another barrier 

may be the difficulty of successful implementation. Many 

providers may not know enough about IT to effectively 

navigate the market, implement choices they make, and 

maintain the system. In addition, introducing IT into the 

workplace may require changes to workplace procedures 

that clinicians and office staff could resist.

Also, the structure of health care payment systems may 

result in the purchasers of IT sometimes not receiving the 

full financial reward of their investments. For example, 

use of EHRs may result in fewer medical errors, which 

may lead to the need for fewer services. Payers would 

benefit because they would have to reimburse physicians 

for fewer services, but the physicians who invest in the 

EHRs may end up with lower revenues.

A final barrier may be the lack of a standard system for 

transmitting data and describing the content of the data. 

This limits providers’ ability to share and use information 

across systems. For example, a physician’s office may 

find that information from an outside source, such as 

a laboratory, may not be compatible with its system. 

Because a patient can receive care in a number of settings, 

providers may be hesitant to invest in systems that cannot 

be linked to other parts of the health care system.

Increasing the presence of health information 

technology in Medicare 

The Commission considered three methods for advancing 

the use of IT in the Medicare program: providing grants 
18 Medicare in the 21st centur y: Changing beneficiar y profile

and loans, requiring use, and establishing quality measures 

that are linked to IT (MedPAC 2005).

The Commission argued against using grants and loans 

because:

• Providers may need to commit to changes and be 

willing to revise work processes to successfully 

implement IT. Effectively targeting grants and loans 

to providers who are willing to make the necessary 

changes could be difficult.

• Grants and loans should be funneled to providers 

most in need of assistance. But it may be difficult to 

identify those most in need.

In regard to requiring providers to implement and use IT, 

the Commission determined that this approach could be 

overly burdensome to providers.

The Commission determined that the best way to 

increase use of IT would be to incorporate into pay-forperformance initiatives the use of quality measures that 

require the use of IT, are facilitated by IT, or are likely to 

improve if providers use IT. The Commission made this 

decision for these reasons:

• Under pay-for-performance initiatives, providers 

would need to collect and report information on 

performance measures, and IT systems may make this 

easier.

• Use of IT can be directly measured, and these IT 

measures could be part of a larger set of quality 

measures.

• Tying payments to quality could increase the financial 

benefit of investing in IT and sustaining its use vis-àvis other investment options.

• Medicare should pay providers for using IT, not just 

for purchasing it.

Expanding use of comparativeeffectiveness analyses

Comparative effectiveness is the process of comparing 

the relative contribution of services to improvements 

in the health of patients. It can help providers and 

patients make well-informed decisions about alternatives 

for diagnosing and treating a condition. A complete 

discussion of the benefits of comparative effectiveness 

and how it can be produced so that public payers, private 

payers, providers, and patients can use it appears in 

Chapter 2 of this report.

Promoting healthy lifestyles

Our expert panel discussed the importance of promoting 

healthy lifestyles. An example of how healthy lifestyles 

could be promoted is through public health campaigns—

not necessarily operated through Medicare—aimed at 

lowering obesity rates by improving diet and exercise. 

To the extent that such a campaign is successful, lower 

obesity rates could reduce the prevalence of costly chronic 

conditions such as diabetes and hypertension.

Some panelists emphasized that it would be important to 

reach beyond the Medicare program and promote healthy 

lifestyles among the population that is nearing Medicare 

eligibility. Their rationale for including future beneficiaries 

in lifestyle promotions is that more beneficiaries would 

have healthy lifestyles when they become eligible for 

Medicare. However, the beneficial effects of promoting 

healthy lifestyles may be limited. For example, the success 

of promotions depends on the willingness of people to 

make behavioral changes. Moreover, some people may 

have genetic predispositions to being overweight that 

lifestyle changes cannot overcome.

Changing benefits and cost sharing

Medicare has long been credited with improving 

beneficiaries’ access to care (MedPAC 2006a). However, 

traditional Medicare—the choice of more than 80 percent 

of beneficiaries—is based on a model of health insurance 

design from the 1960s. Health insurance in the private 

sector has changed since then, so Medicare has a system 

of benefits and cost sharing that is somewhat different 

from most private-sector health plans. The structure of 

the benefits and cost sharing creates incentives that could 

dissuade providers and beneficiaries from choosing 

the most clinically effective options. Moreover, the 

benefit structure of traditional Medicare does not limit 

beneficiaries’ exposure to financial loss, and, because of 

its coverage limitations, beneficiaries often rely on other 

sources to supplement Medicare, which adds inefficiency 

to the health care system by encouraging excessive and 

inappropriate use of services.

In this section, we review the current structure of benefits 

and cost sharing in the Medicare program and then review 

changes discussed by our expert panel or by MedPAC 

in a previous report so that Medicare can better serve 

beneficiaries (MedPAC 2002). In the future, we intend to 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 19

estimate the potential effects of changing the benefits and 

cost sharing on program spending as well as beneficiaries’ 

cost-sharing liabilities and service use.

The benefits and cost sharing in traditional 

Medicare have limitations

The benefit package in traditional Medicare has three 

parts:

• Part A primarily covers acute care services provided 

in hospital inpatient units (including drugs), skilled 

nursing facilities, and hospices. It also covers some 

home health services. Most beneficiaries are entitled 

to Part A and do not pay a premium to participate.

• Part B covers acute care services provided by 

physicians, hospital outpatient departments, and 

ambulatory surgical centers. It also covers home 

health services not covered under Part A, diagnostic 

laboratory tests, outpatient mental health services, 

durable medical equipment, and some preventive 

services. In general, drugs furnished as part of Part B 

services are covered under Part B. Beneficiaries pay a 

subsidized premium to participate in Part B, although 

low-income beneficiaries can have their premium paid 

through their state’s Medicaid program.

• Part D covers outpatient prescription drugs that are not 

covered under Part B. Beneficiaries pay a subsidized 

premium to participate in Part D, but low-income 

beneficiaries can have some or all of the premium 

subsidized further.

The benefit package in traditional Medicare has been 

credited with helping elderly Americans access needed 

care. However, traditional Medicare, which accounts for 

most Medicare enrollment, has some important limitations 

including:

• The structure of the cost sharing may add 

inefficiencies to the health care system.

• The program does not limit beneficiaries’ liability for 

cost sharing on covered services, putting beneficiaries 

at risk for catastrophic losses.

Traditional Medicare may not promote efficient 

health care choices The benefits and cost sharing in 

traditional Medicare are a patchwork system (Table 1-3, p. 

20). The cost-sharing design affects the costs beneficiaries 

face when they use health care services, which may 

affect their decisions—or those of their providers—about 

whether to seek care and what mix of services to use. 

Furthermore, some features of Medicare’s cost sharing 

may lead providers and beneficiaries to make inefficient 

choices.

For example, hospital inpatient care typically depends 

on random events that are beyond beneficiaries’ control. 

By contrast, some—but not all—of the ambulatory care 

covered in Part B is more discretionary. Insurance theory 

suggests that nondiscretionary care should be covered more 

fully than care that is within the insured person’s control. 

The logic behind this theory is to avoid financial penalties 

for events that are beyond beneficiaries’ control, and need 

for inpatient care is typically beyond their control. In other 

words, individuals have no choice but to receive inpatient 

care, so do not punish them for getting sick. 

In contrast, greater cost sharing in many instances is 

appropriate for ambulatory care because beneficiaries’ 

use of it is often discretionary. In these cases, cost sharing 

gives patients an incentive to consider the benefit of the 

care relative to the cost. When faced with cost sharing, 

beneficiaries will not use care that has little benefit to 

them. This implies that inpatient care in most instances 

should have less cost sharing than ambulatory care. But, 

in one respect, the opposite is true in traditional Medicare: 

The inpatient deductible, $992, is much higher than the 

Part B deductible, $131. However, the structure of cost 

sharing for ambulatory care must be considered carefully 

so that it does not give beneficiaries incentive to forgo 

beneficial services.

Traditional Medicare does not limit financial 

risk A limitation in the benefit structure of traditional 

Medicare cited by our expert panel is that it does not limit 

beneficiaries’ financial losses if they experience a costly 

illness. Private health insurance plans typically become 

more generous as a beneficiary’s costs increase. For 

example, insurance in the private sector typically has a 

deductible and coinsurance or copayments at relatively low 

cost levels and a stop loss that limits beneficiaries’ liability 

if they have high costs. Stop-loss provisions are typically 

present even in the high-deductible plans associated with 

health savings accounts.

In contrast to most private-sector plans, traditional 

Medicare lacks a stop loss. Consequently, beneficiaries 

who are in traditional Medicare and lack supplemental 

coverage have no limit on the financial liability they can 

incur from covered medical expenses. The high total costs 

that some beneficiaries incur illustrate the potential risk 
20 Medicare in the 21st centur y: Changing beneficiar y profile

T A B L E

1 –3 Medicare benefits and cost-sharing requirements, 2007

Services Beneficiary cost sharing

Part A

Inpatient hospital

(up to 90 days per benefit period

plus 60 lifetime reserve days)

$992 for the first stay in a benefit period

Days 1–60: Fully covered

Days 61–90: $248 per day

60 lifetime reserve days: $496 per day

Skilled nursing facility

(up to 100 days per benefit period)

Days 1–20: Fully covered

Days 21–100: $124 per day

Hospice care for terminally ill beneficiaries Nominal coinsurance for drugs and respite care

Part B

Premium $93.50–$161.40, per month, depending on income

Deductible $131 annually

Physician and other medical services

(including supplies, durable medical equipment,

and physical and speech therapy)

20 percent of Medicare-approved amount

Outpatient hospital care Greater of 20 percent of Medicare-approved amount or

20 percent of 1996 national median charge updated to 2000

Ambulatory surgical services 20 percent of Medicare-approved amount

Laboratory services None

Outpatient mental health services 50 percent of Medicare-approved amount

Both Part A and Part B

Home health care for homebound beneficiaries

needing skilled care

None

Part D

Premium Depends on plan choice

Deductible $265*

Coinsurance 20 percent on costs from $265 to $2,400,

100 percent from $2,400 to $3,850, and

nominal cost sharing above $3,850*

Note: 	 The Part B premium increases from $93.50 to $161.40 per month based on a sliding scale for individuals with incomes above $80,000 and below $200,000 and

for couples with incomes above $160,000 and below $400,000.

*Standard benefit plans may offer actuarially equivalent or enhanced benefits.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 21

of catastrophic loss (Figure 1-3). Moreover, MedPAC has 

shown that the lack of a stop loss can limit beneficiaries’ 

options for where they can receive care. For example, 

MedPAC found that beneficiaries’ cost sharing for cancer 

drugs has been rising. For those who lack supplemental 

coverage, the cost-sharing liabilities for these drugs can 

be large. When beneficiaries cannot pay the cost sharing, 

providers respond by changing their delivery of care. For 

example, oncology practices in some areas of the country 

have stopped treating patients without supplemental 

insurance in their offices and send them to hospital 

outpatient departments or safety-net facilities (MedPAC 

2006c).

In response partly to the risk of large financial losses in 

traditional Medicare, nearly 90 percent of beneficiaries 

in traditional Medicare have supplemental coverage 

beyond the standard Medicare benefits.10 Some of the 

changes in the profile of beneficiaries’ characteristics 

discussed above may increase their vulnerability to being 

impoverished from health care expenses. This is especially 

true if the decline in the proportion of beneficiaries with 

ESI continues or if beneficiaries’ incomes continue to 

increase more slowly than the cost of health care services 

and premiums. To the extent that beneficiaries’ risk of 

catastrophic loss increases, the lack of a stop loss becomes 

a more pressing issue.

Possible changes to Medicare benefits and cost 

sharing

From the discussion with our expert panel and review 

of previous MedPAC analyses, we have identified some 

possible changes to the benefits and cost sharing in 

traditional Medicare so that the program would better 

serve beneficiaries in the future. These changes include:

Ten percent of beneficiaries have more than $50,000 of annual health care spending

Note:	 Analysis includes fee-for-service beneficiaries living in the community.

Source:	 MedPAC analysis of 2003 Medicare Current Beneficiary Survey Cost and Use file.

Ten percent of beneficiaries spend more than $50,000 on health care FIGURE

1-2

Note and Source in InDesign Per capita total spending (all payers)

60,000

50,000

40,000

30,000

20,000

10,000

0

<10 10–25 25–50 50–75 75–90 >90

Groups of beneficiaries ranked by total spending (percentile ranges)

F i g ure

1 –3
22 Medicare in the 21st centur y: Changing beneficiar y profile

• Create a single deductible for Part A and Part B. 

Beyond the deductible, it may be reasonable to have 

no additional cost sharing for hospital inpatient care 

and require some cost sharing for most other services. 

In addition:

• Most beneficiaries in traditional Medicare have 

supplemental coverage, which can largely reduce 

the effectiveness of cost sharing in deterring 

excessive spending. Limiting the extent to which 

supplemental insurance is allowed to cover cost 

sharing could reduce program spending. However, 

the limitations should not be so severe that 

beneficiaries face excessive risk of catastrophic 

loss.

• Careful thought should be given to the structure 

of the cost sharing because even a small amount 

can have a strong effect on beneficiaries’ use of 

some services and runs the risk of discouraging 

use of beneficial services. For example, research 

suggests that use of physician office visits and 

adherence to drug regimens can be very sensitive 

to cost sharing (Chandra et al. 2007, Goldman 

et al. 2006). In addition, cost sharing can have 

an especially strong effect on low-income 

beneficiaries, who may forgo beneficial services 

if they view cost sharing as too great a financial 

burden (Hudman and O’Malley 2003).

• Cost sharing in Part B should encourage 

preventive care and discourage services of 

marginal value.

• Include a limit (stop loss) on beneficiaries’ liability for 

cost sharing on covered services.

• A stop loss would reduce beneficiaries’ risk 

of incurring health care liabilities that could 

impoverish them. This would improve their 

financial circumstances, especially if their 

incomes continue to rise slowly relative to health 

care costs and if ESI continues to wane as a 

source of supplemental insurance.

• Also, a stop loss may convince some beneficiaries 

to discontinue their supplemental coverage 

because they may begin to view the restructured 

Medicare benefit design as adequate. Fewer 

beneficiaries with supplemental coverage would 

make the health care system more efficient 

because many supplemental plans cover most or 

all of a beneficiary’s cost sharing, which gives 

them an incentive to use services that have little 

or no benefit. In addition, it would reduce the 

administrative expenses providers and insurers 

incur in processing claims and managing multiple 

sources of coverage.

Summary and next steps

The general profile of characteristics among Medicare 

beneficiaries is likely to change in important ways in the 

coming decades. These changes include:

• a greater proportion with several chronic conditions, 

• a smaller proportion with disabilities, 

• fewer with ESI, 

• adult children being less available to provide longterm care in the home,

• a different racial and ethnic mix,

• a changing proportion age 85 or older, 

• more years of formal education, and

• changes in per capita income and the distribution of 

income.

To the extent these changes occur, they will affect 

beneficiaries’ needs and preferences for health care as well 

as costs to the Medicare program.

In this chapter, we discussed details of the changing 

characteristics and offered some possible changes to 

Medicare so that the program could better serve future 

beneficiaries. The changes we presented include:

• Facilitate care coordination, which can be especially 

beneficial to those who have several chronic 

conditions.

• Encourage greater use of IT, which can improve 

quality, efficiency, and care coordination.

• Expand use of comparative-effectiveness analyses, 

which can help beneficiaries and providers make 

informed decisions about health care choices.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 23

• Develop and use public health initiatives that promote 

healthy lifestyles, which could help reduce cost 

pressures on Medicare.

• Change the structure of benefits and cost sharing 

in traditional Medicare, which can help improve 

efficiency in the health care sector and reduce 

beneficiaries’ risk of catastrophic loss.

The analysis we presented in this chapter is intended to be 

the first part of a longer term analysis. In the coming year, 

the Commission plans to revisit ideas for restructuring 

Medicare benefits and what we have learned about the 

changing characteristics of future Medicare beneficiaries. 

We also will be looking in greater depth at how Medicare 

can promote changes to the health care delivery system 

to provide the care coordination that will address the 

changing needs of Medicare beneficiaries. 
24 Medicare in the 21st centur y: Changing beneficiar y profile

1 A chronic condition is a disease that cannot be cured or is 

infrequently cured. Examples of chronic conditions include 

diabetes, hypertension, and coronary heart disease.

2 BMI is calculated as weight in kilograms divided by height in 

meters squared.

3 The complete definition of metabolic syndrome is having 

three or more of the following conditions: abdominal obesity, 

defined as waist circumference of more than 102 centimeters 

(cm) in men and 88 cm in women; high triglyceride levels 

(more than 150 milligrams per deciliter (mg/dL)); low highdensity lipoprotein (below 40 mg/dL in men and below 

50 mg/dL in women); high blood pressure (above 130/85 

millimeters); and high fasting glucose (above 100 mg/dL) 

(Ford et al. 2002).

4 Hyperlipidemia is the presence of elevated or abnormal levels 

of lipids or lipoproteins in the blood.

5 The data sources from AHRQ are the 1987 National Medical 

Expenditure Survey and the 2002 Medical Expenditure Panel 

Survey.

6 A person of recommended weight has a BMI of 20 to 24.9.

7 Private fee-for-service plans are a type of MA plan that has 

little or no restriction on which providers beneficiaries can 

see. See Chapter 3 of this report for a description of the 

enrollment trends in the MA program.

8 Beneficiaries can receive coverage for care in facilities 

through Medicaid. However, they must meet income and 

asset criteria to be eligible for Medicaid coverage. Often, 

beneficiaries have to incur enough medical expenses to 

“spend down” their income and assets to levels that make 

them eligible for Medicaid.

9 The Part B premium increased from $36.60 in 1993 to $58.70 

in 2003 in nominal terms and from $36.60 in 1993 to $46.82 

in 2003 in inflation-adjusted terms. Mean household income 

among Americans age 65 or older increased from $25,965 in 

1993 to $36,893 in 2003 in nominal terms and from $25,965 

in 1993 to $29,429 in inflation-adjusted terms.

10 Another motivation for obtaining supplemental insurance is a 

preference for predictable spending.

Endnotes
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 25

Census Bureau. 2005. Historical income tables—households, 

table H-10. http://www.census.gov/hhes/www/income/histinc/

h10ar.html. 

Centers for Disease Control and Prevention and Merck Institute 

of Aging and Health. 2004. The state of aging and health in 

America 2004. Atlanta, GA: CDC.

Chandra, A., J. Gruber, and R. McKnight. 2007. Patient costsharing, hospitalization offsets, and the design of optimal health 

insurance for the elderly. NBER working paper no. 12972. 

Cambridge, MA: NBER.

Chernew, M. E., D. P. Goldman, F. Pan, and B. Shang. 

2005. Disability and health care spending among Medicare 

beneficiaries. Health Affairs Web Exclusives (September 26). 

http://www.healthaffairs.org/.

Congressional Budget Office. 2003. Baby boomers’ retirement 

prospects: An overview, Chapter 3. Washington, DC: CBO. http://

www.cbo.gov/showdoc.cfm?index=4863&sequence=3&from=0. 

Cutler, D. M., E. L. Glaeser, and A. B. Rosen. 2007. Is the U.S. 

population behaving healthier? NBER working paper no. 13013. 

Cambridge, MA: NBER.

DeVaney, S. A., and S. T. Chiremba. 2005. Comparing the 

retirement savings of the baby boomers and other cohorts. http://

www.bls.gov/opub/cwc/cm20050114ar01p1.htm. 

Enrado, P. 2006. Medicare pilot off to a rocky start. Healthcare IT 

News. December 1.

Fontaine, K. R., D. T. Redden, C. Wang, et al. 2003. Years of life 

lost due to obesity. Journal of the American Medical Association

289, no. 2: 187−193.

Ford, E. S., W. H. Giles, and W. H. Dietz. 2002. Prevalence of the 

metabolic syndrome among U.S. adults. Journal of the American 

Medical Association 287, no. 3: 356−359.

Ford, E. S., W. H. Giles, and A. H. Mokdad. 2004. Increasing 

prevalence of the metabolic syndrome among U.S. adults. 

Diabetes Care 27, no. 10: 2444−2449.

Fowler-Brown, A., G. Corbie-Smith, J. Garrett, and N. Lurie. 

2007. Risk of cardiovascular events and death—Does insurance 

matter? Journal of General Internal Medicine 22, no. 4: 502−507.

Fronstin, P. 2005. The impact of the erosion of retiree health 

benefits on workers and retirees. Issue brief no. 279. Washington, 

DC: EBRI.

Gatz, M. J., A. Mortimer, L. Fratiglioni, et al. 2006. Potentially 

modifiable risk factors for dementia in identical twins. 

Alzheimer’s and Dementia: The Journal of the Alzheimer’s 

Association 2, no. 2: 110−117.

Gist, J. 2006. Boomer wealth−Beware of the median. AARP data 

digest no. 143. Washington, DC: AARP.

Goldman, D. P., G. F. Joyce, and P. Karaca-Mandic. 2006. Varying 

pharmacy benefits with clinical status: The case of cholesterollowering therapy. American Journal of Managed Care 12, no. 1: 

21−28.

Goodman, J. C., and P. R. Orszag. 2005. Common sense reforms 

to promote retirement security. Washington, DC: Retirement 

Security Project.

Henry J. Kaiser Family Foundation and Hewitt Associates. 2005. 

Prospects for retiree health benefits as Medicare prescription 

drug coverage begins. Publication no. 7439. Washington, DC: 

KFF/Hewitt.

Henry J. Kaiser Family Foundation and Hewitt Associates. 2004. 

Current trends and future outlook for retiree health benefits. 

Publication no. 7194. Washington, DC: KFF/Hewitt.

Howard, D. H., S. Busch, and K. E. Thorpe. 2006. Understanding 

recent increases in chronic disease treatment rates: Are we getting 

sicker or getting screened? Unpublished manuscript. October 5.

Hudman, J., and M. O’Malley. 2003. Health insurance premiums 

and cost-sharing: Findings from the research on low-income 

populations. Publication no. 4071. Washington, DC: Kaiser 

Family Foundation. 

Jha, A. K., T. G. Ferris, K. Donelan, et al. 2006. How common 

are electronic health records in the United States? A summary of 

the evidence. Health Affairs Web Exclusives (October 11). http://

www.healthaffairs.org/. 

Joyce, G. F., E. B. Keeler, B. Shang, and D. P. Goldman. 2005. The 

lifetime burden of chronic disease among the elderly. Health Affairs 

Web Exclusives (September 26). http://www.healthaffairs.org/. 

Lakdawalla, D. N., D. P. Goldman, and B. Shang. 2005. The 

health and cost consequences of obesity among the future 

elderly. Health Affairs Web Exclusives (September 26). http://

www.healthaffairs.org/. 

Medicare Payment Advisory Commission. 2007. Report to the 

Congress: Medicare payment policy. Washington, DC: MedPAC.

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26 Medicare in the 21st centur y: Changing beneficiar y profile

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Producing comparativeeffectiveness information

C h a p t er 2
R E C O M M E N D A T I O N

The Congress should charge an independent entity to sponsor credible research on comparative 

effectiveness of health care services and disseminate this information to patients, providers, and 

public and private payers. 

COMMISSIONER VOTES: YES 15 • NO 0 • NOT VOTING 0 • ABSENT 2
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 29

Producing comparativeeffectiveness information

C H A PT E R 2

Chapter summary

For the past several decades, the United States has spent an expanding 

share of its resources on health care. In 1960, national health 

expenditures made up about 5 percent of gross domestic product. 

That share had grown to 16 percent by 2004, and CMS projects that 

it will make up 20 percent by 2015 (Borger et al. 2006). Even though 

substantial resources are devoted to health care in the U.S., the value of 

services furnished to patients is often unknown.

There is not enough credible, empirically based information for 

health care providers and patients to make informed decisions about 

alternative services for diagnosing and treating most common clinical 

conditions. Many new services disseminate quickly into routine medical 

care with little or no basis for knowing whether they outperform 

existing treatments, and to what extent. 

Comparative-effectiveness analysis compares the relative value of 

drugs, devices, diagnostic and surgical procedures, diagnostic tests, 

and medical services. By value, we mean the clinical effectiveness 

In this chapter

• The United States needs 

more credible comparative 

information sponsored by an 

independent entity

• Increasing the capacity 

to produce comparativeeffectiveness information
30 Producing comparative-effectiveness infor mation

of a service compared with its alternatives. Comparative-effectiveness 

information has the potential to promote care of higher value and quality in 

the public and private sectors. 

Comparative information would help patients and providers become better 

informed and make value-based decisions. Most public payers—including 

Medicare—and private payers do not encourage patients or providers 

to consider the value of a service when making health care decisions. 

Information about the value of alternative health strategies might improve 

quality and reduce variation in practice styles. Use of comparativeeffectiveness research might improve health but will not necessarily reduce 

spending. Many effective treatments are underused, and effectiveness 

research might encourage their greater and more appropriate use (McGlynn 

et al. 2003). On the other hand, comparative-effectiveness research might 

reduce spending if, among a set of clinically comparable services, less costly 

services replace more costly services.

Although several public agencies conduct comparative-effectiveness 

research, it is not their main focus. For private-sector groups, conducting 

this type of research is costly. Because it is a public good, the benefits of 

comparative effectiveness—when it is publicly available—accrue to all users, 

not just to those who pay for it. Researchers have shown that some industrysponsored studies are biased. In addition, some health plans have expressed 

reluctance to use comparative-effectiveness information for fear of litigation.

Consequently, the Commission concludes that the Congress should establish 

an independent entity whose sole mission is to produce and provide 

information about the comparative effectiveness of health care services. 

Recommendation The Congress should charge an independent entity to sponsor credible research on 

comparative effectiveness of health care services and disseminate this information to 

COMMISS patients, providers, and public and private payers. IONER VOTES: 

YES 15 • NO 0 • NOT VOTING 0 • ABSENT 2
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 31

Since the information can benefit all users and is a public good, a federal 

role is necessary to produce the information and make it publicly available. 

Such an entity would:

• be independent and have a secure and sufficient source of funding;

• produce objective information and operate under a transparent process; 

• seek input on agenda items from patients, providers, and payers; 

• re-examine comparative effectiveness of interventions over time; 

• disseminate information to providers, patients, and public and private 

payers; and

• have no role in making or recommending coverage or payment decisions 

for payers.

There are different ways to carry out a federal role. The Commission prefers 

a public–private option, to reflect that all payers and patients will gain from 

comparative-effectiveness information. Funding could come from some 

public and some private sources or from all public sources. An independent 

board of experts should oversee the development of a research agenda and 

ensure that the research is objective and methodologically rigorous. 

The entity’s primary mission is to sponsor studies that compare the clinical 

effectiveness of a service with its alternatives. While cost effectiveness is 

not a primary mission, the Commission does not rule it out. In the simplest 

case, cost may be an important factor to consider for two services that are 

equally effective in a given population. But even when clinical effectiveness 

differs, it may be important for end users to be aware of costs. We emphasize 

that the entity would not have a role in how public and private payers 

apply this information—that is, coverage or payment decisions. Instead, it 

would produce and disseminate comparative-effectiveness information to 

purchasers, providers, and patients who would then decide how to use it.

The Commission envisions that the entity would contract out most of the 

research to outside groups, including existing governmental agencies, with 
32 Producing comparative-effectiveness infor mation

experience conducting comparative-effectiveness studies. Thus, a federal 

role need not result in a large expansion of the government. To ensure that 

its research is credible, the entity would collaborate with other researchers to 

help establish high standards for the methods used to conduct comparativeeffectiveness studies. 

Widespread use of the information will depend on the credibility of the 

entity conducting the studies. Operating under a transparent process and 

providing a public forum for stakeholders to critique ongoing work will 

enhance the credibility of the research. Because comparative effectiveness 

is a public good, the entity’s agenda should reflect priorities of public and 

private groups and encompass all patient groups. 

Disseminating the research findings to a wide audience will be an important 

function of the entity; it should not be treated as a minor activity to be 

undertaken after studies are completed. The entity should communicate its 

findings to reach audiences with different levels of sophistication. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 33

The United States needs more credible 

comparative information sponsored by 

an independent entity 

Comparative-effectiveness information would help patients 

and health care providers become informed and make valuebased decisions (see text box, p. 34, for more information 

on comparative-effectiveness analysis). It might also 

help CMS and other public and private payers formulate 

better payment policies. The United States does not have 

an independent entity whose sole mission is to sponsor 

and disseminate information about services’ comparative 

effectiveness. Although manufacturers do sponsor research 

on comparative effectiveness, it does not always focus on 

populations with multiple comorbidities and older and 

disabled populations. In addition, researchers have shown 

that some industry-sponsored studies are biased.

More comparative information could 

help support better decision making by 

providers and beneficiaries

Changes in technology are a major driver of health care 

spending, but public and private payers often incur high 

spending for services whose effectiveness is unknown. 

Providers and payers frequently do not know the extent to 

which the increased use of new, costly services improves 

patients’ outcomes. Providers lack enough scientific 

evidence to determine the likelihood of patients having 

improved outcomes with a certain course of treatment. 

In addition, scant scientific evidence is available to help 

identify which types of patients are most likely to benefit 

from a service. 

Many new services disseminate quickly into routine 

medical care without providers knowing whether they 

outperform existing treatments, and to what extent. For 

example, a recent study showed that inexpensive diuretics 

may control hypertension as effectively as expensive 

calcium-channel blockers (ALLHAT 2002). In other cases, 

providers do not discover side effects of a service until it 

has diffused into medical practice.1

The regulatory process of the Food and Drug 

Administration (FDA) for approving new technologies 

does not in general generate evidence that shows a 

service’s effectiveness relative to its alternatives.2 Most 

manufacturers conduct studies (referred to as phase III 

studies) that show the efficacy and safety of their drug 

or biologic relative to a placebo (inactive) agent. The 

FDA requires information about a drug’s or biologic’s 

effectiveness and safety relative to its alternatives only 

if the manufacturer wants to claim that its product is 

superior. For devices, the FDA requires safety and 

effectiveness information only for high-risk devices, such 

as stents, that pose a significant risk of illness or injury 

to patients.3 Finally, for new diagnostic and surgical 

procedures, less clinical information is available because 

the FDA does not review their safety and effectiveness.

Even for products approved by the FDA, little information 

is available about their long-term safety and effectiveness. 

Phase III clinical studies do not typically provide this 

information for drugs or devices because manufacturers 

usually conduct the studies over a relatively short time 

with a relatively small number of patients. Thus, long-term 

side effects may go undetected during phase III studies 

(Hunter 2006). In addition, the safety and efficacy of 

products in patients with conditions or comorbidities not 

included in phase III studies are unknown. Some clinical 

studies may be limited, excluding older patients and those 

with multiple illnesses. In addition, after the FDA approves 

a product, providers can prescribe it off-label—that is, to 

patients with conditions not evaluated in a clinical trial. 

The FDA has limited authority to require that 

manufacturers conduct postmarketing surveillance 

studies (GAO 2006). Postmarketing studies can either 

be required of or agreed to by a manufacturer after the 

FDA has approved its product for marketing.4 The FDA 

may request that a manufacturer conduct postmarketing 

studies to provide additional information on how a drug 

works in expanded patient populations or to identify 

safety issues that occur rarely or in special patient 

populations. The agency can require that manufacturers 

conduct postmarketing studies only for drugs that: (1) the 

FDA approved under the accelerated approval program 

because they are used to treat life-threatening illnesses, (2) 

providers prescribe to children, or (3) the FDA approved 

without information about their efficacy in humans. 

Once the FDA approves a drug, few manufacturers 

initiate further studies that examine its: (1) long-term 

safety, (2) effectiveness in patients not included in the 

approval clinical trials, or (3) effectiveness relative to its 

alternatives. Manufacturers spent 0.3 percent of sales 

on postmarketing studies in 2003 compared with 15.6 

percent of sales on research and development, which 

includes premarketing studies (Ridley et al. 2006). 

Between 2002 and 2006, the proportion of postmarketing 

commitments—studies that manufacturers are required 

to conduct or have agreed to conduct—that were on 
34 Producing comparative-effectiveness infor mation

schedule ranged from 15 percent to 21 percent of all 

commitments for drugs and 24 percent to 46 percent of 

all commitments for biologics (FDA 2007c, 2006, 2005, 

2004, 2003). During this same time period, the proportion 

of postmarketing commitments that manufacturers had not 

yet started (pending) ranged from 61 percent to 71 percent 

of all commitments for drugs and 24 percent to 37 percent 

of all commitments for biologics (FDA 2007c, 2006, 2005, 

2004, 2003).5 The Government Accountability Office 

found that the FDA lacked clear and effective processes 

Defining comparative effectiveness

Comparative-effectiveness analysis evaluates 

the relative effectiveness, safety, and cost of 

medical services, drugs, devices, therapies, 

and procedures used to treat the same condition 

(AcademyHealth 2005). Effectiveness implies the “realworld” performance of clinically relevant alternatives 

provided to patients with diverse clinical characteristics 

in a wide variety of practice settings. 

The outcomes that researchers assess in comparativeeffectiveness studies may include: 

• clinical outcomes, including traditional clinical 

endpoints, such as mortality and major morbidity; 

• functional endpoints, such as quality of life, 

symptom severity, and patient satisfaction; and

• economic outcomes, including the cost of health 

care services and cost effectiveness.

Some comparative studies only contrast the clinical 

and functional outcomes of alternative treatments while 

others also compare cost and assess cost effectiveness. 

An example of a comparative-effectiveness study is 

the National Emphysema Treatment Trial in which the 

National Institutes of Health compared lung-volumereduction surgery to medical therapy for patients with 

severe emphysema (National Emphysema Treatment 

Trial Research Group 2003). This study concluded 

that surgery increases the chance of improved exercise 

capacity but does not confer a survival advantage 

over medical therapy. It also concluded that the cost 

effectiveness for surgery compared with medical 

therapy was relatively unfavorable because of the high 

costs of the surgical procedure and the hospital stays 

during the first few months after surgery.

Researchers use two basic approaches to conduct 

comparative-effectiveness studies. In trial-based studies, 

they conduct a clinical trial and collect information on a 

wide variety of patient outcomes. Researchers often call 

these studies “practical clinical trials.” Alternatively, 

in review-based studies, researchers combine evidence 

from existing trials, studies published in the scientific 

literature, and other secondary data sources such as 

administrative claims data to answer the research 

questions. Practical clinical trials are more costly to 

conduct than review-based studies.

Researchers can use multiple approaches to assess 

the comparative effectiveness of a given service. For 

example, they might first analyze existing published 

clinical evidence and conduct studies using secondary 

data sources. Conducting head-to-head trials will be 

necessary for services that lack sufficient evidence in 

the literature and with outcomes that secondary data 

sources do not collect, such as tumor growth in cancer 

patients. To evaluate the effectiveness of services in 

different patient populations and to assess changes in 

the effectiveness of services over time, researchers may 

need to conduct more than one head-to-head trial. 

Cost-effectiveness analysis provides information 

about a service’s value relative to its alternatives. It 

synthesizes functional, clinical, and economic data to 

allow users to trace all the consequences of a particular 

decision. Researchers assess cost effectiveness by 

quantifying the incremental net health benefits (e.g., 

reduced mortality) and economic costs of alternative 

services. They calculate a cost-effectiveness ratio by 

dividing the incremental costs by the incremental 

benefits. Researchers refer to services with a smaller 

cost-effectiveness ratio as being more cost effective 

than those with a larger ratio. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 35

for making decisions about, and providing management 

oversight of, postmarket drug safety issues (GAO 2006). 

Patients have some information about differences among 

health care providers and the prices they charge but often 

they have little or no information about how well different 

treatments work. CMS and some private payers post 

information about the quality of care certain providers 

furnish but do not disseminate information to consumers 

on the effectiveness of alternative medical services. 

Often patients cannot make informed decisions rationally 

because the information on which to base the decision 

does not exist or is not understandable. Often, they rely on 

their health provider to decide for them (Slutsky 2007). 

As copayments and deductibles rise, patients may 

become more value conscious and their demand for 

comparative information may increase. For example, 

enrollees in consumer-directed health plans are more 

likely to identify and consider treatment alternatives 

and ask providers about cost than traditionally insured 

patients (McKinsey & Company 2005). Fronstin and 

Collins reported that patients in either high-deductible 

or consumer-driven health plans are more likely to 

use information about quality and cost than patients 

in comprehensive health plans (Fronstin and Collins 

2005). Nonetheless, little information is available to 

patients about the effectiveness of treatment alternatives. 

Even when it is available, the lack of standardization 

in measurement and reporting across treatments and 

providers poses a challenge to patients trying to use the 

information (Buntin et al. 2006).

Comparative information could help CMS

make better policies

In making national coverage determinations, CMS 

considers the clinical effectiveness of a service, but the 

clinical evidence is often for a younger population rather 

than for the elderly and disabled. As mentioned earlier, 

phase III clinical trials that manufacturers conduct to 

obtain FDA approval do not always demonstrate long-term 

safety and effectiveness in all patient populations who 

will eventually receive the service. In addition, evidence 

about the effectiveness of the service compared with its 

alternatives is infrequently available. CMS rarely uses 

clinical information to set payments. 

Some researchers contend that CMS needs to base its 

payment decisions on more complete clinical evidence 

when dealing with costly new services (Redberg 

2007). Investment in building a process for conducting 

comparative-effectiveness studies could lead to future 

use of this information in Medicare’s payment policies. 

Researchers have suggested several ways for CMS to use 

comparative-effectiveness information in the payment 

process including: 

• Creating a tiered payment structure that pays 

providers more for services that show more value to 

the program;

• Creating a tiered cost-sharing structure that costs 

patients less for services that show more value to the 

program; 

• Using the cost-effectiveness ratio to inform the 

payment level; 

• Not paying the additional cost of a more expensive 

service if evidence shows that it is clinically 

comparable to its alternatives; and

• Requiring manufacturers to enter into a risk-sharing 

agreement, which links actual beneficiary outcomes 

to the payment of a service based on its comparative 

effectiveness. Manufacturers might rebate the 

Medicare program for services that do not meet 

expectations for their effectiveness (Chernew et al. 

2007, MedPAC 2006).

Medicare might use comparative-effectiveness 

information to prioritize pay-for-performance measures, 

target screening programs, or prioritize disease 

management initiatives. A pay-for-performance program 

could link providers’ bonuses to the provision of services 

that are clinically effective and of high value. Medicare 

could consider comparative effectiveness when choosing 

measures for pay-for-performance programs; there are 

usually more potential measures than are practical to use.

Finally, Medicare’s national coverage process does 

consider a service’s clinical effectiveness but not its cost 

effectiveness or value. The coverage process may not be 

the area to begin to use cost-effectiveness information. 

Stakeholders raised many concerns when CMS tried to 

use cost-effectiveness information in the national coverage 

process (MedPAC 2005). Rigid use of cost-effectiveness 

information in the coverage process may not be consistent 

with Americans’ fear of limits set by public and private 

organizations and interest in access to new medical 

technology (Neumann 2004). 
36 Producing comparative-effectiveness infor mation

Comparative research sponsored by public 

and private entities 

Private entities assessing comparative effectiveness 

include health plans, pharmacy benefit managers, and 

manufacturers, but none systematically produces and 

publicly reports the information. Conducting this type 

of research is costly and, when it is publicly available, its 

benefits accrue to all, not just to those who pay for it. In 

addition, some health plans do not use the information 

because of concerns about litigation. Some researchers 

have shown that clinical and review studies sponsored by 

manufacturers may contain biases that affect the design of 

the study, methods, transparency, and results. These critics 

postulate that funding a study influences the outcomes 

reported in the study (Peppercorn et al. 2007, Heres et 

al. 2006). These findings color public confidence in the 

conclusions.

There is no comprehensive federal effort to conduct 

comparative-effectiveness studies designed to meet the 

needs of patients, providers, and payers. Conducting 

comparative-effectiveness studies is not the primary 

focus of any agency within the Department of Health and 

Human Services, although the following agencies generate 

this information: 

• CMS reviews and collects information about a 

service’s clinical effectiveness to help guide its 

national coverage decisions. On occasion, CMS 

requests help from the Agency for Healthcare 

Research and Quality (AHRQ) and the National 

Institutes of Health (NIH) to assess a service’s clinical 

and cost effectiveness.

• AHRQ conducts systematic reviews of the literature 

to compare the clinical effectiveness of alternative 

services (see text box). While these reviews do not 

include cost-effectiveness analysis, the prices of the 

comparative services are included in some reviews.6

For other projects, AHRQ has sponsored and 

conducted research examining patients’ outcomes, 

health care costs, and cost effectiveness.

• NIH is the largest sponsor of clinical trials that 

compare alternative treatments.

The FDA does not look at the clinical or cost effectiveness 

of a service relative to its alternatives. As mentioned 

earlier, the FDA typically reviews a service’s efficacy and 

safety compared with a placebo that manufacturers obtain 

from planned clinical trials. Table 2-1 (p. 38) summarizes 

the efforts and uses of clinical- and cost-effectiveness 

information by selected U.S. and international groups.

CMS’s efforts 

CMS assesses the clinical effectiveness of services when 

making national coverage decisions. In the past, the 

agency based these assessments primarily on reviewing 

available literature about the service. CMS is beginning to 

gather information about services’ clinical effectiveness 

through registries and clinical trials for services the agency 

might not have covered in the past because of insufficient 

data about the service’s clinical value. CMS refers to this 

approach as coverage with evidence development. In 

some cases, CMS supplements its research by sponsoring 

outside groups, such as NIH, to conduct head-to-head 

trials and AHRQ and the Medicare Evidence Development 

& Coverage Advisory Committee (MedCAC) to 

conduct and review technology assessments. A 

technology assessment studies the medical and economic 

implications of the development, diffusion, and use of 

services. MedCAC advises CMS on whether a service is 

reasonable and necessary under Medicare by reviewing 

and evaluating medical literature, reviewing technology 

assessments, and examining data and information on the 

effectiveness of the service under consideration.7 CMS 

then uses these recommendations to determine Medicare’s 

coverage policies for the service.

CMS does not consider clinical information in its payment 

process, with few exceptions. CMS uses patients’ anemia 

status when paying for erythropoietin for patients with 

end-stage renal disease on dialysis. In addition, the 

agency uses clinical information to determine when 

new technologies qualify for add-on payments under the 

inpatient hospital prospective payment system and passthrough payments under the outpatient hospital prospective 

payment system.

CMS does not routinely assess a service’s cost 

effectiveness in its coverage or payment process. The 

agency twice considered using information on cost 

effectiveness or value for national coverage decisions. 

Stakeholders raised a number of concerns about its use 

including that: (1) it would impair beneficiaries’ access to 

care and lead to rationing, (2) the methods researchers use 

to conduct the analyses are not sufficiently robust, and (3) 

it might slow innovation of new health care services. The 

Commission’s June 2005 and June 2006 reports discuss 

these issues (MedPAC 2006, 2005).
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 37

AHRQ’S efforts

AHRQ compares the clinical effectiveness of alternative 

treatments under a provision in the Medicare Prescription 

Drug, Improvement, and Modernization Act of 2003 

(MMA) that mandated the agency to conduct and support 

research with a focus on outcomes, comparative clinical 

effectiveness, and appropriateness of pharmaceuticals, 

devices, and health care services. The text box describes 

AHRQ’s comparative-effectiveness research activities. 

To fulfill the MMA mandate, AHRQ has: (1) put 

processes in place to select topics for analysis, review 

and synthesize the scientific literature, and obtain input 

from the public and private sectors; (2) developed the 

infrastructure to conduct comparative-effectiveness 

research and disseminate the information to providers 

and patients; (3) completed 8 effectiveness studies, with 

more than 30 studies in progress; and (4) disseminated the 

research findings to end users. 

Outside of the MMA mandate, AHRQ has conducted 

studies examining both the clinical effectiveness and cost 

Agency for Healthcare Research and Quality sponsors comparative clinical 

effectiveness research

The Medicare Prescription Drug, Improvement, 

and Modernization Act of 2003 (MMA) 

authorized the Agency for Healthcare Research 

and Quality (AHRQ) to synthesize, sponsor, and 

disseminate comparative clinical-effectiveness research. 

Specifically, Section 1013 of the MMA charges AHRQ 

with conducting research on the: (1) appropriateness, 

comparative clinical effectiveness, and outcomes 

of services; and (2) organization, management, and 

delivery of care.

To fulfill this mandate, AHRQ established the Effective 

Health Care Program, a coordinated and transparent 

program that funds: 

• thirteen evidence-based practice centers to perform 

systematic evidence reviews of the comparative 

effectiveness of alternative interventions;

• the DEcIDE (Developing Evidence to Inform 

Decisions about Effectiveness) Network to develop 

new evidence on effectiveness and comparative 

effectiveness of health care services using existing 

data sources, such as registries and electronic 

health records;

• eleven centers to perform research on the safe 

and effective use of drugs, biologics, and medical 

devices; and

• John M. Eisenberg Clinical Decisions and 

Communications Science Center to communicate 

comparative-effectiveness findings to diverse 

audiences, hold symposia on translational issues, 

and provide models for translational work.

Beginning in 2005, the Congress has appropriated 

$15 million per year for the agency to fulfill its MMA 

mandate (the MMA authorized up to $50 million for 

this research effort). Since initiating this effort, AHRQ 

has completed studies on:

• the comparative effectiveness of epoetin and 

darbepoetin for managing anemia in patients 

undergoing cancer treatment,

• the effectiveness of noninvasive diagnostic tests for 

breast abnormalities,

• gastroesophageal reflux disease,

• renal artery stenosis,

• the comparative effectiveness of second-generation 

antidepressants in the pharmacologic treatment of 

adult depression,

• the efficacy and comparative effectiveness of offlabel use of atypical antipsychotics,

• choices for pain medicine for osteoarthritis, and

• Medicare Part D plans’ medication therapy 

management programs.

In addition, 39 studies are ongoing under AHRQ’s 

Effective Health Care Program. The Eisenberg Center 

has held its first symposium on communicating risk 

to consumers, and a series of papers on this topic are 

awaiting publication in a peer-reviewed journal. 
38 Producing comparative-effectiveness infor mation

T A B L E

2 –1 Roles of selected organizations in conducting and using 

information about clinical and cost effectiveness

Organization Type of analysis Description of analysis

CMS Requires and collects clinical-effectiveness

information for some services

Sponsors and uses comparativeeffectiveness studies and technology

assessments*

Uses clinical information when making national coverage decisions.

Limited use in payment decisions (e.g., erythropoietin for dialysis

patients). Beginning to gather information about some services’

clinical effectiveness in the national coverage process—coverage with

evidence development—through registries and practical clinical trials.

AHRQ Conducts and sponsors comparativeeffectiveness reviews, technology

assessments, and CEAs

Has developed infrastructure to conduct comparative-effectiveness

reviews of health care services from the literature. Contracts with

13 evidence-based centers to conduct reviews and technology

assessments. Has conducted CEAs for CMS for selected services

(e.g., fecal occult blood tests). Has sponsored CEAs conducted

together with clinical trials.

NIH Conducts comparative-effectiveness

studies

Largest federal sponsor of clinical head-to-head trials.

FDA Requires information about a service’s

efficacy and safety

Reviews information about the efficacy and safety of drugs, biologics,

and devices for marketing in the U.S.; most manufacturers conduct

trials comparing a service with a placebo (an inactive treatment).

Does not require cost-effectiveness information. May request

manufacturers to collect clinical data after a service’s approval (i.e.,

postmarketing surveillance studies).

VA Conducts and uses clinical and

comparative-effectiveness studies and

CEAs

Requires CEAs from manufacturers of drugs that have small

differences in quality but large differences in cost compared with

their alternatives. Uses information in the formulary decision-making

process.

Cooperative studies program conducts clinical research including

comparative-effectiveness trials. Program on health services research

and development examines the organization, delivery, and financing

of health care. Research on a wide variety of services ranging from

assessing the cost effectiveness of ICDs to improving safety culture

and outcomes in VA hospitals.

Oregon University’s

Drug Effectiveness

Review Project

Sponsors comparative-effectiveness

studies of drugs

Conducts comparative-effectiveness reviews to obtain effectiveness

comparisons between drugs. Collaborative effort of 14 organizations.

Does not review information about cost effectiveness.

Washington state Sponsors technology assessments Recently signed into law a health technology assessment program to

consider evidence about the safety, efficacy, and cost effectiveness of

services.

Note: 	 AHRQ (Agency for Healthcare Research and Quality), CEA (cost-effectiveness analysis), NIH (National Institutes of Health), FDA (Food and Drug Administration), VA

(Department of Veterans Affairs), ICD (implantable cardioverter defibrillator), NICE (National Institute for Health and Clinical Excellence). For more description of the

activities of these organizations, see Chapter 10 in the Commission’s June 2006 report (MedPAC 2006).

*Technology assessments can include a review of the clinical and economic evidence about one or more services. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 39

T A B L E

2 –1 Roles of selected organizations in conducting and using 

information about clinical and cost effectiveness (cont.)

Organization Type of analysis Description of analysis

Manufacturers (of

drugs and devices)

Sponsor comparativeeffectiveness studies and

CEA

Sponsor an increasing proportion of CEAs over the years. Use information

to show value of service to purchasers, determine pricing strategies, and

inform marketing decisions. Also sponsor premarketing studies, comparativeeffectiveness studies, and postmarketing surveillance studies. 

Commercial

payers/plans

Use clinical effectiveness

and CEA for drugs

Use information about

clinical effectiveness and

cost for services other than

drugs

Plans’ pharmacy and therapeutics committees use clinical effectiveness and CEA

for development of drug formularies, treatment guidelines, prior authorization

and step therapy requirements, and tiered copayments.

Less reliance of CEA for services other than drugs. Primarily rely on evidence

about clinical effectiveness. Some consider cost by, for example, requiring use

of less costly alternatives.

Blue Cross Blue Shield

Evaluation Center

Conducts reviews of the

clinical effectiveness of

services

Examines clinical effectiveness and appropriateness of services to subscribing

commercial health plans and provider groups. Does not usually assess costs or

cost effectiveness.

NICE in the United

Kingdom

Sponsors and uses

technology assessments

including CEA

An independent group that provides guidance to the National Health Service

on health care services. Commissions independent academic groups to conduct

technology assessments, which includes CEAs. Uses information to develop

coverage policies. Uses a National Horizon Scanning Centre to identify

significant new and emerging health technologies.

Canadian Agency

for Drugs and

Technologies in Health

Sponsors technology

assessments including CEA

An independent nonprofit body funded by the federal, provincial, and territorial

governments. Provides evidence-based information on services including drugs,

devices, procedures, and best practices. Uses a program that alerts decision

makers to upcoming services that are likely to have a significant impact on the

delivery of health care in Canada. Technology Assessment Program examines

clinical and cost effectiveness of drugs, medical technologies, and health systems.

For drugs, reviews clinical- and cost-effectiveness information submitted by

manufacturers. Recommends reimbursement options (unrestricted use, limited

use, prior authorization) to provinces. Periodically conducts CEAs of a whole

class of drugs and reconsiders past reimbursement decisions.

Pharmaceutical

Benefits Advisory

Committee in Australia

Uses CEA for drugs An independent statutory body that makes recommendations and gives advice

to the Department of Health and Ageing about which drugs should be made

available as pharmaceutical benefits. Reviews information about clinical and

cost effectiveness submitted by manufacturers. 

Note: 	 AHRQ (Agency for Healthcare Research and Quality), CEA (cost-effectiveness analysis), NIH (National Institutes of Health), FDA (Food and Drug Administration), VA

(Department of Veterans Affairs), ICD (implantable cardioverter defibrillator), NICE (National Institute for Health and Clinical Excellence). For more description of the

activities of these organizations, see Chapter 10 in the Commission’s June 2006 report (MedPAC 2006).

*Technology assessments can include a review of the clinical and economic evidence about one or more services. 
40 Producing comparative-effectiveness infor mation

effectiveness of services for CMS and NIH. For example, 

CMS requested that AHRQ assess the cost effectiveness 

of drugs used to treat rheumatoid arthritis and multiple 

sclerosis under a MMA-mandated demonstration 

(CMS 2007). CMS also requests that AHRQ conduct 

technology assessments, such as an assessment of the 

use of neuroimaging techniques in evaluating breast 

cancer, Alzheimer’s disease, and dementia. In other 

instances, AHRQ completed an assessment for CMS 

of the cost effectiveness of fecal occult blood tests. 

AHRQ also collaborates with NIH. On a recurring basis, 

AHRQ provides systematic reviews using its evidencebased practice centers for numerous groups within NIH, 

including the Office of Medical Application Research, the 

Office of Dietary Supplements, the Office of Women’s 

Health Research, the National Cancer Institute, and the 

National Center for Complementary and Alternative 

Medicine.

Conducting comparative-effectiveness research is not 

AHRQ’s main mission, although the agency’s efforts 

in this area are significant. Its primary mission is to 

conduct and sponsor health services research—the 

multidisciplinary field of scientific investigation 

that studies how social factors, financing systems, 

organizational structures and processes, health 

technologies, and personal behaviors affect access 

to health care, the quality and cost of health care, 

and the health and well-being of the U.S. population 

(AcademyHealth 2005). 

NIH’s efforts 

NIH is the largest sponsor of head-to-head trials. 

Researchers can structure head-to-head clinical trials 

comparing alternative services to include a diverse 

patient population, recruit patients from heterogeneous 

practice settings, and collect data on a broad range of 

health outcomes (Tunis et al. 2003). For example, NIH 

and CMS cosponsored the ongoing head-to-head trial 

comparing more frequent hemodialysis with thrice weekly 

(conventional) hemodialysis for patients with end-stage 

renal disease. 

Examples of other public agencies’ efforts 

The Department of Veterans Affairs (VA) also sponsors 

head-to-head clinical trials and cost-effectiveness analyses 

specific to its patient population. Since 1994, the VA 

has required a formal cost-effectiveness analysis from 

manufacturers of drugs that have small differences in 

quality but large differences in cost compared with their 

alternatives (Aspinall et al. 2005). The VA routinely 

requests manufacturers to submit clinical and economic 

data using the Academy of Managed Care Pharmacy 

format and incorporates this information into the drug 

reviews used in the formulary decision-making process. 

The Drug Effectiveness Review Project (DERP) at 

Oregon Health & Science University compares the clinical 

effectiveness of drugs within a given therapeutic class 

using information from the scientific literature. Now in its 

fourth year, the DERP is a self-governing collaboration 

of 14 states that aggregated their resources to review the 

clinical evidence of about 26 drug classes. The project 

does not look at the cost effectiveness of alternative 

drugs because health care costs vary from state to state. 

However, each state can conduct its own cost-effectiveness 

analysis by applying its own costs. 

The private sector does not systematically 

produce and disseminate objective 

comparative-effectiveness information 

Manufacturers conduct studies assessing the clinical and 

cost effectiveness of their products, but some researchers 

have critiqued these studies and raised concerns that these 

efforts may not always be objective and available to the 

public. Researchers have shown that industry-sponsored 

studies were significantly more likely to reach conclusions 

favorable to the sponsor than were non-industry-sponsored 

studies. Jorgensen and colleagues (2006) concluded that 

industry-supported reviews were less transparent, noted 

few reservations about methodologic limitations of the 

included trials, and had more favorable conclusions than 

reviews conducted by an independent nonprofit group 

(Cochrane Collaboration). Bias in drug trials is common 

and often favors the sponsor’s product (Peppercorn et al. 

2007, Heres et al. 2006, Als-Nielsen et al. 2003). Possible 

sources of bias in industry-sponsored trials include: (1) the 

dose of the drug studied, (2) the exclusion of patients from 

the study population, (3) the statistics and methods used, 

and (4) the reporting and wording of results. 

Bekelman and colleagues have shown that financial 

relationships among manufacturers, scientific 

investigators, and academic institutions are widespread 

(Bekelman et al. 2003). Relationships between members 

of institutional review boards and manufacturers are 

common and members sometimes participate in decisions 

about protocols sponsored by companies with which 

they have a financial relationship (Campbell et al. 2006). 

Researchers have also raised concerns that manufacturers 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 41

influence the adoption of clinical guidelines that serve 

their own financial goals (Eichacker et al. 2006). 

Pharmacy benefit managers, health plans, and other large 

providers (e.g., hospitals) consider a service’s clinical 

effectiveness, cost, and cost effectiveness, particularly 

for their drug formularies, but do not necessarily make 

their evaluations public. These groups often focus on 

proprietary internal studies related to their health care 

practices (Kupersmith et al. 2005). Private-sector efforts 

do not typically focus on patients who are 65 years or 

older, disabled populations, or patients with end-stage 

renal disease—the populations of interest to Medicare. 

Few private-sector groups systematically produce clinicaland cost-effectiveness information and make it available 

to the public. One exception is the Technology Evaluation 

Center (TEC) established by Blue Cross Blue Shield 

Association, which posts reports on the Internet. The TEC 

relies on reviewing the existing literature to compare the 

clinical effectiveness of alternative services. 

Concerns about liability might affect some private plans’ 

use of cost-effectiveness information in their decisionmaking process (Jacobson and Kanna 2001). In one 

survey of health plan officials, most respondents said 

they approved equally effective but costlier treatments for 

fear of litigation (Singer et al. 1999). Some health plans 

reluctantly agreed to cover high-dose chemotherapy with 

autologous bone marrow transplant for breast cancer partly 

in response to the threat of litigation, despite its high cost 

and the lack of evidence that it was effective (Mello and 

Brennan 2001). 

A public role is necessary in comparativeeffectiveness research 

Some researchers have noted that comparative 

effectiveness is a public good (Wilensky 2006, Kupersmith 

et al. 2005, Reinhardt 2004, Perry and Thamer 1999). An 

item is a public good if it demonstrates: 

• “Nonexcludability”: Once comparative-effectiveness 

information is publicly available, it is difficult to stop 

other groups from using the research free of charge. 

• “Nonrivalness”: One group’s use of the information 

does not detract from its use by other groups. 

Economic theory argues that the private sector will 

underproduce goods or services (or in this case 

information) that meet this definition and that a 

government role is necessary to ensure that a sufficient 

supply is available. Conducting this type of research is 

costly and, when it is publicly available, its benefits accrue 

to all, not just to those who pay for it (Bloche 2006, 

Kupersmith et al. 2005, Neumann et al. 2005). Although 

health plans have some of the clinical data to conduct 

more of this research, they lack incentives to support it at 

the needed levels. 

Increasing the capacity to produce 

comparative-effectiveness information

Little objective, credible, and high-quality information 

is publicly available that compares the effectiveness and 

costs of health care services furnished to patients. There 

is no independent entity in the U.S. whose sole mission 

is to compare the benefits, risks, and costs of alternative 

services and make this information publicly available. 

Comparative-effectiveness research is costly to generate 

and sponsors have difficulty recouping the costs of 

producing the research because other users will not pay to 

use the research once it is publicly available. Consequently, 

the Commission concludes that a federal role is necessary 

to help increase the capacity to generate comparativeeffectiveness information. 

R ec o mmenda t i o n

The Congress should charge an independent entity to 

sponsor credible research on comparative effectiveness of 

health care services and disseminate this information to 

patients, providers, and public and private payers.

R a t i o nale

More information on the comparative effectiveness of 

health care services could increase the value of health 

care spending. Public and private payers could use 

the information to help inform their payment policies 

and coverage decisions. Current public and private 

organizations do not produce enough objective and 

credible information about which services work best and 

for which populations. This information has the potential 

to improve quality of care and reduce variations in health 

care utilization. 

I m p lica t i o n s

Spending

• Increasing the capacity to examine the comparative 

effectiveness of health care services would likely 

increase federal administrative spending relative to 

current law. 
42 Producing comparative-effectiveness infor mation

Beneficiary and provider

• Information on the comparative effectiveness of health 

care services could improve decision making by 

patients, providers, and payers. 

To improve the evidence base on the effectiveness of 

health care services, the United States needs an impartial 

entity whose mission is to independently develop evidence 

about the comparative effectiveness of alternative 

treatments, including drugs, medical devices, surgical 

and diagnostic procedures, and medical services. The 

entity’s functions would include systematically reviewing 

existing evidence, sponsoring or conducting new studies, 

and reporting the information to patients, providers, and 

public and private payers in a user-friendly format. Such 

an entity would:

• be independent and have a secure and sufficient 

source of funding;

• produce objective and credible information;

• operate under a transparent process and establish 

standardized and credible methods;

• seek input on agenda items from patients, providers, 

and payers; 

• re-examine comparative effectiveness of interventions 

over time; 

• disseminate information to providers, patients, 

decision support vendors, associations, and federal 

and private health plans; and

• have no role in making or recommending either 

coverage or payment decisions for public or private 

payers.

The entity’s primary mission is to sponsor studies that 

compare the clinical effectiveness of a service with its 

alternatives. While cost effectiveness is not a primary 

mission, the Commission does not rule it out entirely. 

In the simplest case, cost may be an important factor to 

consider for two services that are equally effective in a 

given population. But even when clinical effectiveness 

differs, it may be important for end users to be aware of 

costs. We emphasize that the entity would not have a role 

in how public and private payers apply this information—

that is, coverage or payment decisions. Instead, it would 

produce and disseminate comparative-effectiveness 

information to purchasers, providers, and patients who 

would then decide how to use it.

To carry out its activities effectively, the entity needs to 

develop a clear rationale for selecting the services to study, 

use rigorous methods and the best scientific evidence to 

conduct its research, and provide for an opportunity for 

comment and participation from different constituent 

groups, including patients, providers, specialty groups, 

and manufacturers. Setting up a transparent process that 

is understandable, clear, and documented to produce 

objective research will be important; people might not 

use the research if they consider the process subjective 

and the results biased. The entity should help develop 

the “gold standard” of research methods used to conduct 

comparative-effectiveness studies by collaborating with 

other researchers with expertise in this field.

Along with considering the functions of the entity, 

policymakers will also need to consider its characteristics. 

The remainder of this chapter describes options for 

configuring and financing an entity that produces 

comparative-effectiveness information and their 

advantages and disadvantages. The Commission has 

not yet reached a conclusion about the best approach; 

we intend to continue looking at the pros and cons of 

different options. Policy analysts have proposed different 

options, including placing such an entity in an existing or 

new federal agency, a public–private entity, or a private 

entity. Some policy analysts have also proposed including 

a board—a panel of experts—as a way to promote the 

entity’s transparency. The entity could receive funding 

from voluntary or mandatory federal sources, private 

sources, or some combination of the two. 

The independence and stability of the entity will largely 

depend on its governance and funding. For example, 

an entity that relies on federal appropriations might be 

more susceptible to political pressures than an entity 

with mandatory funding (e.g., from the Medicare trust 

fund). Each year, the Congress considers the spending for 

services financed from appropriations; by contrast, the 

statute guarantees spending for services financed from 

mandatory sources. Even so, entities with a mandatory 

funding source face some political pressure because the 

Congress always has the option to alter their funding. 

Private groups who voluntarily fund the entity might 

attempt to control the entity’s research agenda. In addition, 

the entity’s governance and funding will affect some 

constituents’ perception of the research it produces. Some 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 43

stakeholders want an entity that is close to or within 

the government while others are concerned about too 

much government involvement. Finally, the functions 

of the organization should help steer its structure. The 

entity’s staff will need to be proficient in designing 

comparative-effectiveness research but can take advantage 

of experienced public agencies and independent private 

groups by contracting studies to them. 

Functions and activities of a comparativeeffectiveness entity 

Policymakers should consider numerous process issues 

when developing the capacity to sponsor and disseminate 

information about the comparative effectiveness of 

alternative health care services. The rest of this section, 

based on reports submitted by Moon and by Neumann and 

Cohen, discusses some of the process issues to consider 

(Moon 2007, Neumann and Cohen 2007). 

Identifying research priorities

The Commission envisions that the entity’s research 

agenda is broader than Medicare; the agenda would 

include services important to all patient groups. For 

the entity’s research to be relevant, its users—patients, 

providers, and public and private payers—should help 

inform the agenda. To help develop its process for setting 

research priorities, the entity could review the criteria 

used by existing organizations that conduct comparativeeffectiveness research, including AHRQ and the National 

Institute for Health and Clinical Excellence in the United 

Kingdom (NICE). 

For its comparative clinical-effectiveness program 

(Effective Health Care Program), AHRQ’s selection 

criteria include: 

• the severity, incidence, and prevalence of the 

condition; 

• the uncertainty about the service and the availability of 

data to support a systematic review and analysis of the 

topic; 

• the potential impact of the research for reducing 

clinically significant variations in the prevention, 

diagnosis, treatment, and management of a condition 

or in the use of a service; and

• the topic’s policy relevance to Medicare, Medicaid, 

and other federal health care programs.

In addition, AHRQ’s website provides an opportunity for 

stakeholders—patients, providers, policymakers, health 

care scientists, clinical practice organizations, quality 

improvement organizations, and health care plans—to 

suggest topics for future research (AHRQ 2007a). 

NICE uses similar criteria in identifying topics for study. 

Specifically, NICE considers: (1) the burden of the disease 

(e.g., its prevalence and mortality), (2) cost impact, (3) 

policy importance, and (4) whether the service’s use varies 

across the country. Like AHRQ, NICE’s website allows the 

public to suggest a topic for future study; NICE also meets 

with health professionals, patients, and policymakers. 

Finally, the National Horizon Scanning Centre (NHSC) 

provides information on new and emerging technologies, 

including different uses of existing technologies, that 

might require NICE’s evaluation (NHSC 2007). The 

scope of its activity includes pharmaceuticals, devices, 

diagnostic tests and procedures, surgical and other 

interventions, rehabilitation and therapy, public health, 

and health promotion activities. NHSC produces briefings 

that outline what the technology is, its likely patient group, 

the current treatment alternatives, the level and amount 

of research evidence available, and a prediction of its 

relevance both clinically and to the U.K.’s National Health 

Service.

Other researchers have developed methods to set priorities 

for evaluative research by quantifying the gains from 

research. Phelps and Parente, for example, developed an 

index of expected gains from research, which incorporates 

spending levels for a particular condition and the degree of 

variation in intervention strategies to establish a first-cut 

priority list (Phelps and Parente 1990). The researchers use 

variation to suggest the degree of uncertainty associated 

with a particular technology and thus the opportunity 

for research to affect practice patterns. Areas with high 

spending and large variation receive higher priority.

Designing safeguards to ensure that private 

funding sources do not affect study results

If private groups with a vested interest in the outcome 

of the research help fund the research entity, it is 

important to ensure that they cannot influence the study 

results. Otherwise, some stakeholders may not consider 

information the entity produces to be objective. As 

mentioned earlier, researchers have shown that some 

private groups that fund clinical- and cost-effectiveness 

research affect the objectivity of the research and the 

likelihood of publishing the findings.
44 Producing comparative-effectiveness infor mation

No single private group should dominate the process or 

bias the research. Policymakers might consider limiting 

the amount any private group can contribute to funding 

the entity. Requiring all private groups to fund the entity 

might ensure that no single private group can influence 

the entity’s research. For example, assessing a small 

fee on all private health-related groups—including 

manufacturers, payers, and providers—would provide for 

broad-based funding rather than funding limited to one 

group (Reinhardt 2004). Another option is for a nonprofit 

foundation to distribute private contributions to the entity 

conducting comparative-effectiveness research. We discuss 

some pros and cons of different funding approaches later 

in the chapter.

Producing unbiased information

Some clinical- and cost-effectiveness studies show biases 

of investigators and their sponsors. As mentioned earlier, 

industry-sponsored analyses tend to report more favorable 

results than non-industry-sponsored studies (Peppercorn et 

al. 2007). Ensuring that analysts work independently and 

objectively will be a critical issue. Ethics rules might help 

ensure that analysts working on behalf of the entity avoid 

involvement in any real or apparent conflict of interest. 

Ethics rules would address issues such as whether analysts 

can accept compensation from outside sources and 

requirements for regularly reporting financial interests. 

Scope of activities

Whether the entity is new or an existing group, it will 

need to conduct and sponsor comparative-effectiveness 

research. This section describes the scope of activities that 

we envision an entity would carry out. 

Comparative-effectiveness research involves synthesizing 

existing data and research from the scientific literature. 

Another option is to design studies that use administrative 

claims data from public and private payers. There may 

be opportunities to use databases developed by providers 

and other private-sector groups. In the future, electronic 

medical records might become a source of important data 

for comparative-effectiveness research if providers widely 

adopt information technology. When existing data sources 

do not provide sufficient information on comparative 

effectiveness, the entity will need to sponsor head-tohead clinical trials to generate the data needed to assess 

comparative effectiveness. Researchers could collect 

information on patients’ functional and clinical outcomes 

as well as measures of value and resource use. 

The entity will need in-house staff with experience in 

designing and conducting comparative-effectiveness 

research. To avoid duplicating expertise, the entity could 

contract out research to federal and state agencies and 

research groups with experience conducting comparativeeffectiveness research and communicating the information. 

AHRQ, for example, supports 13 evidence-based practice 

centers that review relevant scientific literature to produce 

evidence reports and technology assessments (Clancy et al. 

2004).8

The research the entity sponsors will need to examine 

comparative effectiveness in relevant patient populations 

and in different patient care settings. Because the health care 

delivery system might affect the usefulness of some services, 

it will also be important to consider the effectiveness of 

services provided under different delivery systems. 

The entity will need to establish guidelines for studies 

that it conducts and that it contracts out to public and 

private research groups. Work conducted by other U.S. and 

international organizations can help inform this process. It 

will not be necessary to reinvent mechanisms that are now 

working well. Consensus on the entity’s methods from the 

research community is essential to establish the entity’s 

credibility.

As the key U.S. entity focused on comparativeeffectiveness research, the entity could have other 

responsibilities apart from conducting or sponsoring 

comparative-effectiveness research. For example, 

the organization could also sponsor conferences or 

scientific symposia on a host of issues surrounding the 

use of comparative-effectiveness analysis, including 

methodologic questions. 

The organization should be aware of the comparativeeffectiveness research done by other organizations. As 

mentioned earlier, the research of other groups, such 

as AHRQ, CMS, NIH, and the VA, may overlap with 

the entity’s comparative-effectiveness research agenda. 

Coordination with public and private groups would ensure 

that agencies do not duplicate research.

Transparency and stakeholder input

It will be important for the organization to have a 

transparent process and to obtain input from stakeholders, 

including manufacturers. For example: 

• AHRQ posts draft reports online and accepts public 

comments for about four weeks. AHRQ then considers 

public comments for incorporation into the final report. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 45

• NICE publishes its studies on its website at several 

stages, including the scope of study, the literature 

review, and draft guidance. Moreover, NICE meets 

with all stakeholder groups, including relevant patient 

organizations, doctors, pharmaceutical companies, and 

a citizens’ council.

Re-examining a service’s effectiveness over time

For some services, the entity will need to re-examine their 

clinical and cost effectiveness as new information becomes 

available. Reasons for a service’s re-evaluation include 

its use in populations not examined by the original study, 

new information about the service’s clinical effectiveness, 

and a change in practice patterns that affects the use or 

cost of the service. Moreover, it will be important to 

validate models as new clinical evidence emerges. Some 

researchers have found that predictions from models were 

more optimistic than results in subsequent clinical trials 

demonstrated.

Disseminating information to all users

It will also be important to disseminate the findings 

from the comparative-effectiveness research to multiple 

audiences of different levels of sophistication, in culturally 

appropriate and consumer-friendly ways. Disseminating 

the findings is not a minor activity and should not be 

isolated from the review process. Rather, the entity 

needs to view dissemination as a crucial component 

of developing the capacity to produce comparativeeffectiveness research. Otherwise, efforts to circulate 

the findings may be disorganized and haphazard and the 

findings may not reach all potential users. Matchar and 

colleagues concluded that failing to integrate research 

and dissemination goals could derail efforts to translate 

research into meaningful action, while actively integrating 

research and dissemination goals can promote more 

effective dissemination (Matchar et al. 2005). Thus, the 

entity should consider the tasks involved in disseminating 

the results when it initiates a study. 

It will be important to tailor the reporting of the study and 

its results to its audience. Getting the input of consumers 

and providers early in the process might be valuable in 

designing materials that will reach all potential users. 

Information will be useful to patients only if the entity 

provides the results in a format that is concise and easy 

to understand. AHRQ has experience in developing 

information that targets multiple users. For example, 

the agency developed separate guides for consumers 

and clinicians that summarize in plain language the 

effectiveness, risks, and prices of the different drug 

treatments for osteoarthritis (AHRQ 2007b). AHRQ 

based both guides on the findings of its comparativeeffectiveness review of analgesics for osteoarthritis 

that it carried out under the MMA mandate to conduct 

comparative-effectiveness research.

Researchers will need to translate the technical results 

from comparative clinical- and cost-effectiveness 

analysis to plain language that patients and providers can 

understand. Pearson developed a framework for displaying 

information about a service’s comparative clinical 

effectiveness and value in a user-friendly fashion. For each 

service, a grid ranks the service’s clinical effectiveness as 

superior, incremental, comparable, promising, or uncertain 

and ranks its comparative value as superior, reasonable, or 

poor (Health Industry Forum 2006). 

Training potential users—including patients, providers, 

professional associations, and schools of medicine—is an 

important function to ensure that the information is used. 

The entity could help to set up the process by developing 

standards for training and technical assistance, which can 

take many forms, including face-to-face, by video and 

teleconference, or via the Internet. The goal of training and 

technical assistance is to foster widespread adoption of 

evidence-based practices. Training and technical assistance 

may not be a direct responsibility of the entity, but the 

entity could contribute to this important activity. 

Developing human capital

An adequate supply of qualified researchers will be 

needed to conduct comparative-effectiveness research. 

The entity could develop programs that train investigators 

and institutions to do the research. For example, AHRQ 

provides predoctoral and postdoctoral educational and 

career development grants in health services research. 

AHRQ also provides institutional-level grants to support 

the planning and development of health services research 

in certain types of institutions. NIH also offers a wide 

variety of research training opportunities, including 

programs for postbaccalaureate, postdoctoral, medical, 

and dental students.

Structuring an entity to examine and 

report on comparative effectiveness 

In this section, the Commission begins to explore the 

pros and cons of different ways to configure and finance 

the entity that produces comparative-effectiveness 

information. At this point, the Commission reaches no 
46 Producing comparative-effectiveness infor mation

conclusions and plans to evaluate these options in the 

future. 

In evaluating the different governance and funding options, 

policymakers might consider whether: (1) users will judge 

the research as being objective, credible, and produced 

with minimal or no conflict of interest and bias; (2) the 

entity is independent of various stakeholders and political 

pressures; and (3) the entity is stable (Wilensky 2006). 

Governance options 

One option is to establish the entity within an existing 

federal agency or a new federal agency. An entity within 

an existing federal agency could build on the existing 

capacity of the agency, such as AHRQ, NIH, or CMS. 

Another option is to create a new agency not under an 

executive branch agency. Establishing an external board 

composed of independent experts to advise the entity 

about research priorities and to provide oversight for 

conducting research might promote transparency and the 

credibility of the findings. 

Some constituents are concerned about creating a new 

bureaucracy. Others have raised concerns about placing 

the entity within an existing federal agency. Providers 

and patients may be more distrustful of the motives of an 

entity if an existing federal agency that will ultimately 

use the entity’s research findings (e.g., CMS) houses 

the center. As mentioned earlier, stakeholders in the past 

had many concerns when CMS considered including 

cost effectiveness or value in the national coverage 

process, including that it would lead to rationing of care. 

Another disadvantage of expanding the scope of an 

existing federal agency is that stakeholders who do not 

support conducting comparative-effectiveness research 

could place funding for all its functions at risk. Placing 

an entity within the federal government could limit 

opportunities for private-sector funding, although the 

FDA does accept private funding in the form of user fees 

the manufacturers pay.

A public–private entity with an external board is another 

option to consider. For example, the Federal Reserve 

System (the central bank of the United States) has a 

unique public–private structure that enables it to operate 

independently within government but not independent 

of government. Although the Federal Reserve is required 

to report to the Congress on its activities, neither the 

president nor the Congress approves its decisions. The 

Federal Reserve consists of a federal agency (the Board 

of Governors) and private entities (12 federally chartered 

corporations known as Federal Reserve Banks). The Board 

of Governors, appointed by the president and confirmed 

by the Senate, represents the public sector.9 The Reserve 

Banks and the local citizens on their boards of directors 

represent the private sector. This structure provides 

accountability while avoiding centralized, governmental 

control of banking and monetary policy (GAO 1996). 

Unlike most other federal commissions, the Federal 

Reserve is a self-financing entity; it does not receive 

congressional appropriations. 

Other examples of public–private entities discussed by 

researchers for situating a comparative-effectiveness 

entity include federally funded research and development 

centers (FFRDCs) and congressionally chartered 

nonprofit organizations. The 37 existing FFRDCs are 

organizations that an executive branch agency sponsors 

but an academic or private organization operates and 

that can perform work for organizations other than the 

sponsoring agency (AcademyHealth 2005, CRS 2005) 

(Table 2-2, pp. 48–49). By contrast, congressionally 

chartered nonprofit organizations do not have a “parent” 

agency and can receive more funding from the private 

sector. The text box provides more information about 

FFRDCs, congressionally chartered organizations, and 

other types of public–private entities. 

A public–private entity might address some stakeholders’ 

concerns about too much federal government involvement 

but still provide for strong public-sector involvement 

and oversight. In addition, a public–private entity might 

provide a better balance of different perspectives than 

an entity that is either all public or all private. However, 

voluntary funding of a public–private entity would make it 

as susceptible to stakeholder pressures as an entity within 

a federal agency. 

Another option is to establish a comparative-effectiveness 

entity within a private-sector entity—for example, a new 

or existing independent nonprofit group could take the 

lead generating comparative-effectiveness information. A 

private-sector entity would minimize concerns about the 

government’s influence on the research agenda and the 

entity’s findings. On the other hand, it would be difficult 

for the federal government to fund such an entity without 

being involved in its governance. Some stakeholders who 

are already uneasy about the influence of manufacturers 

on clinical trials and reviews might be concerned about the 

potential for bias if a private-sector group took the lead to 

generate comparative-effectiveness information. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 47

Funding options

Whether public or public–private, mandatory federal 

funding might result in the entity being more stable than 

if it had voluntary federal funding. One option for funding 

is for the Congress to appropriate funds, which would 

require policymakers to annually consider the priority 

of such research compared with other health programs. 

However, variations in the level of federal appropriations 

may reflect factors other than the priority of the research. 

In addition, voluntary funding could result in an unpopular 

report affecting the entity’s budget. 

Voluntary contributions from private groups—such as 

private plans and payers and manufacturers of drugs, 

biologics, and medical devices—could also be vulnerable 

Examples of public–private entities

Public–private (quasi-government) entities are 

organizations that have some legal relation or 

association with the federal government. The 

term includes many different types of organizations that 

share one common characteristic: They are not agencies 

of the federal government (CRS 2005). Researchers 

have considered three types of quasi-government 

entities for housing a comparative-effectiveness center: 

federally funded research and development center 

(FFRDC), agency-related nonprofit organization, and 

congressionally chartered nonprofit organization. We 

also describe government corporations, another public–

private entity, in this text box.

FFRDCs are nonprofit private organizations that federal 

agencies can sponsor to achieve a long-term research 

need that cannot be met as effectively by using in-house 

or contractor resources. The first FFRDC was RAND, 

created by the Air Force in 1947; currently 37 FFRDCs 

exist (Table 2-2, pp. 48–49) (NSF 2007). Academic, 

nonprofit, or corporate organizations operate the 

centers on behalf of the sponsoring agency. FFRDCs 

may perform work for organizations other than the 

sponsoring agency; 30 percent of their funding may 

come from the private sector (AcademyHealth 2005). 

An agency-sponsored nonprofit organization also 

has a legal relationship with a department or agency 

of the federal government, but this relationship may 

differ from one situation and organization to the next. 

Agency-sponsored nonprofit organizations have boards 

and can receive funding through private sources. 

This organization type often performs functions that 

the agency finds difficult to integrate into its regular 

policy and financial tasks. For example, the Congress 

established: 

• The Foundation for the National Institutes of Health 

(NIH) to match the interests of donors—private 

individuals and organizations—to the needs of NIH, 

and 

• The National Park Foundation to accept and 

administer gifts given to the National Park Service. 

There are some 90 congressionally chartered 

organizations (also commonly referred to as “Title 

36” corporations). The federal chartering process is 

honorific; these organizations do not receive direct 

appropriations (CRS 2005). The National Academy 

of Sciences, which includes the Institute of Medicine 

(IOM), is one example of such an organization. These 

organizations can accept private funds; for example, the 

private sector funded about one-quarter of IOM’s grants 

and contracts in 2005. 

Finally, another public–private entity is a government 

corporation. The Congress established government 

corporations to carry out business-type programs 

that need more autonomy and flexibility than what a 

conventional government agency structure provides. 

These organizations: (1) are predominantly of a 

business nature, (2) produce revenue and are potentially 

self-sustaining, and (3) involve a large number of 

business-type transactions with the public (GAO 

1995). Examples of a government corporation include 

the Tennessee Valley Authority, the Federal Deposit 

Insurance Corporation, and the Pension Benefit 

Guaranty Corporation. 
48 Producing comparative-effectiveness infor mation

T A B L E

2 –2 Current FFRDCs

Sponsoring agency FFRDC Administrator

Office of the Secretary

of Defense

Institute for Defense Analyses Studies and Analyses

Federally Funded Research and Development Center

Institute for Defense Analyses

National Defense Research Institute RAND Corporation

C3I Federally Funded Research & Development Center MITRE Corporation

National Security Agency Institute for Defense Analyses Communications

and Computing Federally Funded Research and

Development Center

Institute for Defense Analyses

Department of the Navy Center for Naval Analyses The CNA Corporation

Department of the Air Force Lincoln Laboratory Massachusetts Institute of Technology

Aerospace Federally Funded Research

and Development Center

The Aerospace Corporation

Project Air Force RAND Corporation

Department of the Army Software Engineering Institute Carnegie Mellon University

Arroyo Center RAND Corporation

Department of Energy Idaho National Laboratory Battelle Energy Alliance

Los Alamos National Laboratory Los Alamos National Security

Sandia National Laboratories Sandia Corporation

Savannah River Technology Center Westinghouse Savannah River Co.

Ames Laboratory Iowa State University of Science and Technology

Argonne National Laboratory University of Chicago

Ernest Orlando Lawrence Berkeley National Laboratory University of California

Fermi National Accelerator Laboratory Universities Research Association, Inc.

Lawrence Livermore National Laboratory University of California Livermore

Princeton Plasma Physics Laboratory Princeton University

Stanford Linear Accelerator Center Leland Stanford, Jr., University

Thomas Jefferson National Accelerator Facility Southeastern Universities Research Association, Inc.

Brookhaven National Laboratory Brookhaven Science Associates, Inc.

National Renewable Energy Laboratory Midwest Research Institute; Battelle Memorial

Institute; Bechtel National, Inc.

Oak Ridge National Laboratory UT–Battelle, LLC

Pacific Northwest National Laboratory Battelle Memorial Institute

Note:	 FFRDC (federally funded research and development center), C3I (Command, Control, Communications & Intelligence).

Source:	 National Science Foundation 2007.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 49

to budget uncertainties. Private sponsors might decide to 

withhold or withdraw funding for any number of reasons, 

such as disagreeing with the selection of a service for 

consideration. The influence of private groups that directly 

fund the research on a study’s design and findings could 

be a concern. 

Not linking the funding to either annual federal 

appropriations or voluntary funding from private groups is 

another option. Policy analysts have suggested alternatives 

including: 

• imposing a dedicated tax on products that threaten 

human health, such as tobacco, products with trans 

fats, and alcohol; or

• obtaining financial support from users of the evidence, 

including health plans, payers, and purchasers. 

Review of options other researchers have 

recently discussed

AcademyHealth is the professional society for health 

services researchers and health policy professionals. 

This group issued a report that addressed AHRQ’s role 

as the lead agency for health services research and the 

importance of producing comparative-effectiveness 

research (AcademyHealth 2005). AcademyHealth 

recommended that an agency of the Department of Health 

and Human Services (HHS), currently AHRQ, be the lead 

agency for health services research and that a comparativeeffectiveness research entity be established either within or 

T A B L E

2 –2 Current FFRDCs (cont.)

Sponsoring agency FFRDC Administrator

National Institutes of Health National Cancer Institute at Frederick Science Applications International Corp.; Charles River

Laboratories, Inc.; Data Management Services, Inc.;

Wilson Information Services, Inc.

Department of

Homeland Security

Homeland Security Institute Analytic Services, Inc.

National Aeronautics and

Space Administration

Jet Propulsion Laboratory California Institute of Technology

National Science Foundation National Astronomy and Ionosphere Center Cornell University

National Center for Atmospheric Research University Corporation for Atmospheric Research

National Optical Astronomy Observatories Association of Universities for Research in Astronomy, Inc.

National Radio Astronomy Observatory Associated Universities, Inc.

Science and Technology Policy Institute Institute for Defense Analyses

Nuclear Regulatory Commission Center for Nuclear Waste Regulatory Analyses Southwest Research Institute

Department of Transportation Center for Advanced Aviation System

Development

MITRE Corporation

Department of the Treasury Internal Revenue Service Federally Funded

Research and Development Center

Center for Enterprise Modernization, MITRE Corporation

Note:	 FFRDC (federally funded research and development center), C3I (Command, Control, Communications & Intelligence).

Source:	 National Science Foundation 2007.
50 Producing comparative-effectiveness infor mation

outside of AHRQ.10 AcademyHealth discussed, but did not 

endorse, the following options: 

• AHRQ sponsors and conducts research, with guidance 

from an external board and panel of experts; 

• AHRQ establishes a FFRDC and receives guidance 

from an external board; 

• The Congress creates a quasi-government entity, with 

AHRQ remaining as currently structured; or

• The Congress reconstructs AHRQ as a quasigovernment agency, which would keep most of its 

existing functions and add comparative-effectiveness 

research to its research portfolio. 

Compared with other quasi-government entities, 

AcademyHealth preferred the FFRDC model because 

it would: (1) be more focused on comparativeeffectiveness research, (2) provide for a strong publicsector involvement and oversight, and (3) provide for 

a close link between AHRQ and the entity conducting 

comparative-effectiveness research. Table 2-2 (pp. 48–49) 

lists the 37 FFRDCs.

Reinhardt (2004) endorsed the creation of nonprofit, 

independent institutions to analyze the cost effectiveness 

of drugs. He proposed that the proceeds from a small 

surcharge (0.5 percentage point or less) on the annual 

outlays on prescription drugs could establish permanent 

endowments for the independent nonprofit organizations. 

Reinhardt considered housing the infrastructure in a 

federal agency to which the Congress would appropriate 

funds but concluded that it would be too vulnerable to 

political pressures. Reinhardt also noted that the private 

sector does not produce cost-effectiveness information in 

“socially efficient quantities” because “the private costs 

of producing the information can easily exceed the private 

benefit to its producer, even if the potential social benefits 

of the information far exceed the cost of its production.”

Kupersmith and colleagues (2005) recommended a 

public–private consortium to include federal agencies, 

payers, insurers, drug companies, device companies, 

patient advocacy and interest groups, professional 

societies, hospitals, academics, and health foundations. 

Under this proposal, new federal appropriations would 

fund the consortium, with the expectation that the private 

sector would also contribute. 

Wilensky (2006) considered four options: (1) placing the 

entity within AHRQ, (2) placing the entity within HHS 

as a new or existing entity, (3) placing the entity within a 

quasi-government organization, and (4) placing the entity 

within the private sector. Wilensky concluded that placing 

the center within a quasi-government entity is the most 

attractive alternative and that an FFRDC associated with 

either AHRQ or a newly established board within HHS are 

options worth exploring. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 51

1 Examples of approved drugs and devices in which important 

side effects were not well documented until after the 

technology diffused into medical practice include: drugcoated stents, erythropoietin, telithromycin, and rofecoxib 

(FDA 2007a, 2007b).

2 For certain conditions, such as cancer and AIDS, clinical 

trials often compare the most accepted treatment with a new 

treatment.

3 The FDA approves most devices for marketing in the United 

States based on their similarity to previously approved 

devices.

4 The FDA has the authority to require that manufacturers 

report adverse events to the agency with different reporting 

schedules based on the seriousness of the event and whether 

the event has been previously identified and is included in the 

prescribing label (GAO 2006).

5 According to the FDA, a study that is pending is one that 

the manufacturer has not yet initiated but is not delayed. The 

FDA defines a delayed study as one that is behind the original 

schedule. 

6 For example, the summary guide on choosing pain medicine 

for osteoarthritis includes the prices of the different drugs 

included in the analysis. 

7 MedCAC meets about six times each year. MedCAC 

functions on a committee basis by reviewing and evaluating 

medical literature, reviewing technology assessments, and 

examining data and information on the effectiveness and 

appropriateness of medical items and services that are covered 

or are eligible for coverage under Medicare. Each committee 

includes 13 to 15 members. 

8 The evidence-based practice centers include: Blue Cross 

and Blue Shield Association, Technology Evaluation 

Center; Duke University; ECRI; Johns Hopkins University; 

McMaster University; Oregon Health & Science University; 

RTI International–University of North Carolina; Southern 

California Evidence-Based Practice Center–RAND; Stanford 

University–University of California, San Francisco; Tufts 

University–New England Medical Center; University 

of Alberta, Edmonton, Alberta, Canada; University of 

Minnesota, Minneapolis; University of Ottawa, Canada.

 9 The top officials of the Board are seven members, who are 

appointed by the President and confirmed by the Senate. 

10 Because of renewed interest in comparative-effectiveness 

research, the AcademyHealth Board of Directors established 

a special Committee on the Placement, Coordination, and 

Funding of Health Services Research within the Federal 

Government. 

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C h a p t er 3

Update on the Medicare 

Advantage program 

and implementing past 

recommendations

Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 57

Update on the Medicare 

Advantage program 

and implementing past 

recommendations

Chapter summary

This chapter provides an update on plan participation and beneficiary 

enrollment in the Medicare Advantage (MA) program as of early 2007, 

paying special attention to private fee-for-service (PFFS) plans and 

special needs plans (SNPs). The Commission supports the participation 

of private health plans in Medicare. Beneficiaries should be able to 

choose health plans that seek greater efficiency in the delivery of 

health care and improved outcomes for enrollees. Private plans have the 

flexibility to use care management techniques that fee-for-service (FFS) 

Medicare does not encourage. If paid appropriately, they have greater 

incentives to undertake innovations in care delivery and management 

and to negotiate with providers over levels and methods of payment. 

MA plans can use the savings they achieve through efficiency to 

provide enrollees with extra benefits—reduced cost sharing and 

coverage of items and services not covered by Medicare. In a system 

in which plan payments are no higher than those in FFS and are 

appropriately risk adjusted, a richer benefit package would generally 

signal that one plan is more efficient than a competing plan—and that 

In this chapter

• The Commission’s views on 

private plans in Medicare

• Efficiency in Medicare 

Advantage and extra 

benefits

• Options for moving to 

benchmarks at 100 percent 

of FFS expenditures

• Equity between sectors and 

among plan types

• Special needs plans

• Future work on Medicare 

Advantage

C H A PT E R 3
58 Update on the Medicare Advantage program and implementing past recommendations

a private plan offering extra benefits is more efficient than the traditional 

Medicare FFS program in the plan’s market area. However, for most MA 

plans the current approach to payment does not promote efficiency, primarily 

because county benchmarks—the basis of payment for MA plans—exceed 

Medicare FFS expenditures. 

Our analysis of 2006 benchmarks and program payments in MA showed 

that benchmarks and payments significantly exceeded Medicare FFS 

expenditures. The benchmarks averaged 116 percent of the expected FFS 

spending, and Medicare payments on behalf of MA enrollees averaged 

112 percent of what payments would have been under the traditional FFS 

program. High benchmarks have enabled plans to offer generous extra 

benefits to attract enrollees, resulting in significant enrollment growth in MA. 

The original design of the Medicare private health plan program envisioned 

that extra benefits would be available to enrollees only when plans achieved 

efficiencies. Some MA plans have payments that are lower than FFS 

Medicare, and those payments finance the cost of the Medicare benefit 

as well as extra benefits. However, in many cases (and for PFFS plans 

in particular), the sole source of financing for extra benefits is Medicare 

payments that are significantly above FFS expenditure levels. 

The continuing growth in enrollment in counties with the highest payments 

relative to FFS spending and in the least efficient types of plans heightens 

our concerns about the MA program. Enrollment growth has been greater 

in PFFS than in coordinated care plans. PFFS enrollment experienced 

the fastest growth through 2007, with membership expanding 72 percent 

between July 2006 and February 2007.

The current MA payment policy is inconsistent with MedPAC’s principles 

of payment equity between MA and the traditional FFS program. Moreover, 

the program applies standards and rules inequitably among different types 

of MA plans. Equity and efficiency issues are of particular concern with 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 59

Medicare facing long-run issues of financial sustainability, discussed in our 

March 2007 report to the Congress (MedPAC 2007). 

Beginning with our March 2001 report to the Congress and in subsequent 

years, the Commission recommended payment equity between Medicare’s 

private plans and the FFS program (MedPAC 2001a). In the context of MA, 

Medicare could achieve such equity by setting benchmarks at 100 percent 

of FFS payment levels. However, the Commission recognizes that changing 

MA plan payment rates to achieve financial neutrality too quickly will cause 

disruptions for beneficiaries in some markets, and thus the Congress may 

want a transition period. We discuss possible approaches for moving toward 

benchmarks at 100 percent of FFS expenditure levels:

• Freeze benchmarks at current levels to arrive at 100 percent of FFS rates 

over time, with a possible minimum yearly update.

• Cap the percentage by which benchmarks can exceed FFS expenditures 

and gradually lower the cap.

• Use a blend of 100 percent of FFS rates and historical benchmarks and 

gradually increase the portion attributable to 100 percent of FFS in the 

blend.

• Use plan bids as a factor in determining benchmarks.

We also discuss the large differences among plans in their performance 

on quality measures, highlighting the importance of the Commission’s 

recommendation to institute a pay-for-performance system in MA and the 

importance of having all plans report on quality measures (PFFS plans 

currently are exempt from most quality measurement requirements). 

Two issues of concern provide advantages to particular types of MA plans. 

Medical savings account (MSA) plans consist of a high-deductible health 

plan combined with a savings account with funds deposited by Medicare 

that enrollees can use on a tax-advantaged basis to cover health care costs. 

Unlike other plan types, MSA plans do not have to return 25 percent of 
60 Update on the Medicare Advantage program and implementing past recommendations

the difference between the plan bid and the benchmark to the trust funds. 

Instead, the program deposits the full difference between the benchmark 

and a bid below the benchmark to the enrollee’s savings account. Another 

recently enacted provision allows MA-only plans (i.e., that do not offer 

Part D drug coverage) to have year-round open enrollment. The provision 

provides an advantage to PFFS plans because enrollees choosing other types 

of MA plans must give up their Part D coverage when they enroll in the 

MA-only plan. 

We provide an update on SNP availability and participation. The number of 

SNPs and enrollment in SNPs increased from 2006 to 2007. We intend to 

continue studying what the proper role for SNPs in the MA program should 

be and what criteria to establish for these plans. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 61

The Commission has examined enrollment patterns and 

plan payments for the Medicare Advantage (MA) program 

for different geographic areas and types of plans. In this 

chapter, we pay particular attention to the fastest-growing 

plan type, private fee-for-service (PFFS). We also provide 

an update on special needs plans (SNPs). Our March 2007 

report to the Congress repeated the Commission’s past 

recommendations for the MA program. 

The Commission’s views on private plans 

in Medicare

The Commission supports the participation of private 

health plans in the MA program. Beneficiaries should be 

able to choose alternatives to traditional fee-for-service 

(FFS) Medicare in which health plans use practices that 

promote greater efficiency in the delivery of health care 

and improved outcomes for enrollees. Private plans have 

the flexibility to use care management techniques that 

FFS Medicare does not encourage. Moreover, if paid 

appropriately, plans have greater incentive to undertake 

innovations in the delivery and management of care and 

to negotiate with providers over levels and methods of 

payment. 

The Commission supports financial neutrality between 

payments in the traditional FFS program and the MA 

program and, beginning with the March 2001 report to 

the Congress, has recommended changing the program 

to achieve neutrality (MedPAC 2001a). Financial 

neutrality means that the Medicare program pays the same 

amount, adjusting for the risk status of each beneficiary, 

regardless of which Medicare option a beneficiary 

chooses. Financial neutrality would set benchmarks for 

MA plans in the current bidding system at 100 percent 

of average Medicare FFS expenditures. The Commission 

also recommended that the Congress use the 25 percent 

difference between the benchmark amount and bids 

below 100 percent of the benchmark (now retained in 

the Medicare trust funds) for a pay-for-performance 

program in MA (MedPAC 2005). The Commission 

has also discussed premium support as an approach to 

neutrality. Under premium support, competition between 

health plans and the FFS system would determine the 

contribution Medicare makes on behalf of the beneficiary. 

Although MA is a bidding system, plans bid against 

administratively set benchmarks, which have a strong 

influence on the payments to plans. 

Efficiency in Medicare Advantage and 

extra benefits

Over many years of experience with private plans in 

Medicare, the Congress has looked to private plans to 

provide a source of efficiency in the program. To the 

extent that MA plans provide Medicare benefits at a lower 

cost than the traditional program, they are required to 

return some of the efficiency to the program and to the 

beneficiary. Recent analysis of efficiency in MA shows 

that some types of plans are efficient while others are 

not. High benchmarks used in the bidding formula work 

against the program’s objectives in getting the most for the 

program dollar. We also see differences in the quality that 

plans bring to beneficiaries.

Private plans, efficiency, and benefits

From the time that full risk contracting for HMOs became 

a feature of Medicare through the Tax Equity and Fiscal 

Responsibility Act of 1982, policymakers have tried 

to structure the Medicare private plan program so that 

efficient plans can provide extra benefits to enrollees. 

To the extent that a private plan can provide care more 

efficiently than FFS Medicare (or, prior to the current MA 

program, for less than 95 percent of Medicare FFS costs), 

the program was designed so that plans could use their 

efficiency gains to finance extra benefits.1 Extra benefits 

include reduced out-of-pocket costs for enrollees and 

services not covered by Medicare, such as dental, hearing, 

and vision services; rebates of the Part B premium (as 

of 2001); and (before the advent of Part D) outpatient 

prescription drugs. 

Extra benefits should attract beneficiaries to enroll in 

efficient plans. Having plans compete against each other 

should also promote efficiency. In a system in which 

plan payments are no higher than those in FFS and are 

appropriately risk adjusted, a richer benefit package 

generally signals that one plan is more efficient than a 

competing plan—and that a private plan offering extra 

benefits is more efficient than the traditional Medicare 

FFS program in the plan’s market area.2

In the program’s current design—in which plans bid 

against a benchmark set in law—for bids below the 

benchmark, the law requires that 75 percent of the 

difference (referred to as the rebate) be used to fund extra 

benefits for enrollees. The program keeps the remaining 

25 percent in the Medicare trust funds (for regional plans, 
62 Update on the Medicare Advantage program and implementing past recommendations

Calculating Medicare’s payments to plans

The benchmark is a bidding target under the 

bidding system for Medicare Advantage 

(MA) plans that began in 2006. The local MA 

benchmarks come from the county-level payment 

rates used to pay MA plans before 2006. Those 

payment rates were at least as high as per capita feefor-service (FFS) Medicare spending in each county. 

Some counties had rates significantly higher than FFS 

because of specific statutory changes. The Medicare 

Prescription Drug, Improvement, and Modernization 

Act of 2003 called for updating county benchmarks 

from one year to the next in one of three ways, using 

whichever method results in the greatest increase:

• Generally, local MA benchmarks are updated by the 

national growth rate in per capita Medicare spending 

(subject to certain adjustments that could increase or 

decrease eventual plan payments). 

• A second possibility is that, if the national growth 

rate is less than 2 percent, MA benchmarks 

are increased by 2 percent (subject to certain 

adjustments). This minimum increase provision 

(contained in the Balanced Budget Act of 1997 

(BBA)) applies each year, regardless of the 

economic circumstances and of the expected 

cost growth for an efficient provider. In 1998, for 

example, the year when the 2 percent provision 

went into effect, the provision applied to MA 

payment rates at the same time that overall Medicare 

expenditures declined slightly for the year. 

• A third possibility is to set the benchmark of a 

given county equal to the FFS expenditure for the 

county. That is, 100 percent of FFS becomes the 

benchmark for a county if it yields the highest 

benchmark amount. 

To implement the 100 percent of FFS provision, CMS 

determines FFS rates for each county at least every 

three years, a procedure referred to as “rebasing.” Once 

a county benchmark is set at 100 percent of FFS in a 

given year, even if FFS payments fall, the benchmark 

for plans does not. For example, if in the following 

year CMS finds the FFS rate for the county was far 

below that of the preceding year, the county capitation 

rate would be the preceding year’s FFS rate increased 

by either the minimum increase of 2 percent or, if 

greater, the national growth rate in per capita Medicare 

spending. This policy creates, in effect, an additional 

type of “floor.”

Another source of higher benchmarks is Medicare’s 

treatment of indirect medical education (IME) payment 

to hospitals. See our June 2005 report to the Congress 

for discussion of our recommendation to remove the 

effect on benchmarks of Medicare’s double payment for 

IME for MA enrollees.

MA benchmarks are higher than Medicare per capita 

FFS spending in almost all counties (other than 

for regional plans, which have a different basis for 

determining benchmarks applicable across an entire 

region). One source of the difference is statutory 

provisions that introduced minimum county payment 

rates, or floors, intended to attract or retain private 

plans in Medicare. Floor rates are no longer a basis of 

plan payment, but what were historically floor counties 

generally continue to have higher payment rates than 

nonfloor counties in relation to FFS expenditure levels. 

The BBA initially established a payment floor for 

counties with relatively low FFS expenditures. The 

BBA floor is often called the rural floor because it 

applies mainly to rural counties and was primarily 

intended to attract plans to rural areas. The “large 

urban floor,” which applies to counties within large 

metropolitan statistical areas, was introduced in the 

Medicare, Medicaid, and SCHIP Benefits Improvement 

and Protection Act of 2000 (BIPA) and was effective 

as of March 2001. BIPA also provided an increase in 

the BBA floor rate. The counties that had been floor 

counties have very high relative benchmarks compared 

with other geographic areas; on average, they are 121 

percent of FFS for the large urban floor counties and 

134 percent of FFS—the highest average benchmark 

level—for the floor established in the BBA. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 63

half of the 25 percent is retained in the benefit stabilization 

fund for possible use in 2012 or thereafter to promote 

participation by regional plans). 

Comparing payments to plans with the 

amounts spent under the fee-for-service 

program

As stated in the March 2007 report to the Congress, our 

analysis of plan benchmarks shows that they are well 

above FFS levels (116 percent of FFS expenditures as 

of 2006), with variations by geographic area and type of 

plan that reflect the enrollment patterns of the different 

plan types (Table 3-1).3 The Congressional Budget Office 

independently arrived at a similar finding for 2007: 

Benchmarks are at 117 percent of FFS and program 

payments for MA enrollees are at 112 percent of FFS 

(CBO 2007). 

MedPAC has not estimated the bid-to-benchmark ratio 

for 2007. One factor that should, all else equal, lead to a 

decline in plan payments for 2007 is the phasing out of 

the hold-harmless provision that determines the extent 

to which MA payments are adjusted to reflect the health 

status of enrollees. MA plans are enrolling beneficiaries 

who are healthier than average. A payment system 

incorporating risk adjustment based on health status would 

lower payments for healthier enrollees. However, the holdharmless provision protects plans from the effect of full 

implementation of payments based on health status. This 

provision is phasing out over several years, ending in 2011. 

However, other factors—primarily the trend in enrollment 

toward areas of the country with high benchmarks in 

relation to FFS—will increase the benchmark-to-FFS ratio 

for 2007. 

Some have criticized the accuracy of our estimated ratios 

in connection with three issues: (1) administrative costs 

in the Medicare program, (2) the use of Department of 

Veterans Affairs (VA) facilities by Medicare beneficiaries, 

and (3) the treatment of indirect medical education (IME) 

payments (AHIP 2007a). At the national level, these issues 

would not materially change our findings; that is, the 

ratios would remain unchanged. For example, factoring in 

certain CMS administrative costs and MA user fees would 

result in a change of at most 0.5 percentage point.4 In a 

few geographic areas, beneficiaries’ use of VA facilities 

to receive Medicare-covered care may understate the 

average cost of providing Medicare-covered services in 

the area. That is, CMS estimates of county-level FFS 

expenditures (and thus the benchmarks) do not account 

for some Medicare beneficiaries using VA facilities to 

obtain care that otherwise would be covered and paid for 

by Medicare.5 However, if MA enrollees continued to 

use VA facilities to the same extent as FFS beneficiaries, 

a benchmark adjustment might not be appropriate. 

Another issue is whether our calculations account for 

IME payments. We correctly account for IME dollars by 

removing them from each sector in calculating the ratio of 

MA benchmarks and payments to FFS expenditures. 

T A B L E

3 –1 Program payments exceed FFS expenditures but vary by plan type, 2006

All MA plans 

with bids HMO Local PPO Regional PPO PFFS

Percentage relative to FFS expenditures

Benchmark 116% 115% 120% 112% 122%

Bid (for Medicare Part A and Part B benefits) 99 97 108 103 109

Rebate 13 13 9 7 10

Payment (bid + rebates) 112 110 117 110 119

Enrollment as of July 2006 (in thousands) 6,877 5,195 285 82 774

Note:	 FFS (fee-for-service), MA (Medicare Advantage), PPO (preferred provider organization), PFFS (private fee-for-service). When a bid is below the benchmark, 75

percent of the difference—referred to as the “rebate” amount—is paid to the plan to provide extra benefits and reduced premiums; 25 percent of the difference is

retained by the Medicare trust funds and, in the case of regional PPOs, half of the 25 percent is deposited in the benefit stabilization fund. Enrollment increased

rapidly after July 2006.

Source:	 MedPAC analysis of CMS data on plan bids, benchmarks, and enrollment.
64 Update on the Medicare Advantage program and implementing past recommendations

Recent findings on differences in efficiency 

by type of plan

Our analysis of plan payments and benchmarks showed 

that, for 2006, program payments to plans averaged 

112 percent of FFS expenditures across all plans.Those 

figures vary by plan type, with HMO benchmarks and 

program payments at 115 percent and 110 percent of FFS, 

respectively, on average, and PFFS at 122 percent and 

119 percent of FFS, respectively (Table 3-1, p. 63). These 

differences reflect the areas where these types of plans 

locate as well as variations in efficiencies in care delivery. 

Efficient plans operate in the MA program. They provide 

the traditional Medicare Part A and Part B benefit at a 

lower cost than the FFS program, although plans receive 

additional Medicare payments that are used for extra 

benefits. On average in 2006, HMO plans provided the 

traditional Medicare benefit for 97 percent of Medicare 

FFS expenditure levels (Table 3-1, p. 63). Because 

HMOs had such a large share of the overall enrollment 

in 2006, across all plan types the bid for Medicare Part 

A and Part B services averaged 99 percent of Medicare 

FFS expenditures. However, some plan types were much 

less efficient; for example, PFFS plan bids averaged 109 

percent of FFS expenditures. That is, on average they 

cost Medicare 9 percent more than the traditional FFS 

program to provide Medicare Part A and Part B benefits. 

For each plan type, the numbers we cite are averages; not 

all plans of a particular type (HMOs, preferred provider 

organizations (PPOs), PFFS) operate with the same 

efficiency in relation to FFS in their market areas. 

Plan bids for the Medicare Part A and Part B benefit 

package include costs for administration, marketing, and 

profit or retained earnings. Similarly, the extra benefits 

provided through the additional payments include the 

administrative and marketing costs and profit or retained 

earnings associated with extra benefits.6

Effects of high benchmarks

The high MA benchmarks allow plans to be less efficient 

than they would otherwise be if they faced the financial 

pressure of lower benchmarks closer to Medicare 

FFS levels. As the Commission has stated in the past, 

organizations are more likely to be efficient when they 

face financial pressure. The Medicare program needs 

to exert consistent financial pressure on both the FFS 

program (as detailed in our March 2007 report to the 

Congress) and the MA program, coupled with meaningful 

quality measurement and pay-for-performance programs, 

to maximize the value the Medicare program receives 

for the dollars it spends. MA payment policy is actively 

shaping the market for Medicare health plans. The current 

policy conveys the message that Medicare values private 

plans that cost more than FFS, and Medicare is willing to 

subsidize beneficiary enrollment in MA. 

MA enrollment is growing particularly fast in PFFS plans 

and in counties where the benchmarks are highest in 

relation to FFS. PFFS enrollment tends to be concentrated 

in counties where benchmarks are significantly higher 

than FFS expenditures. This explains why PFFS plan 

benchmarks and payments are so high in relation to FFS. 

Growth in enrollment in less efficient plans heightens 

our concerns about payment equity for MA. The program 

is paying more for MA enrollees than for those in the 

traditional Medicare FFS program, with beneficiaries 

and taxpayers financing those higher payments. The 

Commission also has concerns about an uneven playing 

field among the different types of MA plans. The equity 

and efficiency issues we discuss here are of particular 

concern in an era when Medicare faces long-run 

sustainability challenges.

With MA benchmarks at their current levels, the MA 

program results in higher average costs than FFS Medicare 

and added costs for taxpayers and beneficiaries who 

finance the Medicare program. However, with respect to 

the cost of the Part D program, because MA–Prescription 

Drug (MA–PD) plan bids on average are lower than the 

bids of stand-alone plans, MA–PD bids bring down the 

national average bid for Part D (see discussion in the 

March 2007 report to the Congress on relative premium 

levels in Part D by plan type). For Medicare Part A 

and Part B, while some of the MA payments above 

FFS expenditures are used to finance extra benefits for 

MA enrollees, all beneficiaries, through their Part B 

premium—and all taxpayers, through general revenues—

are paying for those benefits. Most Medicare beneficiaries 

are not MA enrollees, but all beneficiaries pay for benefits 

the 18 percent of beneficiaries enrolled in MA plans use. 

Low-income and minority beneficiaries are more 

likely to enroll in MA plans (AHIP 2007b, Atherly and 

Thorpe 2005), and a reduction in benchmarks may 

disproportionately affect their benefits. Although we 

cannot be certain about the impact on different populations 

(e.g., urban enrollees of MA plans would be more likely 

than rural enrollees to continue to receive generous 

extra benefits if benchmarks were brought closer to FFS 

levels), the benefits do not go exclusively to a subgroup 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 65

of enrollees. All MA enrollees receive the same level of 

benefits. Some are concerned that low-income individuals 

should receive extra help with their cost sharing and other 

expenses for medical care. However, other programs 

target this population more efficiently. Examples are the 

Medicare savings programs and the low-income subsidy 

approach used for the Medicare drug benefit. 

The PFFS option

The high MA benchmarks have allowed PFFS plans 

to attract enrollment in areas with limited competition 

from other plan types. PFFS plans essentially mimic 

FFS Medicare in their structure and in their payment and 

contracting arrangements with providers.

Design and history of the PFFS option

The existing PFFS plans are generally not network plans 

(they do not provide care through a network of contracted 

providers) and do not use many of the techniques that 

network plans can use to encourage the provision of better 

health care at a reduced cost. PFFS plans pay providers 

the same rates as Medicare FFS.7 Although PFFS plans 

may form networks to make payment arrangements 

with providers, to date PFFS plans have relied mainly 

on “deemed” participation of providers to provide care 

to their enrollees. Under this policy, the plan deems a 

provider to be in the PFFS plan if the beneficiary states 

that he or she is a PFFS plan enrollee and the provider 

treats the patient after learning about the plan’s terms and 

conditions of payment. A provider also is deemed if he or 

she has had reasonable opportunity to obtain information 

about terms and conditions (e.g., being provided with an 

Internet source for the terms and conditions). 

The program does not require PFFS plans to meet the 

same quality standards as network plans because, as 

non-network plans, one might argue that they are not 

accountable for the quality of care practiced by physicians 

and other providers that enrollees choose to see. 

The Balanced Budget Act of 1997 (BBA) introduced the 

PFFS option to guarantee access to all Medicare providers 

without imposing utilization controls. Policymakers 

developed this option because, in the 1990s, during the 

period of greatest growth in managed care enrollment, 

they feared that rationing of care would occur because 

of a general movement toward managed care, utilization 

management, and restrictive provider networks. 

Policymakers wanted an option without limitations on 

enrollees’ ability to obtain care through the providers of 

their choice.

While including the PFFS option in the BBA, the 

Congress also intended that enrollees bear the added cost 

of a private health plan offering free access to providers. 

As noted in the BBA conference report, “the private feefor-service Medicare+Choice option authorized by this 

agreement represents the first defined contribution plan 

in which beneficiaries may enroll in the history of the 

program” (House of Representatives 1997). PFFS was a 

defined contribution plan under Medicare+Choice (the 

predecessor to MA) because, unlike other plans, a PFFS 

plan could charge a premium for its cost of providing the 

Medicare Part A and Part B benefit package in excess of 

the actuarial value of Part A and Part B cost sharing in FFS 

Medicare. 

The current benchmarks are high enough to permit PFFS 

plans to finance extra benefits through program payments 

even when such plans are less efficient at providing the 

Medicare Part A and Part B benefit package. In our June 

2001 report to the Congress, we anticipated the possibility 

that PFFS plans would be providing extra benefits solely 

because of the higher payment rates and noted that this 

“would not appear to be paying the cost of an efficient 

provider—the basic axiom of Medicare payment policy. 

Paying PFFS plans at ... [higher] rate[s] is an expensive 

way to get extra benefits for Medicare beneficiaries in 

some counties” (MedPAC 2001b).

Recent growth in PFFS plans

PFFS plans and enrollment continue to grow rapidly 

(Table 3-2). While local coordinated care plans grew 

T A B L E

3 –2 Enrollment in PFFS plans grew faster

 than in other major plan types

Total enrollees

(in thousands)

Plan type

July 

2006

February 

2007

Percentage 

change

Local CCPs 5,480 6,065 11%

PFFS 774 1,328 72

Regional PPOs 82 121 48

Note:	 PFFS (private fee-for-service), CCP (coordinated care plan), PPO (preferred

provider organization). CCPs include HMOs and local PPOs.

Source:	 CMS health plan monthly summary reports.
66 Update on the Medicare Advantage program and implementing past recommendations

about 11 percent between July 2006 and February 2007, 

enrollment in PFFS plans accounted for nearly half the 

growth in MA, rising from about 774,000 to 1.3 million—

a 72 percent increase.8 The number of entities with PFFS 

contracts nearly doubled, from 25 in 2006 to 47 in 2007. In 

addition, for 2007, a direct-contract employer group plan 

(an option authorized in the Medicare Prescription Drug, 

Improvement, and Modernization Act of 2003 (MMA)) 

started operating as a PFFS plan, with 10,000 enrollees. 

Under this option, the employer is an MA contractor, 

assuming risk for providing Medicare services to its 

retirees.

The more common option is for an employer to offer 

retiree coverage through an MA organization that designs 

a plan available only to that employer group or to multiple 

employer groups. The PFFS option is particularly 

attractive for employers and unions covering retirees when 

they retire and move away from their place of work. While 

plans can cover active workers through network plans in 

a specific geographic area, an HMO, for example, would 

need to have a very wide network to provide access to 

retirees. A PFFS plan, on the other hand, because it does 

not need to have a network, can make its service area 

the entire country. This solves the employer’s or union’s 

concerns about ensuring access to care, and PFFS plans do 

not have to form networks in each county where they have 

enrollment.

Enrollment in PFFS continues to come primarily from 

counties where benchmarks reflect statutorily set payment 

floors (Table 3-3). In February 2007, 80 percent of PFFS 

enrollment comes from such counties (31 percent from 

BBA floor counties and 49 percent from metropolitan 

statistical area floor counties (the text box on p. 62 

provides an explanation of floor counties)). The percentage 

of the total PFFS enrollment coming from rural counties 

has decreased slightly, from 39 percent in 2006 to 35 

percent in 2007.9 However, enrollment grew most rapidly 

in nonfloor counties between 2006 and 2007. 

SNPs (discussed separately at the end of this chapter) and 

employer-sponsored plans were the only source of growth 

in local HMO plans between 2006 and 2007. Between July 

2006 and February 2007, the number of HMO enrollees 

who were not in SNP plans and not enrolled through an 

employer-sponsored plan declined by about 2 percent.

Differences among MA plans on quality 

measures

In addition to differences in efficiency among MA 

plans, we see wide differences in plan performance on 

quality measures (Table 3-4, reflecting results for 2005). 

For example, on the quality measure for the percentage 

of enrollees with diabetes who receive eye exams, the 

currently reported scores among HMO plans range from 

14 percent to 87 percent. The rate for providing flu shots 

T A B L E

3 –3 PFFS enrollment comes primarily from floor counties and rural areas

July 2006 distribution February 2007 distribution

Percent 

growth in 

enrollment

Enrollees

(in thousands) Percent

Enrollees

(in thousands) Percent

By historical county payment status

BBA floor counties 284 37% 399 31% 40%

MSA floor counties 390 50 630 49 62

Nonfloor counties 99 13 260 20 162

By rural/urban status

Rural 304 39 451 35 48

Urban 470 61 838 65 78

Note:	 PFFS (private fee-for-service), BBA (Balanced Budget Act of 1997), MSA (metropolitan statistical area). Sums of figures in each group may not be the same due to

rounding. The number of enrollees for July 2006 includes counties with 10 or fewer enrollees; the number of enrollees for February 2007 in this table does not.

BBA floor counties are generally rural counties with a payment set by the BBA at a minimum level. The MSA floor, applicable to counties within an MSA with a

population of over 250,000, was introduced in the Medicare, Medicaid, and SCHIP Benefits Improvement and Protection Act of 2000 (BIPA) and was effective as

of March 2001. BIPA also provided an increase in the BBA floor rate.

Source:	 MedPAC analysis of CMS data on plan-level enrollment.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 67

to members ranged from 16 percent to 88 percent among 

HMOs. CMS reported no data on PFFS performance on 

the flu shot measure; for the three PPOs that reported data, 

flu shot rates ranged from 68 percent to 75 percent.10

The measure on which plans register their best 

performance is the provision of beta blockers after a heart 

attack. Among the 127 plans of any type for which there 

are reported data, the scores range from 28 percent to 100 

percent, with the median at 97 percent and with 22 plans 

having a score of 100 percent. Plans have also shown 

improvement in measures over the years. For example, the 

National Committee for Quality Assurance reports that, 

in Medicare plans, the measure for controlling high blood 

pressure among those with hypertension increased from 47 

percent in 2000 to 66 percent in 2005 (NCQA 2006).

In the past, the Commission has called for two policies 

on quality (MedPAC 2005). One is for CMS to calculate 

measures of quality in the FFS program so that we 

can compare the performance of MA to the traditional 

program. The other is for a pay-for-performance program 

within the MA program that would pay more to plans with 

superior quality and to those that improved their quality 

over time and would pay less to other plans. 

Options for moving to benchmarks at 

100 percent of FFS expenditures

Since benchmarks remain high, MA plans are able to offer 

extra benefits, subsidized by the Medicare program, to 

attract enrollees. This has resulted in significant growth in 

MA enrollment. While the Commission supports plans as 

an option for Medicare beneficiaries, it also supports the 

concept of setting benchmarks at 100 percent of FFS. The 

Commission recognizes that changing MA plan payment 

rates to achieve financial neutrality too quickly will 

cause disruptions for beneficiaries in some markets. The 

T A B L E

3 –4 Quality measures show significant variation across plans

Number 

of plans 

with data

Rate

Quality measure, by plan type Average Median Lowest Highest

Eye exams for members with diabetes

All plans 175 64% 66% 8% 87%

ALL HMO plans 138 65 66 14 87

PPOs 12 53 57 8 74

PFFS plans 4 37 37 28 45

Flu shots

All plans 247 68 73 15 89

ALL HMO plans 206 67 71 16 88

PPOs 3 72 73 68 75

PFFS plans 0 N/A N/A N/A N/A

Beta blockers after heart attack

All plans 127 93 97 28 100

ALL HMO plans 112 94 97 59 100

PPO plans 3 69 80 28 100

PFFS plans 1* N/A N/A N/A N/A

Note:	 PPO (preferred provider organization), PFFS (private fee-for-service), N/A (not applicable). All plans include cost plans (plans contracting with Medicare on a cost

reimbursement basis) and demonstration plans.

*One plan reported, with a rate of 65 percent.

Source:	 MedPAC analysis of data from Medicare personal plan finder downloadable database reflecting 2005 results.
68 Update on the Medicare Advantage program and implementing past recommendations

history of private plan participation in Medicare provides 

a precedent for understanding the possible consequences 

of a change in MA payment policy. Following the payment 

changes in the BBA and because of other market factors 

affecting managed care plans, a large number of plans 

withdrew from Medicare in 1999 and thereafter, and 

enrollment declined significantly (Hurley et al. 2003, 

GAO 2000). On the other hand, the more beneficiaries 

who receive extra benefits subsidized by the Medicare 

program, and the longer beneficiaries have such benefits, 

the more difficult it will be to reduce MA benchmarks. 

In 2006, county benchmarks in counties with any MA 

enrollment ranged from about 104 percent to about 166 

percent of FFS (excluding Puerto Rico). 

Possible approaches might be to: 

• freeze all county benchmark rates at their current 

levels until each county’s rate is at the FFS level, while 

possibly providing for a minimum update; 

• differentially reduce benchmark rates by setting a cap 

on the amount by which benchmarks could exceed 

FFS in a county, thereby having a higher reduction in 

the highest benchmark counties; or 

• use a blend of FFS rates and MA rates that would 

apply to a particular county, increasing the weight of 

the FFS portion over time. 

Other transition strategies are also possible, such as using 

local plan bids as a factor in determining benchmarks. 

Freeze benchmarks

The Congress could freeze benchmarks until FFS 

spending catches up to that level. This policy would 

address all areas with benchmarks above FFS immediately, 

but it would take many years for FFS levels to catch up 

in some areas (e.g., in counties with benchmarks at 166 

percent of FFS). This approach has the disadvantage 

of freezing benchmarks in counties where rates are 

close to FFS, which are likely to be the areas with the 

highest concentration of MA enrollment (currently and 

historically) and areas where competitive plans have bids 

that are low in relation to FFS expenditures or are in fact 

below FFS. Therefore, a better option might be to allow 

a minimum yearly update in MA benchmarks (e.g., 2 

percent each year, which is the current minimum), but this 

would lengthen the time it takes benchmarks to reach FFS 

levels in many counties. 

Under this option, with a minimum increase, for the first 

few years beneficiaries would not be likely to see big 

changes in their benefits and program savings would 

be lower. However, this policy has the effect of keeping 

benchmarks high in areas with the highest benchmarks 

in relation to FFS. Counties with the highest relative 

benchmarks would be the last to reach FFS levels. 

Cap allowable percentage above FFS for 

benchmark and gradually lower cap

The cap option would set a maximum for the benchmark 

equal to some percentage above FFS and gradually reduce 

the percentage. For example, assume the cap was set at 

140 percent and reduced by 10 percentage points each 

year until all benchmarks were set at local FFS spending. 

In year 1, all benchmarks higher than 140 percent of FFS 

would be reduced to 140 percent. In year 2, all benchmarks 

would be limited to 130 percent of FFS, and so on.

This policy would first address areas with the largest 

discrepancies between benchmarks and FFS costs. All 

benchmarks eventually would be brought down to FFS 

levels. Depending on how quickly the benchmarks come 

down, many areas with benchmarks above FFS would not 

see any reductions for several years, and program savings 

would be gradual for the first few years. While there would 

not be an extreme reduction in benefits immediately, there 

would likely be significant reductions annually.

Blend 100 percent of FFS and historical 

benchmarks and gradually increase 

the blend

The blend option would blend an area’s FFS rate with its 

historical benchmark (perhaps increased by a national 

growth percentage), and the historical benchmark would 

be weighted lower each year until it was eliminated. For 

example, in the first year the blend could be 80 percent 

historical and 20 percent FFS. In year 2, the weighting 

could be changed to 60/40, and so on.

Advantages of this policy include that reductions would 

begin immediately and would be proportional to the 

discrepancies between benchmarks and FFS costs. 

There would be more certainty in the timing because all 

counties would be at FFS levels by a certain year. For 

areas where the benchmarks were not relatively high, the 

annual reductions would not be large. All benchmarks 

would be reduced toward FFS. Those areas with relatively 

high benchmarks would see large reductions each 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 69

year. As with other transitions, the savings would build 

gradually, and certain counties would see changes in 

benefits and plan options.

Competitive bidding to set rates

Medicare could use plan bids to help determine the 

benchmarks. There could be several versions of this 

option. We focus on an approach that would operate 

somewhat like the bidding system used to set the regional 

benchmarks. Plan bids in an area would be averaged and 

blended with the area’s FFS spending or the MA county 

benchmarks to calculate a benchmark for a particular 

market area (e.g., a county or an area larger than one 

county). Under this type of policy, Medicare would use 

competition to influence plan payments, which then 

would be more likely to reflect the costs of efficient 

providers. Average bids for the Medicare Part A and 

Part B benefit package are currently well below the 

benchmarks and are often below FFS costs. Therefore, 

the resulting benchmarks may approach FFS spending, 

although it is unlikely that program costs would end up 

at exactly 100 percent of FFS. This option would also be 

complicated to design and implement. For example, not 

all plans in a given market may include every county of a 

multicounty market area or some counties may have only 

one plan.

Equity between sectors and among 

plan types

The Commission supports equity between MA plans 

and the traditional Medicare program. Supporting the 

principle of equity between the sectors takes many forms. 

For example, most private plans participating in Medicare 

are required to report various types of quality measures. 

The Commission believes the same approach should 

apply in the traditional FFS program. That is, CMS should 

report quality information for FFS Medicare that allows 

Medicare beneficiaries to compare FFS Medicare with 

private plans in terms of their performance on quality 

measures. To that end, the Commission has specifically 

recommended that the Secretary of Health and Human 

Services calculate clinical measures for the FFS program 

that would permit CMS to compare the FFS program with 

MA plans (MedPAC 2006a).

The Commission also supports the concept of equity in 

the treatment of different plan types within the private 

plan sector. For example, the Commission recommended 

that the Congress eliminate the benefit stabilization 

fund introduced in the MMA, which provided an unfair 

advantage to the regional PPOs. (In the Deficit Reduction 

Act of 2005, the Congress reduced the stabilization 

fund by $6.5 billion, to $3.5 billion, and restricted the 

availability of the funds to 2012 or thereafter.) 

Table 3-5 (p. 70) shows how different requirements 

apply to different plan types in MA. In general, the 

Commission favors a level playing field for all plan 

types, unless special circumstances dictate otherwise. The 

Commission believes, for example, that PFFS plans and 

medical savings account (MSA) plans should be required 

to report on the quality of care for their enrollees so that 

beneficiaries can use quality as a factor in judging these 

plans. The Congress should eliminate payment rules that 

give one plan an advantage over another—as in the case of 

regional PPO plans. 

In 2008, PFFS plans and MSA plans will have another 

advantage over other plan types. Other types of 

organizations with network plans that wish to offer plans 

tailored for employer-group-sponsored retirees will 

continue to be required to have plans that are available to 

individual, non-group-sponsored beneficiaries. However, 

non-network PFFS plans and MSA plans will not be 

subject to this requirement (CMS 2006).

In the March 2007 report to the Congress, the Commission 

noted its concern about how MA MSA plans are paid. 

The report pointed specifically to the statutory provision 

which required all funds to be used for the enrollee deposit 

when the equivalent of an MSA plan bid is below the 

benchmark. For other MA plans, the trust fund retains 25 

percent of the difference. This provides MSA plans with 

an unfair advantage over other types of MA plans (though 

currently only three MSA plans are in operation).

In a similar vein, we are concerned about a recent 

provision that gives an unfair advantage to certain types 

of plans. The Tax Relief and Health Care Act of 2006 

added section 1851(e)(2)(E) of the Social Security 

Act, effective only for 2007 and 2008, which allows a 

beneficiary who is not an MA enrollee (i.e., is in FFS 

Medicare) to enroll in an MA-only (nondrug) plan outside 

of the open enrollment period. MA-only plans then have 

an advantage over other plans. These MA-only plans have 

year-round enrollment, while other plans may accept new 

enrollees only during the open enrollment period (or if 

a person is newly entitled to Medicare, for example). In 
70 Update on the Medicare Advantage program and implementing past recommendations

particular, the provision affords an advantage to PFFS 

plans. The CMS guidance on this provision states that 

“if an individual in Original Medicare and a stand-alone 

prescription drug plan elects to enroll in an MA-only 

coordinated care plan, such as an HMO, PPO, or Regional 

PPO, his or her enrollment in the PDP [prescription drug 

plan] will be automatically cancelled as of the effective 

date of enrollment in the MA-only plan” (CMS 2007a). 

Beneficiaries without drug coverage may enroll in any 

MA-only plan, but a beneficiary’s Part D coverage 

continues only if the person enrolls in a PFFS MA-only 

plan. In addition to giving an advantage to PFFS plans, 

beneficiaries with Part D coverage can use this provision 

as a way to discontinue their Part D enrollment outside of 

the open enrollment period. 

Special needs plans

The Congress created a new MA plan type known as 

a SNP in the MMA to provide a common framework 

for existing plans for special needs beneficiaries and 

to expand beneficiaries’ access to and choice among 

MA plans. SNPs function essentially like any other MA 

plan but must also provide the Part D drug benefit. In 

exchange, they are allowed to limit their enrollment to 

their targeted populations—a provision that will lapse at 

the end of 2008, absent action by the Congress to extend 

the provision. Targeted populations include dual (Medicare 

and Medicaid) eligibles, the institutionalized, and 

beneficiaries with severe or disabling chronic conditions.

T A B L E

3 –5 Certain requirements and provisions vary by type of MA plan

PFFS MSA

HMO/

Local PPO

Regional 

PPO SNP

Requirements

Build networks of providersa 3 3 3

Report quality measures 3 3 3

Have CMS review and negotiate bids 3 3 3

Return to the trust funds 25 percent of the difference

between bid and benchmarkb 3 3 3 3

Offer Part D coveragec 3 3 3

Have an out-of-pocket limit on enrollee expenditures 3 3

Offer individual MA plan if offering employer group pland 3 3 3

Other provisions

Protected from some risk through risk corridorse 3

Can limit enrollment to targeted beneficiariesf 3

Note:	 MA (Medicare Advantage), PFFS (private fee-for-service), MSA (medical savings account), PPO (preferred provider organization), SNP (special needs plan). aPFFS plans are exempted from other MA plans’ network adequacy requirements if they pay providers Medicare fee-for-service rates. bThis provision applies when bids are under the benchmark. For regional PPO plans, one-half of the 25 percent amount is retained by the trust funds, and the

remainder is included in the stabilization fund that, as of 2012, may be used to retain or attract such plans. c

MSA plans are prohibited from offering Part D coverage. PFFS plans may offer Part D coverage, but special rules apply to such plans (e.g., it is not required that

an enrollee receive drugs at a discounted rate when the deductible applies or the person is in the Part D coverage gap). 	 dAs of 2008, only non-network PFFS plans can operate exclusively as plans limited to employer group enrollees. 	 eRisk corridors are available only in 2006 and 2007. f

MA plans must allow all Medicare beneficiaries in their service area to enroll with few exceptions (e.g., beneficiaries with end-stage renal disease). Other

exceptions apply to MSA plans (e.g., Medicaid beneficiaries may not enroll in an MSA). SNPs are permitted to limit their enrollment to their targeted beneficiary

population (i.e., dual eligibles, beneficiaries who reside in an institution, or those with a chronic or disabling condition). SNPs can be local or regional coordinated

care plans. They cannot be MSAs or PFFS plans.

Source:	 MedPAC analysis of MA statutory and regulatory requirements.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 71

This year again marked a significant increase in the 

number of SNPs available to beneficiaries. In 2004, there 

were just 11 SNPs.11 By 2005, the number grew to 125. In 

2006, the number of SNPs more than doubled to 276 with 

the entry of 151 new SNPs. In 2007, there are 476 SNPs. 

Organizations entering the SNP market include those with 

experience with Medicaid and special needs populations, 

such as Evercare, but also include MA organizations that 

chose to add SNPs to their menu of plans.

The Commission has sought creative ways to deliver highquality health care to special needs beneficiaries. SNPs 

offer the potential to improve care coordination for dual 

eligibles and other special needs beneficiaries through 

unique benefit design and delivery systems.

However, as described in the June 2006 report to the 

Congress, we see that many SNPs are not taking advantage 

of the opportunity to better coordinate care for special 

needs beneficiaries. SNPs, even dual-eligible SNPs, are 

not required to contract with states to provide Medicaid 

benefits. Based on site visits and additional discussions 

with experts, we do not see how dual-eligible SNPs that 

do not integrate Medicaid could fulfill the opportunity to 

coordinate the two programs. We also are unsure whether 

SNP designation is necessary to allow plans to furnish the 

sorts of benefits targeted at beneficiaries in institutions 

and with chronic conditions. For 2008 applications, CMS 

instructed SNPs to describe how they plan to meet their 

enrollees’ special needs but has not specified minimum 

expectations or established an enforcement mechanism. 

SNP availability and enrollment: July 

2006 and March 2007

Since the June 2006 report to the Congress, we have 

further analyzed the availability of and enrollment in 

SNPs. Most SNPs (82 percent) available in 2006 were 

for dual-eligible beneficiaries (Figure 3-1). In 2007, 

dual-eligible plans still account for the largest share of 

The number of SNPs and SNP enrollment increased from 2006 to 2007

Note:	 SNP (special needs plan).

Source:	 CMS special needs plans fact sheet and data summary, February 14, 2006; CMS special needs plans comprehensive report, March 21, 2007; and CMS annual

report by plan, July 26, 2006.

The number of SNPs and SNP enrollment increased from 2006 to 2007 FIGURE

3–1

Note and Source in InDesign. Number of SNPs

600

300

200

400

500

100

0

July 2006 March 2007

276

476

SNP enrollment (in thousands)

900

300

200

400

500

600

700

800

100

0

July 2006 March 2007

532,000

843,000

Dual eligible

Chronic or 

disabling condition

Institutional

Dual eligible

Chronic or 

disabling condition

Institutional

F i g ure

3 –1
72 Update on the Medicare Advantage program and implementing past recommendations

SNPs (67 percent). However, institutional and chronic 

SNPs grew at faster rates, 127 percent and 446 percent, 

respectively (not shown).

In July 2006, most SNP enrollment (83 percent) was 

in dual-eligible plans (Figure 3-1, p. 71). Enrollment in 

chronic condition SNPs was almost entirely (98 percent) 

in a single plan—Medicare y Mucho Más in Puerto 

Rico. Enrollment in institutional SNPs was mostly (88 

percent) in Evercare plans offered by United Healthcare. 

By 2007, most SNP enrollment was still in dual-eligible 

plans (74 percent). Enrollment in institutional SNPs grew 

as a share of total SNP enrollment from 4 percent to 17 

percent. However, this growth is largely accounted for by 

the redefinition of the SCAN demonstration social-HMO 

as an institutional SNP. SCAN qualified as a SNP under 

the disproportionate share rule; approximately 26 percent 

of its enrollees are nursing home certifiable, living in the 

community. This change added 89,222 institutional SNP 

enrollees, 76 percent of institutional SNP enrollment 

growth.

Most SNPs were offered by parent organizations that also 

offer regular MA plans. Only 13 percent of SNPs were 

offered by parent organizations that focused exclusively 

on operating SNPs (Figure 3-2). The other 87 percent were 

offered by parent organizations that also offered regular 

MA plans, which suggests that these organizations offer 

SNPs as one choice in a menu of options. In fact, most 

SNPs (about 60 percent) existed alongside other MA 

plans offered by the same parent organization in the same 

service area.

Future work on Medicare Advantage

The Commission plans to continue monitoring the 

MA program. In addition to continuing our work in 

examining SNPs, we intend to look more closely at 

employer-sponsored plans in MA to learn more about 

their prevalence and where enrollment is concentrated. 

We would like to know more about the standards that 

apply to such plans (particularly in light of the broad 

waiver authority applicable to these plans), the bidding 

patterns compared with nongroup plans, and other issues 

that will permit us to evaluate these plans. Employersponsored plans appear to be growing in popularity, and 

more employers and groups are providing retiree coverage 

through the PFFS option. We will also be looking more 

closely at the MSA plans that began enrolling beneficiaries 

for 2007. MSA plans also appear to be focusing on the 

employer group market as a source of enrollment. 

F i g ure

3 –2 Most SNPs’ parent organizations 

offered other MA plans in 2006

Note:	 SNP (special needs plan), MA (Medicare Advantage).

Source:	 CMS plan benefit packages, 2006.

Most SNPs’ parent organizations

offered other MA plans in 2006

FIGURE

3–2

Note: Note and Source in InDesign.

5%

1 other plan

in same area

54%

2 or more other plans

in same area

26%

Other plans

elsewhere

13%

SNP only:

no other plans

2%

Unknown
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 73

1 Under the Medicare Prescription Drug, Improvement, and 

Modernization Act of 2003, plans with a bid below the 

benchmark (with the bid including administration and profit 

or retained earnings) are required to use 75 percent of the 

difference between the bid and the benchmark to finance 

extra benefits, with the remaining 25 percent (or half of that 

amount, for regional plans) retained by the Medicare trust 

funds. Previously, plans had the option of returning to the 

government all or a portion of the amount by which their 

needed revenue to provide the Medicare benefit package 

exceeded the Medicare payment—an option rarely chosen. 

Plans could also deposit any difference in a “stabilization 

fund” that financed extra benefits provided in a future year. 

2 In the early years of the Medicare risk program, plan 

payments were set at 95 percent of projected FFS 

expenditures, but payments were not risk adjusted by enrollee 

health status.

3 For regional plans, within a given county the benchmark that 

applies to each county in the region may be lower than the 

local benchmark that applies to that county for local plans.

4 The user fee that is the MA plan contribution to the 

Medicare education campaign is 0.059 percent of plan 

payments (see www.cms.hhs.gov/MMAHelp/downloads/

endofyearenrolpayletter07_final.pdf (downloaded 

3/30/2007)). Benchmark amounts include CMS contractor 

administrative costs for claims processing. Noncontractor 

Medicare administrative costs incurred by CMS, after netting 

out administrative costs for the MA program apportioned 

by program expenditures, are in the range of 0.4 percent of 

program expenditures in Medicare (the CMS 2006 Financial 

Report is available at www.cms.hhs.gov/CFOReport/

Downloads/2006_CMS_Financial_Report.pdf). 

5 As required by the statute, CMS anticipates incorporating any 

VA effect in the 2009 MA rates (CMS 2007b).

6 For the range of benefits MA plans provided to enrollees in 

2006, see Chart 10-4 of the June 2006 MedPAC data book 

(MedPAC 2006b).

7 At least one PFFS plan has a hospital network. The plan 

service area consists of two counties. Beneficiaries pay 

different levels of cost sharing for in-network versus out-ofnetwork hospital care. We do not know whether the payment 

arrangements between the plan and the network hospitals call 

for payment at other than Medicare FFS rates.

8 The February 2007 numbers exclude counties with fewer than 

11 enrollees because they are based on data released publicly 

by CMS, which suppresses such data for privacy reasons. For 

February 2007, about 39,000 enrollees live in counties with 

enrollment under 11. About 3 percent of PFFS enrollment 

comes from such counties. In the July 2006 data, about threequarters of the under-11 enrollment in PFFS came from rural 

counties. Assuming a similar pattern in 2007, the rural and 

BBA floor percentages shown in the table for 2007 would 

increase by 1 percentage point. 

9 Note that the February 2007 enrollment numbers of Table 

3-3 are based on data publicly released by CMS and do not 

include counties with fewer than 11 enrollees. About 3 percent 

of PFFS enrollment is in counties with 10 or fewer enrollees 

in 2007. The 2006 numbers in the table include all counties 

with any PFFS enrollment.

10 The reported data are based on a MedPAC analysis of the 

2007 Medicare Personal Plan Finder downloadable database 

available at the CMS website. Note that PFFS plans will no 

longer be reporting quality measures through the Health Plan 

Employer Data and Information Set but will have member 

satisfaction data reported based on the Consumer Assessment 

of Healthcare Providers and Systems. Medical savings 

account plans will have no reported quality or member 

satisfaction measures. 

11 SNP plans, like other MA plans, are benefit packages offered 

by MA organizations, which sign contracts with CMS.

Endnotes
74 Update on the Medicare Advantage program and implementing past recommendations

America’s Health Insurance Plans. 2007a. AHIP raises 

concerns about new MedPAC report and its potential impact on 

beneficiaries. Washington, DC: AHIP. Press release. March 1. 

http://ahip.org/content/pressrelease.aspx?bc=174|19044.

America’s Health Insurance Plans. 2007b. Low-income and 

minority beneficiaries in Medicare Advantage plans. 

Washington, DC: AHIP Center for Policy and Research. February. 

http://www.ahipresearch.org.

Atherly, Adam, and Kenneth E. Thorpe. 2005. Value of Medicare 

Advantage to low-income and minority Medicare beneficiaries. 

Prepared for the Blue Cross and Blue Shield Association. Atlanta, 

GA: Emory University. 

Centers for Medicare & Medicaid Services, Department of 

Health and Human Services. 2007a. New Medicare Advantage 

enrollment period for MA-only plans. Memorandum from 

Anthony Culotta, director, Medicare Enrollment and Appeals 

Group, to all Medicare Advantage organizations. February 7.

Centers for Medicare & Medicaid Services, Department of Health 

and Human Services. 2007b. Announcement of calendar year 

(CY) 2008 Medicare Advantage capitation rates and payment 

policies. Note from Abby Block, director, Center for Beneficiary 

Choices, and Paul Spitalnic, director, Parts C & D, Actuarial 

Group, Office of the Actuary. April 1.

Centers for Medicare & Medicaid Services, Department of 

Health and Human Services. 2006. 2008 employer group waiver 

policy—Elimination of the “nexus” test for PDPs, non-network 

PFFS and MSA MA plans and service area extension for certain 

MA local coordinated care plans. Memorandum from Abby 

Block, director, Center for Beneficiary Choices, to all prescription 

drug plan sponsors, Medicare Advantage organizations, and 

Section 1876 cost plan sponsors offering employer/union-only 

group waiver plans. November 6.

Congressional Budget Office. 2007. The Medicare Advantage 

program: Trends and options. Statement of Peter R. Orszag, 

director, before the Subcommittee on Health, Committee on Ways 

and Means, U.S. House of Representatives. March 21. 

Government Accountability Office. 2000. Medicare+Choice: 

Plan withdrawals indicate difficulty of providing choice while 

achieving savings. GAO/HEHS–00–183. Washington, DC: GAO. 

September.

House of Representatives. 1997. Balanced Budget Act of 1997. 

Conference report to accompany H.R. 2015, p. 585. July 30 

(Legislative Day of July 29).

Hurley, Robert E., Joy M. Grossman, and Bradley C. Strunk. 

2003. Medicare contracting risk/Medicare risk contracting: 

A life-cycle view from twelve markets. Health Services Research 

38, no. 1 (February): 395–417.

Medicare Payment Advisory Commission. 2007. Report to the 

Congress: Medicare payment policy. Washington, DC: MedPAC.

Medicare Payment Advisory Commission. 2006a. Report to the 

Congress: Medicare payment policy. Washington, DC: MedPAC.

Medicare Payment Advisory Commission. 2006b. A data book: 

Healthcare spending and the Medicare program. Washington, 

DC: MedPAC.

Medicare Payment Advisory Commission. 2005. Report to 

the Congress: Issues in a modernized Medicare program. 

Washington, DC: MedPAC.

Medicare Payment Advisory Commission. 2004. Report to the 

Congress: Medicare payment policy. Washington, DC: MedPAC.

Medicare Payment Advisory Commission. 2001a. Report to the 

Congress: Medicare payment policy. Washington, DC: MedPAC.

Medicare Payment Advisory Commission. 2001b. Report to the 

Congress: Medicare in rural America. Washington, DC: MedPAC.

National Committee for Quality Assurance. 2006. The state of 

health care quality 2006. Washington, DC: NCQA. http://www.

ncqa.org/Communications/SOHC2006/SOHC_2006.pdf.

References
C h a p t er 4

Value-based purchasing: 

Pay for performance in 

home health care

Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 77

Chapter summary

In the Deficit Reduction Act of 2005, the Congress asked the 

Commission to discuss the design of a pay-for-performance (P4P) 

system in home health care as part of a broad set of initiatives to 

improve the value of health care that Medicare purchases. Providing 

financial incentives for quality is one tool the Medicare program can 

use in home health and other settings. P4P should be used in tandem 

with other payment reforms (e.g., increasing the accuracy of payments) 

as well as with other quality incentives (e.g., public reporting).

The first key decision in the design of a P4P system is how to fund the 

reward pool. As a principle, the Commission has stated that P4P should 

be budget neutral, neither adding nor removing money from the system. 

Thus, the system could be funded by redistributing payments from 

poor performers to high-quality performers and to providers who are 

improving.

Another set of key decisions involves how to set thresholds for 

performance. One way to set a threshold is to predetermine a 

In this chapter

• Pay for performance 

in Medicare: The 

Commission’s design 

principles

• Pay for performance for 

home health

• Circumstances of the home 

health sector

• Additional technical 

information on home health 

pay for performance

Value-based purchasing: 

Pay for performance in 

home health care

C H A PT E R 4
78 Value-based purchasing: Pay for per for mance in home health care

percentage of providers (e.g., rewarding the top 10 percent of providers). 

Another alternative is to choose a minimum score and use a test of statistical 

significance: High performance is a score statistically significantly above 

the average, poor performance is a score statistically significantly below the 

average, and improvement is a score statistically significantly greater than 

the provider’s previous score.

A system that rewards both attainment of high quality and improvement 

toward high quality must find a balance between the two rewards. If the 

rewards are exclusive (a provider can receive either an attainment reward or 

an improvement reward but not both) then less weight could be placed on 

the improvement rewards since those providers are, by definition, providing 

lower quality care as measured by the P4P system.

A final decision in P4P design is to determine the size of the reward. In 

a budget-neutral system, the size of the reward is constrained by the size 

of the penalty placed on poorly performing providers. One implication of 

the Commission’s principle that P4P should be budget neutral is that when 

money is removed from the system to fund the pool, then the entire reward 

pool should be spent on rewards. The size of the reward for the provider 

should be a percentage of the provider’s Medicare payments.

The circumstances of home health care may pose some challenges for P4P in 

that sector. The payment system has some inaccuracies, and payments have 

been more than adequate. The Commission will continue to consider reforms 

to the payment system. P4P should be put in place at the same time Medicare 

improves the payment system to create stronger incentives to improve 

quality. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 79

MedPAC recommended that Medicare build financial 

incentives for quality into payments to hospitals, 

physicians, home health agencies (HHAs), dialysis 

providers, and Medicare Advantage plans (MedPAC 2004, 

2003). Medicare’s current payment systems are neutral or 

negative toward the quality of services; these systems do 

not promote the program’s goals to provide high-quality 

services to its beneficiaries and to be a good steward of 

public resources. The program should link payment to 

quality through a pay-for-performance (P4P) program to 

increase the value of health care spending. P4P should be 

used as one payment policy tool along with reforms that 

address other weaknesses in the payment system and other 

incentives for quality.

The Congress asked the Commission to address several 

key design issues in developing a system that links 

payment to performance in home health care as part of 

a broad initiative to encourage value-based purchasing 

in the Medicare program. The Deficit Reduction Act of 

2005 requested this mandated report (see text box, p. 80). 

The mandate posed four questions: How should P4P be 

funded? What is the threshold for a reward? How should 

improvement and attainment be balanced? What is an 

effective size for the reward? 

Pay for performance in Medicare: 

The Commission’s design principles

The Commission has developed principles to guide the 

design of a P4P program and to select the quality measures 

that would support it.

Program design features

The Commission calls for P4P programs that:

• Reward providers based on attaining or exceeding 

certain benchmarks and improving at certain 

benchmarks. This principle seeks to encourage 

as many providers as possible to improve, thus 

maximizing the benefit of the program to as 

many beneficiaries as possible. Providers already 

performing at high levels will be rewarded for their 

efforts. Those who score low at baseline will have an 

incentive to improve. If all providers improve over 

time, improvement incentives can be phased out of the 

system.

• Are funded by setting aside a small proportion of the 

current payment—initially 1 percent to 2 percent. The 

first dimension of this principle is whether the policy 

should be funded by withholding dollars or whether 

new spending is necessary.1 Through a separate 

process, the Commission evaluates the adequacy 

of payment levels every year when it recommends 

payment updates for providers. The Commission 

determined that the P4P initiative should be funded 

within current levels of spending. The primary 

rationale was to shift the incentives of payment, not 

the level. 

The second dimension is whether the size of the 

incentive is enough to encourage provider change or 

whether it is too disruptive. Evidence about the “right” 

level for incentives is limited.2 In a budget-neutral 

program, smaller incentives may be more powerful as 

providers perceive the penalty dollars as lost income. 

The much smaller 0.4 percent incentive for hospitals 

called for by the Medicare Prescription Drug, 

Improvement, and Modernization Act of 2003 was 

designed to encourage data reporting as a condition 

for receiving a full update; there was a penalty for 

nonparticipation. It resulted in nearly universal 

hospital reporting on certain process measures. 

Others have suggested that, if the dollars are withheld, 

even 1 percent to 2 percent could be significant and 

potentially harm providers that may be at low levels of 

quality. This concern was one rationale for suggesting 

that improvement from low levels should also be 

rewarded.

Given the limited evidence on the right level, and 

to ensure minimal disruption for beneficiaries and 

providers, the Commission chose to recommend that 1 

percent to 2 percent be set aside, at least initially. The 

Commission expects the percentage to increase as the 

Medicare program and providers gain more experience 

with P4P. 

• Distribute all payments that are set aside to providers 

that meet reward criteria.

• Establish a process for evolution of the program, 

together with private purchasers and other public 

purchasers. The P4P design should be evaluated and 

changed over time. This system should be a learning 

system.
80 Value-based purchasing: Pay for per for mance in home health care

Criteria for quality measures for a payfor-performance program

Based, in part, on the experiences of private-sector 

initiatives, the Commission developed criteria for 

determining whether the measures and measurement 

activities for each provider setting were sufficient to 

distinguish between high- and low-quality performance. 

These criteria are:

• Well-accepted, evidence-based measures must be 

available. They should be accepted by independent 

quality experts and should be familiar to providers. 

While few individual measures are perfectly valid 

or reliable, they should identify real differences in 

provider quality. 

• Collecting and analyzing data should not be unduly 

burdensome for either the provider or CMS. To 

minimize the burden of collection and analysis, CMS 

should base quality measures on data it currently 

collects, wherever possible. The need for additional 

information should be balanced against the value 

of the information to the provider being measured, 

patients, and the Medicare program. 

• Incentives should not discourage providers from 

taking riskier or more complex patients. Appropriate 

risk adjustment is always important when comparing 

provider quality. To address this concern, the program 

could use measures that—in general—are not affected 

by the complexity of the patient, such as process, 

structure, and patient-reported experience of care 

measures. Risk adjustment is critical for outcomes-ofcare measures.

• Most providers should be able to improve on the 

available measures. This criterion has several 

dimensions. For one, the measures should capture 

aspects of care the providers can affect. Another 

dimension is that the measures should be related to 

aspects of quality that most need improvement; there 

should be room for real gains in quality. Another 

dimension is scope. The measures should apply to 

a broad range of care and providers; the greater the 

proportion of providers whose care is measured, the 

broader the impact will be on beneficiaries. It is also 

important to measure a broad range of the types of 

care delivered in the setting. Measures focused on 

specific conditions are already available in most 

settings, but to capture a broad range of care in each 

setting, measures that apply to all types of patients 

(e.g., safe practices, use of patient registries, and 

patient perceptions of care) should be added over time. 

A starter set of measures could satisfy this criterion 

and not necessarily encompass all care, all providers, 

and all patients. 

• A P4P measure set should evolve to become more 

comprehensive. Ideally, measures should also reach 

across settings to align incentives across providers 

such as hospitals, skilled nursing facilities, and 

physicians working together to reduce readmissions 

to acute care hospitals. After Medicare chooses an 

initial measure set, CMS will need to alter, add, and 

drop measures and ensure that research is under way 

to create or validate other measures. A single entity 

could help coordinate public and private efforts 

and, based on the advice of quality experts, make 

recommendations on measures.

Mandate for report

The Deficit Reduction Act of 2005

MedPAC Report on value based purchasing.

Not later than June 1, 2007, the Medicare Payment 

Advisory Commission shall submit to Congress a 

report that includes recommendations on a detailed 

structure of value based payment adjustments for 

home health services under the Medicare program 

under title XVIII of the Social Security Act. Such 

report shall include recommendations concerning 

the determination of thresholds, the size of such 

payments, sources of funds, and the relationship 

of payments for improvement and attainment of 

quality. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 81

Pay for performance for home health 

In this section, we apply the Commission’s general 

principles to the specific challenge of developing a 

Medicare P4P system for HHAs. We use an illustration of 

a home health P4P system to discuss the decisions to be 

made at each point. This illustration is only one of many 

possible designs for a P4P system; factors that influence 

whether P4P is likely to have an impact on quality should 

also be considered (see text box). Our use of a single 

model is for the benefit of clarity and does not imply an 

endorsement of this particular set of design choices. We 

chose six real agencies; using their actual quality and 

financial information from 2005, we present the rewards 

and penalties that would accrue in a system that pays more 

for high-quality care and less for low quality.

There are several decision points in the design of home 

health’s P4P system. At each of these points in the model, 

we discuss the alternatives to the path we chose for the 

purpose of this illustration. The major decision points are:

What will make pay for performance work?

Providing incentives for quality can increase value 

by prompting providers to begin addressing the 

current shortcomings of health care.3 Results 

such as the high level of evidence-based care for cancer 

in the first year of the United Kingdom’s physician payfor-performance (P4P) program (Doran et al. 2006), 

the increase in cholesterol screening during California’s 

physician P4P program (Integrated Healthcare 

Association 2006), and patients receiving aspirin after 

a heart attack under CMS’s hospital P4P demonstration 

provide evidence that providers respond to incentives 

to improve their performance, increasing the quality of 

health care. 

The Agency for Healthcare Research and Quality 

(AHRQ) synthesized economic, psychological, 

decision, and organizational theories to describe other 

factors that could lead providers to respond to—or 

ignore—a P4P program (Dudley et al. 2004). We 

summarize these factors in this text box.

Providers are more likely to respond to financial 

incentives if expected revenue is greater than or equal 

to costs. If the direct costs and opportunity costs of 

responding to the incentive outweigh the financial 

return, then the incentive is likely to fail. However, 

this may be mitigated by some of the nonfinancial 

incentives, such as a commitment to professionalism, 

the mission of the organization, and the provider’s 

potential loss of standing among peers or in the 

community (Town et al. 2004). These “costs,” in terms 

of the provider’s reputation, will be greater if the P4P 

information is widely available. 

Providers who think they have greater control over 

what is measured will have a greater response. For this 

reason, structural and process measures may generate a 

greater response than outcome measures. 

Providers under fee-for-service payment are more 

likely to respond to incentives to produce more units 

of service—more discharges or more episodes of 

home health care—because improving quality in a 

way that increases use of services increases revenue. 

Alternatively, providers in a capitated payment system 

may be less attracted to incentives that require more 

services to be provided within the bundle of payment. 

Researchers at the University of Minnesota expanded 

on AHRQ’s list with provider characteristics that will 

affect a provider’s response to P4P (Town et al. 2004). 

For example, providers that are risk averse will respond 

more strongly to avoid a penalty.

If different payers coordinate their efforts, P4P 

is more likely to succeed because providers can 

receive consistent incentives and avoid duplicative 

or incompatible requests for quality data. Also, the 

coordination of effort leads to a greater impact by 

capturing a larger portion of providers’ total revenue. 
82 Value-based purchasing: Pay for per for mance in home health care

• funding the reward pool

• measuring agency quality

• setting thresholds for reward and penalty 

• balancing improvement and attainment

• calculating the rewards

For the purposes of illustration, we discuss a model 

that funds the reward pool by withholding 5 percent of 

payments from each HHA. While this is not the only 

design consistent with the Commission’s principles, 

it is provided to illustrate one possible configuration 

of P4P in home health care. The model uses a quality 

measure based on improving or stabilizing functional 

outcomes and avoiding potentially preventable unplanned 

hospitalizations and trips to the emergency room (ER). 

To determine whether an agency will be rewarded or 

penalized, its quality score is compared to a national 

benchmark level of quality (the threshold) to determine 

whether it is statistically significantly higher or lower 

than the benchmark. The model also includes a measure 

of the agency’s improvement in quality. The reward for 

attaining high quality is twice as large as the reward 

for improvement in this model. Rewards and penalties 

are calculated as a percentage of the agency’s Medicare 

payments. We also discuss additional design features, 

such as addressing agencies with few patients and ways 

to improve the P4P system and the quality measures over 

time.

Funding the reward pool

The first decision is how to fund the reward pool. This 

involves two issues: (1) whether the funding should be 

budget neutral, new money, or from savings elsewhere 

in the program; and (2) how much funding should go to 

payment for performance.

Source of funding

The Commission has stated as a principle that P4P should 

be budget neutral. In a report on rewarding provider 

performance, the Institute of Medicine (IOM) also 

recommended a budget-neutral funding source (IOM 

2006). The model applies budget neutrality by withholding 

5 percent of Medicare revenue from poor performers to 

fund the reward pool for high performers. Thus, the reward 

and the penalty pools redistribute spending within the 

home health sector and do not add new money to it. 

A P4P system that includes potential penalties (which 

is implicit in a budget-neutral program) may be more 

powerful than a system with the same percentage of 

payment without penalties because economic actors assign 

more value to potential income lost than to rewards won 

(Kahneman and Tversky 1979). If providers are at risk for 

losing revenue, then low-quality providers could perceive 

even 1 percent to 2 percent of payments as significant. 

In contrast to the Commission’s design principle, 

CMS uses savings generated by home health quality 

improvement in other sectors of Medicare to fund rewards 

for HHAs in its proposal for a demonstration. The 

demonstration would increase the amount of spending in 

the home health sector but would not increase Medicare 

spending as a whole because spending would be reduced 

in other sectors. Under the demonstration, if the HHAs in 

the demonstration keep their patients out of the hospital 

more often than agencies outside of the demonstration, 

then the amount saved on hospitalizations avoided will be 

available as rewards to high-quality HHAs that participate 

in the demonstration. If savings are not achieved, then no 

money will be available for rewards.

If a program were funded based on savings, IOM observed 

that it would not be possible to predict the size of the 

reward pool until the experience for the entire year in 

multiple sectors is gathered and analyzed, creating a long 

lag between implementing the program and rewarding 

providers and resulting in instability from year to year. 

IOM also noted that it would be difficult in a generated 

savings funding system to attribute spending decreases 

in one sector (e.g., hospitals) to quality interventions in 

a different sector (e.g., HHAs). This challenge would be 

compounded if and when P4P systems in different sectors 

are running simultaneously. For example, if both home 

health and skilled nursing facility P4Ps were running, 

the program should not “spend” the hospital savings 

twice, even though improvements in both skilled nursing 

facilities and home health care might have contributed 

to reduced hospitalizations. This funding source is likely 

to be unstable because it might be difficult to generate 

increasing savings year after year. 

Providers may not perceive a funding system based on 

savings to be fair if improvements in their quality do not 

generate savings in other sectors. Providers may also 

perceive the complicated calculation of savings to be 

inaccurate. Finally, there may be a “free rider” problem 

if the savings some exemplary providers generate are 

attributed to all. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 83

A positive attribute of funding based on savings is its 

explicit link between high quality and resource use in 

achieving greater efficiency. It may appeal to policymakers 

because it builds an explicit incentive to generate savings 

for Medicare into the P4P program. If such a system 

were effective, one might imagine a future phase of the 

program in which Medicare keeps some of the savings 

and thus lowers total Medicare spending. Finally, such a 

system allows the program to fund a reward pool without 

penalizing (and presumably antagonizing) providers who 

participate in Medicare voluntarily or seeking new money 

from outside the program.

Level of funding 

The Commission recommended starting P4P with a 

small portion of payment. Evidence on the right level for 

incentives is limited (Rosenthal et al. 2005). One survey 

of private-sector efforts found that purchasers report 

needing incentives of 5 percent to 20 percent to influence 

the behavior of physicians and 1 percent to 4 percent to 

influence hospitals. Applying these findings to a program 

as large as Medicare is problematic. We do not know what 

portion of providers’ overall payment these percentages 

represent. Because Medicare payment often represents a 

higher percentage of a provider’s total revenue than does 

a single private payer, a smaller percentage of Medicare’s 

payment may be enough to encourage change. In CMS’s 

Premier hospital demonstration, preliminary results show 

improvement in all conditions in the first four quarters in 

anticipation of financial rewards of either 1 percent or 2 

percent for those in the upper rankings (Premier 2006).4

The Commission expects the percentage to increase as the 

Medicare program and providers gain more experience 

with P4P. 

As a general guide, the Commission suggested that P4P 

programs begin with 1 percent or 2 percent of payments. 

The model withholds 5 percent of payments. One could 

view the model as a program that started with a smaller 

withhold and grew over several years to the 5 percent 

level. In 2005, Medicare payments for home health 

services totaled $12.5 billion. The 5 percent withhold 

would generate $625 million in the pool for rewards. 

Annual Medicare payments to individual agencies ranged 

from about $125,000 to $6.5 million.5 At the agency level, 

a 5 percent withhold would amount to a payment reduction 

ranging from $6,300 for some of the smallest agencies 

to $325,000 for some of the largest. The median agency 

received $1 million in Medicare payments and would have 

a withhold of $50,000.

Illustration of a home health P4P model 

For illustrative purposes, the model (Table 4-1) withholds 

5 percent of revenues from six agencies to demonstrate the 

reward pool. 

Measuring agency quality

The core of home health quality measurement is the 

31-measure Outcome-Based Quality Improvement 

(OBQI) set. CMS developed the OBQIs to use in their 

public reporting of HHA quality and to track changes in 

quality over time. The OBQI set includes the measures of 

outcome, stabilization, and improvement shown in Table 

4-2 (p. 84).

These measures are based on comparison of patients’ level 

of function at the beginning and end of their home health 

treatment as measured by the Outcome and Assessment 

Information Set (OASIS) patient assessment tool. Most 

patients can be included in most measures. 

T A B L E

4 –1 The pay-for-performance model withholds 5 percent of Medicare payments

Agency

1 2 3 4 5 6

Total Medicare payments $192,000 $755,000 $4,706,000 $2,106,000 $415,000 $764,000

Payment withheld $9,600 $37,700 $235,300 $105,300 $20,800 $38,200

Source:	 Outcome Concept Systems analysis of 2003–2005 cost report and Outcome and Assessment Information Set data.
84 Value-based purchasing: Pay for per for mance in home health care

CMS has used about a dozen of these measures to assess 

individual HHAs’ quality for the past several years on the 

Home Health Compare website. These measures satisfy 

most of the Commission’s criteria for use in P4P: They 

are valid, reliable, generally accepted by researchers, and 

familiar to providers.6 Providers can improve on these 

measures. They are derived from data that are routinely 

collected from HHAs and processed by CMS; they do not 

pose a new data burden.

A composite quality score

For illustrative purposes, we used a quality score that 

combines 20 home health outcomes into a score called the 

Standardized Quality Index (SQI). Additional technical 

information is provided at the end of this chapter. The SQI 

includes patients who improve at activities of daily living 

as well as those whose level of functioning is stable. It 

includes penalties for potentially avoidable hospitalizations 

and potentially avoidable use of the ER, both of which 

indicate lower quality and suboptimal resource use. The 

SQI groups patients into categories by their primary 

diagnosis. The measurement is restricted to patients for 

whom Medicare is the primary payer.

The SQI gives agencies credit for stabilizing patients who 

do not improve. This allows the system to capture the 

quality of care provided to patients who use home health 

care to remain safely at home, stabilize their condition, and 

avoid institutional care settings such as a nursing home. 

The score places greater weight on unplanned 

hospitalization and ER use because these outcomes 

also capture the potentially avoidable use of hospitals’ 

and ERs’ resources. The Commission has underscored 

the importance of including both quality and resource 

use in measures of efficiency. A high rate of potentially 

avoidable adverse events indicates not only low quality 

but also inefficient use of hospital resources. By safely 

and appropriately preventing avoidable hospitalizations 

and use of the ER, home health care can efficiently reduce 

the use of hospital resources. The SQI score restricts the 

definition of adverse events to ER and hospital use for 

specific diagnoses that could have been prevented. 

T A B L E

4 –2 OBQI measure set

Outcome Stabilization Improvement

•	 Acute care hospitalization

•	 Any emergency care provided

•	 Discharge to community

Stabilization in:

	 •	 Bathing

	 •	 Grooming

	 •	 Transferring

	 •	 Light meal preparation

	 •	 Laundry

	 •	 Housekeeping

	 •	 Shopping

	 •	 Telephone use

Improvement in:

	 •	 Bathing

	 •	 Grooming

	 •	 Transferring

	 •	 Light meal preparation

	 •	 Laundry

	 •	 Housekeeping

	 •	 Shopping

	 •	 Telephone use

	 •	 Ability to dress lower body

	 •	 Ability to dress upper body

	 •	 Ambulation

	 •	 Bowel incontinence

	 •	 Confusion frequency

	 •	 Dyspnea (shortness of breath)

	 •	 Eating

	 •	 Frequency of pain

	 •	 Management of oral medications

	 •	 Toileting

	 •	 Urinary incontinence

	 •	 Urinary tract infection

Note: OBQI (Outcome-Based Quality Improvement).
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 85

Giving more weight to measures that include resource use 

is consistent with goals established by CMS and IOM. 

In the proposed demonstration of a P4P system in home 

health care, CMS has given additional weight to unplanned 

use of the hospital and use of the ER. In its report, IOM 

stressed the need for P4P to include measurements of 

resource use. 

We discuss specific and additional issues in the 

development of composite quality scores at the end of this 

chapter.

Whether to measure quality for Medicare 

patients only

In our model, we measure quality only for Medicare 

patients cared for by Medicare-certified agencies. 

Choosing to measure only those patients for whom 

Medicare is the primary payer increases the homogeneity 

of the patients compared across agencies: Medicare 

patients tend to share certain characteristics such as age, 

full insurance coverage, and regular sources of care. 

Also, within home health care, patients must meet the 

same conditions of medical necessity and level of need: 

The rules of Medicare stipulate that home health patients 

must be homebound, require skilled medical services, and 

need temporary or intermittent care (rather than 24-hour 

or long-term care). Patients with non-Medicare sources 

of payment might not fit these criteria. The heterogeneity 

of private pay and Medicaid patients might make it more 

difficult to make fair comparisons of patients across 

agencies. In terms of the verification of data, patients 

outside of Medicare pose a special challenge because 

the Medicare program may not have a regular, auditable 

source of data for those patients. 

Alternatively, a P4P system could include all of a 

provider’s patients and not just those whose primary 

payer is Medicare. The Medicare program’s conditions of 

participation maintain the same quality standards for all 

of a provider’s patients. Some patients have both Medicare 

and Medicaid sources of payment; thus, the primary 

source of payment may change but the patient remains 

the same. Measures that are more inclusive allow for 

larger samples, which can result in more accurate quality 

measurement.

Illustration of a home health P4P model

For the model, we used the SQI score for the six agencies’ 

therapy patients. This score summarizes 22 outcomes 

for patients who need physical therapy. Using primary 

diagnosis, which acts as a risk adjuster, we grouped similar 

patients together. Only Medicare patients are included.

On this scale, higher scores indicate that more patients 

achieved better outcomes more frequently. The scores 

ranged from –2 to +2. The average score was 0.84. The 

measurement periods are year 1 (from the second quarter 

of 2004 to the first quarter of 2005) and year 2 (from the 

second quarter of 2005 to the first quarter of 2006). Table 

4-3 presents the average quality scores for the six agencies.

The third row in Table 4-3 displays each agency’s score 

when we pooled data from year 1 and year 2. Pooling data 

across years is an effective tool to address the challenge 

of small sample sizes. Also, pooled data add stability to 

the scores because a two-year average changes less from 

year to year than a single-year average. As we continue to 

discuss the model in this chapter, we will measure these 

agencies by their score on the two years of pooled data. 

T A B L E

4 –3 Agency level quality scores in the model

Agency

1 2 3 4 5 6

SQI score:

Year 1 0.46 0.30 0.66 0.83 0.95 1.09

Year 2 0.60 0.61 0.69 0.86 0.87 1.16

Pooled data 0.50 0.56 0.66 0.85 0.92 1.13

Note:	 SQI (Standardized Quality Index).

Source:	 Outcome Concept Systems analysis of 2003–2005 cost report and Outcome and Assessment Information Set data.
86 Value-based purchasing: Pay for per for mance in home health care

Setting thresholds for reward and penalty

P4P programs measure the quality of each provider and 

compare providers’ quality scores with a threshold to 

determine whether they qualify for a reward for attaining 

high quality. Three components of the program can be set 

in advance: (1) the amount of the payment (necessary for 

budget-neutral systems), (2) the threshold that will trigger 

payment or penalty, and (3) the number of agencies that 

will receive a payment or a penalty.

For illustration, we have set both the funding and the 

threshold in advance. We call the threshold the national 

benchmark. Setting the quality target in advance may 

help some providers develop plans to improve quality, 

focus their efforts, and set milestones over the course of 

the measurement period to calibrate their performance. 

Alternative models that predetermine the proportion of 

agencies to reward or penalize (e.g., a system that rewards 

the top 10 percent or penalizes the worst 100 agencies) 

could penalize or reward average providers because some 

agencies that are statistically the same as the average 

could fall into the reward or penalty group. However, 

predetermining the size of the pool has the advantage of 

producing a stable, predictable pool of agencies to reward 

and penalize.

In comparing the agency’s average quality score to the 

national benchmark, we use a statistically significant 

difference as the threshold: Thus, the threshold for a 

reward is to be statistically significantly above the national 

benchmark. The threshold for a penalty is to be statistically 

significantly below the benchmark and not show any yearto-year improvement. This system minimizes uncertainty 

by reducing the number of times it rewards a provider that 

is actually poor or mediocre or penalizes a provider that is 

actually mediocre or good. 

The national average SQI score for therapy patients for 

the measurement year is 0.84 in the model. Whether a 

given agency is significantly better than average depends 

on three things: (1) the agency’s score, (2) the size of 

the agency, and (3) the variation in outcomes among 

the agency’s patients. High scores, larger samples, and 

more consistency increase the statistical certainty that 

an agency’s score is greater than average; small samples 

and inconsistent outcomes among an agency’s patients 

could lead to a score that is higher than average due to 

chance rather than to high quality of care.7 Two sources 

of variation, measurement error and random variation in 

patients’ response to care, could cause an agency’s score to 

differ from the true quality of the agency.

The national average SQI score for therapy patients for 

the year before the measurement year is the benchmark of 

the system. This system would allow providers to know 

their quality improvement target; they would know what 

score they had to beat to gain a reward or how much they 

would need to improve to avoid a penalty. Thus, setting the 

benchmark with the previous year’s average substantially 

reduces one of the greatest uncertainties providers in a 

P4P system face. Also, by using a national average the 

industry has already obtained, the program can be fairly 

certain that some providers will exceed the benchmark and 

some will fail to meet it. Alternatively, the trend in quality 

improvement that has emerged over the past several years 

of quality reporting in home health care—namely, about 

a 2 percent annual gain in functional outcomes—could be 

applied and the benchmark could be set 2 percent higher 

than the previous year’s national score average so everyone 

would need to continue to improve at the current rate to 

maintain their current status; they would need to expend 

an additional effort to excel. 

The reward group 

When we apply the model to national data for patients 

in the therapy group, we find that we would place 34.4 

percent of all agencies in the reward group (Table 4-4). 

T A B L E

4 –4 The share of agencies in the reward

 group will depend on clinical group

 and statistical confidence level

Confidence level

Clinical group 95% 90%

Therapy 32.0% 34.4%

Acute CVD 16.5 20.0

CHF or COPD 27.3 30.5

Diabetes 21.0 24.3

Pneumonia 15.3 18.9

Skin infection 16.7 20.5

Skin ulcer 14.6 18.3

Note:	 CVD (cerebrovascular disease), CHF (congestive heart failure), COPD

(chronic obstructive pulmonary disease).

Source:	 Outcome Concept Systems analysis of 2003–2005 cost report and

Outcome and Assessment Information Set data.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 87

If we had started with a different clinical group, a different 

proportion of agencies would be eligible for a reward. 

Fewer agencies excel at care for the other six clinical 

groups. Also, if we applied a higher standard of certainty—

for example, if we had used a 95 percent confidence 

interval—we would have a smaller proportion of agencies 

in the reward group.

Alternatively, P4P in home health care could use a model 

that is similar to the system CMS is considering for its 

home health P4P demonstration. This system will reward 

the top 10 percent of eligible agencies. This design has the 

advantage of ensuring that there will always be a group 

of agencies to reward. A system that sets a performancebased threshold runs the risk that very few or even no 

agencies will qualify for a reward. To be eligible, an 

agency must serve at least 25 patients. CMS’s system 

measures all the patients at each agency. It does not restrict 

its measurement to patients in a single clinical group. As 

we noted previously, the CMS design scores each outcome 

separately; thus, an agency could receive a reward for 

its ability to improve patients’ bathing but not receive a 

reward for improvement in walking. 

A weakness of CMS’s method of setting a threshold for 

reward is the potential to make statistical errors. Some 

agencies may score in the top 10 percent due to chance. 

Treating each agency’s reported score as given—without 

accounting for the size of an agency’s caseload or the 

standard deviation of scores within an agency’s caseload—

makes substantial distinctions among small agencies with 

widely variable scores and makes very little distinction 

among larger agencies with more stable scores that remain 

closer to the mean. The high level of variation in the scores 

of small agencies relative to the larger agencies indicates 

that their scores are likely to be the luck of the draw. They 

depend more on chance than on the underlying quality 

of the agency because the sample of patients is small. A 

threshold that ignores statistical significance would reward 

or penalize fewer large agencies with stable scores close 

to the mean and would reward or penalize more small 

agencies because of high variance in outcomes associated 

with small samples of patients. On the other hand, using a 

test of statistical significance implies that a large agency 

with a score close to the threshold may receive a reward 

while a smaller agency with a score well above the 

threshold would not receive a reward. One may wish to 

consider pairing a test of statistical significance with an 

absolute minimum difference from the threshold to limit 

the number of times very small but significant differences 

are rewarded. 

The penalty group

For the purpose of the illustration, we set the threshold 

for penalty at a score statistically significantly lower 

than the national benchmark. The statistical method for 

determining this threshold is the same as the method 

we are using for the illustration to set the threshold for 

reward. In the illustration, we find that 28.9 percent of 

agencies fall into the penalty category. As in the case of 

the reward threshold, if different clinical groups were used, 

the proportion would be different, and, if we used a higher 

level of confidence, the penalty pool would be smaller.

Most P4P systems do not use penalties. There may be 

several reasons not to use them:

• Many P4P programs are voluntary; providers may be 

unlikely to volunteer for a program that could reduce 

their revenue.8

• P4P systems that are funded with generated savings 

or new money do not need a penalty pool to fund the 

rewards.

• Some suggest that the use of penalties will increase 

the amount of gaming that is likely to occur under a 

P4P system.

On the other hand, the possibility of a penalty is likely to 

motivate the providers in the middle and lower-middle 

portion of the quality spectrum to improve so that they 

may avoid losing revenue. A system without penalties 

might not provide enough motivation for some of the 

poorest performers to improve, because there would be no 

cost to them for nonparticipation. 

The average group 

To illustrate how to apply thresholds, the model has a third 

group: agencies with neither reward nor penalty. They 

are neither statistically above nor below the benchmark. 

Not surprisingly, many agencies fit this category. In the 

model, they would receive a refund equal to the amount 

of payments withheld. However, these agencies may be 

eligible for a reward based on improvement, even though 

they do not attain high quality.

Illustration of a home health P4P model

For purposes of illustration, the threshold for reward is set 

at a level that is statistically higher than last year’s national 

average; the threshold for penalty is statistically lower than 

the national average. The national average score was 0.84. 
88 Value-based purchasing: Pay for per for mance in home health care

An agency whose confidence interval falls entirely below 

0.84 is in the penalty group. If the confidence interval 

includes 0.84, the agency is in the no-change group. If the 

confidence interval is entirely above 0.84, the agency is in 

the reward group (Figure 4-1). 

In the national data set, 34.4 percent of all agencies were 

eligible for a reward; a penalty was applied to 28.9 percent 

of agencies. In the proposed model, a third group of 

agencies (36.7 percent of the total) would be in neither the 

reward nor the penalty pool. Their scores are essentially 

the same as the average score; their quality is neither 

excellent nor poor.

Balancing improvement and attainment

Next, the model considers improvement in agencies’ 

performance over time, consistent with the Commission’s 

principle that P4P should reward both attainment of high 

performance and improvement. In the model, agencies 

with average scores in the measurement year but with 

statistically significantly higher scores than they had in the 

previous year are eligible for an improvement reward. The 

award to this “most improved” group is half the size of the 

reward to the group that attained high scores. In the model, 

we also look again at the agencies in the penalty group. If 

they significantly improved over the previous year, they 

are lifted out of the penalty group and put into a group that 

receives neither reward nor penalty.

For the illustrative model, the second component of the 

reward system would acknowledge the improvement 

among agencies that did not attain a score high enough 

for an attainment reward (Figure 4-2). The Commission 

has stated as a principle that P4P should reward both 

attainment and improvement. If the improvement in an 

agency’s score from the previous year to the current year is 

statistically significant, then that agency could be eligible 

for an improvement award. We use exclusive categories 

for attainment and improvement rewards. If an agency 

is eligible for an attainment award, it is not also eligible 

Comparing agencies to the threshold in the model

Note:	 SQI (Standardized Quality Index). The figure shows the agencies’ pooled data score, which includes two years of data.

Source:	 Outcome Concept Systems analysis of 2003–2005 cost report and Outcome and Assessment Information Set data.

Comparing agencies to the threshold in the model FIGURE

4-X

Note and Source in InDesign Agencies

1

4

3

2

5

6

Penalty

No change

Penalty

Penalty

Reward

Reward

0 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 1.10 1.20 1.30

SQI score

Threshold 

(National average)

(0.84)

0.50

0.56

0.66

0.85

0.92

1.13 Score

Confidence interval

F i g ure

4 –1
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 89

for an improvement award. Thus, improvement rewards 

would go to agencies with average scores but that showed 

substantial progress toward the goal of excellence. In the 

model, the rewards for improvement would be one half the 

size of the rewards for attainment.

The illustrative model would reward agencies with 

average scores and any amount of statistically significant 

improvement. Agencies with scores that are statistically 

significantly below the benchmark would not be eligible 

for an improvement reward. Measuring the statistical 

significance of the difference in year 1 and year 2 scores 

would minimize the number of times we would give an 

improvement reward to small agencies with very unstable 

scores—agencies with scores that are likely to be higher 

or lower due to chance rather than to the influence of 

real quality improvement. Alternatively, there may be a 

minimum threshold for improvement such as a 10 percent 

difference between year 1 and year 2 so that small but 

statistically significant differences would not be rewarded. 

The model also uses a measurement of improvement to 

soften the penalty for poor performance. If an agency’s 

score were statistically significantly below the national 

benchmark score, but the agency showed significant 

improvement over its score the preceding year, then it 

would not receive a penalty. This system softens the 

penalty by allowing agencies who are truly getting better 

to avoid losing revenue. Thus, only the worst actors in the 

system would be penalized: They are both poor performers 

relative to the benchmark and are not getting any better 

relative to their own performance. 

Illustration of a home health P4P model

In this step of the illustrative model, agency 3 avoids the 

penalty because its improvement from year 1 to year 2 

was statistically significant. Agency 6 also had significant 

Rewarding attainment and improvement in the model

Note:	 SQI (Standardized Quality Index). Agency’s pooled SQI score includes two years of data.

Source:	 Outcome Concept Systems analysis of 2003–2005 cost report and Outcome and Assessment Information Set data.

NEED TITLE**ABOUT factoring in improvement in the model FIGURE

4-2

Note and Source in InDesign

Compare agency’s 

pooled SQI score

 to the threshold

Agency’s score

statistically

significantly

higher

Agency’s score

not statistically

significantly

different

Agency’s score

statistically

significantly

lower

Attainment

reward

Statistically significant

improvement in agency’s 

score from year 1

 to year 2?

Statistically significant

improvement in agency’s 

score from year 1

 to year 2?

Improvement

reward

No change

Yes

No

Penalty

Yes

No

Attainment Improvement

F i g ure

4 –2
90 Value-based purchasing: Pay for per for mance in home health care

improvement but has already qualified for an award based 

on its attainment, so in the model it cannot also receive the 

improvement reward. The other four agencies did not show 

significant improvement.

In national data, about 5 percent of all agencies that 

would have been in the penalty pool were lifted into 

the no-change pool because they showed significant 

improvement. Another group of 5 percent of agencies 

would be in the improvement group. They showed 

statistically significant improvement from year 1 to year 

2, but in year 2 their score remained statistically similar 

to the average. These agencies would not qualify for an 

attainment reward but would qualify for an improvement 

reward. One could contemplate a further evolution of this 

scoring system in which agencies that attained a high level 

of performance with scores statistically significantly above 

the mean and also improved from year 1 to year 2 might 

be eligible for some additional bonus recognition as a 

breakthrough group. 

Calculating rewards

The final step in the P4P system is distributing the rewards 

to providers. The Commission’s principle that P4P should 

be budget neutral guides this step. The agencies in the 

penalty group will not have their 5 percent withhold 

returned to them. The 5 percent withhold is returned to 

the agencies in the no-change group. The agencies in the 

reward group receive an amount equal to the 5 percent 

withheld plus the reward amount. Because the model 

does not force the reward group and the penalty group to 

be the same size, and the pool was funded by a withhold 

of a predetermined size, the size of the rewards varies to 

fit the size and number of reward recipients. The amount 

returned or rewarded to an agency is proportional to the 

agency’s Medicare payments. The size of the reward will 

also depend on the number and size of the agencies in 

the penalty group relative to the number and size of the 

agencies in the reward group. 

Keeping the rewards proportional to Medicare’s payments 

is consistent with our principle of realigning the payment 

system; that is, Medicare pays agencies in proportion to 

services rendered and so P4P rewards should distribute 

money under the same principle. However, the resources 

required to improve quality might not be proportional 

to revenue. If a minimum investment is required to 

achieve higher quality, then smaller agencies might need 

to commit a greater proportion of their resources than a 

larger agency. Establishing a minimum award amount may 

lead smaller agencies to believe the amount of the reward 

is a reasonable return on investment compared with the 

effort required to improve quality. 

Illustration of a home health P4P model

In the model, we would be ready at this step to assign 

penalties and rewards to the six agencies (Table 4-5). 

The penalties against agencies 1 and 2 were withheld 

throughout the year. In the model, penalized agencies 

would not be required to pay the program any additional 

amount at the end of the year. Agencies 3 and 4 would 

receive a refund equal to the total amount withheld. Recall 

that agency 3 would have been penalized but it showed 

significant improvement and thus moved into the group 

that receives neither penalty nor reward. Agencies 5 and 6 

would receive the reward payment calculated in Table 4-5 

($22,825 and $42,020, respectively) as well as a refund 

of the entire amount withheld ($20,800 and $38,200, 

respectively) for total year-end payments of $43,625 and 

$80,220, respectively. 

Additional design features

The previous section summarizes the five important 

design features for a P4P program. In the process of 

building the illustrative model, we learned that we needed 

to address two additional features of the program—how to 

broaden the program to include the most agencies and how 

to improve the quality measures on which performance is 

rewarded over time.

Including providers with small numbers 

of patients

In the home health sector, like the other sectors of the 

Medicare program, a number of agencies will be too 

small to earn a reward or pay a penalty. In the illustrative 

model, because we consider sample size when we 

calculate statistical significance, many agencies will not be 

statistically distinguishable from the average. In alternative 

systems that compare scores with a threshold without 

considering statistical significance, there is generally a 

minimum sample size for inclusion and smaller providers 

are excluded from the system. 

In the future, we could consider excluding agencies with 

a small number of Medicare patients from P4P. However, 

excluding small agencies introduces some perverse 

incentives that may run counter to the intent of the P4P 

system. An incentive that encourages low volume could 

create an access problem for beneficiaries. It could 

encourage medium-sized agencies to split or reorganize 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 91

in ways that wastefully duplicate administration and 

overhead. It also removes the incentive for the system to 

develop new measures that could include smaller agencies. 

Rather than exclude small agencies, a P4P system could 

address the issue of small agencies in at least two ways. 

One approach is to allow multiple small agencies that 

serve the same areas or contiguous areas to form voluntary 

quality associations. All the patients in the association 

would be pooled to count toward a single measurement. 

The association may generate a reward or a penalty. The 

agencies within the association could choose how best 

to distribute the results. This approach may encourage 

collaboration among agencies as well. 

Another approach we found to be useful is to pool data for 

agencies across two consecutive years rather than use a 

single year of data for measurement. Pooled data yielded a 

substantially higher number of agencies with rewards and 

penalties. To be equitable, this pooling should be applied 

to all agencies and no one would have the opportunity 

to opt out of pooling. This approach has the additional 

advantage of resulting in more stable quality scores from 

year to year. It reduces the variation over time, the impact 

of small samples, and the potential impact of one-time 

events such as a change in management. 

In the model, we had only the two most recent years of 

data, so when we measured improvement over time we 

used two scores, each based on only one year of data. 

A better alternative would be to use pooled data for 

the improvement score as well. The home health sector 

already has more than two years of data available, so 

pooling data over time would not necessarily postpone 

implementation of the program.

Improving the pay-for-performance measure 

set over time

In March 2005, the Commission suggested that additional 

measures be developed to complement those that have 

already been developed, collected, and used for quality 

measurement in home health care. The current set of 

measures focuses on the clinical effectiveness of care 

given to patients whose physical conditions are improving. 

Adding measures could broaden the patient population 

covered by the set, capture safety as an aspect of quality, 

capture a process of care directly under providers’ control, 

reduce variation in practice, and provide incentives to 

improve information technology.

Apply process and safety measures. Process 

measures capture an aspect of care that is under providers’ 

control: whether providers take very specific actions in the 

course of caring for their patients. Both the Commission 

and CMS have been considering adding process measures 

for home health care. The Commission convened a panel 

of researchers, quality measurement experts, and home 

health providers to identify best practices in fall prevention 

and wound care. Interest in these areas is high because 

falls and wounds are prevalent among home health care 

users. In addition, the practices are a part of the care for 

patients whose physical condition is not improving and for 

patients who are improving, and the practices are related 

T A B L E

4 –5 Pay-for-performance reward and penalty amounts in the model

Agency

1 2 3 4 5 6

Total Medicare payments $192,000 $755,000 $4,706,000 $2,106,000 $415,000 $764,000

Payment

Penalty –$9,600 –$37,700 $0 $0 $0 $0

Refund $0 $0 $235,000 $105,000 $20,800 $38,200

Reward $0 $0 $0 $0 $22,825 $42,020

Total –$9,600 –$37,700 $235,000 $105,000 $43,625 $80,220

Source:	 Outcome Concept Systems analysis of 2003–2005 cost report and Outcome and Assessment Information Set data.
92 Value-based purchasing: Pay for per for mance in home health care

to patient safety (MedPAC 2006). CMS is working on 

developing other process measures. 

The National Quality Forum also identified patient safety 

as an important dimension of quality—as outlined by 

IOM in its seminal study—and a priority area for quality 

measurement in home health care (IOM 2001). 

As P4P begins to link reported quality levels with 

payment, the system should improve its ability to audit and 

verify the data. CMS has begun to develop these capacities 

within the Reporting Hospital Quality Data for Annual 

Payment Update program. Under this program, hospitals’ 

quality data are audited to determine whether they are 

complete and whether they include a fair and sufficient 

sample of all their patients. Additional capacity to compare 

quality reports to other sources of administrative data or 

to audits of medical charts would further strengthen a P4P 

program. Adding process measures to the set of outcome 

measures for home health care would allow home health 

quality data to be verified through an audit of medical 

charts or through a comparison to information on the 

claim for payment. 

Expand use of health information technology. The 

Commission recommended that P4P include measures 

of the functions supported by information technology 

(MedPAC 2005). Examples include a registry for patients 

with chronic conditions; a system that tracks test results; 

a system that can directly notify patients of laboratory test 

results; and a system that can aggregate, measure, and 

monitor patients by disease, medication, or other category. 

The functions of a telehealth system to remotely monitor 

patients’ vital signs might be particularly relevant to home 

health care.

Furthermore, financial incentives for measuring and 

reporting care processes could encourage providers to 

improve their systems’ capabilities to meet the new data 

requirements. When nurses, therapists, and other home 

health professionals are encouraged by best practices to 

assess, record, use, and share more information about 

patients’ health status during an episode, wider use of 

information technology may result. These technologies 

include: 

• Electronic medical records. The use of electronic 

medical records to store and provide information 

on a patient’s past medical history, lab reports, and 

medications could greatly enhance the ability of 

health professionals to make informed decisions 

about care. In addition, electronic medical records 

allow an organization to measure its quality of care in 

real time rather than waiting for quarterly or annual 

measurements.

• Management tools. Patient registries, clinical 

reminder systems, and computerized patient 

assessments help providers manage a specific 

aspect of care.9 If nurses used a computer program 

to help prompt and record patient assessments, it 

could reduce the burden of recording important 

clinical information, suggest appropriate tests, and 

immediately identify patients who need special 

interventions to address their needs. 

• Patient communications. Devices used in patients’ 

homes to monitor their health can make it easier for 

patients to monitor their condition, communicate 

with caregivers, and identify the need for a medical 

intervention. 

Patient experience measures. Many agencies 

already collect patient satisfaction information. A basic 

patient experience questionnaire might not be radically 

different from activities many agencies already conduct. 

If the program wished to phase in patient experience 

measurement, it could begin with a pay-for-reporting step 

in which all agencies would have the incentive to develop 

or hire the capacity to survey their patients.

A standardized tool that could be audited and administered 

with some independence from the agency staff being 

evaluated would be necessary to compare patient 

experience measures among agencies. Potential patient 

experience measures include:

• How often did nurses listen carefully to you?

• How often did nurses explain things in a way that you 

could understand?

• How often was your pain well controlled?

• Did you get information about symptoms to watch for 

after you were discharged?

As this partial list suggests, patient experience measures 

can begin to capture concepts such as the adequacy of 

planning for patients’ transitions from professional home 

health care to living in the community or concepts such 

as the patient-centeredness of care (whether patients feel 

adequately informed to actively participate in their care). 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 93

Circumstances of the home health sector

Though the P4P framework discussed in this report 

would realign some funds for incentives to reward quality, 

most Medicare payments for home health care would 

still be administered under the provisions of the current 

prospective payment system (PPS). MedPAC and others 

have cited issues with the PPS, and some of these issues 

could diminish the impact of a P4P incentive (MedPAC 

2006, GAO 2000). Adding a quality incentive to a 

payment system that does not accurately pay providers 

for the costs of different patients could create perverse 

incentives for providers—or overpower the impact of the 

quality incentive. Many factors suggest that the current 

system overpays providers and pays inaccurately for some 

patients. 

Concerns about payment accuracy underscore the need 

to use P4P in tandem with other efforts to reform the 

home health payment system. A quality incentive will 

redirect funds toward a defined outcome that is valuable 

to beneficiaries and improves the incentives under PPS. 

However, maintaining incentives for efficiency under the 

core PPS is critical. Improving quality without maintaining 

incentives for efficiency could cause a conflict between 

efforts to improve quality and efforts to address Medicare’s 

long-term sustainability challenge. Continuing efforts 

to improve the accuracy of payments under the PPS 

will ensure that providers have appropriate incentives to 

provide quality care. 

The aggregate average financial performance of the home 

health industry under PPS has been remarkable (MedPAC 

2006). Since the advent of the PPS, most agencies have 

held per episode cost inflation to about 1 percent per year, 

and margins have exceeded 10 percent despite a onetime reduction in the base rate and numerous reductions 

to the update. The consistent pattern of high margins 

suggests that the base payment in the home health PPS 

may not accurately reflect the costs of efficient providers, 

potentially dimming the impact of a reward or penalty 

for quality. For agencies with significant margins, such 

as the 50 percent of agencies with margins greater than 

16.8 percent in 2007, the impact of a 5 percent reward 

or penalty may be too modest to encourage quality 

improvement. 

Shortcomings in the case-mix measurement may provide 

incentives for HHAs to favor patients with higher case-mix 

scores. Prior analysis has found a small but statistically 

significant relationship between an agency’s case mix 

and its margins (MedPAC 2005). Medicare’s system 

for classifying patient resource needs, the home health 

resource groups (HHRGs), may also inappropriately group 

patients within a single case-mix group though they have 

very different resource needs. MedPAC found a large 

variation in the minutes of service per episode provided 

to patients in the same HHRG (MedPAC 2006). The casemix weights for home health care have never been updated, 

and as a result it is unlikely the current case mix accurately 

reflects the resource intensity of different patients.

 Differences in financial performance among providers 

are to be expected in any PPS, as providers vary in their 

efficiency. However, if some of this variation in margins 

is due to the issues highlighted above, then the variation 

reflects shortcomings in the PPS. This variation may affect 

a quality incentive because providers are likely to assess 

the value of any incentive relative to their margins. For 

example, the top quarter of HHAs, which have margins 

that exceed 27 percent, might not consider a 5 percent 

incentive compelling. Medicare should not expect the 

margins of providers to necessarily be concentrated, but 

failing to address inaccuracies in the payment system that 

can lead to excessive variation may diminish the impact of 

a quality incentive.

Additional technical information on 

home health pay for performance

In this section, we discuss some limitations of the risk 

adjustment currently available for home health outcome 

measures, the composite measure we developed to 

summarize quality at the agency level, and adjusting for 

socioeconomic status.

Adequacy of risk adjustment for home 

health measures

CMS developed risk adjustment for the OBQIs to take into 

account patient health and other characteristics that may 

affect their outcomes. For example, improving patients’ 

pain from cancer is more difficult than improving pain 

in patients with congestive heart failure because of the 

extreme pain associated with many cancers. Early studies 

found that risk adjustment was accounting for the impact 

of patients’ primary diagnosis on pain and giving “credit” 

for the difficulty of cancer patients’ pain management. In 

essence, taking these patient characteristics into account 

should level the playing field among agencies with 

different patient populations.
94 Value-based purchasing: Pay for per for mance in home health care

However, when we applied the risk-adjustment 

methodology that was calibrated in 2001 to the most 

recent data available from 2005, we found that it did not 

adequately account for differences in patient mix at the 

agency level (Shaughnessy et al. 2002). Some of the 

limitations of CMS’s risk model might be explained by the 

fact that it has not been recalibrated since the measures 

were implemented more than five years ago. In the 

calibration year, the expected values and the actual values 

were almost the same. As time passed, the gap between the 

model’s expected values and the actual values widened. 

For example, by 2005, the predicted rate of success in 

improvement in ability to dress the upper body was 60 

percent, and the actual national rate was 67 percent. If the 

changes that led to the gaps in the model’s performance 

have not been consistent among patient types, that would 

explain the model’s limitations in predicting current 

outcomes by patient type. 

Our two tests of the risk-adjustment system applied to the 

most recent available data suggest that the risk adjustment 

does distinguish between patients with very low likelihood 

of good outcomes and those with very high likelihood of 

good outcomes. However, the system is not as capable of 

making finer distinctions. The risk adjustment correctly 

identifies the general patterns in outcomes, but it is not 

very precise. 

In one test, we divided the patients into deciles (10 groups 

of equal size). The groupings were based on CMS’s riskadjustment model’s prediction of the relative likelihood of 

their success at the outcome we were measuring. In each 

test, the model predicts the broad pattern in the relative 

rate of success for patients: Those in deciles with the lower 

predicted rates of success do achieve lower rates of success 

than those in higher deciles. However, the risk-adjustment 

model is imprecise; there is often a wide gap between the 

predicted rate and the actual rate. 

In another test, we found that the risk-adjustment 

model did not precisely account for differences in 

outcomes that were related to patient characteristics. 

We considered patient characteristics such as primary 

diagnosis, comorbidities, informal caregiver availability, 

and functional limitation. We chose these characteristics 

because previous research indicated that they are likely to 

influence outcomes (Shaughnessy et al. 2002). 

We found statistically significant differences among the 

outcomes for different patient types after we applied 

the risk-adjustment model. In other words, though we 

had tried to account for the effects of each of the patient 

characteristics in our expectations, we still found that 

patients of certain types had much better outcomes than 

patients of other types. The results of this second test 

reinforced the evidence from our first test: The CMS riskadjustment model seems to have some limitations in its 

ability to level the playing field among different types of 

patients. Even with risk-adjusted data, many differences 

will exist between the outcomes of patients of different 

types. This will reduce the validity of the quality score, 

will give an advantage to agencies with certain mixes of 

patients, and could lead to access problems for patients of 

certain types. 

A composite home health quality measure 

to combine measures of quality and 

address shortcomings in risk adjustment

A composite can bring several measures together to create 

a picture of quality that is more complete than a single 

measure can be. Any single measure of quality excludes 

some providers, some patients, or some trait of quality. We 

studied quality composites from scorecards for hospitals 

from states and private plans and worked with technical 

experts to develop potential criteria for good composite 

measures. The composite measure should:

• apply to most providers, most patients, and most 

quality traits;

• account for differences in patient characteristics;

• reflect the relative importance of each measure in the 

composite;

• be easy to describe and understand; and

• acknowledge the extent of uncertainty and identify 

where it exists.

Both the selection of measures to include in the composite 

and the construction of the composite determine whether 

the composite meets the criteria. 

We contracted with a quality benchmarking organization 

to help us construct a composite measure for HHAs. They 

applied expertise in clinical logic, statistics, and measure 

design to the national data set of all OASIS patient 

assessments to develop a composite quality measure: the 

SQI. The SQI is risk adjusted by clinical stratification 

instead of by CMS’s regression-based system. This allows 

us to identify a relatively homogeneous set of patients at 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 95

each agency and compare each agency’s score for those 

patients rather than rely on risk adjustment to account for 

all the differences among all of each agency’s patients. 

Clinical stratification groups patients with similar 

diagnoses and treatment plans. This allows the 

measurement system to compare the outcomes for similar 

patients at different agencies. It also establishes a clear 

link between patient groups and outcome for the agency. 

If an agency wishes to target a particular outcome, 

the measurement system has already identified the 

patients and treatment plans that need to be addressed. 

However, clinical stratification is generally regarded 

as incomplete risk adjustment because of the variables 

it does not address. In the long run, CMS may wish to 

explore a hybrid model that groups patients into clinical 

classifications and also applies regression-based risk 

adjustment within groups to account for additional sources 

of variation. 

The SQI measure relies on the OASIS patient assessments 

performed by home health nurses and therapists at 

admission, at some intervening events, and at discharge 

to determine the outcomes of patients’ home health care: 

whether patients’ functional levels improved or stabilized 

and whether patients experienced any adverse events. The 

components of the measure are detailed in Table 4-6.

The SQI set incorporates the seven publicly reported 

functional measures from the Home Health Compare 

public data report, adds more functional outcome 

measures, and adds the four potentially avoidable adverse 

events listed in Table 4-6. These are gross measures not of 

all hospital and ER use but of that specifically due to four 

events the agency is thought to be able to manage.

We tested the correlations among the components of each 

measure. Using the statistical measure Cronbach’s alpha, 

we determined that relationships among the constituent 

measures of each measure were acceptable. This statistical 

measure indicates the extent to which a set of test items 

can be treated as measuring a single construct. In this 

context, we are measuring whether we should use a set 

of functional outcomes and adverse events together to 

measure the quality of an HHA. We compared the SQI 

with an alternative measure that was limited to the public 

data report measures. We found an alpha of 0.71 for the 

measures in the SQI and an alpha of 0.60 for the measures 

in the simpler alternative. The alpha score for the SQI 

exceeds the rule-of-thumb standard for reliability of 0.70 

(Streiner and Norman 1989). The lower score for the 

simpler alternative suggests that adding the additional 

components to the SQI is an improvement. 

The steps to calculate an agency’s SQI score are fairly 

simple. The system starts at the patient level. For each 

patient, all the functional outcomes are scored 2 points 

for improvement, 1 point for stabilization, and –1 point 

for decline. The scores for all the functional outcomes 

are summed and a point is subtracted for each incidence 

of a potentially avoidable unplanned hospitalization or 

ER use. The resulting total is divided by 20 to obtain an 

average. Finally, the scores for all of the patients in an 

agency are averaged. In our data, agencies’ SQI scores 

range from –4 to +2.

Some patients who qualify for the home health benefit 

have limited potential for improvement. In the illustrative 

measure, points are available for stabilizing patients whose 

illness or functional level otherwise could have declined. 

The measure also includes a penalty for potentially 

avoidable hospitalizations and use of the ER, which has 

the effect of rewarding agencies who manage patients with 

T A B L E

4 –6 Components of MedPAC’s quality score

 for home health pay for performance

Functional outcome 

measures

Potentially avoidable 

event measures

•	 Getting out of bed

•	 Walking

•	 Bathing

•	 Using the toilet

•	 Urinary incontinence

•	 Bowel incontinence

•	 Upper body dressing

•	 Lower body dressing

•	 Shortness of breath

•	 Caregiver managing

medical equipment

•	 Managing oral medications

•	 Managing inhaled medications

•	 Managing injectable medications

•	 Managing medical equipment

•	 Ulcer, stasis

•	 Ulcer, pressure

•	 Surgical wound

•	 Pain

•	 Confusion

•	 Anxiety

Unplanned hospitalizations or

uses of the ER caused by:

• Diabetes out of control

• Injury caused by a fall

at home

• Wound infection

or deterioration

• Improper medication

administration

Note:	 ER (emergency room).
96 Value-based purchasing: Pay for per for mance in home health care

declining health safely in their homes while preventing 

unnecessary hospitalizations and trips to the ER. 

The SQI score incorporates steep penalties for unplanned 

hospitalization and ER use to reflect the importance of 

these measures as adverse events—and thus indicative 

not only of low quality but also of actual harm to 

beneficiaries—and measures of the efficiency of home 

care. One of the most important contributions home health 

care spending can make to the efficient resource use 

of the Medicare program is to safely and appropriately 

prevent avoidable hospitalizations and use of the ER. The 

Commission has underscored the importance of including 

both quality and resource use in measures of efficiency. 

For these reasons, the score is designed to give additional 

weight to adverse events. The SQI score restricts the 

definition of adverse events to ER or hospital use for four 

reasons: diabetes out of control, injury caused by fall, 

wound infection, and improper medication use. These four 

reasons describe events that were potentially preventable. 

We calculate an agency’s SQI for patients within a clinical 

group. Because the evidence reviewed in the previous 

section demonstrates that CMS’s risk-adjustment model 

does not sufficiently account for differences in patients’ 

outcomes based on their primary diagnosis, we chose 

to stratify patients into groups based on their primary 

diagnosis using the clinical classification system. We 

applied factor analysis to our large database to identify 

seven categories that included most patients and that put 

them in clinically related groups (patients who would 

receive similar treatments during the course of their home 

health care). The clinical classifications are listed in Table 

4-7. Most agencies treat patients in these common clinical 

groups.

This measure is not as simple as an “off-the-shelf ” 

solution, but it better meets the criteria for good measures 

that we have developed and discussed. The SQI is 

applicable to most providers, most patients, and most 

quality traits. The stratification into clinical groups 

accounts for differences in patient characteristics. The 

scoring method reflects the relative importance of 

improvement, stabilization, and adverse events for each 

measure in the composite. In our P4P model, we show how 

the SQI can be used to describe the extent of uncertainty 

and identify where it exists. Finally, the measure uses data 

that are part of the currently collected home health data.

Basing patient groups on primary diagnoses makes a clear 

link between patient groups and outcome for the agency: 

The measurement system identifies patients with similar 

treatment plans that need to be addressed. Focusing the 

P4P program on one group of patients or on several groups 

of patients provides guidance to agencies on how to focus 

their quality improvement efforts and might decrease the 

burden compared with a program that started with all of 

an agency’s patients. However, relying solely on primary 

diagnosis for risk adjustment is generally regarded as 

incomplete risk adjustment because of the variables it does 

not address. In the long run, CMS may wish to explore a 

hybrid model that groups patients by primary diagnosis 

and also applies regression-based risk adjustment within 

groups to account for additional sources of variation. 

Accounting for differences in 

socioeconomic status

In a program as comprehensive as Medicare, there may 

be wide differences in the socioeconomic status (SES) 

of patients in addition to differences in the clinical 

characteristics we have discussed thus far. Some suggest 

that socioeconomic differences among patients may lead to 

differences in the quality of care measured at the provider 

level for reasons beyond agencies’ control. Patients in a 

lower socioeconomic group may lack access to competent 

informal care, may have fewer tools to make informed 

decisions, or may have a poorer quality diet than those of 

higher SES. However, deciding whether and how to adjust 

for socioeconomic differences is difficult.

T A B L E

4 –7 Nearly all home health

 agencies treat patients

 in selected clinical groups

Agencies with more than:

Clinical group

2 patients in 

clinical group

25 patients in 

clinical group

Acute CVD 6,360 1,040

CHF or COPD 7,710 4,520

Diabetes 7,240 2,610

Pneumonia 5,980 1,070

Skin infection 6,870 1,520

Skin ulcer 6,510 1,450

Therapy 7,530 4,940

Note:	 CVD (cerebrovascular disease), CHF (congestive heart failure), COPD

(chronic obstructive pulmonary disease). Between 2003 and 2005, there

were about 8,000 agencies in Medicare.

Source:	 Outcome Concept Systems analysis of 2003–2005 cost report and

Outcome and Assessment Information Set data.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 97

Choosing whose socioeconomic traits, which traits, and 

what scales to use to measure SES can be challenging. In 

home health care, the characteristics of the patient’s family 

might be as important as, or even more important than, 

those of the patient. This raises the question: Whose status 

should be measured—the patient, the immediate family, or 

the extended family? 

There is some room for doubt about the relationship 

between SES and health outcomes. A recent study on breast 

cancer mortality found higher rates of mortality among 

women in higher socioeconomic groups than in lower ones 

(Strand et al. 2007). Another study found that much of 

the relationship between SES and health is a function of 

known health factors, such as obesity and smoking, which 

are measured directly and accounted for in the clinical risk 

adjustment (Kuper et al. 2007). SES may relate to different 

measures in different ways: It may have little impact on a 

process measure such as giving hospitalized patients an 

aspirin but it may have a larger impact on whether patients 

will purchase and consistently use medications to manage 

blood pressure after they return home. 

Finally, adjusting for SES has the effect of setting lower 

expectations for the providers who are in a position to 

have the greatest impact on vulnerable populations. For 

example, if a Medicare P4P program were to use an SES 

adjustment that incorporated race, it could have the effect 

of setting a lower expectation for quality of care delivered 

to blacks than for whites, Hispanics, or other racial groups. 

Some may view lower standards for the care of vulnerable 

populations to be one of health care’s critical problems; 

the impacts of disparities in health care have been widely 

studied. A P4P system that expects good care for all patients 

regardless of race, income, or education could be one policy 

tool to address the issue of disparities in health care.

Despite the difficulties associated with measuring SES and 

establishing its relationship with health outcomes, some 

contend that P4P should be used to address disparities in 

health care (Rosenthal and Dudley 2007). One approach 

for the future is to develop direct measures of health care 

disparity that can be attributed to providers and patients 

and reward providers for addressing it. Another approach 

to consider—using currently available measures—is to 

offer greater incremental payments to providers who 

achieve high quality for underserved populations. This 

would have the effect of increasing the incentive to better 

serve vulnerable beneficiaries as well as providing some 

adjustment to acknowledge that achieving high quality for 

underserved populations could require a greater effort than 

achieving these goals among other populations. 

An alternative to SES-based adjustments to risk scores 

would allow providers to identify noncompliant patients 

and exclude them from their data. The United Kingdom 

uses this system in its nationwide physician quality 

incentive program (Doran et al. 2006). A comprehensive 

study of this design option found that most physicians 

exempted few of their patients. There was some evidence 

of abuse at the extreme, and they found a moderate 

correlation between the number of patients exempted and 

the quality score achieved by the physician. However, 

the opportunity that exception reporting presents to 

manipulate quality scores could be counterbalanced by 

publicly reporting the providers’ noncompliance rates, 

auditing providers with exceptionally high rates, or 

requiring providers with a noncompliance rate above 

a certain threshold to develop and implement a plan to 

increase compliance. 
98 Value-based purchasing: Pay for per for mance in home health care

1 The Institute of Medicine and CMS have also considered 

funding P4P through savings generated by quality 

improvements.

2 One survey of private-sector efforts found that purchasers 

report needing to provide incentives of 5 percent to 20 

percent for physicians and 1 percent to 4 percent for hospitals 

(MedVantage 2004). Yet, it is difficult to know what portion 

of overall payment these percentages represent. Because 

Medicare payment is often a higher percentage of any one 

provider’s total revenue than a single private payer, a smaller 

percentage of Medicare’s payment may encourage change. In 

CMS’s Premier hospital demonstration, preliminary results 

show improvement in all conditions in the first four quarters 

in anticipation of financial rewards of 1 percent or 2 percent 

for those in the upper rankings (Remus 2005).

3 Numerous studies suggest that patients frequently do not 

receive evidence-based care and often experience illness or 

injury as a result of contact with the medical system (Jencks 

et al. 2003, McGlynn et al. 2003, IOM 2001).

4 Both the study by the Premier group and a later study by 

a group of researchers outside of the system found greater 

improvement among hospitals within the demonstration 

than in hospitals outside the demonstration (Lindenauer et 

al. 2007). The Premier study was very positive about the 

implications of the results of the demonstration for P4P. The 

outside researchers concluded that the quality differences 

were small compared to the costs of operating the quality 

incentive program and suggested that the demonstration has 

negative implications about the cost effectiveness of P4P on 

a larger scale.

5 Based on MedPAC analysis of freestanding agencies’ cost 

reports, in 2005, 5 percent of agencies received less than 

$125,000 and 5 percent of agencies received more than $6.5 

million. The smallest agency in terms of Medicare revenue 

received $2,500 and the largest received $18.4 million.

6 Research that supports the reliability of OASIS items was 

conducted on the research and development sample of OASIS 

data. Later tests on OASIS from the field indicate lower levels 

of reliability for some items (Kinatukara et al. 2005).

7 Conceptually, we are treating each agency’s case load for 

the measurement year as if it were a sample of patients 

drawn from the population of all patients at all agencies 

and measuring the sample mean, sample size, and standard 

deviation of scores within the sample. We are testing whether 

it is likely that the sample’s average score is higher or 

lower than the population’s average score due to chance or 

whether the sample is really different from the population; 

theoretically, it would be different because the quality of the 

agency is truly good or truly bad. We chose to apply a twostage, one-tailed test of significance at a 90 percent level of 

confidence. We determine whether each score that is higher 

than the benchmark is significantly higher in stage 1, and 

then we determine whether each score that is lower than the 

benchmark is significantly lower in stage 2. For each of these 

two tests, we apply a 95 percent confidence coefficient. 

8 In the case of CMS’s proposed home health P4P 

demonstration, for example, the designers thought a penalty 

was not consistent with voluntary participation. We note, 

however, that CMS’s hospital P4P demonstration was also 

voluntary and it did incorporate the possibility of a penalty. 

9 These management tools are often embedded in an electronic 

medical record; however, they are also available on their own. 

Endnotes
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 99

Doran, T., C. Fullwood, H. Gravelle, et al. 2006. Pay-forperformance programs in family practices in the United Kingdom. 

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Dudley, R. A., A. Frolich, D. L. Robinowitz, et al. 2004. Strategies 

to support quality-based purchasing: A review of the evidence. 

Technical Review 10. Prepared by the Stanford–University of 

California San Francisco Evidence-based Practice Center under 

contract no. 290–02–0017. AHRQ publication no. 04–0057. 

Rockville, MD: AHRQ. July.

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(CMS)/Premier hospital quality incentive demonstration project: 

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References

C h a p t er 5

Payment policy for

inpatient readmissions

Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 103

Chapter summary

Hospital readmissions are sometimes indicators of poor care or missed 

opportunities to better coordinate care. Research shows that specific 

hospital-based initiatives to improve communication with beneficiaries 

and their other caregivers, coordinate care after discharge, and 

improve the quality of care during the initial admission can avert many 

readmissions. Medicare does not reward these efforts.

In addition to adversely affecting beneficiaries’ health and peace of 

mind, the failure to adequately attend to the care transition at discharge 

from the hospital results in additional Medicare spending; 17.6 percent 

of admissions result in readmissions within 30 days of discharge, 

accounting for $15 billion in spending. Not all of these readmissions are 

avoidable, but some are. 

To encourage hospitals to adopt strategies to reduce readmissions, 

this chapter explores a two-step policy option that starts with public 

reporting of hospital-specific readmission rates for a subset of 

conditions and goes on to adjust the underlying payment method to 

In this chapter

• Why focus on readmissions?

• How common are 

readmissions?

• How can hospitals reduce 

readmissions?

• How can Medicare policy 

encourage hospitals to 

adopt strategies to reduce 

readmissions?

Payment policy for 

inpatient readmissions

C H A PT E R 5
104 Payment policy for inpatient readmissions

financially encourage lower readmission rates. We recognize the importance 

of pay-for-performance (P4P) measures to improve quality, but find that 

the underlying payment method may undercut the behavior P4P is trying to 

encourage. When this is true, other policies may be needed to create stronger 

incentives to reduce readmissions.

We focus on the hospital’s role but recognize that other types of providers, 

including physicians and various post-acute care providers, can be 

instrumental in avoiding readmissions. MedPAC continues to explore 

ways to encourage those providers to avoid hospital readmissions (see 

Chapter 4 on home health P4P and Chapter 8 on skilled nursing facilities). 

Beneficiaries also have responsibility in the effort to avoid readmissions and 

should be encouraged to be engaged in their own care. Aligning incentives 

across all those who can influence the outcome of care would induce 

needed collaboration among fee-for-service providers and foster greater 

“systemness” and integration in the delivery of health care. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 105

Hospital readmissions sometimes indicate poor care or 

missed opportunities to better coordinate care. Research 

shows that specific hospital-based initiatives to improve 

communication with beneficiaries and their other 

caregivers, coordinate care after discharge, and improve 

the quality of care during the initial admission can avert 

many readmissions. Medicare does not reward these 

efforts. It pays for all admissions based on the patient’s 

diagnosis regardless of whether it is an initial stay or a 

readmission for the same or a related condition.1

Policy changes could encourage more hospitals to 

adopt successful strategies and continue to experiment 

with new ones. This chapter explores a two-step policy 

option to provide a financial incentive for hospitals to 

reduce readmissions. The first step is public disclosure 

of readmission rates followed by payment changes to 

encourage hospitals to reduce their readmission rates—

that is, the number of readmissions to both their own 

hospital and others. The Commission also plans to explore 

bundling Part A and Part B payments for inpatient care. 

In this chapter, we focus on the hospital’s role but 

recognize that other providers—including physicians, 

skilled nursing facilities (SNFs), and home health 

caregivers—can also be instrumental in avoiding 

readmissions. MedPAC continues to explore ways to 

encourage these providers to meet their patients’ needs 

over the course of an episode of care (see Chapters 4 

and 8). Similarly, beneficiaries have responsibility in the 

effort to avoid readmissions and should be encouraged to 

be engaged in their own care. Aligning incentives across 

all those who can influence the patient’s outcome would 

induce the needed collaboration among providers, which is 

the foundation for fostering “systemness” in the delivery 

of health care.

Why focus on readmissions?

Discharge from the hospital is a critical and vulnerable 

care juncture for Medicare beneficiaries. Patients often 

experience difficulties during the transition to home or 

post-acute care. While in the hospital, patients tend to 

defer to their professional caregivers. Upon discharge, 

however, they may suddenly be expected to assume a 

self-management role in recovery with little support 

and preparation (Coleman and Berenson 2004). Patients 

and families may not realize how much support patients 

need, particularly if the patient has not returned to his or 

her baseline physical or cognitive functional state after 

discharge. Further, they may not know which provider to 

call with questions during the interval between discharge 

and follow-up—if there is a follow-up visit—as it is 

not always clear which provider is responsible for and 

informed about the patient’s care (HMO Workgroup 2004).

Discharge is also a time when patients are more likely 

to be receptive to health care recommendations. The 

chances of long-term adherence to medication regimens 

are significantly higher when medications are prescribed 

at hospital discharge, and this increased adherence is 

associated with decreased mortality rates (Lappe et al. 

2004). Experts have noted that hospital-based interventions 

(e.g., ensuring that appropriate medications are prescribed) 

can be more easily implemented, can be more effectively 

managed and measured, and could be more cost effective 

than other potential outpatient intervention strategies 

(Lappe et al. 2004). 

Despite these needs and opportunities associated with 

this transition, hospitals and other providers have not 

broadly invested in their role in managing the transition. 

Two related factors account for this. First, providers 

often operate independently of one another (in “silos”). 

Each is focused on his or her performance rather than 

on the collective performance across an episode of care. 

Accordingly, incentives to coordinate or standardize care 

processes across providers and settings are limited. These 

problems are exacerbated by the increasing degree of 

specialization in health care. Patients today are more likely 

than ever to pass across different settings of care with 

different physicians supervising their care, particularly 

given the increasing prevalence of hospitalists. 

A second related factor is Medicare’s (and other insurers’) 

fee-for-service payment policy. Medicare pays each 

provider separately, and the payment amount is not 

affected by providers’ ability to coordinate care across 

settings. Hospitals that invest in reducing readmissions 

reap none of the reward of their investment (unless they 

are able to fill the unused beds with more profitable 

patients). And because Medicare does not explicitly pay 

for care management services, such as follow-up phone 

calls or scales and blood pressure cuffs for recently 

discharged patients to use at home, providers often do 

not provide these beneficial services. With case-based 

payments that reward hospitals for shorter lengths of 

stay, hospitals focus on discharging patients as soon as is 

medically appropriate. Indeed, in navigating the gray area 

of “medical appropriateness,” hospitals and physicians 
106 Payment policy for inpatient readmissions

may be more likely to discharge patients earlier and accept 

a higher risk of readmission. Aside from moving the 

patient out of the hospital, effective management of the 

discharge and transition is not financially rewarded. The 

Congress has recently considered financial incentives for 

hospitals to avoid complications during the stay and “never 

events” (see text box), but these measures do not create 

incentives to provide needed care at discharge.

The specific causes of avoidable readmissions are varied. 

An adverse event, which may be due to a medical error, 

may have occurred during the initial admission, making 

recovery more complicated and ultimately necessitating 

readmission. Another cause might be that the patient 

was discharged without the proper mix and doses of 

medications being prescribed. The patient may not have 

fully understood when to take the medication, may not 

be fully equipped to arrange for follow-up care without 

assistance, or may not know what symptoms indicate the 

need for outpatient medical attention. Family members 

may not be adequately informed and prepared for how 

to care for their loved one. Patients and family members 

also may not know about end-of-life options and resort to 

rehospitalization as a default. Also, community physicians 

and post-acute care providers receiving the patient may not 

be sufficiently informed about the patient’s care needs and 

history to enable effective care.

African American and dually eligible beneficiaries (about 

one-fifth of whom are African American) appear to be 

Complications and “never events”

Under current policy, Medicare sometimes pays 

hospitals a higher diagnosis related group (DRG) 

payment if a complication develops during the 

hospitalization due to poor care. For example, decubitus 

ulcers, surgical infections, or pneumonia acquired while 

on a ventilator could all lead to increased payments to a 

hospital because a DRG with complications is paid more 

than a DRG without complications. This payment policy 

fails to reward hospitals for investing in quality and 

process improvements to reduce the frequency of these 

adverse events. 

To address this problem, the Congress included a 

provision in the Deficit Reduction Act of 2005 that 

requires CMS, by October 2007, to identify at least 

two preventable hospital-acquired complications that 

are either high cost or high volume. To determine 

whether a complication is acquired in the hospital or 

is present on admission, CMS will require a presenton-admission indicator on claims as of October 2007. 

This is consistent with MedPAC’s recommendation in 

our March 2005 report and is critical to ascertaining 

whether the hospital should be held partially or fully 

responsible for the complication (MedPAC 2005). 

By October 2008, CMS must no longer increase 

payments purely due to patients acquiring one of these 

preventable hospital-acquired complications. The 

magnitude of this policy’s impact may be less than 

was intended, however. CMS believes the provision 

will apply in a small minority of cases because it is 

rare that one of the selected conditions will be the only 

complication or comorbidity on the claim. If there is 

another complication or comorbidity, it will trigger the 

higher payment amount. 

On the related issue of payment for “never events,” 

the Tax Relief and Health Care Act of 2006 included 

a provision requiring the Department of Health and 

Human Services Office of Inspector General to conduct 

a study on the incidence of never events for Medicare 

beneficiaries and the extent to which Medicare pays for 

them. Never events are defined as “serious reportable” 

events as identified by the National Quality Forum and 

include such things as leaving unintended objects in the 

patient as well as death or serious disability from falls, 

medication errors, and administration of incompatible 

blood during hospitalization. The Leapfrog Group 

has recommended to the Congress that Medicare 

adopt policies to require reporting of these events and 

preclude hospitals from billing for them (Leapfrog 

Group 2006). 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 107

at higher risk for readmissions, particularly for stroke, 

diabetes, and asthma (Kind et al. 2007). DartmouthHitchcock Clinic, which is participating in CMS’s 

Physician Group Practice demonstration, also reports 

that—in its experience—dual eligibles were more likely 

to be readmitted than others (Trisolini et al. 2006). 

This finding suggests that lack of coordination during 

transitions may affect beneficiaries unevenly, contributing 

to racial disparities in health care delivery.

In addition to the human consequences, the failure to 

adequately attend to the care transition results in additional 

Medicare spending. Readmissions are a costly aspect of 

Medicare-covered services. While not all that spending is 

avoidable or a sign of poor care, some of it is the result of 

preventable readmissions. 

One study of coronary artery bypass graft (CABG) 

patients in New York attempted to identify the 

prevalence of related readmissions—that is, those due 

to complications directly related to the initial surgery. It 

found that 85 percent of the patients readmitted within 

30 days after surgery were readmitted for complications 

directly related to the CABG. Examples of complications 

included infections, heart failure, myocardial ischemia/

acute myocardial infarction (AMI), and arrhythmias 

(Hannen et al. 2003). 

To further explore the question of what portion of 

readmissions is clinically related and potentially 

preventable (some may be related but not preventable, such 

as staged surgeries), the Commission applied 3M software 

that flags some readmissions as potentially preventable 

to Medicare claims data. Our intent is to illustrate an 

approach rather than to endorse a specific product. 

How common are readmissions?

A significant number of Medicare hospitalizations result 

in readmissions. In 2005, 6.2 percent of hospitalizations 

among beneficiaries resulted in readmission within 7 

days, and 17.6 percent of hospitalizations resulted in 

readmission within 30 days.2 The readmission rates for 

beneficiaries with end-stage renal disease are considerably 

higher than average, which suggests that certain 

subgroups of beneficiaries (particularly beneficiaries 

with comorbidities) are at greater risk of readmission 

(Table 5-1). This finding highlights the importance of risk 

adjustment in calculating comparable readmission rates. 

The readmission rates in Table 5-1 reflect the total number 

of readmissions, including those that may have been 

unrelated to the initial diagnosis, such as a readmission 

for trauma after a discharge for pneumonia or AMI. 

Policymakers will need to consider the importance of 

distinguishing the clinical underpinning of readmissions. 

For the purposes of this analysis, we explored identifying 

potentially preventable readmissions with software 

developed by 3M (see text box, pp. 108–109). Potentially 

preventable readmissions are those that in many cases may 

be prevented with proven standards of care; however, not 

all potentially preventable readmissions can be avoided, 

even if hospitals follow best practices. 

We used the software to identify which of the readmissions 

were potentially preventable. The 7-day rate for potentially 

preventable readmissions is 5.2 percent, the 15-day rate 

is 8.8 percent, and the 30-day rate is 13.3 percent (Table 

5-2). Accordingly, 84 percent of 7-day readmissions, 78 

T A B L E

5 –1 Hospital readmission rates

Percent of patients readmitted

 to hospital within:

7 days 15 days 30 days

Total 6.2% 11.3% 17.6%

Non-ESRD 6.0 10.8 16.9

ESRD 11.2 20.4 31.6

Note:	 ESRD (end-stage renal disease).

Source:	 MedPAC analysis of 2005 Medicare Provider Analysis and Review

file data.

T A B L E

5 –2 Potentially preventable 

hospital readmission rates

Patients readmitted

 to hospital within:

7 days 15 days 30 days

Rate of potentially

preventable readmissions 5.2% 8.8% 13.3%

Spending on potentially

preventable readmissions

(in billions) $5 $8 $12

Source: 3M analysis of 2005 Medicare discharge claims.
108 Payment policy for inpatient readmissions

percent of 15-day readmissions, and 76 percent of 30-day 

readmissions were flagged as potentially preventable. 

Medicare spending on these potentially preventable 

readmissions is substantial: $5 billion for cases readmitted 

within 7 days, $8 billion for cases readmitted within 15 

days, and $12 billion for cases readmitted within 30 days. 

In 2005, the average Medicare payment for a potentially 

preventable readmission totaled approximately $7,200 

(almost $1,400 less than the payment for the original stay). 

Potentially preventable readmission rates vary 

substantially across hospitals (Figure 5-2, p. 110). The 

15-day readmission rate ranges from 6 percent at the 10th 

How can readmissions be defined?

I

n measuring readmission rates, policymakers must 

address whether they want to count all readmissions 

in the rate or a subset of readmissions that are 

clinically deemed to be potentially preventable. In 

addition, policymakers must define a time period 

within which a subsequent admission is considered a 

readmission (e.g., within 7 days, 15 days, or 30 days of 

discharge from the initial admission).

Purchasers, plans, and vendors have pursued a number 

of ways to define readmissions. Some have defined 

all readmissions within a certain number of days 

(e.g., 7 days, 15 days, or 30 days) to count toward the 

rate, regardless of the clinical link between the two 

admissions. For example, under its program measuring 

hospitals’ relative efficiency to help employers in their 

purchasing decisions, the Leapfrog Group counts all 

readmissions within 14 days of discharge. It specifically 

acknowledges that some readmissions counted are not 

related to the earlier discharge. 

Others have begun to develop algorithms, or rules, to 

identify which admissions could have been reasonably 

prevented. For reporting purposes, UnitedHealthcare 

counts all readmissions to the same major diagnostic 

category or for infections in disclosing readmission 

rates for hospitals in California. Physicians with the 

Geisinger Health System in Pennsylvania agreed not 

to be paid for certain readmissions within 90 days of 

nonemergency coronary artery bypass graft surgery. 

These types of readmissions include acute myocardial 

infarction; atrial fibrillation; venous thrombosis; 

infections due to an internal prosthetic device, implant, 

or graft; and postoperative infections. Their approach 

includes all readmissions “not unrelated,” reflecting 

their desire to avoid litigating the difference between 

“definitely related” and “possibly related.” Researchers 

with 3M have also developed algorithms for a wide 

range of conditions that identify related readmissions 

within 7 days to 30 days of the initial admission. 

Florida is proposing to use this product for reporting 

purposes. 

Different decision rules can inform which readmissions 

are potentially preventable. The rules could be very 

narrow, identifying only those readmissions that 

with near certainty could have been avoided, such 

as complications resulting from a perforation during 

surgery. Or they could be broader, identifying types 

of readmissions that likely could have been prevented, 

such as readmissions for chronic obstructive pulmonary 

disease (COPD) after cardiac surgery—some of which 

may be avoided if COPD medications are appropriately 

adjusted at discharge. 

To illustrate this broader approach to identifying 

clinically related and potentially avoidable 

readmissions, MedPAC has begun to explore 3M’s 

software and its implications for defining Medicare 

readmissions. In so doing, our intent is to explore an 

approach, not endorse a specific product.

After excluding certain readmissions—including those 

related to trauma, cancer, and burns—3M combed 

through all permutations of diagnoses for an initial 

stay and for a readmission and evaluated the likelihood 

that a given readmission diagnosis was related to 

the first admission and, therefore, was potentially 

preventable. In general, most medical readmissions 

following an initial medical admission were flagged 

as potentially preventable. Most medical readmissions 

following a surgical readmission were also likely to 

be potentially preventable. In contrast, most surgical 

readmissions following either a medical or a surgical 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 109

percentile to 12 percent at the 90th percentile, for example. 

Some of this variation is due to differences in the mix 

of cases and severity level of patients treated in different 

hospitals. Readmission rates also vary substantially by 

diagnosis; thus, a hospital’s mix of cases will affect its 

overall readmission rate. The 15-day readmission rate for 

heart failure and shock, for instance, is 12.5 percent; the 

rate for pneumonia is 8.9 percent; and the rate for major 

joint replacements (hips and knees) is just 5.1 percent. 

Thus, hospitals that concentrate on joint replacements are 

likely to have lower readmission rates than hospitals that 

concentrate on cardiac care. 

How can readmissions be defined? (cont.)

admission were not likely to be preventable. The logic 

in the software allows for exceptions to these general 

rules (Figure 5-1).

With this approach, many diagnoses qualify to be a 

potentially preventable readmission. For example, 

potentially preventable readmissions following an 

initial admission for congestive heart failure (CHF) 

could be for CHF again or for other conditions, such as 

renal failure, pneumonia, COPD, and septicemia and 

other infections. 

F i g ure

5 –1 Examples of logic used to define potentially preventable readmissions to hospitals

Note:	 AMI (acute myocardial infarction), PTCA (percutaneous transluminal coronary angioplasty), CABG (coronary artery bypass graft).

Source: 3M.

Examples of logic used to define potentially preventable readmissions to hospitals

FIGURE

5-4

Note: Note and source in InDesign. Reason for initial admission

Reason for readmission

Medical Surgical

Surgical

Medical

Potentially preventable

Example: Admission for diabetes following 

discharge for AMI

Exception: Unrelated acute events

Example: Admission for trauma following 

discharge for AMI

Potentially preventable

Example: Admission for angina following 

discharge for PTCA

Exception: Unrelated acute events

Example: Admission for eye infection following 

discharge for PTCA

Not potentially preventable

Example: Admission for appendectomy following 

discharge for pneumonia

Exception: Prior discharge diagnosis was reason 

for surgery

Example: Admission for appendectomy following 

discharge for abdominal pain

Not potentially preventable

Example: Admission for cholecystectomy

following discharge for CABG

Exception: Surgery for complications of 

prior surgery

Example: Admission for PTCA following

discharge for CABG
110 Payment policy for inpatient readmissions

In comparing hospitals, we need to adjust for differences 

in the types of cases and the severity level of patients.3

If we control for disease-specific and severity-related 

differences in the incidence of readmissions, the variation 

across hospitals in readmissions narrows a little but 

overall continues to be fairly wide. Figure 5-3 shows 

how hospitals’ actual readmission rates differ from what 

is expected, given their mix of cases (controlling for all 

patient refined diagnosis related group (APR–DRG) and 

the severity of illness level of the patients).4

About 30 percent of hospitals have 15-day readmission 

rates that are more than 1 percentage point above expected 

and 17 percent have rates that are more than 2 percentage 

points above expected. These are the hospitals with the 

greatest potential to reduce their readmission rates. We 

also see, however, that a substantial portion of hospitals 

have readmission rates that are lower than expected; 13 

percent of hospitals, for instance, achieve readmission 

rates that are more than 2 percentage points below what is 

expected given their mix of cases. Thus, not only is there 

potential for hospitals with above-average readmission 

rates to lower their rates, but hospitals that have rates close 

to the expected rate also have the potential to reduce their 

rates of readmission. 

If we look at specific conditions, such as congestive heart 

failure (CHF), the distribution in the difference between 

actual and expected readmission rates is wider (Figure 

5-4, p. 112). CHF is one of the conditions with the most 

readmissions. Some experts believe there is an especially 

large potential for reductions in readmission rates for CHF 

if proven clinical practices are followed (Lappe et al. 2004, 

IHI 2004a, 2004b). 

The average 15-day readmission rate for CHF is 12.5 

percent, but 20 percent of hospitals that treat CHF have 

readmission rates that are more than 4 percentage points 

higher than expected. Another 20 percent have CHF 

readmission rates that are more than 2 percentage points 

lower than expected. The practices of the hospitals with 

Hospital readmission rates vary widely

Note:	 Readmissions are across all diagnosis related groups and are not severity adjusted. Readmissions are defined using 3M’s software that identifies potentially

preventable readmissions.

Source:	 3M analysis of 2005 Medicare discharge claims data.

Hospital readmission rates vary widely FIGURE

5-1

Note and Source in InDesign Percent of hospitals

25

10

15

20

5

0

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20+

Percent of patients readmitted within 15 days of discharge

F i g ure

5 –2
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 111

low readmission rates could inform a new expectation of 

what could be achieved. That is, CHF readmission rates 

could be lower even for hospitals that currently have 

rates 1 to 2 percentage points lower than expected. We 

see wide variation in readmissions for other conditions, 

such as chronic obstructive pulmonary disease (COPD), 

pneumonia, and CABG surgery. 

How can hospitals reduce readmissions?

Research and the experience of individual hospitals 

suggest that hospitals can reduce the number of 

readmissions. We discuss effective initiatives and 

strategies.

Provide better, safer care during the 

inpatient stay

The Agency for Healthcare Research and Quality has 

found that by providing better, safer care in the inpatient 

setting, hospitals can lower the incidence of adverse 

patient safety events that occur during hospitalization. 

These events, such as anesthesia complications, pulmonary 

embolism, infection due to medical care, hemorrhage, 

and acute respiratory failure, increase the chance that a 

patient will need to be readmitted. A study that looked at 

California non-Medicare data found that the likelihood 

of readmission doubled (from 14 percent to 28 percent) 

with an adverse patient safety event during the initial 

hospitalization (Bernard and Encinosa 2004). 

Similarly, the Pennsylvania Health Care Cost Containment 

Council found that rates of readmission after CABG with 

hospital-acquired infections are more than double those of 

uninfected CABG patients; 13.2 percent of beneficiaries 

with infections were readmitted within 7 days while only 

5 percent of those without an infection were readmitted. 

Over a 30-day window, 27.9 percent of those with 

complications were readmitted, compared with 12.9 

percent without complications (PHC4 2006). 

In addition, by incorporating best practice guidelines into 

clinical care, providers can avoid some complications that 

Readmissions vary across hospitals even after adjusting for severity

Note:	 Expected rates are based on the average rate of readmission across all hospitals, controlling for all patient refined diagnosis related group and severity class of

patients. Readmissions are identified using 3M’s software that defines potentially preventable readmissions.

Source:	 3M analysis of 2005 Medicare discharge claims data.

Potentially preventable readmissions vary across

 hospitals even after adjusting for severity

FIGURE

5-1

Note and Source in InDesign Percent of hospitals

25

10

15

20

5

0

< –5 –5 to –4 –4 to –3 –3 to –2 –2 to –1 –1 to 0 0 to 1 1 to 2 2 to 3 3 to 4 4 to 5 > 5

Percentage point difference between actual and expected readmission rates

F i g ure

5 –3
112 Payment policy for inpatient readmissions

can occur after discharge. For example, after hip and knee 

replacement, the use of blood thinning medications, elastic 

stockings, and exercises to increase blood flow in the leg 

muscles may help avoid blood clots, which can surface a 

few weeks after surgery. In Pennsylvania, blood clots are 

among the top three reasons for readmission, accounting 

for 889 hospital days in 2002 (PHC4 2005). Similarly, 

early extubation or use of beta blockers and aspirin on 

discharge for CABG patients could also contribute to 

lower readmission rates (Hannen et al. 2003).

Attend to patient’s medication needs at 

discharge

Medication errors pose a significant threat to patients after 

discharge. One study found that 19 percent of all patients 

discharged from the hospital experienced an associated 

adverse event within three weeks; 66 percent of them 

were adverse drug events (Forster et al. 2003). Another 

study found that elderly patients who had medication 

discrepancies at discharge were more than twice as 

likely to be rehospitalized within 30 days as those who 

did not experience a discrepancy. Discrepancies, which 

can be thought of as potential errors, include separate 

prescriptions for both the brand name and the generic 

name of the same drug or patients simply being unaware 

they should be taking a medication one of their doctors 

prescribed (Coleman et al. 2005). 

Hospitals have found effective strategies to reduce such 

postdischarge complications. For example, one hospital 

found that if, upon discharge of cardiovascular patients, 

physicians and nurses referred to a checklist of indications 

and contraindications for five medications known to 

prevent complications and save lives, appropriate use of 

the medications increased dramatically. Prescriptions for 

beta blockers, which can prevent heart attacks, increased 

from 57 percent of patients who needed them to 98 

percent; prescriptions for warfarin, which can protect 

certain patients from strokes, increased from 40 percent of 

patients who needed it to about 90 percent. This discharge 

medication protocol significantly improved mortality rates 

after discharge and 30-day readmission rates, particularly 

Adjusted readmission rates for congestive heart failure vary substantially

Note:	 Expected rates are based on the average rate of readmission across all hospitals, controlling for all patient refined diagnosis related group and severity class of

patients. Readmissions are identified using 3M’s software that defines potentially preventable readmissions.

Source:	 3M analysis of 2005 Medicare discharge claims data.

Adjusted readmission rates for congestive heart failure vary substantially FIGURE

5-3

Note and Source in InDesign Percent of hospitals

25

10

15

20

5

0

< –5 –5 to –4 –4 to –3 –3 to –2 –2 to –1 –1 to 0 0 to 1 1 to 2 2 to 3 3 to 4 4 to 5 > 5

Percentage point difference between actual and expected readmission rates

F i g ure

5 –4
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 113

for patients with CHF and for cardiovascular patients 

without CHF. This initiative did not require additional 

employees and was integrated into the hospital’s existing 

medical informatics infrastructure (Lappe et al. 2004). 

Improve communication with patients 

before and after discharge

Another way hospitals can reduce readmissions is 

to improve communication with patients and better 

coordinate their care transition on discharge. Patients 

might not be equipped to care for themselves at home, 

know who to call with questions, or fully understand 

their new health care needs. Hospitals have demonstrated 

their ability to address these problems by improving 

coordination and communication. 

One study found that several Philadelphia hospitals were 

able to better meet patient needs and reduce readmissions 

by 45 percent over the 24 weeks of the study by having 

nurses repeatedly meet with patients at high risk for poor 

outcomes after discharge, both during hospitalization and 

at home after discharge. During these visits, interventions 

focused on medications, symptom management, diet, and 

activity. The nurses also provided written instructions and 

medication schedules, addressed patients’ and caregivers’ 

questions, and worked with community physicians to 

obtain needed services and adjustments to therapies 

(Naylor et al. 1999). Another study tested the effect of a 

“transition coach” to empower elderly patients. The coach 

reviewed medication, provided a personal health record to 

aid cross-site information transfer, and encouraged timely 

follow-up, among other things. This intervention also 

resulted in lower readmission rates at 30 days, 90 days, and 

180 days after discharge (Coleman et al. 2006).

Less comprehensive approaches are also effective, 

particularly in reducing CHF readmissions. Hackensack 

University Medical Center implemented an initiative in 

which nurses telephone CHF patients after discharge 

to check on specific health indicators, such as weight, 

swelling, shortness of breath, pain, appetite, and activity 

level. With this information, they can monitor patients’ 

needs and communicate with their physicians if there is 

an indication that medications should be adjusted. The 

hospital reported a 78 percent decrease in readmission 

rates (IHI 2004a). Baylor University Medical Center also 

reports reducing CHF readmissions by redesigning the 

discharge process to emphasize patient education, having 

a nurse call within 24 hours of discharge, and improving 

communication among providers in the hospital (IHI 

2004b). 

Billings Clinic and Park Nicollet Health Services, which 

are both integrated delivery systems, use a program in 

which CHF patients, who first receive education about 

their self-care and postdischarge intervention program 

during hospitalization, call or log-in each morning to 

report their weight and symptoms. Each day, nurses 

identify patients with worsening conditions. Nurses can 

modify medication and become involved in end-of-life 

issues. Because they actively manage only those with 

worsening conditions, each nurse can have a caseload up 

to 300 patients (Berenson 2006).

Stroke patients may also present an opportunity for 

preventing readmissions. Beneficiaries readmitted after 

suffering a stroke were much more likely than other 

patients to be dehydrated and have electrolyte imbalances 

(Kind et al. 2007). These findings suggest that if hospitals 

monitor those symptoms and address them in a timely way, 

they could avoid readmissions. 

Simply providing complete discharge instructions can 

also help. One study in a Minnesota hospital found that 

CHF patients who received all instructions about how 

to care for themselves upon discharge were less likely 

to be readmitted than those who did not. The types of 

instructions concerned drug interactions, worsening 

symptoms, activity, diet, follow-up appointments, and 

weight monitoring. Only 68 percent of the patients 

received all appropriate discharge instructions (VanSuch et 

al. 2006).5

Improve communication with other 

providers

Improved communication with community physicians 

and post-acute care providers can also lower readmission 

rates. For context, consider that most readmissions, 64 

percent, are for beneficiaries discharged home without 

any additional post-acute care, 20 percent are for 

patients discharged to SNFs, 11 percent are for patients 

discharged home with home health care, and 5 percent 

of readmissions are for patients initially discharged 

to rehabilitation hospitals or units, long-term care 

hospitals, or psychiatric hospitals. Of particular concern 

is the disproportionate share of readmissions for patients 

in SNFs—although 16 percent of patients are discharged 

to SNFs, they account for 20 percent of readmissions—

and the recent growth in their readmission rates (see 

Chapter 8). 

While the receiving facility or provider is responsible for 

providing good care to avert readmissions, the hospital 
114 Payment policy for inpatient readmissions

has an important role in enabling effective follow-up 

care. Perhaps most importantly, it provides key clinical 

information, in the form of a discharge summary, to 

these other providers. Research is beginning to show 

that discharge summaries are not executed in a timely 

and complete way, resulting in discontinuity of care and 

adverse clinical outcomes such as readmissions. Patients 

treated in follow-up by a physician who did not receive 

a discharge summary appear to be at greater risk for 

readmission. Indeed, the discharge summary is often 

unavailable at follow-up: Only between 12 percent and 

34 percent of physicians report having the discharge 

summary at the first postdischarge visit. The discharge 

summaries are also not always sufficiently complete. They 

may lack information on diagnostic test results, discharge 

medications, and follow-up plans (Kripalani et al. 2007).

The experience of group practices provides some insight 

on the ability of providers to address these problems. 

For example, Healthcare Partners, a large group practice 

in Southern California, has focused on improving the 

distribution and content of its discharge summaries, 

establishing a goal that primary care physicians receive 

discharge summaries within one business day of their 

patients’ discharge. The Lahey Clinic in Massachusetts 

conducts quarterly meetings with SNF physicians focusing 

on hospital readmissions from SNFs and whether they can 

be prevented (HMO Workgroup 2004).

Review practice patterns 

Aside from greater adherence to evidence-based 

guidelines and better communication with patients and 

other providers, hospitals can review other aspects of 

practice patterns that influence the likelihood of patients 

being readmitted. For example, hospitals may consider 

keeping some patients an extra day to be more certain their 

condition has stabilized. They may provide comparative 

information to physicians about their readmission rates 

and encourage a dialogue between physicians with 

high readmission rates and those with low rates. For 

example, some cardiothoracic surgeons prefer to manage 

postdischarge atrial fibrillation on an outpatient basis for 

patients who have recently undergone CABG surgery, and 

others prefer to readmit such patients (Hannen et al. 2003). 

A hospital may also reevaluate expansion plans. Higher 

bed supply is associated with higher rates of admission for 

patients with chronic illnesses such as CHF, COPD, and 

cancer as well as end-of-life care (Wennberg et al. 2004). 

The local bed supply, rather than patient preferences, 

explained the differences in end-of-life care among 

patients in one study (Pritchard et al. 1998). 

Physician Group Practice demonstration 

participants implement systems to reduce 

readmissions

Physician groups participating in CMS’s Physician Group 

Practice demonstration have strong incentives to improve 

quality and lower total spending. They have put in place 

better systems to reduce readmissions. One clinic reduced 

readmissions by scheduling all elderly patients for their 

first follow-up visit within 4 to 10 days of discharge. In 

addition, all the sites have considered ways to influence or 

modify physicians’ practice patterns, including modifying 

work processes (e.g., color-coded disease management 

worksheets to remind physicians and other clinical staff 

to order certain tests) and providing feedback reports to 

physicians (Trisolini et al. 2006). 

How can Medicare policy encourage 

hospitals to adopt strategies to reduce 

readmissions?

This section explores a two-step policy option to provide 

an incentive for hospitals to reduce their readmission 

rates, particularly if they have high rates compared with 

their peers. The first step is public disclosure of hospitalspecific, risk-adjusted readmission rates. This will ensure 

that hospitals know their rates and how they compare with 

those of their peers and will allow beneficiaries and other 

providers to use this information when they make health 

care decisions or admit patients. After a year or two, public 

disclosure could be complemented by a change in payment 

rates, so that hospitals with high risk-adjusted rates of 

readmission receive lower average per case payments. 

Depending on design, the policy not only could encourage 

hospitals with excessive rates to reduce readmissions but 

also could encourage top-performing hospitals to consider 

opportunities for continued improvement.

An important parallel policy would be to encourage other 

providers, including physicians, SNFs, and home health 

providers to prevent readmissions. Holding each entity 

accountable will motivate them to collaborate with one 

another because their success will partly depend on the 

success of their care partners. 

Medicare should pursue other policies, such as pay-forperformance (P4P) that includes both process and outcome 

measures and accreditation standards, as well. Currently, to 

receive the full Medicare payment update, most hospitals 

report their performance on the frequency with which they 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 115

give discharge instructions during an admission for heart 

failure. Next year, hospitals will report patients’ responses 

on two specific care transition questions: “Did hospital 

staff talk with you about follow-up care?” and “Did you 

get information about symptoms to look out for?” But 

if the underlying payment method creates a stronger 

counterincentive than a P4P measure or an accreditation 

standard, the effectiveness of the P4P initiative and the 

institutional commitment behind improving performance 

on a process measure may be limited.

A related issue that is beyond the scope of this chapter is 

the lack of funding for care management services. The 

Commission discussed two approaches in its June 2006 

report to the Congress. Perhaps once experience is gained 

in how much hospitals can improve and what resources 

are needed to achieve improvement, policymakers can 

consider the need for any explicit financing for care 

management services as a complement to a change in 

readmission payment policy.

While the rationale for changing hospitals’ incentives 

and aligning them with other providers’ incentives to 

avoid readmissions may be clear, the technical aspects of 

measuring and comparing readmission rates, which are the 

foundation of any public disclosure and payment change 

option, require careful navigation. The following section 

discusses a two-step policy approach and several of the most 

pertinent issues.

Start with select conditions 

It may be prudent to focus on disclosure and payment 

changes for a limited number of conditions at the outset. 

DRGs with high volume and high rates of readmission 

are good candidates. Ideally, the subset would include 

conditions for which some hospitals have successfully 

reduced readmissions. 

By focusing on a subset of conditions, Medicare 

and hospitals can gain needed experience to refine 

measurement techniques and assess the value of expanding 

the policy to a broader set of DRGs. Among the key 

measurement issues and aspects to assess are:

• What is the time period within which readmissions are 

defined? For example, should it be 7 days, 15 days, or 

30 days? (For illustrative purposes, we have provided 

data analysis on 15-day readmissions throughout the 

chapter.) 

• Should all readmissions be counted in the selected 

time period or just the subset that are clinically 

determined to be potentially preventable?

• What is the benchmark against which hospitals are 

measured? Should it be average readmission rates 

across all peers? Or should it reflect a higher standard, 

perhaps the readmission rate of top performers, to 

raise expectations? 

• How does this policy affect discharge destination 

patterns and overall episode costs? For example, do 

hospitals respond by discharging more patients to 

rehabilitation services rather than home? If so, do 

outcomes improve and how do overall episode costs 

compare?

Good candidates for this starter set include CHF, COPD, 

and CABG. In Table 5-3 (p. 116), we list those conditions 

as well as several others to illustrate a potential starter set. 

Together, these conditions account for nearly 30 percent of 

readmissions in the 15-day window after discharge from 

the initial hospitalization. 

Significant variation in readmission rates for these 

conditions suggests the opportunity for improvement. The 

wide variation in CHF is illustrated earlier in the chapter.

Value in disclosure

Once the starter set of DRGs has been identified and 

measurement issues have been addressed, Medicare 

could begin public disclosure of hospital readmission 

rates. A few years of experience with disclosure allows 

for refinement in measurement techniques in preparation 

for a change in payment policy. It would also ensure 

that all hospitals know their readmission rates, including 

readmissions to other hospitals, and have the opportunity 

to improve their rates before a penalty is imposed.

Disclosing these rates would also allow beneficiaries, 

physicians, and other providers to act on this information. 

Beneficiaries may use it to select which hospital to use and 

physicians and other post-acute care providers may use it 

in their admitting and affiliation decisions.

Structuring the payment incentive

A first consideration in designing a payment incentive is 

whether the policy should be a penalty only or whether 

it should include a positive reward for high-performing 

hospitals. A penalty-only policy may be sufficient to 

motivate hospitals to better meet patients’ needs during 
116 Payment policy for inpatient readmissions

the transition from the hospital to home or post-acute 

care. By not paying more than under current law to high 

performers, Medicare saves money and encourages all 

hospitals to be efficient. Also, reducing the frequency of 

readmissions may pay off financially for hospitals under 

current payment and be its own reward for incurring 

additional discharge planning and other such costs. 

MedPAC analysis shows that for patients who are later 

readmitted, hospitals have lower margins on both the initial 

admission and readmissions, compared with patients who 

are not readmitted. By reducing the frequency of these 

patients’ readmissions, hospitals may be able to fill the 

beds with other patients who are more profitable.

On the other hand, a policy that pairs a penalty with a 

reward for good performance could help to offset possible 

lost revenue associated with lower rates of readmissions 

(if the hospital does not fill the beds with more profitable 

patients) and the costs for the actions (e.g., additional 

nursing and discharge planning staff, longer lengths of 

stay) hospitals would take to reduce readmissions. 

We explore illustrative approaches for each type of 

incentive below. Then we consider how to adjust payment 

for readmissions to a hospital other than the one with the 

initial stay. This issue is pertinent regardless of whether a 

penalty-only or a combined reward and penalty approach 

is pursued. Lastly, we discuss risk adjustment issues.

A penalty-only approach

Under an approach that creates a penalty for hospitals with 

high readmission rates but holds top-performing hospitals 

harmless, Medicare could identify those hospitals with 

a higher rate of readmissions and impose the penalty 

only on them. To do this, Medicare could first calculate 

each hospital’s readmission rate based on the prior year’s 

performance and then select a benchmark rate (e.g., the 

average risk-adjusted readmission rate across all hospitals). 

For the next year, Medicare would reduce payment for 

each related readmission only for those hospitals with 

readmission rates above the benchmark rate. This approach 

combines several attractive features. It does not affect 

hospitals with lower rates of readmissions; the penalty can 

be applied in real time rather than assessed at the end of 

the year, which may have greater operational impact; and it 

can be designed to reduce Medicare’s spending.

In this illustration (Table 5-4), hospitals with a readmission 

rate greater than 10 percent would receive the penalty. 

Because hospital A has a 5 percent readmission rate, it has 

no change in its payment. Hospital B, with a 20 percent 

readmission rate, receives the penalty and would be paid 

less for each readmission. With no change in the ratio of 

T A B L E

5 –3 Hospital readmissions for seven conditions make up

 almost 30 percent of spending on readmissions

Condition

Type of 

hospital 

admission

Number of 

admissions with 

readmissions

Readmission 

rate

Average 

Medicare payment 

for readmission

Total spending 

on readmissions

Heart failure Medical 90,273 12.5% $6,531 $590,000,000

COPD Medical 52,327 10.7 6,587 345,000,000

Pneumonia Medical 74,419 9.5 7,165 533,000,000

AMI Medical 20,866 13.4 6,535 136,000,000

CABG Surgical 18,554 13.5 8,136 151,000,000

PTCA Surgical 44,293 10.0 8,109 359,000,000

Other vascular Surgical 18,029 11.7 10,091 182,000,000

Total for seven conditions 318,760 $2,296,000,000

Total DRGs 1,134,483 $7,980,000,000

Percent of total 28.1% 28.8%

Note:	 COPD (chronic obstructive pulmonary disease), AMI (acute myocardial infarction), CABG (coronary artery bypass graft), PTCA (percutaneous transluminal coronary

angioplasty), DRG (diagnosis related group). Analysis is for readmissions within 15 days of discharge from the initial inpatient stay. Readmissions are identified

using 3M’s software that defines potentially preventable readmissions.

Source: 3M analysis of 2005 Medicare discharge claims data. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 117

admissions to readmissions, its average payment per case 

declines by $200. Hospital C has the same readmission 

rate as hospital B at the outset, but, in the face of the 

penalty, reduces its readmission rate and slightly increases 

its admission rate. As a result, it experiences a smaller 

decrease in its average payment per case than hospital B.

A reward and penalty approach

If policymakers prefer to couple a reward for highperforming hospitals with a penalty for low performers, 

Medicare could adjust its current payment method for 

initial admissions and readmissions at the beginning of a 

year so that, in any given year, hospitals with fewer than 

expected readmissions would receive higher average 

case payments than under current law. This illustrative 

approach would reward hospitals with low rates, but not 

necessarily those that reduced their readmission rates. 

Those with a higher than expected rate of readmission 

would receive lower average case payment. To create this 

result, Medicare could increase its payment for initial 

admissions while decreasing its payment for readmissions. 

The magnitude of the two adjustments and their calibration 

relative to one another would be critical to the success 

of the policy. They will determine the degree of the 

incentive for hospitals to change behavior and the effect on 

Medicare spending. Because there are many more initial 

admissions than readmissions, the increase in payment 

for initial admissions should be smaller than the reduction 

in readmission payment. Ideally, the payment would 

be high enough to change behavior but not too high to 

increase spending. To illustrate the concept, we provide the 

following hypothetical example in Table 5-4.

In this example, hospital A has 5 percent readmissions for 

a certain DRG while hospital B and hospital C have a 20 

percent readmission rate. Their average per case payment 

is $5,000. If, under the new policy, Medicare increased 

payment for initial admissions by 2 percent and decreased 

payment for readmissions by 24 percent, average per 

case payment would go up for the hospital with fewer 

readmissions and down for those with more. If there is no 

change in the ratio of initial admissions and readmissions, 

hospital A would have increased its Medicare payment 

per case ($5,035). With no change in the ratio of initial 

admissions to readmissions, hospital B would have 

lower payment per case ($4,883). Hospital C, in this 

example, responds to the policy by reducing the number 

of readmissions but uses the extra capacity to increase 

its initial admissions. Its new average payment per case 

is higher than that for hospital B (which did not change 

behavior) at $4,943. 

Medicare savings in this illustration come from two 

sources: reduced payments for readmissions (partially 

T A B L E

5 –4 Payment effects on providers from two illustrative readmission payment policies

Initial admissions Readmissions

Readmission 

rate

Total 

payment 

across 

all stays

Average 

payment 

Number per case

Per case 

payment

Total 

payment Number

Per case 

payment

Total 

payment

Current policy

Hospital A 570 $5,000 $2,850,000 30 $5,000 $150,000 5% $3,000,000 $5,000

Hospital B 500 5,000 2,500,000 100 5,000 500,000 20 3,000,000 5,000

Hospital C 500 5,000 2,500,000 100 5,000 500,000 20 3,000,000 5,000

Penalty only: Decrease payment for readmissions 24 percent for hospitals with readmission rate >10 percent

Hospital A 570 5,000 2,850,000 30 5,000 150,000 5 3,000,000 5,000

Hospital B 500 5,000 2,500,000 100 3,800 380,000 20 2,880,000 4,800

Hospital C 510 5,000 2,550,000 70 3,800 266,000 12 2,816,000 4,855

Reward and penalty: Increase payment for initial admissions 2 percent; decrease payment for readmissions 24 percent

Hospital A 570 5,100 2,907,000 30 3,800 114,000 5 3,021,000 5,035

Hospital B 500 5,100 2,550,000 100 3,800 380,000 20 2,930,000 4,883

Hospital C 510 5,100 2,601,000 70 3,800 266,000 12 2,867,000 4,943
118 Payment policy for inpatient readmissions

offset by higher payment for initial admissions) and fewer 

readmissions (partially offset by an increase in initial 

admissions that could result given more available beds). 

Accounting for readmissions to other hospitals 

Another payment design issue to consider is how to adjust 

a hospital’s payment when a patient is readmitted to a 

hospital other than the one that had the initial admission. 

This happens about 30 percent of the time. Because the 

readmitting hospital, in this situation, has a minimal 

ability to prevent the readmission, it is not reasonable to 

reduce payment for the readmission. The penalty should 

apply to the hospital with the initial admission. This can 

be accomplished in a number of ways. It could be part 

of “netting,” the routine process in which CMS makes 

retroactive payment adjustments to hospitals. CMS could 

deduct the penalty for the readmission to the same or 

a different hospital from future claims payments. This 

approach requires an added layer to the existing claims 

reconciliation process. 

Alternatively, Medicare could withhold a percentage of 

payment for the initial stay. If the claims did not reflect a 

readmission within 15 days (or whatever time period is 

specified), the withhold could be returned to the hospital 

with the initial stay. If a related readmission were detected, 

the withhold would not be returned. Whichever hospital 

had the readmission would be paid in full. This approach 

keeps the penalty with the hospital that had the greatest 

ability to prevent the readmission. To be administratively 

manageable, the process of detecting preventable 

readmissions would need to be highly automated and an 

integrated step in fiscal intermediaries’ claims review and 

payment process.

Another option is to apply the policy only to readmissions 

to the same hospital, thereby avoiding the administrative 

challenges associated with accounting for readmissions 

that occur across hospitals. Under that approach, payment 

for readmission to the same hospital could be reduced. 

However, this approach would limit the scope of the 

policy significantly and create perverse incentives. 

Hospitals would have an incentive to have patients who 

needed follow-up inpatient care go to a different hospital, 

jeopardizing continuity and quality of care.

Importance of risk adjustment and addressing 

patient nonadherence

It will be necessary to risk adjust hospitals’ rates. 

Readmission is generally more likely the more severely 

ill a patient is, even within the same DRG. Refined DRGs 

that better account for severity of illness should help in 

adjusting for this factor, which is beyond the hospital’s 

control. 

Patients’ adherence to discharge instructions also affects 

hospitals’ readmission rates. Care provided by family, 

which can be important in avoiding readmissions, may be 

declining, as we discuss in Chapter 1. Certain hospitals 

may have patient populations with language and cultural 

barriers that might contribute to readmissions. If a hospital 

has a larger portion of nonadherent patients than other 

hospitals, its performance may look worse than that of its 

peers. 

One way to address this problem is to allow hospitals to 

indicate that a patient was nonadherent upon discharge or 

readmission. Readmissions for those patients would not be 

counted in the providers’ overall rate. Britain has pursued 

a similar exemption process in measuring adherence to 

quality-of-care measures as part of its P4P program. It 

found that relatively few family practices claimed a large 

portion of patients as exempt or nonadherent—only 1.1 

percent excluded more than 15 percent of their patients 

(Doran et al. 2006).

To temper the incentive to declare a high proportion of 

patients as nonadherent, Medicare could keep and publicly 

report a tally of the number of patients who were exempt 

from the rate for each facility. In addition, perhaps an 

objective and verifiable standard for nonadherence could 

be established to limit ambiguity and variation in how 

hospitals use this exceptions process. CMS might require 

providers who had excessive numbers of nonadherent 

patients over time to have plans in place to reduce the 

incidence of nonadherence. 

Even with these sorts of strategies, an exceptions process 

might be counterproductive. Ideally, a provider facing the 

challenges associated with nonadherent patients will invest 

in strategies to encourage patients to adhere to their care 

plans. Allowing hospitals to exempt these patients from 

their readmission rates could undercut the incentive to 

make this investment and fail to address an important part 

of the problem. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 119

1 Quality improvement organizations and other CMS 

contractors have authority to review readmissions claims for 

medical necessity and potential unbundling of services. There 

appears to be wide variation in how aggressively these entities 

focus on readmissions. Interviews with hospital administrators 

suggest that, in some regions of the country, the review is or 

has been so robust that administrators believe Medicare does 

not pay for readmissions within 30 days of discharge of a 

prior hospitalization.

2 Readmissions are identified as cases that are readmitted 

to an acute care hospital (either the same or a different 

hospital) after an acute care stay within a specified time 

frame—7 days, 15 days, or 30 days for this analysis. People 

transferred from one hospital to another hospital are not 

considered readmissions. In calculating readmission rates, the 

denominator in the equation excludes people who died in the 

hospital or were transferred to another acute care hospital. 

3 For example, according to 3M’s analysis, patients with 

congestive heart failure (CHF) in severity level 1 have a 

readmission rate of 9.7 percent, while CHF patients in 

severity level 4 have a 16.3 percent readmission rate over a 

15-day window.

4 Using national data from all hospitals, the percentage of 

discharges with at least one readmission for each APR–DRG 

and severity-of-illness (SOI) level is calculated to establish 

a national readmission rate norm. The expected number of 

discharges with at least one readmission for each APR–DRG 

and SOI level in a hospital is calculated by multiplying the 

readmission rate for the APR–DRG and SOI level from the 

national readmission norm by the number of patients in the 

hospital in that APR–DRG and SOI level. The expected 

number of patients with at least one major readmission in 

each APR–DRG and SOI level summed across all APR–

DRGs and SOI levels is the hospital’s expected number of 

patients with at least one readmission. 

5 Lack of good communication at discharge also appears to 

influence the broader patient experience and recovery, aside 

from readmission rates. A study focused on patient recovery 

after knee replacement surgery found that patients reporting 

coordination problems were more likely to experience joint 

pain and delayed resumption of functioning than those who 

did not report coordination problems (Weinberg et al. 2007).

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Pritchard, R. S., E. S. Fisher, J. M. Teno, et al. 1998. Influence 

of patient preferences and local health system characteristics on 

the place of death. SUPPORT investigators. Study to understand 

prognoses and preferences for risks and outcomes of treatment. 

Journal of the American Geriatrics Society 46: 1242–1250.

Trisolini, M., G. Pope, J. Kautter, et al. 2006. Medicare physician 

group practices, innovations in quality and efficiency. New York: 

The Commonwealth Fund. December.

VanSuch, M., J. M. Naessens, R. J. Stroebel, et al. 2006. 

Effect of discharge instructions on readmission of hospitalized 

patients with heart failure: Do all of the Joint Commission on 

Accreditation of Healthcare Organizations heart failure core 

measures reflect better care? Quality and Safety in Healthcare 15: 

414–417.

Weinberg D. B., J. H. Gittell, R. W. Lusenhop, et al. 2007 Beyond 

our walls: Impact of patient and provider coordination across the 

continuum on outcomes for surgical patients. Health Services 

Research 42, no. 1, pt. 1 (February): 7–24.

Wennberg, J. E., E. S. Fisher, T. A. Stukel, et al. 2004. Use of 

hospital, physician visits, and hospice care in the last six months 

of life among cohorts loyal to highly respected hospitals in the 

United States. British Medical Journal 328 (March 13): 3.

References
An alternative method to 

compute the wage index

C h a p t er 6
R E C O M M E N D A T I O N S

6A The Congress should repeal the existing hospital wage index statute, including 

reclassifications and exceptions, and give the Secretary authority to establish new wage 

index systems.

COMMISSIONER VOTES: YES 15 • NO 0 • NOT VOTING 0 • ABSENT 2

 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6B The Secretary should establish a hospital compensation index that:

• uses wage data from all employers and industry-specific occupational weights,

• is adjusted for geographic differences in the ratio of benefits to wages,

• is adjusted at the county level and smooths large differences between counties, and 

• is implemented so that large changes in wage index values are phased in over a 

transition period.

COMMISSIONER VOTES: YES 15 • NO 0 • NOT VOTING 0 • ABSENT 2

 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6C The Secretary should use the hospital compensation index described in recommendation 

6B for the home health and skilled nursing facility prospective payment systems and 

evaluate its use in the other Medicare fee-for-service prospective payment systems.

COMMISSIONER VOTES: YES 15 • NO 0 • NOT VOTING 0 • ABSENT 2
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 123

An alternative method to 

compute the wage index

C H A PT E R 6

Chapter summary

In the Tax Relief and Health Care Act of 2006 (TRHCA), the Congress 

mandated that MedPAC submit a report on a revision of the wage 

index by June 30, 2007, including Commission recommendations on 

alternatives for computing the wage index. The Secretary then has to 

consider MedPAC’s recommendations and include in the fiscal year 

2009 inpatient prospective payment system (IPPS) proposed rule one 

or more proposals to revise the wage index. The TRHCA also requires 

that CMS consider specific issues of concern to the Congress such as 

eliminating exceptions, minimizing variation in the wage index across 

county borders, and using the hospital wage index in other settings. 

In this chapter, we explore a new method for calculating wage indexes 

for hospitals and other sectors that addresses the Congress’s concerns. 

It is based on wage data from the Bureau of Labor Statistics and the 

Census Bureau and on benefits data from the provider cost reports 

submitted to CMS. The MedPAC wage index isolates differences in 

wage rates that are solely due to geography and is not highly influenced 

In this chapter

• Current approach

• New approach

• Results

• Wage index differences 

across sectors

• Caveats

• Conclusion

• Additional technical 

information on constructing 

a compensation index from 

BLS data
124 An alter native method to compute the wage index

by an individual hospital’s choices about the type of employees to hire or the 

type of services to offer.

The current hospital wage index adjusts Medicare payments for differences 

in reported hospital wages across geographic areas in the United States. By 

law, CMS calculates the index using data only from hospitals paid under 

Medicare’s IPPS. It uses self-reported data in hospital cost reports. However, 

it uses the index to adjust payments for other sectors such as home health and 

skilled nursing facilities (SNFs), even in counties without IPPS hospitals.

Over the years, the Congress and the Secretary have created exceptions to 

the calculated wage index that now change the calculated values for about 

one-third of IPPS hospitals. These exceptions can be overlapping and lead 

to nonintuitive results. The new method eliminates the need for the many 

exceptions by limiting the extent of the differences between adjacent areas. 

It is also less volatile from year to year than the current index and does 

not require a separate survey to untangle the effect of occupational mix 

differences from wage differences—which is an inherent problem in the 

current system.

The Commission recommends first that the Congress should repeal the 

existing hospital wage index statute, including reclassifications and exceptions, 

and give the Secretary authority to establish new wage index systems.

Second, the Commission recommends that the Secretary should use this new 

authority to establish a hospital compensation index that:

• uses wage data representing all employers and industry-specific 

occupational weights,

Recommendation 6A The Congress should repeal the existing hospital wage index statute, including 

reclassifications and exceptions, and give the Secretary authority to establish new wage 

COMMISSIONER VOTES: index systems.

YES 15 • NO 0 • NOT VOTING 0 • ABSENT 2
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 125

• is adjusted for geographic differences in the ratio of benefits to wages,

• is adjusted at the county level and smooths large differences between 

counties, and 

• is implemented so that large changes in wage index values are phased in 

over a transition period.

Because it uses the same underlying data for all settings, the method can 

easily be tailored to SNFs and home health agencies. However, we find that 

the SNF, home health agency, and hospital wage indexes under the new 

approach are highly correlated. Therefore, the Commission also recommends 

that the Secretary should use that hospital compensation index for the home 

health and SNF prospective payment systems and evaluate its use in the 

other Medicare fee-for-service prospective payment systems. 

The Secretary should establish a hospital compensation index that:

• uses wage data from all employers and industry-specific occupational weights,

• is adjusted for geographic differences in the ratio of benefits to wages,

• is adjusted at the county level and smooths large differences between counties, and 

• is implemented so that large changes in wage index values are phased in over a 

transition period.

Recommendation 6B

COMMISSIONER VOTES: 

YES 15 • NO 0 • NOT VOTING 0 • ABSENT 2

The Secretary should use the hospital compensation index described in recommendation 

6B for the home health and skilled nursing facility prospective payment systems and 

evaluate its use in the other Medicare fee-for-service prospective payment systems.

Recommendation 6C

COMMISSIONER VOTES: 

YES 15 • NO 0 • NOT VOTING 0 • ABSENT 2

Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 127

Introduction

The role of the wage index in Medicare prospective 

payment systems is to adjust payments for the differences 

in wage rates across geographic areas. The basic idea is 

that if it costs more to hire a nurse in New York City than 

it does in rural Alabama, then payments should reflect that 

difference because area labor costs are beyond a health 

care provider’s control. The text box shows how CMS uses 

the wage index to calculate payments for hospitals. 

Computing a wage index requires:

• determining geographic labor market areas,

• determining the underlying wage level in those 

markets for the relevant occupations, and

• comparing those levels with the national average to 

derive an index value. 

The market areas in the current system are metropolitan 

statistical areas (MSAs), which usually include a city and 

its surrounding suburbs, and a residual called the statewide 

rural area, which includes all counties in the state that 

are not in MSAs.1 A system that adjusts for geographic 

differences in labor input costs should isolate differences 

in wage rates that are solely due to geography. An index 

should reflect overall market conditions and not be highly 

influenced by an individual hospital’s choices about the 

types of employees to hire or the types of services to offer. 

Calculation of base payment in fiscal year 2007

CMS computes the hospital base payment for 

inpatient prospective payment system (IPPS) 

hospitals by splitting the base rate into a laborrelated share and a non-labor-related share and then 

multiplying the labor-related share by the wage index 

for the geographic area. In fiscal year 2007, the hospital 

wage index ranged from about 1.56 to 0.74. The base 

payment for hospitals in these areas is calculated as 

shown below.

Base payment 

= [(base rate) × (labor share) × (wage index)]

+ [(base rate) × (1 – labor share)]

For fiscal year 2007, all hospitals paid under the IPPS 

have the same base rate, $4,874. The wage indexes for 

the areas with the highest and lowest wage indexes in 

the country are as shown in Table 6-1. CMS estimated 

the labor share to be 0.697 across the nation using 

Medicare cost report data, and CMS uses that figure 

for the area with the highest wage index. However, the 

Congress set the labor share at 0.62 for hospitals with 

wage indexes less than or equal to 1; therefore, CMS 

uses that amount for the area with the lowest wage 

index. Because areas with a wage index less than or 

equal to 1 have a smaller labor share, the differences 

in base payments do not fully reflect the differences in 

wage indexes. CMS calculates the labor-related base 

payment by multiplying the base rate, the labor share, 

and the wage index, and it calculates the non-laborrelated base payment as (1 minus the labor share) times 

the base rate; the sum of those two columns is the base 

payment. That amount ranges from about $4,079 for 

hospitals with the lowest wage index to about $6,783 

for hospitals with the highest wage index. 

T A B L E

6 –1 How the wage index affects the base payment for hospitals, 2007

Wage index area

FY07 

base rate

Labor 

share

Wage 

index

Labor-related 

portion

Non-labor-related 

portion

Base 

payment

Highest $4,874 0.697 1.5617 $5,306 $1,477 $6,783

Lowest 4,874 0.620 0.7368 2,227 1,852 4,079

Note:	 FY (fiscal year).

Source:	 Final FY07 wage indexes and payment factors from Federal Register 71, no. 196 (October 11, 2006): 59890.
128 An alter native method to compute the wage index

Exceptions to the current wage index

Lugar counties: Entire counties may be reclassified to 

an adjacent metropolitan statistical area (MSA) if they 

are adjacent to more than one MSA and, taken together, 

the commuting pattern to those MSAs would classify 

them to a single MSA under Office of Management and 

Budget (OMB) rules. For example, if 13 percent of the 

workers in a county commute to MSA 1 and another 

13 percent commute to MSA 2, the sum of those 

commuting would be 26 percent. Under OMB rules, 25 

percent of workers must commute to a single MSA for 

a county to be part of that MSA; thus, the county would 

qualify as a Lugar county.

Medicare geographic classification review board 

decisions: Hospitals may request reclassification to an 

adjacent labor market area if they meet conditions of 

geographic proximity and comparable wage costs:

• Close geographic proximity is defined as being 

located within 15 miles (if urban) or 35 miles (if 

rural) from the border of the area to which they 

seek to be reassigned. Proximity may also be 

demonstrated if at least 50 percent of the hospital’s 

employees reside in the reassigned area.

• Comparable wage costs are defined as having an 

average hourly wage rate at least 108 percent (if 

urban) or 106 percent (if rural) of the average hourly 

wage in their actual labor market location, and 

having an average hourly wage at least 84 percent (if 

urban) or 82 percent (if rural) of the average wage 

rate in the area to which they seek to be reassigned. 

Comparable wage costs are based on weighted threeyear average hourly wages.

Sole community hospitals and rural referral centers are 

not required to meet the proximity criteria. In addition, 

hospitals that are currently classified or have ever been 

classified as rural referral centers are not required to 

meet the 106 percent criterion (they can reclassify 

even if their wages are not higher than their regional 

average).

Hospitals that do not meet the geographic 

reclassification regulations have also been reclassified:

• The Section 508 reclassifications were created in 

the Medicare Prescription Drug, Improvement, and 

Modernization Act of 2003; they now expire at the 

end of fiscal year 2007.

Section 401: Section 401 allows hospitals to be 

classified for wage index purposes as rural although 

they are in an urban area.

Special exceptions: Special exceptions are 

reclassifications allowed at the discretion of the 

Secretary for certain providers that previously qualified 

under rules for group (countywide) reclassifications, 

where statutory changes related to other prospective 

payment system provisions would otherwise have 

disqualified these providers from reclassification. 

These exceptions were implemented in fiscal year 2005 

(CMS 2004).

Outcommuting adjustment: The outcommuting 

adjustment allows wage indexes for counties in lower 

wage index areas to be blended with higher wage index 

areas in proportion to the number of county residents 

who are hospital workers and who commute to those 

higher wage index areas.

Rural floor:

• The rural floor exception requires that any MSA 

wage index in a state be equal to or greater than the 

statewide rural wage index in that state.

• The rural floor exception was extended to states 

without rural areas and an imputed rural floor was 

created for those states. 

Hold harmless: Under the hold-harmless provision, 

hospitals now in rural but formerly in metropolitan 

markets are allowed to retain former metropolitan 

designation for three years, fiscal years 2005–2007. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 129

Therefore, the sample of wages used to construct the index 

should come from all employers of similar workers in the 

market. 

First, we describe the approach currently used in the 

hospital wage index, which is part of both hospital 

inpatient and outpatient prospective payment system 

(PPS) payment formulas; then we describe an alternative 

approach for computing a hospital wage index and how 

to extend that approach to other sectors such as skilled 

nursing facilities (SNFs) and home health agencies.

Current approach

The current hospital wage index adjusts payments for 

differences in hospital-reported average wages across 

geographic areas in the United States. By statute, it should 

adjust for area differences in hospital wage levels by a 

factor that reflects the relative hospital wage level in the 

geographic area of the hospital compared with the national 

average hospital wage level. It is updated on the basis of a 

survey conducted by the Secretary of the wages and wagerelated costs of inpatient prospective payment system 

(IPPS) hospitals (see text box, pp. 151–152, for text of 

42 U.S.C. 1395ww(d)(3)(E)). In practice, it is based on 

data hospitals reported four years prior in their Medicare 

IPPS cost reports. The reports include detailed instructions 

on which employees, what lines of business, and what 

elements of compensation—including salaries and wagerelated costs—to include. Audits show the results can 

sometimes be inaccurate (OIG 2007). Areas with only one 

or two hospitals may also see volatility in the wage index 

if wages change suddenly—for example, because of a new 

labor agreement or because of errors in reporting costs and 

hours (OIG 2007). 

Exceptions

The basic wage index system, which uses MSAs and 

statewide rural areas as its labor markets, can result 

in large differences between adjoining geographic 

areas. Because a hospital near a border may consider it 

inequitable that its wage index value is lower than that of a 

nearby hospital, over the years numerous exceptions to the 

basic calculation have been incorporated into the system 

that permit hospitals to have their payments adjusted by a 

higher wage index value. Those exceptions now increase 

the calculated wage index for more than one-third of IPPS 

hospitals (Table 6-2, p. 130). Each type of exception is 

explained in the text box (opposite page). 

Adjudicating this exception process and maintaining 

a wage index system with so many exceptions is 

burdensome to CMS. The text box (p. 131) shows that 

the numerous exceptions and the interactions among 

them create a number of troubling anomalies in the 

current system. Dalton and colleagues have compiled 

a history of the wage index legislation and exceptions 

(Dalton et al. 2007).

Occupational mix

A second problem with the Medicare wage index relates 

to the occupational mix across hospitals. The average 

wage might be higher in one hospital than another not 

because of differences in underlying wages but because 

of differences in the share of higher or lower wage staff 

employed by one hospital relative to another. Payments 

to a hospital should not increase because one hospital 

chooses to use a mix of labor that is higher cost than 

another. For example, if one hospital chooses to use 

information technology (IT) specialists and invest in an 

IT system instead of employing many billing clerks, that 

choice should not change its wage index. In addition, 

if a higher skill mix is a result of caring for higher 

intensity patients, the additional costs should be reflected 

in the case mix for the hospital, not in the wage index. 

Medicare’s diagnosis related group (DRG) system captures 

differences in costs—including those associated with 

the mix of staff. Hospitals with more high-cost DRGs (a 

higher case mix) receive higher payments.

Historically, the wage index reflected a hospital’s average 

wage without adjusting for the skill level of its employees. 

In an attempt to correct this problem, the Medicare, 

Medicaid, and SCHIP Benefits Improvement and 

Protection Act of 2000 mandated that CMS remove the 

effect of differences in occupational mix from the index. 

In fiscal year 2005, CMS introduced an occupational mix 

adjustment to control for the effect of skill level on the 

wage index. Because of uncertainties about the data, the 

method, and the impacts on hospitals, CMS limited the 

adjustment to 10 percent of the wage index. 

CMS introduced a new system to survey hospitals for their 

occupational mix in 2006 and was planning to adjust for 

occupational mix using data from the survey beginning 

in fiscal year 2008. However, a court ruling, Bellevue 

Hospital Center v. Leavitt, 443 F.3d 163 (2nd Cir. 2006), 

required that the wage index be adjusted 100 percent for 

the effect of occupational mix in fiscal year 2007 using 
130 An alter native method to compute the wage index

data from the new survey. The latest survey collects 

salaries and hours for hospital employees and contractors 

but only adjusts for differences in the mix of nursing 

personnel.

This limited occupational mix adjustment had a small 

effect on most hospitals in 2007. The occupational mix 

adjustment differentiates between management RNs, 

other RNs, licensed practical nurses (LPNs), nurse aides, 

and medical assistants. It does not account for differences 

in the mix of other occupations. An occupational-mixadjusted wage index was published in October 2006 after 

the final rule. The occupational mix adjustment resulted in 

the wage index increasing in 47 percent of the 386 urban 

wage index areas and decreasing in about 52 percent (4 

areas had no change). The greatest urban increase was 8.4 

percent and the greatest decrease was 6.0 percent. In the 

47 rural areas, 70 percent saw increases and 30 percent 

saw decreases. The greatest rural increase was 3.2 percent 

and the greatest decrease was 2.7 percent. 

Circularity

The current system relies solely on hospital-reported 

data and hence is prone to the problem of circularity. For 

example, hospitals that successfully moderate increases in 

hourly wages relative to the national average increase will 

see a decrease in their wage index. They will then receive 

lower payments, which may create pressure to exert even 

tighter control over costs. (The magnitude of the pressure 

depends on the overall financial condition of the hospital.) 

If hospitals succeed at keeping wage increases below the 

national average again, their wage index could decrease still 

further. This is particularly a problem in a market area with 

few hospitals where any idiosyncratic characteristic of the 

hospital (e.g., labor mix or unusual labor agreements) can 

T A B L E

6 –2 Exceptions to the wage index in fiscal year 2007

Number of hospitals

Percent 

Urban Rural Total of total

Total inpatient prospective payment system hospitals 2,590 1,005 3,595 100%

Labor market reclassifications and special exceptions:

Lugar counties (“deemed urban,” since 1988) 49 N/A 49 1

MGCRB decisions, standard criteria 280 358 638 18

MGCRB decisions (MMA Section 508 special appeals) 81 27 108 3

Urban to rural (BIPA Section 401 providers) 30 N/A* 30 1

“Special exceptions” by the Secretary 13 5 18 1

Subtotal, all reclassified and special exceptions 453 390 843 23

Outcommuting adjustments (MMA Section 505) 133 91 224 6

Rural floor index

Original: Providers in states with rural markets 216 N/A** 216 6

Imputed: Providers in “all urban” states 40 N/A 40 1

“Hold-harmless” providers from MSA-to-CBSA change N/A 46 46 1

Total hospitals with exceptions and adjustments 842 481 1,323 37

Note:	 N/A (not applicable), MGCRB (Medicare Geographic Classification Review Board), MMA (Medicare Prescription Drug, Improvement, and Modernization Act of

2003), BIPA (Medicare, Medicaid, and SCHIP Benefits Improvement and Protection Act of 2000), MSA (metropolitan statistical area), CBSA (core-based statistical

area). Total is number of hospitals with at least one exception; hospitals may qualify for more than one. Numbers may not sum to total due to rounding.

	 *Five providers in this category were identified in the hospital impact file as rural (presumed error; placed in urban column).

	 **Two providers in this category were identified in the hospital impact file as rural (presumed error; placed in urban column).

Source:	 Dalton et al. 2007.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 131

determine the wage index. About half the market areas have 

three or fewer reporting hospitals; 58 markets (13 percent) 

have only one reporting hospital. This characteristic of 

the current system has concerned many providers. Using 

data from all employers will reduce (but not eliminate) 

the circularity problem, with the greatest benefit for 

occupations—such as clerical, housekeeping, and food 

service—with many employers other than hospitals.

New approach

In this chapter, we present a pragmatic approach to 

constructing a wage index that starts from the following 

principles. It should:

• be theoretically sound;

• address limitations in the current approach (large 

numbers of exceptions, occupational mix, circularity);

• use readily available data from all employers;

• use boundaries for geographic areas that are 

commonly used and understood and for which data are 

available;

• take into account all compensation costs, both wages 

and benefits;

• not create greater data-reporting burdens on hospitals; 

and

• be seen as fair by providers in other sectors as well as 

by hospitals.

A system for adjusting for geographic differences in 

labor input costs should isolate the labor-related costs of 

doing business that differ solely because of geography. 

The goal is an index that is more reflective of overall 

market conditions and less reflective of individual 

hospitals’ market power and their choices about the type of 

The system of wage index exceptions causes anomalies in wage index values

The current wage index system has become laden 

with exceptions, distorting area wage indexes. 

For example, the rural floor exception is built on 

the faulty assumption that rural wages should always be 

lower than urban wages. In the 2008 proposed inpatient 

rule, CMS stated that if two hospitals in a certain state 

decide to change status from critical access hospital 

(CAH) to prospective payment system (PPS), a rural 

floor would be created for all wage index values in the 

state (CMS 2007). Because of the high wages paid 

in these two rural communities, the rural floor would 

cause Medicare payments to urban hospitals in the state 

to rise by more than $220 million per year. The fact that 

the movement of one or two CAHs in or out of the PPS 

system can increase (or decrease) Medicare payments 

by $220 million suggests there is a flaw in the design of 

the wage index system.

While some exceptions have odd outcomes, others 

have reasonable outcomes but lack a firm theoretical 

foundation. The rural counties of North Dakota have 

a base wage index more than 10 percent below the 

rural wage index in all neighboring states. The Section 

508 exception temporarily erased this differential (see 

text box, p. 128). However, the Section 508 exception 

assumed that all North Dakota hospitals were part of 

the Fargo labor market even if they were hundreds of 

miles away. Increasing the North Dakota wage indexes 

to a level similar to indexes in neighboring states (i.e., 

removing the cliff) was a good outcome, but other 

mechanisms could remove the differences between 

adjacent counties without distorting the concept of 

labor markets. 

A combination of exceptions in Connecticut results 

in 27 of 32 hospitals reclassifying in some way to a 

different area’s wage index in fiscal year 2007. Twelve 

hospitals are lifted up by the Connecticut rural floor, 

10 are reclassified under the 508 provision, 3 receive 

special exceptions, and 2 are just reclassified. Only 5 

hospitals receive a wage index based on wages paid in 

their own market.

The current system of multiple exceptions, distorted 

concepts of labor markets, and rural floors has created 

enough distortions to the current system to motivate a 

new approach to the wage index. 
132 An alter native method to compute the wage index

employees hired and the types of services offered. Because 

all employers participate in the labor market, the sample 

of wages used to construct the index should come from 

all employers of similar workers. Hospitals must compete 

with all other potential employers for workers, not just 

with other hospitals. 

One could argue that many separate labor pools may 

exist for each occupation. For example, if nurses at IPPS 

hospitals represent one labor pool and nurses in doctors’ 

offices represent a different labor pool, there may be 

imperfect competition across these pools when employers 

hire nurses. However, because we are interested in the 

relative wages across areas, this would be a disadvantage 

only if a market had a disproportionate share of nurses 

working for hospitals and if wages for nurses varied 

substantially by industry. This does not appear to be a 

significant problem because, at the national level, wages 

for RNs working in hospitals average $27.80, while wages 

for those working in physicians’ offices average $27.03. 

(Physicians’ offices are the second largest employer of 

RNs.) For the most part, relative wages between areas are 

usually well represented by treating each occupation as 

one labor pool in a market area.

The new approach starts with readily available allemployer wage data and then uses a fixed-weight 

(Laspeyres) wage index approach to construct the index. A 

Laspeyres index reflects wage variation and not variation 

caused by occupational mix (Pope 1989). Because of its 

advantages, a Laspeyres index was chosen to construct 

the geographic practice cost index used in the physician 

payment system (Zuckerman et al. 1990). The Government 

Accountability Office found that approach to be valid 

in its design, although the data and methods need some 

refinement (GAO 2005). RTI found that the fixed-weight 

Laspeyres form is reasonable for creating a hospital wage 

index and that it is simple, widely used, accepted, and 

understood (Dalton et al. 2007).

We start with Bureau of Labor Statistics (BLS) survey data 

that provide wages for specific occupations by MSA and 

for the balance of each state across all industries. These 

data meet our criteria because they are from a sample 

of all employers, are readily available, are credible, and 

are aggregated by useful geographic areas.2 We then 

create occupational weights for each industry (hospital, 

nursing facility, home health agency) using BLS-reported 

industry-specific national average employment and wages 

by occupation. For example, if RNs represent 37 percent 

of employee wages in hospitals nationwide, we weight 

RN wages 37 percent in each labor market area when 

calculating the area’s average hospital wage. This approach 

(detailed in the section on additional technical information, 

p. 145) automatically adjusts for occupational mix; thus, 

CMS would not have to conduct an additional survey, 

calculate an adjustment, and recalculate the wage indexes 

as required in the current approach. For each area, the 

occupation weights are multiplied by the ratio of wages for 

that occupation compared with the national average wage 

paid to that occupation and then summed to create a wage 

index value for the area. (Physicians providing patient care 

are not included as an occupation because Medicare pays 

them through the physician fee schedule. Physicians who 

are managers and classified as such by their employer are 

included as managers.)

We then use county-level, occupation-specific wage data 

from the census to further refine the MSA and statewide 

rural wage indexes. We do this because statewide rural 

areas may contain distinct labor markets within them, 

areas within an MSA may have differing wage levels, 

and there could still be large differences in wage indexes 

between adjoining areas.3 This step produces countylevel wage indexes. As a last step, we smooth differences 

between county-level wage indexes to reach a target level 

of tolerable difference between adjoining counties. 

We do not use the county-level wage data from the census 

directly to create a county-level index for two reasons. 

First, MSAs are constructed based on commuting patterns 

and hence roughly represent a labor market. Therefore, 

we want to preserve the information at that level. Second, 

census wage data have limitations. They have gaps for 

some occupations in sparsely populated counties, are selfreported, and are difficult to use alone; also, they are not 

updated as frequently as the BLS data. The most current 

census data are from 2000. Because of the limitations of 

the census data, we limit their impact on the wage index 

to a 5 percent deviation from the wage index based on 

BLS data.

In addition to the wage data from BLS and the Census 

Bureau, we have also developed data on benefits such 

as health insurance, pensions, and mandatory payroll 

taxes from hospital, SNF, and home health provider cost 

reports submitted to CMS. We included an adjustment for 

benefits because they differ as a percent of wages across 

geographic regions (Dalton et al. 2007, BLS 2006). We 

incorporated those data into our wage index algorithm 

to create a compensation index, which can be compared 

more directly with the CMS hospital wage index because 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 133

the CMS index also includes benefits. We used the same 

cost report benefit data that CMS used in the current wage 

index; the only difference is that we used benefit data from 

hospitals, SNFs, and home health agencies while CMS 

used only hospital data.

There are no perfect definitions of labor market areas, and 

wage and benefit data are also imperfect. This new method 

represents a pragmatic compromise in many respects. 

Recognizing that current market area definitions (MSA 

and statewide rural) can be too large and counties can 

be too small to represent labor market areas, we created 

a hybrid that allows variation by county within a market 

area, but within limits. Because too great a difference 

between adjoining areas can seem arbitrary, we introduced 

county-level indexes and smoothing to reduce differences. 

Because data on benefits at the market level are not 

available from BLS, we introduced data gathered from 

provider cost reports to adjust for differences in benefits 

across market areas, knowing those data have limitations. 

This alternative balances the limitations of some data 

sources with the strengths of others to create an index with 

some desirable properties—less year-to-year volatility, 

smaller differences between adjoining areas, and automatic 

adjustment for occupation mix. These results are described 

in detail in the following section.

Results

In this analysis we use data from BLS, including a 

calculation of each MSA’s and rural rest-of-state’s 

MedPAC wage index. We also add benefit data from 

hospital, SNF, and home health agency cost reports to 

compute a MedPAC compensation index (described in 

the section on additional technical information). The 

MedPAC wage and compensation indexes have a fairly 

high correlation with the index Medicare currently uses 

(correlation coefficient (R2) of about 0.90). Nonetheless, 

there are some systematic differences. Compared with 

Medicare’s hospital wage index, MedPAC’s wage and 

compensation indexes:

• have smaller differences between adjoining geographic 

areas,

• are less volatile from year to year, 

• have lower wage index values in the (currently) 

highest wage index areas and higher values in the 

(currently) lowest wage index areas, 

• explain slightly less of the variation in hospital costs 

(R2 of 0.823 versus 0.836), 

• automatically take into account occupational mix 

rather than requiring additional adjustments, and

• would lessen the burden on hospitals to collect data. 

Table 6-3 (p. 134) summarizes some important points of 

comparison between the current (fiscal year 2007) CMS 

hospital wage index and the two alternatives we studied. 

This analysis of our two alternatives is at the hospital level, 

using the current CMS post-reclassification wage index 

values for each hospital as the reference point. (It does 

not include the Section 508 additions to the wage index 

because those adjustments use additional money (are not 

budget neutral) and expire at the end of fiscal year 2007.) 

We show the MedPAC wage index and the MedPAC 

compensation index. The latter includes an adjustment for 

benefits.

Another way of evaluating the results is to consider how 

each system treats the hospitals that are exceptions under 

the current system. Table 6-4 (p. 135) uses as its reference 

point the basic (prefloor pre-reclassification) wage index 

that CMS calculates and shows the percentage change in 

the wage index from it to the final CMS system and to 

the new MedPAC compensation index. Table 6-4 shows 

that, if the current exceptions were created to meet a need, 

the new system might meet that need better and do so 

automatically without resorting to an exception process:

• The 2,096 hospitals with no exceptions experience no 

change moving to the final CMS wage index and have 

a small increase of 1.7 percent moving to the MedPAC 

index. These hospitals see an increase in part because 

they no longer have to pay for the reclassification of 

other hospitals through a budget-neutrality adjustment.

• The 224 hospitals in counties receiving an 

outcommuting exception (often located in counties 

bordering higher wage index markets) would receive 

a 5.8 percent increase above the pre-reclassification 

wage index compared with the 4.8 percent they 

receive under the current set of CMS exceptions.

• For hospitals with geographic reclassification, which 

arguably are overrewarded under the current system 

and can now receive very large increases (some 

hospitals have a wage index increase of more than 20 

percent), there would still be an increase under the 
134 An alter native method to compute the wage index

new system, but it would be smaller. The adjustment 

would be, on average, similar to the adjustment for the 

previous category. Those 758 hospitals would receive 

an increase of 4.8 percent, which is less than the 8.3 

percent increase under the current system. 

• Finally, hospitals with special exceptions, which 

by definition meet none of the other criteria, would 

receive a very small increase instead of a 7.9 percent 

increase under the current system.

Arguably, the new system would remove the need for 

exceptions by automatically adjusting the market area 

(MSA and statewide rural) index values to remove 

large differences between adjoining areas. It does so by 

calculating county-level index values and then smoothing 

any remaining large differences. The new system would 

automatically target those adjustments to where they are 

most needed. Under the new system, a similar increase 

would result for other providers in the same counties, 

such as SNFs and home health agencies. Currently, other 

providers receive no adjustment when hospitals reclassify 

to another geographic area.

T A B L E

6 –3 Comparison of CMS hospital wage index and two alternatives

Current CMS

wage index 

(without Section 508)

MedPAC 

wage index

MedPAC 

compensation index

Unit of analysis Each hospital’s average

hourly wage

Each occupation’s average

wage in the market

Each occupation’s average

wage in the market

Occupational mix Separate survey used in

an attempt to correct for

occupational mix differences

Fixed weights for each

occupation, equal to that

occupation’s share of

national hospital wages

Fixed weights for each

occupation, equal to that

occupation’s share of

national hospital wages

Employers surveyed Hospitals only All employers of

hospital-type workers

(e.g., include SNF RNs)

All employers of

hospital-type workers

(e.g., include SNF RNs)

Source of wage data Hospital cost report BLS/Census surveys BLS/Census surveys

Source of benefits Reported on hospital

cost reports

None Estimated for all employees

from hospital, SNF, and

home health cost reports

Market definition MSA/statewide rural MSA/statewide rural

and county blend

MSA/statewide rural

and county blend

Lowest index value 0.7368 0.7659 0.7535

Highest index value 1.5617 1.4734 1.5028

Largest difference in index

values between hospitals in

neighboring counties 28% 10%* 10%*

Ability to explain hospital costs

(R2 value)** 0.836 0.819 0.823

Note:	 SNF (skilled nursing facility), BLS (Bureau of Labor Statistics), MSA (metropolitan statistical area). The second column is the 2007 CMS wage index without the

Section 508 reclassifications.

	 *Difference is constrained to be no more than 10 percent by algorithm in MedPAC indexes.

	 **Percentage of the variation in hospital costs explained in a regression using the specified wage index.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 135

Wage index cliffs

If there are large differences between the wage indexes of 

adjoining market areas—for example, between a statewide 

rural area and an adjoining MSA—hospitals near the 

border may object if they receive the lower wage index. In 

reaction to these objections, geographic reclassification 

was instituted so that hospitals that are near other hospitals 

with higher wage indexes can seek a higher wage index. 

(The text box (p. 128) specifies when reclassification 

is allowed.) We refer to large differences in wage index 

values between adjoining geographic areas as wage index 

cliffs.

We compute our alternative wage and compensation 

indexes in three steps. The first step in the alternative 

method is to calculate market area wage indexes at the 

MSA and statewide rural areas using BLS area wage data 

and BLS industry-specific occupational weights. For the 

compensation index, benefit data are also incorporated 

at this step. (The section on additional technical 

information provides details on constructing the alternative 

compensation index, p. 145.) 

To lower wage index cliffs, in the next step, we vary the 

wage index within market areas—that is, within MSAs 

and within the statewide rural areas. We use county-level 

census data to vary the market area wage index and create 

a county-level wage index. As discussed in the section 

on additional technical information, we set limits on the 

extent to which counties within a market area can vary 

from the market wage index. In the current model, we 

restrict each county to be within 5 percent of the MSA or 

statewide wage index. Given that some counties may have 

a wage index up to 5 percent below their MSA’s mean 

wage index and some may have a wage index 5 percent 

above their MSA’s mean wage index, the maximum 

difference in wage indexes between counties in the same 

MSA would be 10 percent.4

The last step further lowers the remaining differences 

between adjoining counties. We call this step smoothing. It 

is accomplished by: 

• comparing all counties with each of their neighbors; 

• finding the greatest difference between each county 

and its neighbors;

• if that difference is greater than an acceptable 

threshold, reducing it to the threshold (10 percent in 

this example) by increasing the lower wage index; and

• revaluing the entire set of wage index values to be 

budget neutral to the original set of wage index values, 

which is necessary because the previous steps would 

have increased some wage index values and not 

reduced any others.

The algorithm is then repeated until no difference greater 

than the specified threshold remains. The section on 

additional technical information discusses the smoothing 

algorithm and limits in more detail.

For example, in the Atlanta MSA, the calculation using 

BLS data yields a market level wage index of 0.99. 

Adjusting for county-level census wage data, we would 

calculate wage indexes for the 28 counties in that MSA 

ranging from 0.93 to 1.01.5 Similarly, within the statewide 

rural area, we would calculate a wage index of 0.88 from 

BLS data with the county-level wage index varying from 

0.83 to 0.93 when we use census data.

The results of these steps are shown in the maps in Figure 

6-1 (p. 136).

T A B L E

6 –4 MedPAC compensation index is 

higher than the current 

pre-reclassification index for many 

hospitals benefitting from exceptions

Percent increase from 

pre-reclassification 

index value to:

Exception 

status

Number 

of 

hospitals

Current 

system 

with 

exceptions

MedPAC 

compensation 

index

No exception 2,096 0.0% 1.7%

Outcommuting only 224 4.8 5.8

Reclassification 758 8.3 4.8

Special exception 18 7.9 0.3

Other hospitals 429 3.9 4.9

Note:	 Some hospitals were eliminated from this table because their reported

pre-reclassification wage index appeared to be in error; therefore, this

table includes only 2,096 rather than 2,135 hospitals without exceptions.

Changes are all positive because the budget-neutrality adjustment in the

current system is made to the base payment amount, not to the wage

index values. However, the MedPAC compensation index is constructed

to be budget neutral to the CMS wage index with exceptions, so values in

the two columns are comparable. Other hospitals include Lugar counties,

Section 401, rural floor, and hold-harmless exceptions. For details, see

text box on p. 128.

Source:	 MedPAC analysis of Bureau of Labor Statistics May 2005 Occupational

Employment Statistics Survey, 2000 census data, and fiscal year 2007

CMS impact file.
136 An alter native method to compute the wage index

For a portion of Georgia, Figure 6-1A shows the CMS 

pre-reclassification wage indexes for the CMS MSA 

and statewide rural market areas. The darkest area is 

the Atlanta MSA, which has the highest CMS wage 

index value (0.98). It borders directly on the Georgia 

statewide rural area, which has an index value of 0.78. 

The difference, or cliff, is 0.20, which a provider in the 

statewide rural area just across the MSA border may 

consider inequitable. 

Figure 6-1B shows the result of our county-level wage 

index calculation and smoothing. Differences now appear 

among counties inside the Atlanta MSA, with those at the 

center having higher wage indexes than those farther out. 

Variation also appears in the statewide rural area, with 

higher values appearing nearer the MSAs. As a result, 

differences among adjoining counties decrease. Very 

light areas do not often adjoin very dark areas, and no 

differences among adjacent counties exceed 10 percent. 

Year-to-year volatility

Large changes in an area’s wage index from one year to 

the next cause concerns among providers. Theoretically, 

it seems unlikely that relative wage rates would change 

substantially from year to year other than to reflect very 

unusual circumstances. 

Volatility in the wage indexes

Wage index values for hospitals showed some large 

changes from 2006 to 2007. Comparing Medicare’s postreclassification hospital wage index values, the median 

absolute change was 1.4 percent, with a 4.0 percent change 

at the 90th percentile and a 5.4 percent change at the 95th 

percentile. The top 1 percent of hospitals experienced 

changes of more than 13 percent. In 100 hospitals 

wage indexes decreased by more than 5 percent. This is 

noteworthy because, assuming a labor share of around 

70 percent, that amount would have more than offset the 

hospital update, which was 3.4 percent for 2007.

The MedPAC compensation index reflecting the same 

time period was slightly less volatile. The median change 

was 1.0 percent, with a 2.4 percent change at the 90th 

percentile, and a 3.9 percent change at the 95th percentile. 

The top 1 percent of hospitals saw changes of more than 

6.0 percent. Only 21 hospitals had decreases of more than 

5 percent. 

MedPAC compensation index reduces wage index differences between adjacent counties

Source:	 MedPAC analysis of the 2005 Bureau of Labor Statistics Occupational Employment Survey and 2000 census data.

Note and Source in InDesign.

XXXNeed TitleXXX FIGURE

6-1

Atlanta MSA

Georgia

6-1A: Current wage index:

Sharp differences between adjoining areas

Wage index: 1.00 to 1.10 0.95 to 1.00 0.90 to 0.95 0.85 to 0.90 0.75 to 0.85

6-1B: MedPAC county-level compensation index:

No sharp differences remain

Georgia

Atlanta MSA

F i g ure

6 –1
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 137

We expect the index using BLS data to be less volatile 

for two reasons. First, the BLS wage data are an average 

of the last three years of wage surveys for the region and 

should be less volatile than the CMS data, which are from 

a single year’s cost reports. (For example, to compute 

the fiscal year 2004 wage index, 2001 BLS data—which 

incorporate data from 2001, 2000, and 1999—would be 

used. In contrast, CMS would use data from fiscal year 

2000 hospital cost reports.) Second, the BLS surveys 

a sample of all employers rather than a single industry. 

Changes in the wages paid by all employers in an area 

should be less volatile than wages in a single industry. 

Averaging three years of CMS data would also reduce 

year-to-year volatility by about the same magnitude as our 

new approach, so the averaging process alone probably 

accounts for most of the improvement. However, averaging 

three years of cost report data would mean using cost 

report data with as much as a six-year lag.

Volatility in the underlying data

Our contractor, RTI, analyzed changes in the underlying 

data over six years and found that, in addition to being 

less volatile over the entire period, the BLS data were less 

volatile for all but one year-to-year change.

RTI also analyzed the underlying hospital cost report data 

and found that benefits were more volatile than wages, 

but total compensation (benefits plus wages) was not. RTI 

also found that benefits as a percentage of total hourly 

compensation have been increasing and they differ by 

region, being lower in the South. Therefore, it is important 

to include benefits when adjusting for labor costs across 

geographic areas (Dalton et al. 2007).

Impact analysis

Next we examine the impact of moving from the CMS 

wage index to the MedPAC compensation index. The 

MedPAC compensation index is highly correlated with the 

CMS index (0.92). This analysis is at the hospital level, 

using the CMS post-reclassification wage index values 

for each hospital. It excludes the Section 508 additions to 

the wage index because those adjustments use additional 

funds (are not budget neutral) and expire at the end of 

fiscal year 2007.

Table 6-5 compares the MedPAC compensation index 

and the CMS post-reclassification wage index by hospital 

group. The wage index for all hospitals as a group would 

increase by 0.5 percent on the basis of an unweighted 

average across hospitals under the alternative wage index 

system. (It is unweighted in the sense that all hospitals 

count equally regardless of their size or payments.) By 

definition, there is no change overall on a dollar-weighted 

basis because the MedPAC compensation index is 

T A B L E

6 –5 Most hospital groups would have 

a slightly higher wage index under 

MedPAC’s compensation index

Mean percent change 

from current 

(post-reclassification) 

index to MedPAC 

compensation index

Hospital group

Number 

of 

hospitals

Inpatient 

payments 

(dollar 

weighted)

Wage 

index value 

(hospital 

weighted)

Total 3,586 0.0% 0.5%

Exception status

No exception 2,135 0.4 1.6

Outcommuting only 227 0.1 1.2

Reclassification 777 –1.3 –2.8

Special exception 18 –4.2 –6.9

Other hospitals 429 0.1 1.1

Rural 1,010 –0.7 0.7

<100 beds 722 0.2 1.6

100+ beds 288 –1.3 –1.6

Urban 2,576 0.1 0.5

<300 beds 1,988 –0.2 0.3

300+ beds 588 0.3 1.0

Teaching status

Major teaching 298 0.2 0.9

Other teaching 786 0.2 0.6

Nonteaching 2,502 –0.2 0.5

Ownership

Not for profit 2,114 0.0 0.5

Proprietary 873 0.0 0.1

Government 596 0.1 1.1

Note:	 Outcommuting only includes Section 505 hospitals. Reclassifications

are geographic reclassifications under the Medicare Geographic

Classification Review Board standard criteria. Special exceptions are as

defined in the text box, p.128. Other hospitals include Lugar counties,

Section 401, rural floor, and hold-harmless exceptions. Post-reclassification

refers to the 2007 CMS wage index with all adjustments except Section

508 reclassifications.

Source:	 MedPAC analysis of Bureau of Labor Statistics May 2005 Occupational

Employment Statistics Survey, 2000 census data, and fiscal year 2007

CMS impact file.
138 An alter native method to compute the wage index

constructed to be budget neutral to the CMS index. The 

change in wage index is often greater than the dollarweighted change because the labor share of the base 

payment is less than 1 (either 0.62 or 0.69). 

As we discussed earlier, the new system would eliminate 

the need for the current exception process. For the 2,135 

hospitals with no exception, both the dollar- and the 

hospital-weighted average wage index values increase. The 

227 hospitals with an outcommuting adjustment to their 

wage index on average would see more of an increase in 

their wage index under the new system. Those hospitals 

now being reclassified would see a decrease of 1.3 

percent dollar weighted and 2.8 percent hospital weighted. 

T A B L E

6 –6 Contribution of different parts of methodology to total 

impact of MedPAC compensation index

Percent change in wage index

Parts of methodology

Hospital group

Number of 

hospitals BLS data

Adjusting 

for benefits

County level 

with smoothing Total

Total 3,586 0.5% –0.2% 0.2% 0.5%

Exception status

No exception 2,135 2.1 –0.4 0.0 1.6

Outcommuting only 227 –0.7 0.5 1.4 1.2

Reclassification 777 –3.6 0.3 0.5 –2.8

Special exception 18 –8.3 1.4 0.0 –6.9

Other hospitals 429 1.0 –0.1 0.2 1.1

Rural 1,010 –0.2 0.3 0.6 0.7

<100 beds 722 0.7 0.2 0.6 1.6

100 + beds 288 –2.6 0.5 0.6 –1.6

Urban 2,576 0.7 –0.4 0.1 0.5

<300 beds 1,988 0.5 –0.4 0.1 0.3

300+ beds 588 1.4 –0.3 0.0 1.0

Census region

New England 147 –2.0 0.3 0.8 –0.9

Mid-Atlantic 429 –1.5 1.2 0.2 –0.2

South Atlantic 610 2.7 –0.8 0.0 2.0

East North Central 520 –0.3 1.0 –0.1 0.7

West North Central 272 0.3 –0.6 0.3 0.1

East South Central 344 3.1 –0.6 0.2 2.7

West South Central 567 1.9 –1.9 0.0 0.0

Mountain 230 –0.8 –1.2 0.6 –1.4

Pacific 467 –2.0 1.0 0.9 –0.2

Note:	 BLS (Bureau of Labor Statistics). All changes are calculated relative to the post-reclassification index. Post-reclassification refers to the 2007 CMS wage index with

all adjustments except Section 508 reclassifications. All entries are hospital weighted not dollar weighted, so average percent change in wage index does not

sum to zero. Outcommuting includes only Section 505 hospitals. Reclassifications are geographic reclassifications under the Medicare Geographic Classification

Review Board standard criteria. Special exceptions are as defined in the text box, p. 128. Other hospitals include Lugar counties, Section 401, rural floor, and

hold-harmless exceptions.

Source: MedPAC analysis of Bureau of Labor Statistics May 2005 Occupational Employment Statistics Survey, 2000 census data, and fiscal year 2007 CMS impact file.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 139

Hospitals granted a special exception would see an even 

larger decrease. 

Urban and rural hospitals would gain about the same 

percentage hospital weighted. However, because some 

large rural hospitals reclassify under special provisions, 

rural hospitals have a 0.7 percent decrease dollar 

weighted. If the MedPAC approach were also used for 

SNF and home health providers, rural SNF and home 

health providers on average would see payment increases. 

Aggregating across all sectors, total rural payments would 

increase slightly.

Most categories of hospitals would see fairly small 

percentage changes in their wage index as a group, 

although some individual hospitals would see large 

percentage changes. Some hospitals that currently receive 

large benefits from reclassification could experience a 

significant decline in their wage index, and some hospitals 

in counties next to high-wage-index areas—but that have 

not been able to reclassify—will see significant increases 

because of county-level data and smoothing.

Table 6-6 shows the effect of each step in the new system 

relative to the current CMS hospital wage index. For each 

hospital group, we look first at the change resulting from 

using BLS wage data, next at the effect of adding benefits 

to our calculation, and then at the effect of county-level 

wage indexes and smoothing. 

For example, using BLS data increases the average wage 

index of hospitals with no exception by 2.1 percent, 

adding benefits reduces that increase by 0.4 percent, and 

moving to a county-level index with smoothing adds a 

small amount. Using the BLS data noticeably reduces the 

wage index for hospitals that reclassify and receive special 

exceptions. The effect of adding benefits to the calculation 

is most noticeable regionally. The wage indexes in the 

Mid-Atlantic, East North Central, and Pacific census 

regions increase by 1 percent or more, and those in the 

West South Central and Mountain regions decrease by 

more than 1 percent. The direction of this effect accords 

well with differences BLS reported in all-employer 

benefits across census regions (BLS 2006). The countylevel data and smoothing step increase the wage index 

for hospitals now receiving an outcommuting exception, 

which makes sense because they are in counties adjacent 

to a market area with a higher wage index. Otherwise, 

this step does not have a large systematic effect on these 

hospital groups. 

Even though the change in wage index is budget neutral, 

slightly more hospitals would see their wage index go 

up rather than down, because small rural hospitals tend 

to benefit from the MedPAC index. Figure 6-2 (p. 140) 

shows the distribution and magnitude of these changes. 

For example, almost all hospitals in North Dakota would 

see their wage index values increase and become similar 

to the South Dakota values. In the CMS system, there are 

only seven rural hospitals in North Dakota because wage 

data from critical access hospitals (CAHs) are not included 

in the calculation. (Sixteen states have 10 or fewer IPPS 

hospitals in their statewide rural area.) Our new approach 

uses data from all employers so CAHs are included as well 

as all other employers with workers in the occupations 

considered. This addresses a concern of IPPS hospitals in 

areas where their competitors are principally CAHs. Those 

hospitals argue that they are competing with CAHs for 

employees, yet the CAH wages are not in the wage index 

for the area. If the CAHs offer higher wages, the IPPS 

hospitals think they are at a disadvantage.

Figure 6-3 (p. 141) shows the changes in inpatient 

payments moving to the new MedPAC compensation 

index from the current post-reclassification hospital 

index. There are fewer large changes in payments because 

the wage index adjusts less than 70 percent of the total 

payment. (In other words, the labor share is about 0.7 for 

areas with wage indexes above 1 and 0.62 for areas with 

wage indexes below 1.)

One would find a similar result for outpatient payments. 

However, the result would not be exactly the same because 

the labor share in the outpatient PPS is lower (0.6) and 

because of other differences in the payment systems.

Nonetheless, some hospitals would see a large change 

in their payments. Therefore, a transition period may be 

warranted; abrupt, large changes could be avoided by 

phasing in the change for providers with a large change 

in their wage index value. One option is to scale changes 

in the wage index to the update in a way that considers 

the joint effect of the update and the change in the wage 

index. Other options include phasing in large changes 

proportionally over three or four years and specifying 

a maximum permissible change per year. The MedPAC 

compensation indexes for each county, computed with 

the data available in January 2008, are available on the 

MedPAC website at www.medpac.gov.
140 An alter native method to compute the wage index

Explaining inpatient hospital costs

We used a regression model to evaluate the degree to 

which Medicare payment variables—including a wage 

index—can explain variation in hospital costs per 

discharge. The question was whether Medicare payments 

would more closely match hospital costs if we switched 

from the current Medicare wage index to our alternative 

index. Our regression results show that the current wage 

index explains slightly more of the variation in hospital 

costs than the MedPAC compensation index. The R2 value 

is 0.836 using the CMS post-reclassification index in our 

model and 0.823 for the MedPAC index. The CMS wage 

index may be more closely related to hospital costs than 

the alternative because of the circularity of the present 

system. If hospitals report high labor costs, their wage 

index increases either directly or because they are allowed 

to reclassify. In addition, if a hospital is reclassified into 

a higher wage index area, it may spend the additional 

income it receives. To the degree that this is true, any 

existing wage index will be biased toward fitting hospital 

costs better than new alternative wage indexes. 

Wage index differences across sectors

Medicare uses average hospital wages as reported on 

Medicare cost reports for hospitals to determine the wage 

indexes used in the PPSs for many of the provider types 

in Medicare (e.g., long-term care hospitals, inpatient 

rehabilitation facilities, SNFs, home health agencies, 

hospices, dialysis facilities). This assumes that relative 

wages for hospital workers are representative of relative 

wages for all other types of providers and that labor market 

areas are the same for all provider types. Using only IPPS 

hospital data means that almost half of the counties in the 

country do not have data for the wage index calculation. 

For example, a home health agency in Martha’s Vineyard 

recently objected because it was assigned last year’s rural 

wage index in Massachusetts, even though there were no 

rural hospitals to base it on and the old value was for a 

distant hospital.

Slightly more hospitals would see an increase than a decrease in

 their wage index value under the MedPAC compensation index

Note:	 Percent change in wage index value from current post-reclassification hospital wage index (not including Section 508 reclassifications) to MedPAC compensation index.

Source:	 MedPAC analysis of Bureau of Labor Statistics May 2005 Occupational Employment Statistics Survey, 2000 census data, and fiscal year 2007 CMS impact file.

More hospitals would see an increase than a decrease in their wage index value

under the MedPAC compensation index

FIGURE

6-2

Note and Source in InDesign Number of hospitals

1,200

600

800

1,000

400

200

0

<–10 –10 to –5 –5 to –2 –2 to 2 2 to 5 5 to 10 10+

Percent change in wage index from current to MedPAC compensation index

F i g ure

6 –2
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 141

Constructing sector-specific compensation 

indexes

With the alternative method, provider sector-specific 

indexes can be constructed using the same multiprovider 

data and varying the occupational weights. For example, 

the proportion of RNs used by home health agencies 

nationwide would be used to compute the weight for RNs 

in the home health agency index. Those sector-specific 

weights would then be multiplied by the occupationspecific wages for each area (used for all sectors) to 

compute the home health agency average wage for each 

area. This average would then be compared with the 

product of the weights and national average wages for 

these occupations to create a compensation index value.

We have constructed compensation indexes for hospitals, 

nursing facilities, and home health agencies. We used BLS 

national-level data to determine the share of occupations 

represented in each industry. Table 6-7 (p. 142) shows 

the share of wages the top 10 occupations represent in 

each sector. RNs account for about 37 percent of wages 

in hospitals, followed by office workers with 10 percent. 

Health care support workers account for about 32 percent 

of wages in nursing homes, followed by LPNs with about 

16 percent. RNs account for about 26 percent of wages 

in home health agencies, followed by health care support 

workers with about 21 percent. 

As an example, we created a nursing facilities index 

using our technique and the occupational weights for 

nursing facilities and compared it with the current prereclassification hospital wage index, which is used for 

SNF payment. (SNFs do not receive any exceptions to 

the wage index, even when hospitals near them do.) We 

found that SNFs seeing increases over the current wage 

index tended to be in counties adjacent to MSAs with 

higher wage indexes. On average, payments for SNFs in 

Changes in hospital payments are smaller than changes in wage index values

Note:	 Percent change in Medicare inpatient payments is from current post-reclassification hospital wage index (not including Section 508 reclassifications) to MedPAC

wage index.

Source:	 MedPAC analysis of Bureau of Labor Statistics May 2005 Occupational Employment Statistics Survey, 2000 census data, fiscal year 2007 CMS impact file, and

MedPAC payment model simulations.

Changes in payments are smaller than changes in wage index values FIGURE

6-3

Note and Source in InDesign Number of hospitals

2,000

500

1,000

1,500

0

<–10 –10 to –5 –5 to –2 –2 to 2 2 to 5 5 to 10 10+

Expected percent change in inpatient payments from current to MedPAC wage index

F i g ure

6 –3
142 An alter native method to compute the wage index

rural areas increased by 1.6 percent, with 1,961 SNFs 

gaining and 1,551 SNFs losing. In urban areas, the average 

decrease was 0.3 percent, with 4,250 SNFs gaining and 

4,330 SNFs losing. 

Using the same technique to construct compensation 

indexes for each sector, while accounting for the 

differences in occupational weights, we find that the 

compensation indexes for all three sectors are highly 

correlated. The correlation between the hospital and 

the home health agency indexes is 0.96, the correlation 

between the nursing facility and the hospital indexes is 

0.94, and the correlation between the nursing facility and 

the home health agency indexes is 0.97. 

One argument for using separate compensation indexes 

for each sector despite the high correlation is that the 

administrative burden of developing unique sector indexes 

would be fairly low. All compensation indexes use the 

same raw BLS and census data and the same benefit 

information from provider cost reports.6 Differences 

in sector compensation indexes would result only from 

differing occupational weights, the level of benefits, and 

the subsequent adjustments to market area values in the 

county refinements and smoothing. Providers would 

experience no additional burden. 

On the other hand, we cannot be sure that a “nursing 

facility” or “home health” compensation index would 

be a better compensation index for Medicare skilled 

nursing facilities or Medicare home health services than 

the hospital compensation index. One problem with 

the nursing facility index is that these facilities have 

two distinct products. One is long-term care, often for 

Medicaid recipients. The second is post-acute care of 

Medicare beneficiaries. We have data on the wages of 

workers in nursing facilities that provide both services 

combined. However, the mix of workers serving Medicare 

patients may be more likely to be therapists, RNs, and 

LPNs, while the mix of people serving the long-term 

residents of the nursing facility will be weighted more 

toward nursing aides and other lower wage workers. The 

compensation index we calculated for nursing facilities 

is already highly correlated with the compensation index 

calculated for hospitals (0.94). Because the mix of workers 

providing post-acute care to Medicare beneficiaries is 

often more highly paid than the average nursing facility 

employee, a true SNF compensation index for Medicare 

services would probably be even more highly correlated 

T A B L E

6 –7 Top 10 occupation categories by sector, share of wages

Occupation Hospitals Nursing facilities Home health agencies

Registered nurses 36.8% 13.8% 25.9%

Office and administrative support occupations 10.0 4.5 7.6

Health care support occupations 7.3 32.4 21.1

Management occupations 6.4 6.5 7.8

Licensed practical and licensed vocational nurses 2.8 15.7 8.1

Radiologic technologists and technicians 2.4

Medical and clinical laboratory technologists 2.2

Pharmacists 2.1 0.6

Building and grounds cleaning and maintenance occupations 1.8 4.8

Respiratory therapists 1.6

Food preparation and serving related occupations 7.8

Personal care and service occupations 2.2 12.9

Physical therapists 1.4 4.8

Occupational therapists 1.9 1.6

Speech–language pathologists 0.8

Total of top 10 occupations 73.3 90.0 91.1

Source:	 MedPAC analysis of Bureau of Labor Statistics May 2005 Occupational Employment Statistics Survey.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 143

with the hospital index than the nursing facility index we 

computed. 

A similar argument holds for home health agencies. The 

compensation index calculated for home health agencies 

already has a 0.96 correlation with the hospital index. 

If a Medicare-specific occupation mix for home health 

agencies were defined, with more therapy and less personal 

aide services, the correlation would be even higher. 

Using one compensation index for 

all sectors

Given the high correlations between the compensation 

indexes and the imperfect occupational mix data for 

Medicare nursing facility services and Medicare home 

health services, one compensation index may be roughly 

as accurate as three compensation indexes. One index 

would not mean that average wages in hospitals, SNFs, 

and home health agencies are the same in a geographic 

area. Rather, it would mean that relative wages among 

geographic areas are similar for the three types of 

providers. For example, the ratio of SNF wages in county 

A to SNF wages in county B would be similar to the ratio 

of hospital wages in the same counties, although hospital 

wages might be higher than SNF wages in each county.

One index may seem more equitable as well. All providers 

in the same county would have the same compensation 

index; no one could reclassify out. The compensation 

index would also be based on all-employer wage data 

and all-provider benefit data. Thus, even if the hospital 

occupational weights were used, it would no longer be a 

hospital-only compensation index as is the current one. 

This might make it more acceptable to other providers.

If SNFs and home health agencies were paid based on 

the MedPAC compensation index, rural SNF payments 

would increase on average by roughly 2.4 percent and 

urban SNF payments would decline on average by 0.5 

percent. There would be roughly 2,412 rural SNFs with 

increasing payments, 1,100 rural SNFs with decreasing 

payments, 4,223 urban SNFs with increasing payments, 

and 4,353 urban SNFs with decreasing payments if the 

MedPAC compensation index were used for all providers. 

Because home health payments are based on the location 

of the beneficiary, and not the location of the agency, we 

cannot easily categorize home health agencies as rural or 

urban. However, we can examine how payments change 

for the care of rural and urban beneficiaries. Payments 

to home health agencies for care for rural beneficiaries 

would increase on average by about 2.6 percent; in urban 

counties, they would decrease on average by about 0.6 

percent. In general, the rural and urban impacts using the 

MedPAC compensation index for all providers are similar 

to the impacts we showed earlier using sector-specific 

compensation indexes for SNFs and home health agencies.

Caveats

We have demonstrated that it is possible to construct 

compensation indexes for each provider sector 

from available BLS and census data. The resulting 

compensation index has several advantages over the 

current hospital wage index. However, our compensation 

index requires deciding how to handle missing data, how 

much variation to allow between counties in a market 

area, and what limit to choose when smoothing between 

adjacent geographic areas. Making different decisions on 

these points would result in different index values. The 

sensitivity of the compensation index to these decisions 

could be further investigated by CMS—but our analysis 

indicates that the results appear fairly robust in the 

variations we have investigated. 

Data limitations should also be recognized. BLS data 

are gathered in surveys of employers but do not include 

data on self-employed persons. An occupation with 

many self-employed people would be underrepresented 

in the national and local data. However, unless wages 

for the self-employed differ significantly from wages for 

employees in the same occupation, and the propensity for 

self-employment varies significantly by region, the effect 

on relative wages for that occupation would be minimal. 

Such a situation might happen if there were shortages of 

workers who tend to be self-employed in some markets 

and not others. (However, to noticeably affect the 

compensation index values, these occupations would also 

have to have a significant weight within the index.) 

The BLS data also refer only to wages, not to wages and 

benefits. Because the ratio of wages to benefits differs 

across markets, we introduced an adjustment for benefits 

to address this limitation. The adjustment uses the benefit 

data in hospital, SNF, and home health agency cost reports 

submitted to CMS and shares the limitations of those data. 

CMS would have to audit worksheet A of the cost reports 

to ensure that providers report all their benefit expenses 

on worksheet A on the benefit line of the cost report in 

accordance with Medicare accounting rules.
144 An alter native method to compute the wage index

The wage data we used for county-specific adjustments are 

from the 2000 decennial census. However, the age might 

not be a major limitation because we use the census data 

only to adjust (within a 10 percent corridor) the underlying 

BLS data, which are gathered semiannually. In addition, 

the American Community Survey is replacing the longform census data and should provide more timely data in 

the future, particularly for more populous areas. Finally, 

the census data do not include wages for some occupations 

in some geographic areas because not enough people are 

in the sample. We use two techniques to correct for this 

problem. For the initial computation from BLS data, we 

compare those occupations for which we have data in an 

area with the same occupations at the national level, leaving 

out the local and national values for those occupations for 

which data are missing.7 The magnitude of this problem 

is modest; we have data on occupations representing more 

than 95 percent of all wages in all markets. In the next step 

of the computation, we use county-level wage data from 

the census. If data for certain occupations are missing, we 

make the county wage equal to the market area (MSA or 

statewide rural) wage for that occupation. Other methods 

could be used or less-detailed occupational breakouts could 

be made to overcome this limitation.

Conclusion

There are no perfect definitions of labor market areas, 

and wage and benefit data are also imperfect. Our new 

method represents a pragmatic compromise in many 

respects. Recognizing that current market area definitions 

(MSA and statewide rural) can be too large and counties 

can be too small to represent labor market areas, we 

created a hybrid that allows variation by county within 

a market area, but within limits. Because too great a 

difference between adjoining areas can seem arbitrary, we 

introduced county-level indexes and smoothing to reduce 

differences. Because data on benefits at the market level 

are not available from BLS, we introduced data gathered 

from provider cost reports to adjust for differences in 

benefits across market areas, aware that those data have 

their limitations and that this method would require CMS 

to make some additional calculations. This alternative 

balances the limitations of some data sources with the 

strengths of others to create an index that represents a 

major improvement over the current wage index system. 

The MedPAC index approach:

• more fully reflects true labor input costs in the market 

by using occupational-level data that represent all 

employers and reduce circularity,

• automatically captures occupational mix without any 

burden on providers or CMS,

• reduces year-to-year volatility and wage index cliffs, 

and

• eliminates the need for exceptions.

Some providers in other sectors think the system is 

inequitable if the wage index they are assigned is less than 

that assigned to a nearby hospital, because the hospital is 

able to reclassify and they are not. At the same time, there 

are providers in parts of the country without any nearby 

IPPS hospitals. Providing a compensation index based 

on information from a sample of all employers in every 

area of the country and creating adjustments within the 

compensation index that obviate the need for exceptions 

solve both of these problems.

R ec o mmenda t i o n 6 A 

The Congress should repeal the existing hospital wage 

index statute, including reclassifications and exceptions, 

and give the Secretary authority to establish new wage 

index systems.

R a t i o nale 6 A 

The current law is prescriptive; CMS must use hospitalspecific wage data and updates based on a survey 

of hospital costs (see text box, pp. 151–152, for the 

hospital wage index statute). The Secretary cannot 

make the changes to the wage index we have discussed 

administratively; the recommended change to the statute 

will give the Secretary that power. This expanded authority 

would include the ability to implement the new wage 

index and to refine it as necessary in the future under the 

normal notice and comment rule-making process.

I m p L I ca t i o n s 6 A

Spending

• The change is budget neutral.

Beneficiary and provider

• Granting this authority has no impact on providers or 

beneficiaries.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 145

R ec o mmenda t i o n 6 B

The Secretary should establish a hospital compensation 

index that:

• uses wage data from all employers and industryspecific occupational weights,

• is adjusted for geographic differences in the ratio of 

benefits to wages,

• is adjusted at the county level and smooths large 

differences between counties, and 

• is implemented so that large changes in wage index 

values are phased in over a transition period.

R a t i o nale 6 B 

The current wage index calculation produces large 

differences between neighboring areas, which are 

modified through a complex exceptions process, but that 

process in turn creates new inequities. The new approach 

results in smaller differences between areas and thus 

lessens the need for exceptions. To protect providers 

from abrupt, large changes in their wage index value, we 

recommend a transition period. One option is to scale 

changes in the wage index to the update in a way that takes 

into account the joint effects of the update and the change 

in the wage index. Other options include phasing in 

large changes proportionally over three or four years and 

specifying a maximum permissible change per year.

The compensation index should be used for both hospital 

inpatient and hospital outpatient PPSs.

I m p lica t i o n s 6 B

Spending

• The change is budget neutral.

Beneficiary and provider

• Redistributes payments and has no impact on 

beneficiaries.

A wage index calculated as described will more fully 

reflect input prices, automatically adjust for occupational 

mix, reduce circularity, and reduce large differences 

between adjoining areas compared with the current 

system. It will also reduce the administrative burden on 

providers.

R ec o mmenda t i o n 6 C

The Secretary should use the hospital compensation index 

described in recommendation 6B for the home health 

and skilled nursing facility prospective payment systems 

and evaluate its use in the other Medicare fee-for-service 

prospective payment systems.

R a t i o nale 6 C 

All providers in these sectors in the same county will 

have the same wage index because no reclassification 

will be allowed; this would be more consistent across 

providers. Separate indexes would add complexity without 

necessarily improving accuracy.

I m p lica t i o n s 6 C

Spending

• The change is budget neutral.

Beneficiary and provider

• Redistributes payments and has no impact on 

beneficiaries.

We did not evaluate use of the calculated index for 

long-term care hospitals, inpatient psychiatric facilities, 

inpatient rehabilitation facilities, facilities that treat 

patients with end-stage renal disease, ambulatory surgical 

centers, and hospices, but CMS should do so. 

This recommendation would not be used for the physician 

fee schedule. 

Additional technical information on 

constructing a compensation index from 

BLS data

We constructed wage indexes and compensation indexes 

using three sources of data: the BLS Occupational 

Employment Statistics (OES) survey, the 2000 decennial 

census data, and data from provider cost reports submitted 

to CMS. 

We start with data from the BLS OES survey, which is 

published each May. For each MSA, state, and the nation, 

BLS staff estimate hourly wages by occupation across all 

employers in the geographic area. At the national level, 

they also provide for each industry an estimate of the 

share of employment in that industry for each occupation. 

They construct the estimates from a sample of 1.2 million 
146 An alter native method to compute the wage index

establishments drawn over three years. Hourly wages in 

the OES survey do not include benefits, and the sample 

does not include self-employed workers. 

At the national level, the share of each of the top 10 

occupations employed in hospitals, nursing homes, and 

home health agencies is shown in Table 6-8. The analysis 

does not include occupations that typically bill Part B for 

their services, such as physicians.

The occupation accounting for the greatest share of 

workers differs by industry. For example, RNs are the most 

common in hospitals, and health care support occupations 

are most common in nursing homes and home health 

agencies. However, there is significant overlap in the 

occupations all three industries employ, which implies 

that they compete for those types of workers. Nonmedical 

workers—for example, office workers—account for a 

large share of hospital payrolls. Presumably, hospitals 

compete with many industries to hire those workers.

To construct the occupational weights used in our hospital 

analysis, we limited our fixed-weight index to the 30 

occupations shown in Table 6-9, which differs from 

Table 6-8 in that the occupations are weighted by the 

share of wages in the industry rather than by the share 

of employees. Higher paid occupations will have a wage 

share higher than their employment share. Further, we 

express the wage share as the percentage of wages that 

occupation accounts for relative to the total wage share 

these 30 occupations represent. For example, RNs have 

higher than average wages and account for 43.22 percent 

of the wages in the hospital industry represented by these 

30 occupations. However, they represent only 28.1 percent 

of hospital employees. Conversely, health care support 

occupations have a wage share of 8.54 percent and an 

employment share of 12.9 percent because their wages are 

lower than the average hospital wage.

Computing relative compensation for each 

MSA and statewide rural wage area

In our first step, we compute compensation index values 

for each market area—the MSA (or divisions of MSAs) 

and the balance of state areas, which are the non-MSA 

counties in the state. We start by finding the relative wage 

for each occupation in each MSA. The relative wage 

for an occupation is the ratio of the mean wage for that 

occupation in the MSA to the mean wage for the same 

occupation nationally. The wages are for all employers 

of the occupation (as stated previously, this reduces the 

circularity of the wage index). In each market, the relative 

wages are then multiplied by the wage share weights for 

the set of 30 occupations shown in Table 6-9. The result 

T A B L E

6 –8 Top 10 occupation categories by sector, share of employees

Occupation Hospitals Nursing facilities Home health agencies

Registered nurses 28.1% 7.6% 14.5%

Office and administrative support occupations 15.5 4.5 8.0

Health care support occupations 12.9 42.3 33.0

Building and grounds cleaning and maintenance occupations 3.8 6.9 0.4

Licensed practical and licensed vocational nurses 3.6 11.8 6.5

Management occupations 3.4 2.8 3.1

Food preparation and serving related occupations 2.9 11.2

Radiologic technologists and technicians 2.3

Medical and clinical laboratory technologists 2.0

Respiratory therapists 1.5

Medical records and health information technicians 0.8 0.5

Physical therapists 0.6 2.0

Occupational therapists 0.7

Personal care and service occupations 2.8 25.0

Total of top 10 occupations 75.7 91.4 93.8

Source:	 MedPAC analysis of Bureau of Labor Statistics May 2005 Occupational Employment Statistics Survey.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 147

is the compensation index for the market (wages only, no 

benefits).

BLS computed the MSA and balance of state (statewide 

rural in CMS parlance) wage indexes for us. In some 

areas, the BLS data do not have a value for every 

occupation. In those cases, we asked BLS to compare 

those occupations for which they had data in an area with 

the same occupations at the national level to compute 

the wage index. This is equivalent to the assumption that 

the missing occupations have the same relative wages in 

that area as the occupations with data. In every market, 

BLS has wages for occupations representing more than 

95 percent of hospital payrolls; hence, these missing data 

have little effect on the computed wage indexes. BLS 

does not report some occupation-specific data in markets 

where one dominant employer could be identified. By 

computing the index value and thus combining data for all 

the occupations, BLS was able to include those data and 

preserve confidentiality. 

BLS uses New England city and town areas (NECTAs) 

rather than MSAs in some New England areas. Some 

argue that NECTAs better represent labor markets than 

MSAs or counties in New England. In these cases, we 

use the NECTA as the market and attempt to map it to a 

county. In some cases, counties and NECTAs do not match 

exactly, and we had to assign a county to a particular 

NECTA or an average of two NECTAs that are both in the 

county. If our recommendations are implemented, CMS 

could consider the option of using smoothed NECTAs 

rather than counties. 

Source of benefit data 

The hospital, SNF, and home health cost reports provide 

data on total wages and total benefit costs for each facility. 

The benefits (wage-related costs) and wages are currently 

reported in two places on cost reports: on worksheet S-3 

(which is used for the wage index) and on worksheet A. 

Wage-related costs include the employers’ share of the 

Federal Insurance Contributions Act (FICA) tax, Medicare 

taxes, unemployment insurance, health insurance, employer 

401k contributions, pension costs for defined benefit 

plans, and other smaller categories of wage-related costs. 

Because benefits include payroll taxes (FICA, Medicare, 

unemployment), we know hospitals should be reporting 

benefit costs that are more than 7 percent of wages. Wagerelated costs are reported on line 5 column 2 of worksheet 

A. Total wages are also reported on worksheet A on line 

101. We computed benefits as a share of wages using 

worksheet A, excluding outliers (greater than 35 percent 

or less than 15 percent). When the worksheet A data were 

outliers, we used worksheet S-3. To eliminate the need for 

hospitals to file worksheet S-3 in the future, CMS should 

require that all hospitals state all their benefits’ costs on 

worksheet A. In most cases, worksheet A data are exactly 

or approximately equal to worksheet S-3 data. In some 

cases, they may differ because hospitals use generally 

T A B L E

6 –9 Share of hospital 

wages by occupation

Key hospital occupations

Share of 

wages

Registered nurses 43.22%

Office and administrative support occupations 11.73

Health care support occupations 8.54

Management occupations 7.45

Licensed practical and licensed vocational nurses 3.27

Radiologic technologists and technicians 2.83

Medical and clinical laboratory technologists 2.54

Pharmacists 2.48

Building and grounds cleaning

and maintenance occupations 2.14

Respiratory therapists 1.88

Food preparation and serving related occupations 1.65

Physical therapists 1.55

Medical and clinical laboratory technicians 1.18

Surgical technologists 1.13

Medical records and health information technicians 0.99

Diagnostic medical sonographers 0.76

Occupational therapists 0.74

Pharmacy technicians 0.73

Cardiovascular technologists and technicians 0.72

Health technologists and technicians, all other 0.72

Emergency medical technicians and paramedics 0.61

Protective service occupations 0.58

Dietitians and nutritionists 0.43

Nuclear medicine technologists 0.41

Respiratory therapy technicians 0.41

Radiation therapists 0.35

Speech–language pathologists 0.34

Personal care and service occupations 0.28

Dietetic technicians 0.17

Psychiatric technicians 0.17

Note:	 Share of wages as percent of share represented by these 30 occupations.

Source:	 MedPAC analysis of Bureau of Labor Statistics May 2005 Occupational

Employment Statistics Survey.
148 An alter native method to compute the wage index

accepted accounting principles for worksheet S-3 and 

Medicare accounting for worksheet A. However, Office 

of Inspector General (OIG) auditors informed us that 

they believe the data should match and that hospitals are 

required to follow Medicare accounting even on worksheet 

S-3. OIG reported cases in which this difference of 

opinion about accounting standards had a material impact 

on reported benefit expense (OIG 2007). Clarifying the 

reporting rules should resolve this problem. We did not 

obtain CAH benefit information for this analysis; however, 

in the future CMS may want to include it. 

Computation of area benefit-to-wage ratios

The 2005 BLS survey data are based on surveys of 

establishments in 2003, 2004, and 2005. Therefore, to 

match the data as closely as possible with benefits, we 

compute the mean level of benefits over the same three 

years. We first create three-year averages of the benefit-towage ratios for hospitals, SNFs, and home health agencies 

in each market area. Because BLS wage data come from 

all employers, we create a weighted average of benefits 

to wages for each occupation in the region based on the 

national share of employment in that occupation across 

hospitals, SNFs, and home health agencies. We then create 

a weighted average benefit-to-wage ratio for the type of 

workers hospitals employ, the type of workers home health 

agencies employ, and the type of workers SNFs employ by 

multiplying the estimated benefit-to-wage ratio for each 

occupation by the national wage share of that occupation in 

each industry. The result is that every MSA and statewide 

rural area has its own benefit share for hospital-type 

workers, home-health-type workers, and SNF-type workers. 

On average, across all markets, hospital-type workers have a 

benefit-to-wage ratio of 24 percent, SNF-type workers have 

a ratio of 22 percent, and home-health-type employees have 

a ratio of 23 percent. Benefits tend to be slightly higher in 

high-wage areas and slightly lower in the South.8

Computation of the benefit-adjusted 

compensation index

The MSA-level compensation index is computed as follows. 

We start with the national occupation weights from BLS, 

national mean hourly wages by occupation for the 30 

occupations we examine, and an MSA’s hourly wages by 

occupation. The data for two occupations are shown in 

Table 6-10. 

For simplification, assume that data were available only 

for these two occupations (in reality BLS provided us with 

data on occupations representing more than 95 percent 

of wages in every market). Also assume that the ratio of 

benefits to wages was 27 percent in the market shown 

compared with a national average of 24 percent. The 

MSA-level benefits’ adjusted wage index (before budgetneutrality adjustments) would then be equal to:

Wage index without benefits

= [(7.45% × 57.12/39.36) + (43.22% × 33.72/27.80)]/

(7.45% + 43.22%) 

= 0.6325/0.5066 

= 1.2485 

Compensation index with benefits

= (wage index without benefits) × (1 + 0.27)/(1 + 0.24) 

= 1.2787 

The value of 1.2787 would not be the final value for 

the MSA-level compensation index. It is adjusted for 

budget neutrality to make the total payments provided to 

all hospitals under the current wage index (without the 

Section 508 adjustment) equal total payments under the 

new compensation index. Total payments are computed 

with an inpatient payment model that takes into account 

hospital-specific factors such as indirect medical 

T A B L E

6 –10 Applying BLS data to hospital industry at a national and MSA level, 2005

Hospital industry national

Occupation code Job title

Mean hourly 

wage

Share of 

wages

Mean hourly wage 

for MSA

11–1000 Manager $39.36 7.45% $57.12

29–1111 Registered nurse 27.80 43.22 33.72

Note:	 BLS (Bureau of Labor Statistics), MSA (metropolitan statistical area). Data shown are for MSA code 35644 (NY, NY).

Source:	 MedPAC analysis of Bureau of Labor Statistics May 2005 Occupational Employment Statistics Survey.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 149

education payments, disproportionate share payments, 

sole community hospital status, outlier payments, and 

Medicare-dependent hospital status. Instead of modeling 

outpatient effects for budget neutrality, we assumed that 

outpatient shifts would be proportional to inpatient shifts.

Creating county-specific compensation 

indexes

As a second step, we used data from the 2000 census to 

adjust the wages within market areas by county. For each 

county, the Census Bureau provided data on wages by 

occupation and place of employment. The key occupations 

were RNs (census occupation 313), LPNs and licensed 

vocational nurses (census occupation 350), management 

(census occupations 001 to 043), and office and 

administrative support (census occupations 500 to 593). 

All the occupations in Table 6-9 (p. 147) were matched 

to census categories. We then aggregated county-level 

employment and wages from census data to create data for 

MSAs and statewide rural areas. 

Next we screened and cleaned the county-level data. For 

all occupations except RNs, if there were 30 or fewer 

observations for an occupation in a county we replaced 

the county data with the census MSA or statewide rural 

average wage for that occupation. For RNs, we required 

that 50 respondents reported working in the county in the 

2000 census. We set a higher threshold for RNs because 

of their high weighting in the compensation index. After 

replacing data in counties where the sample size was 

small, we screened for outliers. The purpose of the sample 

size and outlier screens was to acknowledge that the 

census data are imperfect (as are CMS and BLS data) and 

we did not want one or two errant responses to distort a 

county’s compensation index. Because of this replacement, 

the county-level adjustment has a significant effect only 

for counties with a significant number of health care 

workers. Counties with few health care workers will be 

assigned the market-area wage level for most occupations. 

The county-level wages are then weighted based on the 

weights in Table 6-9 to create a weighted average wage for 

each county and for each MSA or statewide rural area.

Next, the ratio of the county-level weighted average 

wage to the market-level (i.e., MSA, statewide rural area) 

weighted average wage was computed. For example, if the 

weighted county wage was 110 percent of the average for 

the counties in the MSA, the ratio would be 1.1. We then 

took the compensation index for that market area computed 

from BLS data and adjusted it by the county wage ratio 

computed above. This is the county-specific portion of 

the compensation index. To compute the compensation 

index for a county, we weighted the county-specific wage 

by 50 percent and the original market-level compensation 

index by 50 percent. (If some of the county-level data were 

replaced with MSA-level data as part of our screening 

of the county-level data, then the MSA-level data have a 

weight of more than 50 percent.) For example, if the census 

indicates that county A has wages 10 percent above the 

average for the market area (after replacing missing data), 

we elevated that county’s compensation index 5 percent 

above the compensation index for the market area. 

We are implicitly saying that MSA-wide conditions affect 

the wages a hospital has to pay its workers, but countyspecific conditions also affect those wages. We used a 

weighting of 50 percent at the MSA level and 50 percent at 

the county level, although other weightings could be used. 

In computing county-specific compensation indexes, we 

limited the total adjustment to a maximum of 5 percent 

above or below the market-area value. An example is that 

Manhattan wages will affect overall wage patterns in the 

MSA, but counties on the fringe of the New York MSA 

may be able to pay a slightly lower wage than Manhattan 

hospitals because their workers have lower commuting 

costs. The difference between Manhattan and the county 

with the lowest compensation index in the MSA could 

be up to 10 percent, with the lowest wage index county 

being up to 5 percent below the mean, and the Manhattan 

compensation index being 5 percent above the mean. One 

may have to pay workers up to a 10 percent premium ($20 

per day for a worker at $25 per hour) to commute from the 

lower wage counties of an MSA to the core of the city.

The compensation index for a high-wage county in an 

MSA would be computed as follows:

County-to-MSA ratio 

= (census-weighted county wage)/

(census-weighted MSA wage) 

= 1.1

County-specific portion of compensation index

= (BLS index) × (county-to-MSA ratio) 

= BLS index × 1.1

Blended county/MSA compensation index 

= 0.5 (BLS compensation index) +

0.5 (county-specific compensation index) 

= (0.5 × BLS index) + (0.55 × BLS index) 

= 1.05 times the BLS compensation index for the MSA.
150 An alter native method to compute the wage index

Smoothing

After blending the BLS and census data, the third step 

in our calculation is to smooth the county-level blended 

compensation index values to eliminate large differences 

between adjoining counties. We created a data set of 

county pairs. The data set pairs each county with each 

county that adjoins it. The difference in compensation 

indexes for each county pair is then computed and the 

pair with the greatest difference for each county is 

chosen. If that difference is greater than 10 percent of 

the larger compensation index, the county with the lower 

compensation index value is assigned a compensation 

index equal to 90 percent of its highest neighbor. This 

process is followed for each county pair, resulting in a 

new set of compensation indexes. The same algorithm is 

repeated with the new set of compensation indexes until 

no difference greater than 10 percent remains. Because 

compensation indexes are only increased in this process, 

the entire set of compensation indexes must be revalued to 

keep it budget neutral with the original set. 

If we had selected a smoothing threshold other than 10 

percent, results would differ and the number of iterations 

required to satisfy the condition could differ as well. We 

chose 10 percent to illustrate the mechanism and because 

differences of that magnitude between neighbors might be 

tolerable to providers while still accounting for regional 

differences. In addition, a 10 percent differential is in 

the ballpark of what a hospital would have to pay if it 

were recruiting workers from neighboring counties. For 

example, a 10 percent difference for a worker making the 

national mean RN wage of $25.00 an hour would be $2.50 

per hour, or $20.00 for an eight-hour day. A lower or a 

higher bound could be chosen. However a lower bound, 

such as 5 percent, would cause the smoothing effect to 

ripple out long distances, especially in California due to 

the large size of counties. A smoothing bound significantly 

larger than 10 percent may become large enough to give 

the hospital in the higher wage county an opportunity to 

recruit workers from the lower wage county by offering 

them a wage differential that exceeds the financial and 

time costs of commuting. 

The end result of smoothing and limits on differences 

within MSAs is that the compensation indexes for any 

provider will always be at least 90 percent of its neighboring 

provider’s compensation index and 90 percent of the highest 

compensation index in its MSA.

Adjusting for budget neutrality

After each of the three steps, we adjusted the 

compensation index for budget neutrality. To do this, we 

excluded Maryland hospitals because they are not paid 

under the IPPS system. We estimated inpatient payments 

with our hospital payment simulation model and altered 

compensation index values until the estimated payments 

with the new compensation index differed by less than 

0.1 percent from simulated payments using the CMS 

compensation index (without Section 508 adjustments). 

The payment model takes into account hospital-specific 

factors such as indirect medical education payments, 

disproportionate share payments, sole community 

hospital status, outlier payments, and Medicare-dependent 

hospital status. Instead of modeling outpatient effects for 

budget neutrality, we assumed outpatient shifts would be 

proportional to inpatient shifts.

Limitations

One limitation is that the BLS survey is voluntary. When 

data are missing, BLS imputes data for the missing 

provider. The end result is that BLS provided us with 

data representing occupations that receive at least 95 

percent of hospital payrolls in every MSA, NECTA, 

and statewide rural area. There is a concern that some 

providers would not respond to the survey if it were used 

for payment purposes. However, the incentive to do this 

will be mitigated by the fact that BLS imputes the wages 

for nonresponders. The provider would not know if the 

imputed value would be slightly above or slightly below 

actual wages. To check for accuracy of survey responses, 

BLS uses data screens and can cross-check the OES data 

with other sources of employment and payroll data. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 151

Mandate from the Tax Relief and Health Care Act of 2006

and wage index statute

SEC. 106. HOSPITAL MEDICARE REPORTS 

AND CLARIFICATIONS.

(b) REVISION OF THE MEDICARE WAGE 

INDEX

CLASSIFICATION SYSTEM.—

(1) MEDPAC REPORT.—

IN GENERAL.—The Medicare Payment 

Advisory Commission shall submit to Congress, 

by not later than June 30, 2007, a report on 

its study of the wage index classification 

system applied under Medicare prospective 

payment systems, including under section 

1886(d)(3)(E)10 of the Social Security Act 

(42 U.S.C.1395ww(d)(3)(E)). Such report 

shall include any alternatives the Commission 

recommends to the method to compute the wage 

index under such section.

(2) PROPOSAL TO REVISE THE HOSPITAL 

WAGE INDEX CLASSIFICATION SYSTEM.— 

The Secretary of Health and Human Services, 

taking into account the recommendations described 

in the report under paragraph (1), shall include 

in the proposed rule published under section 

1886(e)(5)(A) of the Social Security Act (42 U.S.C. 

1395ww(e)(5)(A)) for fiscal year 2009 one or more 

proposals to revise the wage index adjustment 

applied under section 1886(d)(3)(E) of such Act 

(42 U.S.C. 1395ww(d)(3)(E)) for purposes of the 

Medicare prospective payment system for inpatient 

hospital services. Such proposal (or proposals) 

shall consider each of the following:

(A) Problems associated with the definition of 

labor markets for purposes of such wage index 

adjustment.

(B) The modification or elimination of 

geographic reclassifications and other 

adjustments.

(C) The use of Bureau of Labor Statistics data, or 

other data or methodologies, to calculate relative 

wages for each geographic area involved.

(D) Minimizing variations in wage index 

adjustments between and within Metropolitan 

Statistical Areas and Statewide rural areas.

(E) The feasibility of applying all components 

of the proposal to other settings, including home 

health agencies and skilled nursing facilities.

(F) Methods to minimize the volatility of wage 

index adjustments, while maintaining the 

principle of budget neutrality in applying such 

adjustments.

(G) The effect that the implementation of the 

proposal would have on health care providers and 

on each region of the country.

(H) Methods for implementing the proposal, 

including methods to phase-in such 

implementation.

(I) Issues relating to occupational mix, such as 

staffing practices and any evidence on the effect 

on quality of care and patient safety and any 

recommendations for alternative calculations.

(continued next page)
152 An alter native method to compute the wage index

Mandate from the Tax Relief and Health Care Act of 2006

and wage index statute (cont.)

42 U.S.C. § 1395ww. Payments to hospitals for 

inpatient hospital services ww(d)(3)(E)

(E) Adjusting for different area wage levels.— 

(i) In general.— Except as provided 

in clause (ii), the Secretary shall adjust the 

proportion (as estimated by the Secretary from 

time to time) of hospitals’ costs which are 

attributable to wages and wage-related costs, of 

the DRG prospective payment rates computed 

under subparagraph (D) for area differences in 

hospital wage levels by a factor (established by 

the Secretary) reflecting the relative hospital 

wage level in the geographic area of the hospital 

compared to the national average hospital 

wage level. Not later than October 1, 1990, and 

October 1, 1993 (and at least every 12 months 

thereafter), the Secretary shall update the factor 

under the preceding sentence on the basis of a 

survey conducted by the Secretary (and updated 

as appropriate) of the wages and wage-related 

costs of subsection (d) hospitals in the United 

States. Not less often than once every 3 years 

the Secretary (through such survey or otherwise) 

shall measure the earnings and paid hours of 

employment by occupational category and shall 

exclude data with respect to the wages and 

wage-related costs incurred in furnishing skilled 

nursing facility services. Any adjustments or 

updates made under this subparagraph for a fiscal 

year (beginning with fiscal year 1991) shall be 

made in a manner that assures that the aggregate 

payments under this subsection in the fiscal year 

are not greater or less than those that would have 

been made in the year without such adjustment. 

The Secretary shall apply the previous sentence 

for any period as if the amendments made by 

section 403(a)(1) of the Medicare Prescription 

Drug, Improvement, and Modernization Act of 

2003 had not been enacted. 

(ii) Alternative proportion to be adjusted 

beginning in fiscal year 2005.— For discharges 

occurring on or after October 1, 2004, the 

Secretary shall substitute “62 percent” for the 

proportion described in the first sentence of 

clause (i), unless the application of this clause 

would result in lower payments to a hospital than 

would otherwise be made. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 153

1 To be specific, CMS defines as market areas MSAs and 

metropolitan divisions within those MSAs. The Office 

of Management and Budget defines 370 MSAs and 29 

metropolitan divisions within 11 of those MSAs (OMB 2003). 

CMS also defines 47 statewide rural areas. Three states have 

no counties defined as rural. In some cases where MSAs 

contain parts of several states, CMS defines separate wage 

indexes for each state, which yields 397 urban market areas. 

Altogether CMS has 444 pre-reclassification market areas. 

In this analysis we do not include Puerto Rico and its eight 

MSAs, but the same methodology could be used for Puerto 

Rico. We use the 362 MSAs in the United States and the 47 

statewide rural areas. There are 3,142 counties in the United 

States; 1,090 of them in the 362 MSAs, leaving 2,052 in the 

statewide rural areas (OMB 2003).

2 The wage data are from the BLS Occupational Employment 

Statistics (OES) survey, which is published each May. For 

each MSA, state, and the nation they provide an estimate 

of hourly wages by occupation across all employers in the 

geographic area. At the national level, they also provide for 

each industry an estimate of the share of employment in that 

industry for each occupation. The estimates are constructed 

from a sample of 1.2 million establishments drawn over 

three years. The data are carefully collected, edited, and 

verified. Employment benchmarks for the survey are derived 

from employment data tabulated from the reports of the 

unemployment insurance program, and the sample is designed 

to yield reliable occupation employment estimates by industry. 

Nonsampling errors are addressed through quality control 

tools. BLS reduces errors through reviewing and editing and, 

if necessary, through contact with respondents whose data 

are internally inconsistent or appear to involve errors (BLS 

1997). States supply data on the number of employees and 

total wages of each employer, which BLS can use to verify the 

reasonableness of provider responses. Hourly wages on the 

OES survey do not include benefits and the sample does not 

include self-employed workers. 

3 In some states, such as California, the counties may be so 

large that they contain distinct labor markets. It might be 

possible to aggregate census data at a subcounty level but 

we did not ask the Census Bureau to do so. The large county 

size also means that smoothing across county boundaries 

can extend the effects of a high wage index area many miles. 

This limitation informs the choice of a tolerance level for 

smoothing; if the allowed difference is too small, many areas’ 

wage indexes could be increased by the existence of one high 

wage index MSA.

4 Limiting a county’s wage index to within 5 percent of the 

MSA’s (or statewide rural area’s) wage index generally results 

in a maximum difference of 10 percent among hospitals in the 

same market area. In the case of an MSA with a wage index 

of 1.0 and the national mean hospital RN wage of $27.80 

per hour, the highest wage index for a county in the MSA 

would be 1.05 and the lowest would be 0.95. The implied 

RN wage in the county with the highest wage in the MSA 

would be $29.19 (1.05 × $27.80), and implied RN wage in the 

county with the lowest wage would be $26.41. The maximum 

differential would be $2.78 an hour or $22.24 a day. This can 

be thought of as the cost, in terms of time and transportation 

cost, of commuting from the far reaches of an MSA to the 

central core. Because this is less than one hour’s wage, it 

seems to be a fairly conservative assumption and does not 

allow for exaggerated differences to arise between counties 

in an MSA or statewide rural area. While we believe the 10 

percent differential is reasonable, an 8 percent or 12 percent 

maximum differential among counties in the same MSA may 

also be reasonable.

5 The county-level wages average slightly less than the MSAlevel wage due to the cost of smoothing. Smoothing, which 

raises some hospitals’ wage indexes, is paid for with a slight 

budget-neutrality adjustment (less than 0.5 percent) applied to 

all hospitals. 

6 The benefit adjustment varies by occupation to reflect the 

proportion of workers in that occupation employed in each 

sector. Thus, using the same data, in each market area, the 

benefit adjustment will automatically differ from sector to 

sector, reflecting the differing mix of workers employed 

in each sector. See the section on additional technical 

information for details (p. 145).

7 This technique is mathematically equivalent to estimating 

the area wages for the missing occupations to be the national 

wage for that occupation times the estimated wage index for 

that area.

8 Our estimates of hospital benefits relative to hospital wages 

had a national mean almost exactly equal to the national ratio 

of hospital benefits to hospital wages that BLS reported. We 

had to use cost report benefit data because the industry-level 

BLS benefit data are available only on a national basis and not 

on a market-by-market basis. 

Endnotes
154 An alter native method to compute the wage index

Bureau of Labor Statistics, Department of Labor. 2006. News: 

Employer costs for employee compensation. September. USDL 

06–2069. Washington, DC: BLS. 

Bureau of Labor Statistics, Department of Labor. 1997. BLS 

handbook of methods. Washington, DC: BLS. 

Centers for Medicare & Medicaid Services, Department of Health 

and Human Services. 2007. Proposed changes to the hospital 

inpatient prospective payment systems and fiscal year 2008 rates. 

Proposed rule. Federal Register 72, no. 85: 24786–24787.

Centers for Medicare & Medicaid Services, Department of Health 

and Human Services. 2004. Medicare program; changes to the 

hospital inpatient prospective payment systems and fiscal year 

2005 rates. Final rule. Federal Register 69, no. 154: 49104–49105.

Dalton, K., G. Pope, W. Adamache, et al. 2007. Potential 

refinements to Medicare’s wage indexes for hospitals and other 

sectors. RTI report for MedPAC. 

Government Accountability Office. 2005. Medicare physician 

fees: Geographic adjustment indices are valid in design but data 

and methods need refinement. GAO–05–119. Washington, DC: 

GAO. 

Office of Inspector General, Department of Health and Human 

Services. 2007. Review of hospital wage index data used to 

calculate inpatient prospective payment system wage indexes. 

A–01–05–00504. Washington, DC: OIG. 

Office of Management and Budget. 2003. Revised definitions 

of metropolitan statistical areas, new definitions of micropolitan 

statistical areas and combined statistical areas, and guidance on 

uses of the statistical definitions of these areas. OMB Bulletin no. 

03–04, June 6.

Pope, Gregory. 1989. Occupational adjustment of the prospective 

payment system wage index. Health Care Financing Review 11, 

no. 1 (Fall): 49–61. 

Zuckerman, Stephen, W. P. Welch, and G. Pope. 1990. A 

geographic index of physician practice costs. Journal of Health 

Economics 9: 39–69.

References
Issues in Medicare 

coverage of drugs

C h a p t er 7
R E C O M M E N D A T I O N S

7A The Congress should direct CMS to identify selected overlap drugs and direct plans to 

always cover them under Part D. Identified drugs should be:

• low cost

• covered under Part D most of the time.

COMMISSIONER VOTES: YES 15 • NO 0 • NOT VOTING 0 • ABSENT 2

 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7B The Congress should allow plans to cover a transitional supply of overlap drugs under Part 

D under the same conditions as the general transition policy applied by CMS.

COMMISSIONER VOTES: YES 15 • NO 0 • NOT VOTING 0 • ABSENT 2

 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7C The Congress should permit coverage for appropriate preventive vaccines under Medicare 

Part B instead of Part D.

COMMISSIONER VOTES: YES 15 • NO 0 • NOT VOTING 0 • ABSENT 2
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 157

Issues in Medicare 

coverage of drugs

7

Chapter summary

Medicare’s Part D prescription drug benefit is built on the delivery 

system used by the commercial market. Pharmacy benefit managers 

process claims and design formulary systems. Most outpatient drugs 

are provided through retail or mail-order pharmacies. Claims are 

adjudicated in real time. When drugs are provided in settings or under 

conditions that do not fit this model, patients, physicians, plans, and 

pharmacies can experience difficulties navigating the system. In this 

chapter, we explore two such situations: overlapping coverage of drugs 

under Part B and Part D, and delivery of Part D benefits to Medicare 

beneficiaries who reside in long-term care facilities.

Overlapping coverage between Part B and Part D drugs

In most cases, stakeholders know whether Part B or Part D covers 

specific drugs. In some cases, however, they need additional 

information. For example, an immunosuppressive drug is covered under 

Part B only if it follows a Medicare-covered organ transplant; otherwise, 

it comes under Part D. Since a drug plan must determine whether a drug 

should be covered under Part B before it can approve a claim, plans 

In this chapter

• Overlapping coverage 

between Part B and Part D 

drugs

• Delivering Part D benefits to 

residents of long-term care 

facilities

• Directions for future 

research

C H A PT E R 
158 Issues in Medicare coverage of drugs

often require prior authorization before the pharmacist can dispense the 

drug. In other words, the plan will not approve the claim until it has collected 

additional information. 

We offer recommendations to address three issues with overlap drugs. First, 

plans and pharmacists agree that drugs that can be prescribed for many 

indications pose a problem. To mitigate this problem, the Commission 

recommends that the Congress change the law to allow CMS to identify lowcost drugs that are sometimes covered under Part B but are covered under 

Part D more than 90 percent of the time and direct plans always to cover 

them under Part D. 

A second issue we identified in our research is permitting plans to cover 

a transitional supply of drugs under Part D. Interviewees report that, until 

a plan determines whether a drug is covered under Part B or Part D, it is 

not allowed to provide emergency supplies to beneficiaries under Part D. 

Some pharmacists do provide emergency supplies but must absorb the 

cost if coverage is denied and the beneficiary cannot pay out of pocket. We 

recommend that the Congress authorize prescription drug plans (PDPs) to 

approve transition supplies while coverage is being determined.

Recommendation 7A The Congress should direct CMS to identify selected overlap drugs and direct plans to 

always cover them under Part D. Identified drugs should be:

• low cost

COMMISSIONER VOTES: • covered under Part D most of the time.

YES 15 • NO 0 • NOT VOTING 0 • ABSENT 2

Recommendation 7B The Congress should allow plans to cover a transitional supply of overlap drugs under 

Part D under the same conditions as the general transition policy applied by CMS.

COMMISSIONER VOTES: 

YES 15 • NO 0 • NOT VOTING 0 • ABSENT 2
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 159

A third issue concerns vaccines administered by physicians. Physicians 

and public health agencies are concerned that new preventive vaccines are 

covered under Part D instead of Part B. Since physicians have no direct 

billing relationship with drug plans, patients might have to pay directly for 

vaccines and seek repayment from their drug plan. Interviewees reported that 

the high out-of-pocket cost of new vaccines under an indemnity model might 

discourage beneficiaries from seeking recommended preventive care.

Delivering Part D benefits to residents of long-term care facilities

When policymakers created Part D, they gave most attention to how 

competing private plans would work for beneficiaries who fill prescriptions at 

retail pharmacies. However, about 5 percent of Medicare beneficiaries reside 

in long-term care facilities, and more than half of them are dually eligible for 

Medicare and Medicaid. Residents in nursing facilities (NFs) are typically 

sicker and frailer than Medicare beneficiaries in the community: They take, on 

average, 6 to 10 prescription drugs per day compared with 2 to 4 among the 

noninstitutionalized. They do not fill prescriptions at retail pharmacies.

A different system is used to dispense drugs to residents of NFs, and Part 

D has affected that system. Previously, long-term care pharmacies (LTCPs) 

interacted most frequently with one payer—a state Medicaid program. Under 

Part D, LTCPs must negotiate with numerous plan sponsors over payments 

for services delivered to NF residents. LTCPs consider some Part D plans 

“friendlier” than others—for example, covering drugs that NF residents 

currently use and requiring prior authorization less frequently. Yet tensions 

have grown between some Part D plans and LTCPs over pharmacies’ desire 

for timely dispensing and plans’ desire to determine whether prescriptions 

are covered and appropriate before paying for them. Also, CMS is concerned 

The Congress should permit coverage for appropriate preventive vaccines under 

Medicare Part B instead of Part D.

Recommendation 7C

COMMISSIONER VOTES: 

YES 15 • NO 0 • NOT VOTING 0 • ABSENT 2
160 Issues in Medicare coverage of drugs

that the separate rebates LTCPs receive directly from drug manufacturers 

could interfere with the formularies Part D plans use and could raise 

program costs.

No empirical analyses based on drug and medical claims evaluate the effects 

of Part D on NF residents. The Commission intends to monitor this issue 

and will look at data as they become available. Policymakers may want 

to evaluate various approaches for providing Part D benefits in long-term 

care settings. Although the Commission does not make recommendations 

on these issues, this chapter looks at three alternatives as an initial step in 

exploring potential options:

• continuing with multiple Part D plans, but adding requirements to 

report data on the quality and appropriateness of drugs dispensed to 

residents of NFs;

• holding periodic competitions to select a single PDP for all residents of 

NFs within the same geographic region; and

• reimbursing LTCPs directly for delivering Part D benefits to residents.

Under all three approaches, Medicare could require the entity delivering Part 

D benefits to bear insurance risk for the drug spending of its enrollees in the 

same way that it does today. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 161

Since 2006, Medicare beneficiaries have had the option 

of receiving outpatient drug benefits through standalone prescription drug plans (PDPs) or Medicare 

Advantage–Prescription Drug (MA–PD) plans that offer 

drug benefits under Medicare Part D. Before the program 

started, policymakers were concerned that few private 

organizations would be willing to offer stand-alone drug 

coverage. Another uncertainty was whether Medicare 

beneficiaries would enroll in the voluntary program. 

However, many drug plans and beneficiaries now 

participate in the program. In 2007, plan sponsors offered 

1,866 PDPs, about 30 percent more than the previous year. 

According to the most recent figures from CMS, about 24 

million beneficiaries are enrolled in PDPs or MA–PDs, 

or about 56 percent of all Medicare beneficiaries. Our 

March 2007 report provides an update on enrollment, plan 

participation, and benefit design.

In this chapter, we explore two particular issues we 

identified in the Part D program. Medicare’s Part D 

prescription drug benefit is built on the delivery system 

used by the commercial market. Pharmacy benefit 

managers process claims and design formulary systems. 

Most outpatient drugs are provided through retail or mailorder pharmacies. Claims are adjudicated in real time. 

When drugs are provided in settings or under conditions 

that do not fit this model, patients, physicians, plans, and 

pharmacies can experience difficulties navigating the 

system. Two such situations include: 

• overlapping coverage of drugs under Part B and Part 

D, and

• delivery of Part D benefits to beneficiaries who reside 

in long-term care facilities.

Overlapping coverage between Part B 

and Part D drugs 

Before 2006, Medicare covered few outpatient drugs. 

Those that were covered under Part B include drugs 

administered by physicians, drugs used with durable 

medical equipment (DME), and drugs specifically named 

in statute. Since 2006, Medicare beneficiaries have been 

able to obtain coverage for most other types of outpatient 

drugs through Part D stand-alone PDPs or MA–PDs. 

Some drugs are covered under both Part B and Part D. For 

example, immunosuppressive drugs are covered under Part 

B for beneficiaries who received Medicare-covered organ 

transplants, and they are covered under Part D if Medicare 

did not cover the transplant.

In the course of research for our mandated report to the 

Congress on the effect of Medicare payment changes for 

Part B drugs (MedPAC 2007a), interviewees reported 

instances when the overlap in drug coverage under Part 

B and Part D increased the administrative burden for 

physicians, pharmacists, and health plans and delayed 

beneficiary access to needed medications. To further 

examine the issue of overlap drugs, a research team from 

NORC and Georgetown University conducted structured 

interviews with drug plans, pharmacists, beneficiary 

advocates, and trade associations. Staff also met with 

representatives from CMS and other government agencies. 

We further discussed Part B/Part D overlap issues with 

physicians, beneficiary advocates, and other stakeholders.

In most cases, stakeholders know whether drugs are 

covered under Part B or Part D. In general, Medicare 

covers drugs that must be administered by physicians 

under Part B and drugs that are purchased at pharmacies 

through Part D. In some cases, however, pharmacists 

and PDPs are unable to determine which program 

covers a particular drug without additional information. 

Stakeholders estimate that about 6,000 products (unique 

national drug code (NDC) numbers) potentially could be 

covered under either Part B or Part D (PCMA/NACDS 

2006). Interviewees told us that coverage overlaps can 

result in delays for patients and increased costs. Since 

PDPs must determine whether a drug should be covered 

under Part B before they can approve a claim, plans often 

require prior authorization, meaning that physicians must 

provide additional information before the pharmacist can 

dispense the drug. Interviewees report that this process 

takes time and can delay beneficiaries getting their drugs.

In addition, some interviewees are concerned about 

coordination of coverage when drugs are covered under 

Part D but necessary supplies or clinical support are 

covered under Part B, Medicaid, or not at all. For example, 

home infusion specialists note that Part D does not 

cover the pharmacy and nursing services, supplies, and 

equipment needed to administer home infusion therapies. 

Beneficiaries may be unable to receive their medication 

because of lack of coordination among coverage sources.

Interviewees described how they manage these situations. 

In some cases, they also discussed possible solutions. 

Many interviewees believed that particular products were 

best suited to one type of coverage. 
162 Issues in Medicare coverage of drugs

Overlapping drug coverage

The Congress gradually has expanded the type of drugs 

eligible for Part B coverage. For example, as some older 

chemotherapy drugs became available in oral form, 

the Congress decided to cover oral chemotherapy and 

antiemetic drugs that are exact replacements for covered 

infusible drugs under Part B. When beneficiaries take 

these medicines orally, Medicare does not have to pay for 

drug administration services and beneficiaries spend less 

time undergoing infusion sessions. The Congress also 

extended coverage to some vaccines, immunosuppressive 

drugs used after a Medicare-covered organ transplant, 

blood products, and drugs used with DME. Retail and 

mail-order pharmacies dispense some of these drugs (e.g., 

immunosuppressive drugs), although physicians continue 

to provide most Part B drugs. 

With the addition of Part D, Medicare beneficiaries 

now have access to coverage for most outpatient drugs 

pharmacies dispense. As in the private sector, plans 

adjudicate claims in real time. Pharmacists know instantly 

whether a drug is covered, requires prior authorization, or 

is off the plan’s formulary. 

Most drugs are clearly covered under one or the other 

program, but in some instances pharmacists need 

additional information to determine which program covers 

a particular drug. In this section, we explore how drug 

coverage depends on:

• patient diagnosis

• timing of treatment

• use of DME

• where the drug is dispensed

Coverage depends on patient diagnosis

The Food and Drug Administration (FDA) approves many 

drugs for multiple indications and patients may find that 

their drug coverage depends on the condition for which 

they are being treated. In 2005, CMS advised plans to 

use prior authorization processes to determine whether an 

overlap drug should be covered under Part B or Part D. 

Although prior authorization processes differ among plans, 

prescribing physicians generally have to contact the plan 

to explain why a drug is prescribed. With that information, 

plans determine whether the drug meets the criteria for 

coverage.1 Examples include:

• Physicians use immunosuppressive drugs to treat 

many conditions. If physicians prescribe them after 

a Medicare-covered organ transplant, the drugs are 

covered under Part B. The same drugs are covered 

under Part D for all other indications.

• When physicians prescribe oral antiemetics in 

conjunction with chemotherapy, they are covered 

under Part B; for all other indications, they are covered 

under Part D.

• Parenteral nutrition is covered under Part B only 

for beneficiaries with permanent dysfunction of the 

digestive tract.

• Erythropoietin, if dispensed at a pharmacy, is covered 

under Part B only for patients undergoing dialysis.

Coverage depends on timing

Patients may also find that drug coverage depends on 

when they had a particular medical procedure or treatment 

that requires additional medication. This issue is similar to 

the one described above but applies to drugs covered for 

the same indication. 

For example: 

• Immunosuppressive drugs prescribed after a 

Medicare-covered transplant are covered under Part 

B; for individuals who had a transplant before they 

were covered by Medicare, the drugs are covered 

under Part D.

• Most oral antiemetics dispensed within 48 hours 

of chemotherapy are covered under Part B; after 

that time, they are covered under Part D even if 

they still are being used to treat nausea caused by 

chemotherapy.

To determine coverage in these cases, plans must know 

both the patient’s diagnosis and the timing of treatment.

Coverage depends on use of durable medical 

equipment

Drugs may be covered under Part B if they are 

administered to beneficiaries in their homes through 

covered DME. PDPs cover the same drugs if beneficiaries 

take the medication using other devices that are not 

included in the DME benefit. For example, pharmacies 

would process an inhalation drug administered through 

a nebulizer or an intravenous drug administered with an 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 163

infusion pump under Part B. PDPs would cover the same 

drugs administered in an alternative fashion under Part D. 

Coverage depends on setting

Interviewees report that drug coverage can depend 

on where beneficiaries live or where their drugs are 

dispensed. In written guidance, CMS noted that Part B 

coverage for drugs administered through DME is limited 

to beneficiaries living in their homes (CMS 2005). They 

explain that most long-term care facilities do not meet 

the statutory definition of “home,” so these drugs must be 

covered through Part D.2

Medicare covers most physician-administered drugs such 

as those used for chemotherapy under Part B. Physicians 

purchase these drugs and bill the carriers.3 However, 

long-term care pharmacies (LTCPs) (discussed in greater 

depth in the second part of this chapter) typically provide 

injectable and infusible drugs to patients in long-term 

care facilities. Facility personnel then administer these 

drugs under medical direction. CMS determined that by 

definition a pharmacy cannot provide a drug “incident to” 

a physician’s service. Thus, Part D covers these drugs in 

long-term care settings. 

In further guidance, CMS determined that any drug 

dispensed at a retail pharmacy cannot be considered 

incident to a physician service and should be eligible 

for coverage by PDPs (CMS 2006a). As a result, some 

physicians have begun requiring patients to purchase 

drugs at a pharmacy (in such cases their PDP pays for 

them under Part D) and bring them to the office for 

administration in a process called “brown-bagging.” Some 

physicians say this allows them to provide drugs to their 

patients without assuming the financial risk of buying 

them or the administrative burden of acquiring them 

through the competitive acquisition program. 

Interviewees did not describe the practice as widespread. 

A number of pharmacists reported that they knew about 

the practice and asked physicians to order the drugs 

in advance because they did not routinely stock these 

medications. They also tried to arrange for patients to 

pick up their medication on the way to their physician’s 

office so that the drug would not be improperly stored. No 

plan representative reported brown-bagging as a problem, 

although plans may not have any way of knowing whether 

patients are going to self-administer a drug or take it to 

their doctor’s office.

While some physicians were experimenting with the 

practice, others raised concerns about the use of brownbagging. Doctors did not want to put patients in charge of 

maintaining the proper storage environment for drugs. In 

addition, many pharmacies do not regularly stock these 

drugs, and waiting for them to acquire the drug could 

create problems with a patient’s treatment schedule. 

With Part D claims data, analysts will be able to measure 

how widespread the practice of brown-bagging is. The 

Commission will monitor this issue to determine whether 

brown-bagging affects spending and quality of care.

Managing overlap drugs

Interviewees told us that sorting out who to bill for 

drugs that could be covered under either program took 

considerable time and resources at the beginning of 

2006. Since plans are legally prohibited from covering 

drugs under Part D that are eligible for Part B coverage, 

many plans require prior authorization to determine 

coverage for all overlap drugs. Plan procedures vary 

but, in general, they require physicians to provide 

information on why the drug is being prescribed. Then the 

plan determines whether the drug meets the criteria for 

coverage. Interviewees reported that this practice resulted 

in significant delays and many patients had to make more 

than one trip to the pharmacy before they could receive 

their medications. In addition to the added burden placed 

on pharmacists, plans also had to devote considerable 

resources to staffing prior authorization requests. If plans 

pay for a drug under Part D that should be covered by 

Part B, they could be in legal jeopardy. For example, they 

would be counting their payments for drugs that should 

be covered under Part B as part of beneficiary out-ofpocket spending for purposes of calculating risk corridors 

and reinsurance. Auditors could interpret this as a false 

claim. Physicians also had to spend time and resources 

responding to requests for information. 

Stakeholders told us that the process of determining 

whether a drug is covered under Part B or Part D has 

improved. CMS made some decisions early in 2006 

that allowed plans to determine program coverage more 

quickly. The agency determined that plans could accept 

physician diagnosis codes on prescriptions as sufficient 

information to determine which program should cover 

a drug. As noted above, CMS also decided that no 

drug dispensed at a pharmacy could be classified as a 

physician-administered drug. Thus, plans could assume 

that such drugs were covered under Part D. In addition, 

plans have developed various strategies to streamline 

some decisions on overlap drugs, including the use of 
164 Issues in Medicare coverage of drugs

information systems that track what patients are being 

treated for and whether they live in long-term care 

facilities. 

These administrative changes and plan program edits 

eased many of the problems, but significant issues remain. 

Interviewees reported that the most common continuing 

problem is determining correct coverage for drugs 

prescribed for many medical conditions. When they are 

unsure which program covers a drug, pharmacists have 

difficulty coordinating with Part B and Part D. A PDP may 

produce an online denial of a claim, while some medical 

carriers require a paper denial from a plan to process the 

claim under Part B. 

In addition, drug claims are processed quite differently 

under Part B and Part D. Pharmacists told us that 

claim adjudication under Part D is simpler for them. 

Determination is generally made instantly. Under Part B, 

they dispense a drug, submit a claim, and wait to see if it 

will be covered. Carriers may take several weeks to make a 

determination. If the claim is denied, the pharmacist must 

then submit the claim to the beneficiary’s PDP. During this 

period, the beneficiary may not be receiving the drug. 

Prior authorization

When a drug is placed on a prior authorization list, 

pharmacists cannot dispense it until the PDP receives 

information that shows the drug meets the criteria for Part 

D coverage. Many plans use information collected from 

an initial prescription for a drug on prior authorization 

to automate the process for refills. If a PDP learns that a 

patient is taking immunosuppressive drugs because of a 

Medicare-covered transplant, the plan knows that future 

immunosuppressive prescriptions are covered under Part 

B. Plans include codes in their information systems to 

track whether a beneficiary is living in a long-term care 

facility. If that is the case, plans know that physicianadministered drugs for that patient are covered under Part 

D. However, stakeholders told us that the use and accuracy 

of codes denoting that a patient lives in a long-term care 

facility vary considerably among plans.

In general, pharmacists report that plans have different 

prior authorization requirements for Part B overlap drugs. 

Plans are most likely to ask physicians for diagnosis 

information. Some plans also request information on 

the indications for the drugs, a faxed statement from the 

physician, or proof of denial from Part B. Some plans 

allow pharmacists to ask physicians about their patient’s 

diagnosis, while others require that physicians complete 

written authorization forms. 

If a physician writes the needed information on the 

prescription, plans can provide immediate authorization.4

However, few physicians do this. Physicians who prescribe 

a large volume of drugs covered by both Part B and Part D 

(e.g., rheumatologists) are most likely to include diagnosis 

on the prescription, but other doctors are less likely to do 

so. In addition, many physicians are reluctant to include 

diagnosis on prescriptions because of concern about 

patient privacy. 

Some researchers believe that including diagnosis codes 

on all prescriptions would provide valuable information in 

examining treatment outcomes. In the future, plans might 

develop information systems that include medication, 

dosage, and diagnosis for each claim. Researchers could 

use the resulting database to inform studies of evidencebased medicine.

Trade groups representing pharmacy benefit managers and 

chain drugstores issued a white paper recommending that 

CMS eliminate the need for prior authorization of low-cost 

overlap drugs (PCMA/NACDS 2006). They suggest that 

CMS identify low-cost drugs that are covered under Part 

D more than 90 percent of the time but could be covered 

under Part B and allow plans always to cover them under 

Part D. Interviewees most often mentioned prednisone 

and methotrexate, two inexpensive generic drugs that 

are frequently prescribed for many indications. Part B 

covers these drugs when physicians prescribe them after 

a Medicare-covered transplant. PDPs cover them for all 

other indications. IMS Health estimates that PDPs cover 

the drugs 98 percent of the time. 

Both pharmacists and plan representatives repeatedly 

told us that placing very inexpensive drugs on prior 

authorization delayed beneficiary access and increased 

costs for them. They noted that the cost of determining 

whether the drug should be covered by Part D may be 

higher than the cost of covering the drug. In interviews, 

some plan representatives said that they had already 

instructed pharmacists to cover these drugs without prior 

authorization but are concerned about future audits. If an 

audit determined that a plan had paid for a drug under 

Part D that should have been covered by Part B, Medicare 

could consider the payment a false claim and the plan 

could be in legal jeopardy. If the Congress gave CMS the 

authority, the agency could draft a regulation that lists 

drugs that should always be covered under Part D.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 165

R ec o mmenda t i o n 7 A

The Congress should direct CMS to identify selected 

overlap drugs and direct plans to always cover them 

under Part D. Identified drugs should be:

• low cost

• covered under Part D most of the time.

R a t i o nale 7 A

Prior authorizations placed on inexpensive drugs that are 

nearly always covered by Part D may delay beneficiary 

access, increase costs for plans and pharmacists, and 

increase the administrative burden on physicians.

I m p lica t i o n s 7 A

Spending

• None

Beneficiary and provider

• This recommendation would improve beneficiary 

access and reduce provider costs and administrative 

burden.

Transition supplies

According to law, plans may not provide emergency 

supplies to beneficiaries while the plan decides whether 

coverage should be under Part D or Part B. If a patient’s 

drug requires prior authorization, beneficiaries may not 

receive their medication until coverage is resolved. Some 

pharmacists do provide emergency supplies but they 

sometimes must absorb the cost if coverage is denied and 

the beneficiary cannot pay out of pocket. The Commission 

recommends that the Congress authorize PDPs to approve 

transition supplies to beneficiaries under Part D while the 

plan determines coverage.

When a physician submits a request for a drug that 

requires prior authorization and supplies accompanying 

information, the plan must complete a coverage 

determination or an expedited coverage determination 

within a set time frame. For example, plans must 

decide on an expedited request for coverage within 24 

hours. However, this time frame does not begin until a 

physician or the enrollee has attempted to fulfill the prior 

authorization requirement. When a beneficiary brings 

a prescription to a pharmacy and the claim is denied 

because the plan does not know whether the drug should 

be covered under Part B or Part D, the plan is not making a 

coverage determination because the plan has not received 

a request for coverage with accompanying information and 

no time frame applies (CMS 2007a). Since it is unlikely 

that a pharmacist can contact the beneficiary’s physician 

and the physician can complete a prior authorization 

form while the beneficiary is waiting at the pharmacy, the 

beneficiary may have to pay for the drug out of pocket or 

leave without needed medication. 

Part D plans currently must maintain a transition policy to 

provide temporary supplies of medications for new plan 

enrollees who are stabilized on drugs that are not on their 

plan’s formulary or are subject to utilization management 

requirements. This policy requires plan sponsors to provide 

a temporary supply of the requested medicine and to send 

the enrollee written notice explaining when the supply 

will end and the procedures for requesting a coverage 

determination or exception. The transition supply is 

limited to a 30-day fill and is subject to the plan’s general 

cost-sharing requirements.5 However, the transition policy 

does not apply to overlap drugs that may be covered under 

Part B or Part D (CMS 2007a). 

R ec o mmenda t i o n 7 B

The Congress should allow plans to cover a transitional 

supply of overlap drugs under Part D under the same 

conditions as the general transition policy applied by CMS.

R a t i o nale 7 B

If PDPs were able to apply the transition policy to 

overlap drugs while coverage is being determined, 

beneficiaries would not risk disruptions in their medical 

regimens. Physicians would have more time to meet prior 

authorization requirements to determine the coverage 

status of the prescribed drugs. Both pharmacy and 

pharmacy benefit management trade associations support 

this policy (PCMA/NACD 2006). 

I m p lica t i o n s 7 B

Spending

• Negligible

Beneficiary and provider

• This recommendation would improve beneficiary 

access and reduce risk for pharmacists. 
166 Issues in Medicare coverage of drugs

Vaccines and Part D

Physicians report that coverage of preventive vaccines 

under Part D is problematic for them. By statute, under 

Part B Medicare covers preventive vaccines for influenza, 

pneumonia, and hepatitis B for patients at high or 

intermediate risk. Medicare covers other vaccines under 

Part B if they are administered to treat an injury or direct 

exposure to a disease. For example, Part B covers rabies 

vaccine for beneficiaries bitten by animals. However, 

Medicare covers any other preventive vaccines under Part 

D. Currently, PDPs are paying for few preventive vaccines. 

Interviewees mentioned that the most likely new vaccine 

to be covered under Part D is a vaccine for shingles 

licensed by the FDA in 2006. However, if more vaccines 

become eligible for Part D, physicians are likely to have a 

problem billing plans. Like most Part B drugs, physicians 

purchase vaccines and provide them in their offices, but 

most have no direct way of billing PDPs. 

Currently, CMS is seeking to clarify how plans intend 

to pay for vaccines under Part D. Plans would also have 

to develop a method to pay providers to administer the 

vaccines. To date, plans have suggested a variety of 

methods to pay for Part D-covered vaccines including: 

• delivering vaccines directly to the physician’s office,

• providing vaccines to network pharmacies,

• reimbursing patients after administration of the 

vaccine, and

• developing a web-based tool that allows physicians to 

submit claims electronically (Banner 2007).

These methods are largely untested. Recognizing this 

concern, the Academy of Managed Care Pharmacy notes 

that physicians do not have the appropriate information 

systems to bill under Part D (AMCP 2007). They endorse 

moving all vaccines to Part B.

If beneficiaries have to pay the full payment rate for 

vaccines and then seek reimbursement from their plans, 

physicians are concerned that the out-of-pocket cost will 

discourage beneficiaries from seeking preventive care 

when appropriate vaccines are available. Public health 

agencies—for example, the Centers for Disease Control 

and Prevention (CDC) and the National Vaccine Program 

Office in the Department of Health and Human Services 

(HHS)—share this concern. Beneficiaries without Part D 

coverage might also be unable to receive recommended 

vaccines unless they are able to pay the full payment rate.

Before implementation of Part D, Medicare covered 

preventive vaccines only if they were listed in statute. The 

Congress could simplify the process for coverage. For 

example, Medicare carriers could decide on coverage for 

preventive vaccines based on medical evidence as they do 

with other Part B services. Medicare payment for vaccines, 

like other Part B drugs, would be based on the average 

sales price methodology.

One source of information about Part B coverage 

for vaccines could be the recommendations of the 

Advisory Committee on Immunization Practices (ACIP), 

which consists of 15 experts in fields associated with 

immunization who have been selected by the Secretary 

of HHS to provide advice and guidance to the Secretary, 

the Assistant Secretary for Health, and the CDC on the 

most effective means to prevent vaccine-preventable 

diseases. The Committee develops recommendations for 

the administration of preventive vaccines to the pediatric 

and adult populations, along with schedules regarding 

the appropriate time frame, dosage, and contraindications 

applicable to the vaccines. ACIP recommendations are 

currently used to determine vaccines covered under the 

Vaccines for Children program. ACIP could develop 

similar recommendations for the Medicare population. 

Medicare could use this source of information for help 

making coverage decisions.

Although this section relates only to preventive vaccines, 

beneficiaries might have better access to some other 

drug products under Part B than under Part D.6 CMS is 

studying whether some drugs should be moved from one 

part of the program to the other. The Commission also 

will study potential cases in future work. Any significant 

shift of drugs from one part of the program to the other 

should consider the time needed for drug plans to take the 

changes into account before submitting their bids to CMS 

for the following year.

R ec o mmenda t i o n 7 C

The Congress should permit coverage for appropriate 

preventive vaccines under Medicare Part B instead of Part D.

R a t i o nale 7 C

Since physicians have no direct way to bill Part D plans, 

they face administrative barriers to providing appropriate 

preventive care to beneficiaries. Under Part B, physicians 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 167

would be able to administer new vaccines in their offices 

as they do current covered vaccines and beneficiaries 

would have more access to preventive care. 

I m p lica t i o n s 7 C

Spending

• This recommendation would increase spending by less 

than $50 million for 1 year and by less than $1 billion 

over 5 years.

Beneficiary and provider

• This recommendation would improve beneficiary 

access to preventive care and reduce the administrative 

burden for physicians.

Delivering Part D benefits to residents of 

long-term care facilities

The overall fit between Part D and the nursing home 

pharmacy sector is a matter of debate. Some stakeholders 

characterize the Part D benefit as a better fit for 

community-based beneficiaries who fill prescriptions in 

retail pharmacies than for institutionalized beneficiaries 

because the latter often have cognitive as well as physical 

impairments. However, current law states that Medicare 

beneficiaries in nursing facilities (NFs) should have 

the same freedom as community-based beneficiaries to 

choose among Part D plans. Here we examine how the 

introduction of Part D is affecting pharmacy services 

for residents of NFs and other stakeholders. We also 

describe several approaches policymakers can consider for 

delivering Part D benefits in this care setting but do not 

offer recommendations.

Medicare beneficiaries in NFs

According to data from the Medicare Current Beneficiary 

Survey (MCBS), in 2003, about 5 percent of all 

beneficiaries lived in long-term care facilities.7 This group 

is made up disproportionately of individuals age 85 or 

older (43 percent vs. 12 percent of the entire Medicare 

population), and they are much more likely to be widows 

or to have never married (only 14 percent remain married 

vs. 52 percent overall) (CMS 2006b). More than half of 

beneficiaries in long-term care facilities did not complete 

high school compared with 30 percent overall, and about 

half have incomes of $10,000 or less compared with 

22 percent overall. Individuals who reside in NFs often 

are there because they are in a weak physical state with 

difficulty performing activities of daily living. About 

two-thirds of the institutionalized are also mentally or 

cognitively impaired (Table 7-1).

The population of beneficiaries in long-term care facilities 

is made up disproportionately of individuals who are dually 

eligible for Medicare and Medicaid.8 In 2003, 19 percent of 

duals lived in long-term care facilities compared with about 

2 percent of nondual Medicare beneficiaries. More than 

half of all institutionalized Medicare beneficiaries are duals 

(Table 7-1). Definitions of institutionalization can vary, 

but this result corresponds roughly with other data. As of 

April 2007, data from CMS’s On-line Survey, Certification, 

and Reporting system suggest that nearly 14 percent of 

residents at certified NFs were on a Medicare Part A stay, 

65 percent were on a Medicaid stay, and the remaining 21 

percent either had another source of coverage or paid out of 

pocket (CMS 2007b). 

Providing prescription drugs to NF 

residents before and after Part D

The distribution system for drugs dispensed to residents 

of NFs is quite different from that for beneficiaries 

living in the community, and Part D has affected how 

providers operate. NFs and the LTCPs with which they 

contract have always had to interact with multiple insurers 

because residents with individual or employer-sponsored 

supplemental drug policies had coverage through different 

T A B L E

7 –1 About two-thirds of institutionalized

 Medicare beneficiaries are 

mentally or cognitively impaired

Dual 

eligibles

Nondual 

eligibles All

All institutionalized

beneficiaries 56% 44% 100%

Percent who are mentally

or cognitively impaired

Aged 32 26 58

Disabled 8 1 10

Total 40 28 68

Note:	 Dual eligibles are individuals who receive both Medicare and Medicaid

benefits. Mentally or cognitively impaired includes beneficiaries who have

dementia, mental illness, or mental retardation. Sums may not add to

totals due to rounding.

Source:	 MedPAC analysis of Cost and Use files, 1999–2001 Medicare Current

Beneficiary Surveys.
168 Issues in Medicare coverage of drugs

companies. Nevertheless, overall LTCPs interacted more 

frequently with just one payer because so many NF 

residents had their drugs paid by a single state Medicaid 

agency. Previously, most state Medicaid programs paid 

NFs a daily rate to cover the room, board, and nursing 

care services of dually eligible residents and made 

separate fee-for-service payments to LTCPs for pharmacy 

services (Figure 7-1).9 LTCPs were reimbursed directly by 

residents with third-party coverage and those who paid out 

of pocket. 

Regulatory requirements and current market practices 

make it advantageous for many NFs to rely on a single 

LTCP for all pharmacy-related services (Lewin 2004). 

Nearly one-third of all states require NFs to let residents 

use a pharmacy of their own choosing, and some NF 

residents with retiree drug coverage or coverage through 

the Veterans Administration receive medications through 

mail-order pharmacies or other providers. Nevertheless, 

many NF providers cite efficiency, predictability, and 

standardization of dispensing practices as advantages of 

contracting with one vendor. 

Federal and state laws and regulations have led to certain 

standards of practice for delivering pharmacy services in 

NFs. For example, the Omnibus Budget Reconciliation 

Act (OBRA) of 1987 and OBRA 1990 require each 

state’s Survey and Certification Agency to verify that 

NFs accepting Medicaid funding meet conditions of 

participation, including reviewing drug regimens monthly, 

documenting and maintaining low medication error 

rates, and reducing unnecessary drug use (Leavitt 2005). 

States also regulate and license NFs and pharmacies 

(including institutional pharmacies such as LTCPs) and the 

professionals they employ.

NFs must obtain the services of a licensed pharmacist 

to provide mandated standards of pharmacy practice. 

While NFs do not necessarily need to use LTCPs for these 

services, most do. In addition to the services that retail 

pharmacies provide, LTCPs often:

• develop and maintain an advisory formulary specific 

to the geriatric population;

• prepare and dispense unit doses of prescribed 

medicines, typically in blister packs, and provide 

medication carts with locked, nonremovable drawers 

for each resident’s drugs;

• provide 24-hour drug delivery, provide emergency 

drug supplies, and handle unused medications;

• provide the services of consultant pharmacists who 

review residents’ prescriptions prospectively, maintain 

records of drugs dispensed, coordinate documentation 

for prior authorization or proof of medical necessity, 

review drug regimens retrospectively, and help train 

facility nursing staff on how to administer certain 

complex therapies and monitor residents.

Prior to Part D, LTCPs provided many of these services 

for charges that were thought to approximate the cost 

of drug ingredients and dispensing. National chain 

LTCPs maintain their own formularies and are able to 

obtain rebates from pharmaceutical manufacturers in 

return for filling a certain volume of drug sales and for 

moving market share toward specific drugs. In turn, some 

analysts believe those rebates finance much of the cost of 

additional LTCP services such as drug regimen reviews. 

However, others believe that rebate revenues have led 

primarily to higher profits for some LTCPs than providers 

experience in other health sectors. 

The nature of LTCP formularies differs somewhat from 

formularies that pharmacy benefit managers (PBMs) 

use. PBMs’ formularies are continually updated lists of 

medications that a plan or payer will cover. A PBM covers 

all drugs listed on its formulary in some way; however, 

most formularies do not list all drugs and enrollees must 

pay out of pocket for drugs that are not listed. In addition, 

PBMs’ formularies typically set different levels (tiers) of 

cost sharing or require that a particular condition is met 

before certain drugs or groups of drugs will be covered 

(MedPAC 2004). By comparison, LTCPs’ formularies are 

more advisory in the sense that the pharmacy generally 

does not decline to cover prescriptions, except for limited 

circumstances. Under OBRA 1987, NFs must provide 

their residents with all needed care, including prescription 

drugs, whether or not the facility has identified a source 

of payment. Nursing home regulations also tend to focus 

on making sure that NFs provide the prescription to the 

correct resident in a timely manner. As a result, NFs pay 

close attention to the timeliness with which LTCPs deliver 

prescribed medicines rather than focusing on whether a drug 

should be covered. Nevertheless, LTCP formularies divide 

drugs into categories—for example, preferred, acceptable, 

or unacceptable. In the case of both LTCPs and PBMs, the 

designation of certain drugs as preferred or nonpreferred 

may reflect both clinical and economic factors.

One important issue to consider is whether the role of 

pharmacists in long-term care settings differs from that 

in community retail pharmacies. Some states require 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 169

all types of pharmacies to substitute generic medicines 

automatically for brand-name drugs that have a generic 

available unless the physician explicitly asks for the 

brand-name drug. However, before making therapeutic 

substitutions—an alternative drug within the same 

therapeutic class but with a different molecular structure—

pharmacists must first contact the prescribing physician 

for approval. Beneficiaries who live in the community and 

their pharmacists must get such approval from prescribing 

physicians to obtain coverage or pay lower copays under a 

PBM’s formulary. 

Before Part D: How prescription drugs were typically provided to dually eligible residents

Note:	 LTCP (long-term care pharmacy). Dual eligibles are individuals who receive both Medicare and Medicaid benefits. Other nondual residents of long-term care

facilities either paid out of pocket or used third-party drug coverage to reimburse the LTCP on a fee-for-service basis.

	 *Many of the services listed are required of nursing facilities under federal and state laws and regulations.

Before Part D: Flow of funds for prescription drugs provided to dually eligible residents FIGURE

7-1

Note and Source in InDesign.

Drug manufacturer

Drug distribution chain

Wholesaler

Medicaid Nursing facility

Beneficiaries

Long-term care pharmacy*

• Develops and maintains formulary 

specific to geriatric population

• Repackages, prepares, dispenses unit

doses of drugs

• 24-hour drug delivery, emergency

supplies, handles unused medications

• Provides services of consultant 

pharmacists (e.g., for drug regimen

reviews, maintaining records of

dispensing, coordinating documentation 

for proof of medical necessity, helping 

train nursing facility staff in administering

complex drug therapies and monitoring 

residents) 

Facilitate prior 

authorizations 

and therapeutic 

substitutions Payment for room, board, 

and nursing care services

Payment thought to equal 

the cost of drugs and 

dispensing services

Negotiated

rebates that 

cover some of 

the costs of 

LTCP services

Preferred 

placement 

on LTCP

formulary

Flow of:

Drugs

Funds

Services

F i g ure

7 –1
170 Issues in Medicare coverage of drugs

By contrast, in long-term care settings, consultant 

pharmacists review drug regimens monthly and can use 

that information to suggest therapeutic substitutions to 

the prescribing physician. Such suggestions may reflect 

clinical and safety concerns of the consultant pharmacist 

and may also reflect a drug’s preferred status on the 

LTCP’s formulary. In some states and under specific 

circumstances, consultant pharmacists can change a 

prescription without seeking the physician’s approval.10

Under current law, if a consultant pharmacist recommends 

changing a resident’s prescription, the prescribing 

physician must consider that recommendation and respond 

to it (Lewin 2004). Some analysts believe that because 

most consultant pharmacists are employed directly by 

LTCPs, they may have the financial interests of the LTCP 

in mind when recommending therapeutic switches.11

It is worth noting that a number of providers share 

responsibility for the quality and safety of drug use among 

NF residents, including prescribing physicians, LTCPs, 

and NFs.

LTCPs serve more than 80 percent of all nursing home 

beds nationwide (Stevenson et al. 2007). The LTCP market 

is highly concentrated, with the top three firms accounting 

for two-thirds of nursing home beds: Omnicare covers 

about 850,000 of the nation’s 1.7 million beds (50 percent), 

PharMerica covers 220,000 (13 percent), and Kindred 

Pharmacy Services (KPS) covers 100,000 (6 percent). 

AmerisourceBergen and Kindred Healthcare, which own 

PharMerica and KPS, respectively, are in the process of 

spinning off those units to create a single firm. Smaller 

local or regional pharmacies (both retail and long-term 

care) serve the remaining one-third of nursing home beds. 

In the past, larger LTCPs had some important competitive 

advantages because they could negotiate large rebates 

from drug manufacturers. More recently, smaller LTCPs 

have turned to group purchasing organizations (GPOs), 

which give them greater bargaining power in negotiations. 

(GPOs bargain on behalf of member organizations that are 

smaller, thereby pooling their purchasing power.)

The introduction of Part D brought about a major shift 

in the financing of LTCP services. Previously, sources of 

nonrebate revenue for LTCPs mirrored the distribution 

of residents in NFs: 60 percent to 65 percent came from 

Medicaid, 10 percent to 15 percent came from Medicare 

Part A (for stays in skilled nursing facilities), and the 

remaining 20 percent to 24 percent was divided between 

private-pay residents and those with supplemental drug 

coverage (Lewin 2004). Most frequently, LTCPs were 

reimbursed on a fee-for-service basis. Now Medicare 

makes monthly payments to competing private plans that 

administer prescription drug benefits on behalf of the 

program, including for enrollees who reside in NFs (Figure 

7-2). As of January 1, 2006, Medicare Part D replaced 

Medicaid as the primary source of drug coverage for fullbenefit duals. Other residents may enroll in Part D plans if 

they choose. This means that LTCPs must now negotiate 

with numerous organizations that sponsor Part D plans 

to become part of each plan’s pharmacy network. Part 

D plans are required to offer a contract to any pharmacy 

willing to participate in its LTCP network so long as the 

pharmacy is capable of meeting certain performance 

criteria, relevant state laws, and other contract terms. Many 

NFs continue to contract with a single LTCP, and the larger 

LTCPs have effectively negotiated contracts with all PDPs 

to join their pharmacy networks.

Now that Part D is in place, each facility’s residents 

are enrolled among several different Part D plans with 

corresponding differences in formularies. Although 

the number will likely decline for 2008, during 2006, 

representatives of NFs and chains reported that residents 

were often enrolled in 6 to 10 or more plans (Stevenson 

et al. 2007).12 CMS automatically assigned NF residents 

who are duals randomly among qualifying Part D plans 

prior to January 1, 2006 (the date their entitlement to 

drug coverage through Medicaid ended). Typically, duals 

are enrolled in a qualifying stand-alone PDP rather than 

an MA–PD because most duals are in fee-for-service 

Medicare.13 NF residents who are not duals had until 

May 15, 2006, to select and enroll in a PDP. All Medicare 

beneficiaries who reside in NFs may switch to a different 

plan up to once per month. NF residents who are duals 

automatically qualify for Part D’s low-income subsidy 

(LIS) program, which covers their monthly premiums 

if they are enrolled in a qualifying plan. Full-benefit 

dual eligibles who reside in NFs face no cost-sharing 

requirements. Otherwise, residents must pay premiums 

and cost sharing.

Throughout 2006, LTCPs maintained their own 

formularies, and stakeholder interviews suggest that 

LTCPs still receive rebates from drug manufacturers. 

These formularies and rebates are separate from those 

developed and negotiated by Part D plans with which 

LTCPs have contracts. 

Findings from stakeholder interviews

MedPAC contracted with researchers at Harvard 

Medical School’s Department of Health Care Policy to 

interview key stakeholders about how Part D is changing 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 171

After Part D: How prescription drugs are typically provided to dually eligible residents

Note:	 LTCP (long-term care pharmacy). Dual eligibles are individuals who receive both Medicare and Medicaid benefits. Other nondual residents of long-term care

facilities may or may not choose to enroll in Part D plans.

After Part D: Flow of funds for prescription drugs provided to dually eligible residents FIGURE

7-2

Note and Source in InDesign.

Drug 

manufacturer

Drug distribution chain

Wholesaler

Nursing 

facility Medicare Medicaid

Part D

plan Beneficiaries

Long-term 

care pharmacy

Facilitate prior 

authorizations 

and therapeutic 

substitutions

Payment for room, 

board, and nursing 

care services

Negotiated

rebates that 

cover some of

the costs of 

LTCP services

Negotiated

rebates

Preferred 

placement 

on LTCP 

formulary

Flow of:

Drugs

Funds

Services

Payment for basic 

Part D coverage plus 

enrollees’ premiums 

and cost sharing

Plan payment for pharmacy services

Preferred 

placement 

on Part D 

plan 

formulary

F i g ure

7 –2
172 Issues in Medicare coverage of drugs

the provision of drugs in NFs (Stevenson et al. 2007). 

They conducted 31 semistructured interviews between 

November 2006 and January 2007 with nursing homes 

and chain operators (6), LTCPs (6), GPOs/LTCP networks 

(2), Part D plans (4), financial analysts who cover the 

LTCP sector (3), physicians working in NFs (4), consultant 

pharmacists (2), state and federal policymakers (2), and 

advocates for nursing home residents (2). The researchers 

tried to select specific stakeholders to maximize 

representation of Medicare beneficiaries; for example, 

they tried to interview the larger nursing home chains, 

LTCPs, and PDPs. 

The research team found that, by some accounts, the 

transition to Part D has been challenging in the long-term 

care sector. Part D brought about a substantial departure 

from how prescription drugs were previously financed and 

administered in NFs, and providers are struggling to adapt 

to some of these changes. At the same time, meeting the 

needs of NF residents and working with LTCPs are new 

challenges for most PDP sponsors as well. Interviewees 

identified a range of longer term issues that merit attention 

to ensure that Part D works well for residents of NFs. The 

Harvard team reported the following conclusions:

• The overall fit between Part D and the nursing home 

pharmacy sector is a matter of contention among 

the stakeholders interviewed. Many stakeholders 

characterized the Part D benefit as being a better 

fit for community-based beneficiaries who 

access medications in retail pharmacies than for 

institutionalized beneficiaries.

• Medicare beneficiaries in nursing homes have the 

same freedom to choose plans as community-based 

beneficiaries; however, stakeholder interviews 

highlighted sensitivities between ensuring this 

freedom of choice and allowing nursing home 

providers to encourage enrollment into plans they 

perceive to be a better fit with residents’ medication 

needs and that minimize facility and pharmacy 

administrative burden.

• Part D increased the variation around formularies 

and drug management processes for residents at 

the facility level. In general, stakeholders described 

tension between cost-saving strategies PDPs used, 

such as prior authorization, and the burden these 

utilization management processes can place on clinical 

staff at NFs and pharmacy staff.

• Formulary coverage appears adequate for many 

medications used by NF residents, and the special 

protections required for six medication classes plus 

Part D transition coverage requirements helped to 

shield residents from coverage limitations. However, 

stakeholders noted what they consider to be important 

exceptions to overall formulary adequacy for the 

institutionalized population and instances when the 

application of utilization management policies was 

particularly problematic.

• Empirical analyses are needed to assess the impact of 

Part D on utilization patterns, outcomes, and quality 

of care. Noting this important caveat, stakeholders 

pointed to within-class drug utilization shifts but did 

not report a change in overall use of drugs. To date, 

stakeholders have not perceived any adverse impact 

on resident outcomes or quality of care attributable to 

Part D.

• Stakeholders indicated that Part D’s financial impact 

on nursing homes is evolving. Part D altered the 

relationship between nursing homes and their LTCPs, 

introducing a tension between facilities’ need to 

dispense medications quickly and LTCPs ensuring 

coverage for those drugs. Nursing homes and LTCPs 

have an incentive to minimize prescriptions for 

noncovered drugs, but how these entities will share the 

financial impacts of these costs depends on nursing 

home–LTCP contracting, which will likely continue to 

vary across providers.

• The impact of Part D on the future competitiveness 

of the LTCP sector is also evolving. Although the 

LTCP sector is concentrated, financial analysts 

with whom we spoke characterized the sector as 

very competitive overall, with few barriers to entry. 

The prominent role of GPOs and LTCP network 

organizations in particular has helped smaller LTCPs 

access more favorable pricing from manufacturers 

and PDPs so that most small LTCPs have joined these 

organizations.

• Consensus among stakeholders was that LTCP 

rebates—which currently continue—will likely 

decline in future years. CMS has not disallowed LTCP 

rebates under Part D, but it has expressed strong 

reservations about them, raising the possibility that 

they could constitute fraud and abuse.

• If LTCP rebates decline or disappear, these changes 

could lead to increased transparency of pricing 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 173

and safety. For example, in a meeting with investment 

analysts, CMS officials noted that about half of all ADEs 

occur in long-term care settings, despite the presence of 

consultant pharmacists and procedures intended to limit 

medication errors (Stifel Nicolaus 2007). Underlying 

this comment is the view that past standards of practice 

among LTCPs may not have served NF residents well. At a 

separate investors’ conference, officials argued that LTCPs 

“have a very strong incentive to promote the use of drugs 

for which they receive rebates” (Lueck 2006). 

An important goal for policymakers should be that, in the 

midst of changes brought about by Part D, beneficiaries 

in NFs receive safe and appropriate drug therapies. Data 

on drug claims are not yet available to examine the degree 

to which Part D has affected residents’ drug utilization, 

health outcomes, and quality of care. Stakeholder 

interviews suggest that they have seen shifts among 

drugs within specific classes—for example, therapeutic 

substitution of one cholesterol-lowering statin for another. 

To date, however, stakeholders do not report broad 

changes—increases or decreases—in gross utilization of 

drugs. Nor have stakeholders reported changes in quality 

of care as Part D has gotten under way. Researchers need 

to conduct empirical analyses to examine the issue more 

systematically. 

The apparent lack of change thus far is likely due to 

protections for residents within current law, such as OBRA 

1987, which obligates NFs to provide residents’ prescribed 

medicines even when financing for such services has 

not been identified. Other protections are through CMS 

guidance that requires Part D plans to supply all or nearly 

all drugs in certain therapeutic classes and to provide 

transition supplies during the first 90 days of enrollment. 

Although stakeholders dealt with requirements for prior 

authorization under state Medicaid before, physicians and 

some NFs characterized these as more challenging under 

Part D.14 NF and LTCP interviewees highlighted access 

challenges under Part D for drugs to treat Alzheimer’s 

disease, selected antibiotics, erythropoietin, and some 

alternative formulations of medicines such as injectables 

and inhalation therapies. Importantly, the clinical impact 

of plans’ coverage limitations or prior authorization 

requirements depends on a number of factors, including 

the prevalence of a drug’s use, available alternatives, 

and the efficacy and safety of specific medications. For 

instance, limited access to therapies widely used among 

NF residents could affect the clinical quality of care. 

Alternatively, if a drug is seldom used because clinically 

because LTCPs would need to unbundle their services 

and begin charging for services explicitly. Although 

reduced rebates would likely have a greater negative 

impact on larger LTCPs, these entities would still 

likely maintain certain economies of scale that 

might be advantageous in terms of service pricing, 

dispensing costs, and negotiating power.

• A reduction or elimination of rebates also could result 

in LTCPs passing increased administrative costs 

or a greater share of costs for items like consultant 

pharmacist services on to the nursing homes with 

which they contract.

• PDPs generally did not express reluctance to have 

institutionalized enrollees in their plans; however, 

there seemed to be a level of uncertainty among PDPs 

about the adequacy of payment and risk adjustment for 

this population as risk corridors widen. Reassessing 

the methodology of risk adjustment going forward 

and possibly making future refinements could be 

important to ensuring adequate availability of plans 

for dual-eligible beneficiaries.

Policy concerns related to pharmacy 

benefits for NF residents

The use of Part D’s system of competing private plans in 

the long-term care sector has led to a number of concerns 

among stakeholders. Some predate Part D and relate to the 

appropriateness of drug use among residents. Others arose 

because Part D uses multiple competing plans. 

Concerns about quality and appropriateness of 

drug use in NFs

Historically, NFs have struggled with appropriate 

prescribing and dispensing for residents (Stevenson et al. 

2007). Individuals who reside in NFs take 6 to 10 drugs 

per day, which raises their risk for adverse drug events 

(ADEs)—a term that describes harm caused by the use 

of a drug or the inappropriate use of a drug (Nebeker 

et al. 2004). Recent research suggests that preventable 

ADEs remain a significant problem (Gurwitz et al. 2005). 

Reducing medication errors and ADEs has been a focus 

of past federal reforms, such as those in OBRA 1987 and 

OBRA 1990.

While NFs operate within a highly regulated environment, 

processes for referring noncompliant facilities and 

enforcing standards do not always work well (OIG 2005). 

With respect to residents’ drug regimens, CMS officials 

recently raised strong concerns about the overuse of drugs 
174 Issues in Medicare coverage of drugs

superior alternatives are available, a plan’s coverage 

limitations may be appropriate. 

Policymakers need to monitor closely the quality and 

appropriateness of prescription drug use among NF 

residents as the relationship among NFs, LTCPs, and Part 

D plans evolves. A few independent organizations such 

as the Pharmacy Quality Alliance are working to develop 

quality measures specific to long-term care settings. 

CMS has begun measuring certain aspects of quality for 

Part D plans, but initially the agency’s efforts focused on 

measures such as call center performance, complaint rates, 

and generic dispensing rates rather than on measures of 

drug safety, polypharmacy, and the appropriateness of 

prescribing. 

Concerns about directing residents into specific 

Part D plans

CMS guidance restricts providers who serve NF residents 

(the NFs themselves, physicians, and pharmacies) in the 

information they give residents about particular plans. 

Providers may give objective information to residents, 

including how well drug plans cover medications of 

interest, but they are restricted from directing residents to a 

smaller number of plans and from distributing information 

that could be construed as having that aim (CMS 2006c). 

This guidance was designed to limit conflicts of interest—

for example, by keeping NFs from steering residents into 

plans solely out of concern for the facility’s administrative 

ease or to retain a past working relationship. 

At the same time, cognitive impairment among so many 

NF residents complicates the issue of plan selection. 

Some stakeholders believe that the marketing guidelines 

undercut an advisory role that many residents and families 

want providers to play. Along those lines, the Washington 

Legal Foundation filed suit against CMS, arguing that 

the guidelines violate the first amendment rights of NF 

providers (WLF 2006). Formularies of Part D plans vary 

a great deal, and CMS’s random assignment process may 

have placed some NF residents in plans that typically do 

not cover the drugs that residents used (Long Term Care 

Pharmacy Alliance 2007, OIG 2006).

On the other hand, some NFs strongly support CMS’s 

marketing guidelines because of concerns about undue 

pressure from large LTCPs to take actions that may not 

be in either the resident’s or a facility’s best interest. Not 

all NFs want such a responsibility—some are reluctant to 

assume liability for recommending a particular plan. Some 

NFs turned to outside assistance: Workers from State 

Health Insurance Assistance Programs (SHIPs) helped 

residents use CMS’s Part D Plan Finder tool at www.

medicare.gov to select among plans based on their current 

mix of prescribed drugs. Although SHIP resources are 

often limited, this approach is one way to provide residents 

with unbiased help in choosing a plan.

Stakeholders report that the existence of multiple Part D 

plans has increased the workload for NFs. One reason 

is that NFs must now educate residents and family 

members about their choices of Part D plans. This task 

was especially burdensome during 2006, the year Part D 

began, and the challenge associated with such education 

may diminish. An ongoing challenge is that NFs often 

have residents enrolled in several different Part D plans, 

and staff (with the help of their LTCPs) must navigate 

each plan’s utilization management techniques. For 

example, to meet prior authorization requirements or 

initiate grievances and appeals procedures, NF staff 

may need to provide supporting documentation from 

residents’ medical records on the clinical reasons why 

the prescribing physician prefers a specific drug. This 

task can be a logistical challenge when the prescribing 

physician is not on site—in other words, the drug order 

is written by a physician in a community practice rather 

than by the facility’s staff or medical director. Under 

these circumstances, NF staff or the LTCP must contact 

the prescribing physician’s office to initiate the process. 

Although CMS requires plans to accept standardized 

forms for prior authorization, providers report that some 

Part D plans continue to require their own forms. 

Stakeholders describe this increased burden as sizable, 

but its costs are difficult to quantify. Interviewees from 

NFs report that so far they have largely handled higher 

administrative burden with current staff. However, at 

least for some NFs, financial constraints kept them from 

increasing staff levels to address the workload, and staff 

were reportedly stretched thin.

Concerns about how to manage relations 

between PDPs and LTCPs

In guidance to plans for 2007, CMS requires LTCPs to 

begin reporting rebate information to the Part D plans 

with which they contract and, in turn, to CMS (CMS 

2006d). Under current law, Part D plans rather than LTCPs 

are expected to create and maintain formularies. Yet, in 

practice, LTCP formularies and consultant pharmacists 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 175

still influence NF prescribing practices. Agency officials 

have voiced concern about continuing rebates directly to 

LTCPs because, from their point of view, it could lead to 

higher Medicare program spending. This could occur if 

an LTCP is steering residents toward higher cost drugs 

for which they receive higher rebates, particularly if the 

LTCP’s formulary placement is at odds with that of the 

Part D plan’s formulary.

Disclosing rebates could change the way LTCPs do 

business. Rebate information is highly proprietary, and we 

do not know the magnitude of those revenues. However, 

given that LTCPs have the capacity to achieve significant 

formulary compliance, it is reasonable to assume that 

rebates have been sizable (Lueck 2006). If manufacturers 

begin to reduce or eliminate rebates, LTCPs may need 

to begin charging explicit fees for services such as drug 

regimen reviews.15 In turn, this could have implications for 

other payers such as Medicaid.

CMS’s guidance to plan sponsors and LTCPs regarding 

rebates raises the broader issue of the overall fit between 

Part D and the nursing home pharmacy sector. A specific 

question is whether the program’s consumer choice 

approach will lead Part D plans to manage quality and 

costs well for the long-term care sector, including the 

potentially higher costs associated with separate rebates 

to LTCPs. 

Ideally, Part D’s competitive system should provide the 

incentive for plans to strike a balance between providing 

enrollees with access to appropriate and high-quality 

drug therapies while controlling drug spending. Under 

the program’s approach, competing plans attract enrollees 

by offering formularies that cover many of the drugs they 

want at reasonable copayments and an attractive premium. 

Informed consumers should also look at the quality of plan 

services and the convenience of their pharmacy networks 

when selecting a plan. Medicare’s payments to plans 

are based on plan bids and their relative popularity as 

measured by past enrollment. For dual eligibles who pay 

no premiums and minimal or no cost sharing, the program 

maintains competitive pressure by limiting the number of 

Part D plans that qualify to receive autoassigned enrollees 

to those with premiums at or below regional premium 

thresholds. 

In the opinion of some stakeholders, several characteristics 

of NF residents make Part D’s approach a questionable fit. 

About two-thirds of NF residents have cognitive or mental 

impairments, and some do not have family members or 

legal representatives to help them choose among plan 

options or consider alternatives to assigned plans. NF 

residents who are eligible for full Medicaid benefits pay 

no cost sharing and thus are not concerned whether they 

are enrolled in a plan that covers the drugs they currently 

use on tiers with lower cost sharing. It is also unclear how 

much attention, on average, Part D plans pay to managing 

this population. Stakeholder interviews suggest that, while 

this share varies among plans, NF residents typically make 

up just 3 percent to 5 percent of total plan enrollment—

roughly the same percentage as in the overall Medicare 

population. 

On the other hand, plans may pay more attention to 

managing this population than enrollment levels might 

suggest. NF residents have higher average drug spending, 

and thus Part D plans have more incentive to manage their 

care. Since the advent of Part D, some plans have made 

strides in becoming more knowledgeable and attuned 

to the long-term care setting, both to manage their own 

risk and to work effectively in meeting the needs of their 

nursing home enrollees. In addition, CMS adjusts monthly 

payments to plans for the higher average spending of 

institutionalized enrollees to provide an incentive for plans 

to enroll these individuals. Plan representatives generally 

did not express reluctance to enroll NF residents. For 

the future, however, there is some uncertainty about the 

adequacy of payment and risk adjustment as Part D’s 

risk corridors widen and plans bear a greater degree of 

insurance risk.

Alternative approaches for providing drug 

benefits to residents

For the future, policymakers may want to consider whether 

Part D’s consumer choice approach is most appropriate 

for Medicare beneficiaries who reside in nursing homes. 

Would other approaches better serve this population? 

When evaluating options, decision makers should consider 

whether policy alternatives address the following goals:

• less complexity of plan choice for long-term care 

residents and their legal representatives;

• close attention to the appropriateness of drug therapies 

provided to each resident to improve patient safety and 

reduce ADEs;

• timely access to appropriate drug therapies;
176 Issues in Medicare coverage of drugs

• reasonable administrative burden for carrying out 

requirements for prior authorization, exceptions, 

grievances, and appeals; and

• incentives for controlling drug spending.

What follows is a discussion of alternative delivery 

approaches. The Commission does not have 

recommendations on these issues. How the program 

evolves for beneficiaries and providers may influence 

whether these options make sense as alternatives to the 

existing structure.

Continue with multiple Part D plans

One alternative is to keep today’s approach of using 

multiple PDPs to deliver pharmacy benefits in NFs. 

Since general enrollment in Part D is concentrated among 

relatively few plans, the number of plans available could 

decline. If plans with relatively few enrollees decide to 

exit the market, enrollment could become even more 

concentrated and there would likely be fewer plans with 

premiums at or below regional benchmarks for LISs. CMS 

has also announced certain policies for 2008 that will 

reduce the number of plans that qualify for autoassigned 

enrollees. In its notification of changes in Part D payments 

for 2008, the agency stated that Medicare will transition 

to enrollment-weighted averages for calculating monthly 

plan payments and regional low-income premium subsidy 

thresholds (CMS 2007c).16 CMS will also lower its de 

minimus policy from $2 to $1—the monthly dollar amount 

by which a plan’s premiums may surpass its regional lowincome premium threshold before the plan is no longer 

premium-free to beneficiaries who receive Part D’s LIS. 

Moreover, Part D’s risk corridors are scheduled to widen in 

2008 and beyond. Widened risk corridors mean that plan 

sponsors will bear relatively more insurance risk for the 

drug spending of their enrollees than they do today. 

Fewer Part D plans would make choosing a plan less 

complex for residents and their representatives and would 

also reduce administrative burden for NFs and LTCPs. 

PDPs would gain experience at managing pharmacy 

benefits within long-term care settings and might 

become more attuned to the needs of this population. 

(The Commission is also exploring the pros and cons of 

autoassigning LIS beneficiaries into Part D plans based on 

the current mix of drugs they use—an approach that could 

be applied to the long-term care population.)

On the other hand, it is unclear how the relationship 

between PDPs and LTCPs will evolve and to what 

extent the incentives of those providers for controlling 

drug spending will align. Most PDPs are relatively 

inexperienced at providing benefits in long-term care 

settings, and plans use LTCPs to deliver benefits to 

comparatively few enrollees. Given the degree of 

concentration in the LTCP market, the largest providers 

retain considerable market power in their negotiations 

with PDPs.

Given the agency’s concerns about ADEs and the overuse 

of drugs, CMS might want to consider requiring Part 

D plans to report specific quality data based on drug 

claims for their institutionalized enrollees. However, 

when discussing the quality of pharmacy services to NF 

enrollees, one central issue is the extent to which standalone PDPs, which manage only pharmacy benefits, 

should be held accountable for prescribing behavior. 

Policymakers may want to share responsibilities for 

quality reporting between Part D plans, which have more 

detailed information about individual drug claims, and 

NFs, which have access to the residents’ medical history 

and may have more influence with providers about 

their prescribing behavior. Ideally, one would include 

physicians among the group responsible for such quality 

reporting as well. Policymakers may want to also ensure 

that CMS monitors Part D plans for compliance with 

provisions for patient protection.

Another approach toward improving and monitoring 

quality for NF residents could involve the medication 

therapy management programs (MTMPs) of Part D plans. 

The law requires each plan to administer an MTMP 

in cooperation with pharmacists and other providers, 

with the goals of improving therapeutic outcomes for 

targeted beneficiaries and reducing the risk of ADEs. 

Part D plans submit proposals to CMS for how their 

individual MTMP will operate. Current CMS guidance 

defines targeted beneficiaries as a Part D plan’s enrollees 

who have multiple chronic diseases, are taking multiple 

Part D drugs, and (for 2007) are likely to incur annual 

covered drug costs of $4,000 or more. Although many NF 

residents probably fit these criteria already, policymakers 

might want to consider requiring Part D plans to enroll 

all residents in plans’ MTMPs. CMS may also need to 

make MTMPs somewhat more uniform than they are 

today; for example, not all currently review drug regimens 

(Touchette et al. 2007). However, a key legal question to 

investigate before using MTMPs in this manner is whether 

Part D payments to LTCPs for drug regimen reviews 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 177

would duplicate services required under NFs’ conditions 

of participation for Medicaid and whether that would 

constitute an impermissible double payment. 

Hold periodic competitions to select regional 

PDPs for NFs

Under a different approach, CMS would hold competitions 

periodically to select a PDP that would become the sole 

plan for all NF residents within a given PDP region 

(Frank and Newhouse 2007). This option would reduce 

complexity for residents, NFs, and LTCPs by virtue of 

using a single plan rather than multiple plans. 

An open question is how well this approach would 

address CMS’s concerns about patient safety, quality, and 

appropriateness of drug therapies. Arguably, if a PDP’s 

sole focus is to deliver benefits to NF residents, that plan 

can devote greater attention to concerns about quality 

and safety. However, to promote quality improvement, 

policymakers may want to make a portion of payment and 

future contract awards conditional on attainment of certain 

performance goals. 

Medicare could continue to pay the regional PDP the 

same way it pays other Part D plans; alternatively, the 

program could pay on a fee-for-service basis. Under 

the current approach, Medicare pays plans a monthly 

amount based on the nationwide average bid to provide 

basic Part D benefits.17 This approach means that Part D 

plans bear some insurance risk for the drug spending of 

their enrollees, which, in turn, gives plans an incentive to 

control drug spending. One risk of using a single regional 

PDP is that only smaller or more specialized organizations 

may choose to bid for such contracts. Since NF residents 

tend to use many prescription drugs, smaller sponsoring 

organizations would probably have a combined pool 

of Part D enrollees with relatively high drug spending. 

This could lead to higher premiums for long-term care 

plans and, since Medicare pays the Part D premiums for 

many NF residents, potentially higher Medicare program 

spending. Using a single regional PDP for NFs would 

avoid any problems stemming from incentives among 

multiple plans to enroll relatively less costly NF residents 

or to avoid this population altogether. Paying a regional 

PDP on a fee-for-service basis would probably address 

concerns about timely access to residents’ medications, but 

it would not create incentives to control drug spending or 

to examine patterns of drug use more closely. 

If CMS pursued this approach, the agency may want to 

consider limiting the number of regions in which any one 

sponsoring organization could operate a long-term care 

PDP. By limiting sponsors to a few geographic regions, 

CMS could keep several organizations providing services 

in this market. A credible threat of losing the next period’s 

regional contract to another sponsor would provide plans 

with a greater incentive to keep an eye on the cost and 

quality of their services.

To keep all sponsors of long-term care PDPs interested 

in this specific market, CMS would need to verify 

periodically that risk adjusters the agency uses to pay for 

the higher cost of institutionalized enrollees are accurate. 

This point may be especially relevant for 2008 and beyond 

as Part D’s risk corridors widen. CMS would also want to 

consider explicitly requiring MTMPs for these long-term 

care PDPs as well as public reporting of quality measures.

Reimburse LTCPs directly for delivering Part D 

benefits 

Under a third approach, Medicare could reimburse LTCPs 

directly for drugs delivered to residents who are Part D 

enrollees. This option would eliminate complexity for 

residents because they would no longer need to select a 

plan. It would also reduce the administrative burden for 

LTCPs, NFs, and prescribing providers. The incentives for 

how closely LTCPs monitor drug spending and the safety 

and appropriateness of drugs dispensed would depend on 

how policymakers structure reimbursements—on a feefor-service basis or with risk-based payments. 

Paying LTCPs on a fee-for-service basis would establish 

a system similar to what those providers experienced 

when Medicaid was the primary payer for the pharmacy 

benefits of dually eligible NF residents. This approach is 

also similar to the idea of a “fallback” plan—a provision 

in current law for situations in which no plan sponsor 

is willing to bear insurance risk. (To date, CMS has not 

needed to operationalize the law’s fallback provision.) 

Unless coupled with strong pay-for-performance 

provisions, fee-for-service payments would provide no 

incentive to manage residents’ drug spending; indeed, the 

incentive would be to increase utilization.

By comparison, requiring LTCPs to bear some insurance 

risk would provide strong incentives for cost control and, 

particularly if coupled with performance measures and 

formulary reviews, for examining concerns about safety 

and overuse. Policymakers may want to give LTCPs the 
178 Issues in Medicare coverage of drugs

same incentives to manage residents’ drug use as Part D 

plans face today: risk-based payments to ensure attention 

to cost control, coupled with individual reinsurance and 

risk corridors to mitigate incentives to stint on benefits. 

This approach would also likely change the relationship 

between NFs and LTCPs more than it has already changed 

under Part D. Specifically, NFs would continue to be most 

concerned with timely access to drug therapies for their 

residents, while LTCPs would face stronger incentives to 

consider prescription costs before dispensing.

Requiring LTCPs to bear insurance risk raises practical 

problems. For example, LTCPs would likely need to 

partner with insurers to meet state licensing requirements 

for risk-bearing entities. Pharmacies would need to 

develop information systems for verifying eligibility, 

enrolling and disenrolling residents in their plan, bidding, 

collecting premiums and cost sharing, and adhering to 

CMS’s reporting requirements. Given stronger incentives 

for controlling drug spending, LTCPs might also apply 

utilization management tools such as prior authorization 

to a much greater degree than they do currently as well as 

administer processes for formulary exceptions, grievances, 

and appeals. Each of these new functions could raise costs, 

and it is not clear how well smaller LTCPs could take on 

these roles and compete with the larger ones. 

Directions for future research

The Commission intends to continue monitoring the two 

topics discussed in this chapter—overlapping coverage 

of drugs under Part B and Part D and delivery of Part 

D benefits for residents of long-term care facilities. A 

unifying theme between the two topics is the need for 

performance measures to help watch for any problems that 

arise. For example, if overlapping Medicare drug coverage 

is leading to problems with beneficiaries’ access to needed 

medications because of prior authorization requirements 

in certain plans, performance measures that capture wait 

time until dispensing or timeliness of handling requests 

for prior authorization presumably would reflect these 

difficulties. Likewise, monitoring performance measures 

such as ADEs for enrollees who reside in long-term care 

facilities might shed light on whether Part D’s approach 

is addressing long-standing concerns about the safety and 

appropriateness of drug therapies for this population.

CMS has taken initial steps to measure the performance of 

Part D plans using metrics such as call-center wait times, 

complaint rates, and generic dispensing rates. However, 

the agency has considerably more work to do before 

measuring other important facets of pharmacy benefits 

(see text box). 

The Commission urges CMS to capture more dimensions 

of Part D plan operations in its performance measures 

and make those measures available publicly in a timely 

manner. Part D’s approach to delivering drug benefits 

provides consumers with a broad choice among 

private plans. However, for that approach to work well, 

beneficiaries need to be able to distinguish among plans 

by the characteristics they think are most important. 

Today, the Medicare Prescription Drug Plan Finder tool 

available at www.medicare.gov provides considerably 

more information about plans’ premiums, cost-sharing 

requirements, and formularies than it does about important 

factors related to plan quality. 

Evaluating Part D

Before the start of Part D, the Commission 

discussed how policymakers would need to 

monitor implementation of the new drug 

benefit to evaluate plan performance and to measure 

how well the program meets cost, quality, and access 

objectives (MedPAC 2005). In 2005, MedPAC staff 

convened a panel of experts to discuss performance 

measures and to identify ways policymakers could use 

measures to monitor the Part D program over time. The 

panelists represented health plans, pharmacy benefit 

management companies, employers, pharmacies, 

consumers, quality assurance organizations, and 

researchers. Panelists discussed several areas of 

performance that purchasers often use when selecting 

and monitoring health plans or pharmacy benefit 

management companies. Table 7-2 lists these areas 

of performance as well as examples of more specific 

measures in each area. 

(continued next page)
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 179

Evaluating Part D (cont.)

T A B L E

7 –2 Examples of performance measures for evaluating drug benefit management

Measurement area Example

Cost control

Plans’ drug spending Average drug spending per member per month (risk-adjusted)

Out-of-pocket drug spending Average annual out-of-pocket spending on covered drugs (risk-adjusted)

Pharmacy discounts on drugs Average rate of discount on brand and generic drugs

Pharmacy dispensing fees Dispensing fees for brand and generic drugs

Manufacturer rebates Total aggregated rebates as a percent of total drug spending, annually

Drug utilization Average number of prescriptions per member per year, by therapeutic category

Generic use Ratio of generic drugs to total drugs that have an available generic

Formulary adherence Ratio of preferred to nonpreferred brand-name drugs covered

Access and quality assurance

Pharmacy network Ratio of preferred network pharmacies to all pharmacies in service area

Enrollee refill adherence Percentage of members who refill chronic medications

Formulary review process Average time P&T committee takes for initial review of new drug

Prior authorization and nonformulary exceptions Average time for plan decision on prior authorization request

Appeals process and rates Percentage of appeals that are overturned

Point-of-sale electronic messaging to pharmacists Frequency of updates to clinical safety messaging software

Utilization of drugs contraindicated for the elderly Percentage of drugs contraindicated for the elderly on prior authorization

Adverse drug interactions, events Number of adverse drug interactions and/or adverse drug events

per 1,000 members

Drug utilization review Presence of screening to identify drugs filled beyond maximum

therapeutic duration

Electronic prescribing use Percentage of prescriptions submitted through e-prescribing per year

Benefit administration and management

Claims processing Percentage of claims processed accurately per year

Eligibility determination Percentage of claims processed for ineligible individuals per year

Data management for coordination of benefits Accuracy of benefit-spending calculations

Enrollee satisfaction

Enrollee survey results Member satisfaction rates

Call-center availability Hours per day the call center is open

Call-center response times Abandonment rates (percentage of time caller hangs up while on hold)

Grievance reporting Average number of complaints reported per 100 members per year

Plan retention and disenrollment Percentage of enrollees who voluntarily disenrolled

Note:	 P&T (pharmacy and therapeutics). The measures included in the second column are examples meant for illustrative purposes. Drug benefit purchasers (e.g.,

employers) may use many other more detailed measures to assess health plan or pharmacy benefit manager performance. In some cases, results from

these measures can be interpreted differently, depending on other plan variables.
180 Issues in Medicare coverage of drugs

The Commission and other stakeholders also need more 

performance measures to evaluate how well Part D plans 

are complying with CMS’s procedures and guidelines. 

The agency carried out the provisions of the Medicare 

Prescription Drug, Improvement, and Modernization Act 

of 2003 and implemented the Part D program admirably 

within very tight time constraints. Understandably, much 

of CMS’s attention focused on attracting private plans to 

this new market, creating bidding processes and payment 

systems for those plans, and enrolling beneficiaries into 

the program. However, now that Part D plans are more 

established, policymakers may want to turn their attention 

to ensuring that CMS enforces the rules it created for 

the program, such as making sure that plans keep to 

the agency’s timelines for reviewing requests for prior 

authorization, exceptions, and appeals. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 181

1 Plans use prior authorization for many purposes, such as to 

prevent the overuse of certain high-cost medications. For 

additional information see MedPAC’s June 2005 report to the 

Congress (MedPAC 2005).

2 Pharmacists do not bill separately for medication provided to 

patients in Medicare-covered stays in skilled nursing facilities. 

3 Carriers are private organizations that contract with CMS to 

make coverage and payment decisions for items provided by 

physicians and suppliers.

4 Ultimately, plans have the final decision over what 

information to accept for coverage determinations.

5 In long-term care settings, plans must provide a 90-day supply 

of medication. They also must provide an emergency supply 

of a new prescription outside the transition policy.

6 Interviewees representing home infusion therapy companies 

and some specialty pharmacies also said they found it easier 

to obtain coverage for their patients under Part B. PDPs 

cannot cover the supplies, equipment, and nursing services 

necessary to administer some therapies. Under Part B, 

physicians are not limited by a formulary when they choose a 

drug for their patient. 

7 Definitions of long-term care facilities vary. The MCBS (from 

which the 5 percent estimate was taken) includes facilities 

that have three or more long-term care beds and provide either 

personal care services to residents, continuous supervision of 

residents, or long-term care services throughout the facility 

or in a separately identifiable unit. Types of facilities include 

licensed nursing homes, skilled nursing homes, intermediate 

care facilities, retirement homes, domiciliary or personal care 

facilities, distinct long-term care units in a hospital complex, 

mental health facilities and centers, assisted and foster care 

homes, and institutions for the developmentally retarded and 

developmentally disabled.

8 See Chapter 3 of MedPAC’s June 2004 report to the Congress 

for an analysis of the characteristics of all dual eligibles 

(MedPAC 2004).

9 By contrast, a few states such as New York bundle payment 

for most drugs with that for daily care. NFs must then 

reimburse LTCPs for their services. Likewise, Medicare 

bundles reimbursement for drugs provided during a covered 

stay in a skilled nursing facility within a broader prospective 

per diem rate. 

10 Some states have collaborative practice agreements 

that permit the pharmacist to change prescriptions for a 

predetermined list of drugs. These agreements are between the 

pharmacist and the physician (Lewin 2004).

11 In 2006, one large LTCP reached a settlement agreement with 

42 states and the federal government in a dispute over dosage 

switches for three drugs. In a separate settlement agreement, 

the same LTCP agreed to pay the state of Michigan over 

accusations of Medicaid overbilling. The company admitted 

no wrongdoing in either settlement (Lueck 2006).

12 The number will likely decline for 2008 because CMS 

announced changes to payment policy that will reduce the 

number of plans with premiums at or below regional lowincome premium thresholds. See a further discussion of this 

point on p. 176.

13 To qualify for autoassigned enrollees, Part D plans must have 

monthly premiums at or below regional threshold amounts. In 

2006 and 2007, PDP regions had a median of 13 and 15 plans, 

respectively, that qualified for autoassigned enrollees.

14 See, for example, a recent survey of long-term care physicians 

by the American Medical Directors Association (AMDA 

2006). 

15 One alternative is for LTCPs to negotiate with Part D plans 

to be paid for drug regimen reviews through each plan’s 

medication therapy management program (MTMP). To date, 

however, the services provided in MTMPs vary widely and 

not all provide regimen reviews (Touchette et al. 2007). NFs 

have a clearer regulatory requirement to review drug regimens 

of their residents under their conditions of participation 

for Medicaid than do the MTMPs of Part D plans. One 

important question is whether drug regimen review services 

provided under MTMPs would duplicate those reviews 

required for participation in Medicaid. In turn, this would 

have implications for whether such arrangements constitute a 

double payment to the LTCP.

16 See Chapter 4 of MedPAC’s March 2007 report to the 

Congress for a discussion of how enrollment weighting 

influences Medicare’s payments to plans and enrollee 

premiums in Part D (MedPAC 2007b).

17 Plans also receive federal individual reinsurance subsidies that 

cover much of the cost of benefits for enrollees above a high 

threshold of drug spending as well as risk corridor payments 

that limit each plan’s aggregate losses or profits.

Endnotes
182 Issues in Medicare coverage of drugs

Academy of Managed Care Pharmacy. 2007. Medicare Part 

B versus Part D coverage. The future of Medicare Part D.

Alexandria, VA: AMCP. http://www.amcp.org.

American Medical Directors Association. 2006. Long term care 

physicians still experience difficulties in prescribing selected drugs 

for patients in Medicare Part D. Columbia, MD: AMDA. October 

12. http://www.amda.com/news/releases/2006/101206.cfm.

Banner, V. 2007. Billing for vaccine depends on Part D plan. Part 

B News Online. DecisionHealth, January 8.

Centers for Medicare & Medicaid Services, Department 

of Health and Human Services. 2007a. Prescription 

drug benefit manual. Chapter 18, sections 30 and 30.1, 

updated March 2. Baltimore, MD: CMS. http://www.

cms.hhs.gov/MedPrescriptDrugApplGriev/Downloads/

PartDManualChapter18.pdf. 

Centers for Medicare & Medicaid Services, Department of Health 

and Human Services. 2007b. E-mail message to author from the 

Division of Nursing Homes, April 23.

Centers for Medicare & Medicaid Services, Department of Health 

and Human Services. 2007c. Notification of changes in Medicare 

Part D payment for calendar year 2008 (Part D payment 

notification). Baltimore, MD: CMS.

Centers for Medicare & Medicaid Services, Department of 

Health and Human Services. 2006a. Clarification of plan due 

diligence in prior authorization of Part B versus Part D coverage 

determinations. Guidance issued March 24, 2006. Baltimore, 

MD: CMS.

Centers for Medicare & Medicaid Services, Department of 

Health and Human Services. 2006b. Medicare current beneficiary 

survey. Data tables for 2003. http://www.cms.hhs.gov/apps/mcbs/

PubCNP03.asp.

Centers for Medicare & Medicaid Services, Department of Health 

and Human Services. 2006c. Medicare marketing guidelines 

for: Medicare Advantage plans (MAs), Medicare Advantage 

prescription drug plans (MA-PDs), prescription drug plans 

(PDPs), and 1876 cost plans. Baltimore, MD: CMS.

Centers for Medicare & Medicaid Services, Department of Health 

and Human Services. 2006d. Memorandum to State Survey 

Agency Directors from CMS Director, Survey and Certification 

Group regarding nursing homes and Medicare Part D. Baltimore, 

MD: CMS. May 11.

Centers for Medicare & Medicaid Services, Department of Health 

and Human Services. 2005. Long term care guidance. Baltimore, 

MD: CMS. 

Frank, R. G., and J. P. Newhouse. 2007. Mending the Medicare 

prescription drug benefit: Improving consumer choices and 

restructuring purchasing. Washington, DC: The Brookings 

Institution. 

Gurwitz, J. H., T. S. Field, J. Judge, et al. 2005. Incidence of 

adverse drug events in two large academic long-term care 

facilities. American Journal of Medicine 118, no. 3 (March): 

251–258.

Leavitt, M. O., Department of Health and Human Services. 2005. 

Report to Congress: Review and report on current standard of 

practice for pharmacy services provided to patients in nursing 

facilities. Washington, DC: HHS.

Lewin Group. 2004. CMS review of current standards of practice 

for long-term care pharmacy services: Long-term care pharmacy 

primer. Prepared for CMS. December 30. 

Long Term Care Pharmacy Alliance. 2007. State by state 

formulary variability in Medicare prescription drug plans for 

auto-assigned long-term care residents. Washington, DC: LTCPA. 

Lueck, S. 2006. In nursing homes, a drug middleman finds bid 

profits. Wall Street Journal (December 23): A1.

Medicare Payment Advisory Commission. 2007a. Report to the 

Congress: Impact of changes in Medicare payments for Part B 

drugs. Washington, DC: MedPAC.

Medicare Payment Advisory Commission. 2007b. Report to the 

Congress: Medicare payment policy. Washington, DC: MedPAC.

Medicare Payment Advisory Commission. 2005. Report to 

the Congress: Issues in a modernized Medicare program.

Washington, DC: MedPAC.

Medicare Payment Advisory Commission. 2004. Report to 

the Congress: New approaches in Medicare. Washington, DC: 

MedPAC.

Nebeker, J. R., P. Barach, and M. H. Samore. 2004. Clarifying 

adverse drug events: A clinician’s guide to terminology, 

documentation, and reporting. Annals of Internal Medicine 140, 

no. 10 (May 18): 795–802.

References
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 183

Office of Inspector General, Department of Health and Human 

Services. 2006. Dual eligibles’ transition: Part D formularies’ 

inclusion of commonly used drugs. OEI–05–06–00090. January. 

Washington, DC: OIG.

Office of Inspector General, Department of Health and Human 

Services. 2005. State referral of nursing home enforcement cases. 

OEI–06–03–00400. December. Washington, DC: OIG.

PCMA/NACDS. 2006. White paper: Proposed solutions to the 

Medicare Part B/Part D coverage overlap. Unpublished paper. 

November 15.

Stevenson, D. G., H. A. Huskamp, and J. P. Newhouse. 2007. 

Medicare Part D, nursing homes, and long-term care pharmacies.

Contractor draft report. Submitted to MedPAC March 27. http://

www.medpac.gov/.

Stifel Nicolaus. 2007. Takeaways from meeting with key CMS 

staff. Baltimore, MD: Stifel Nicolaus. February 8.

Touchette, D. R., A. L. Burns, M. A. Bough, et al. 2007. Survey 

of Medicare Part D plans’ medication therapy management 

programs. Effective Health Care Research Report No. 1. Prepared 

by University of Illinois-Chicago DEcIDE Center, contract no. 

HSA290200500381. Rockville, MD: Agency for Healthcare 

Research and Quality. 

Washington Legal Foundation. 2006. WLF files suit against CMS, 

charging that CMS speech suppression violates First Amendment. 

Washington, DC: WLF. August 24. http://www.wlf.org/upload/

082406RS.pdf.

Skilled nursing facilities: 

The need for reform

C h a p t er 8

Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 187

Chapter summary

This chapter discusses issues related to Medicare’s payment system 

for skilled nursing facilities (SNFs) and the measures used to assess 

the quality of care provided in them. The first section outlines the 

research CMS funded to examine ways to improve the accuracy of 

SNF payments. The current design of the SNF prospective payment 

system (PPS) results in impaired access for certain beneficiaries who 

require expensive nontherapy ancillary (NTA) services and encourages 

providers to furnish therapy even when it is of little or no value. CMS’s 

researchers explored ways to establish and calculate a separate payment 

for NTA services, to base therapy payments on a patient’s predicted 

need for the service, and to defray some of the costs of treating 

unusually expensive cases through outlier payments. We conclude that 

options can be designed that better target payments for NTA services 

and for stays with unusually high costs. The options vary in their 

ability to predict cost differences across patients, the resources required 

for CMS to implement them, the changes required of providers, 

whether the option makes clinical sense, and the incentives to furnish 

In this chapter

• Options for reforming the 

skilled nursing facility 

prospective payment system

• Hospital-based SNFs: 

Analysis from the hospital 

perspective

• Understanding the declines 

in SNF quality

Skilled nursing facilities: 

The need for reform

8 C H A PT E R 
188 Skilled nursing facilities: The need for refor m

inappropriate care. Better data on the use of NTA services during the SNF 

stay, patient diagnoses, nursing costs, and patient assessment information at 

admission and discharge would facilitate redesign efforts. 

The second section considers why some hospitals continue to operate SNFs, 

despite their apparent poor financial performance, while other hospitals 

have closed their units. Many hospitals opened hospital-based SNFs during 

the 1990s to take advantage of the cost-based payments but began closing 

them after the PPS was implemented in 1998. We examine the considerable 

differences between hospital-based and freestanding facilities in their 

facility and patient characteristics, patterns of care, daily costs, and financial 

performance. In site visits and interviews, hospital administrators told us their 

reasons, including nonfinancial factors, for keeping their SNFs open or for 

closing them. The administrators indicated that they consider how the SNF 

contributed to the combined financial performance of both the hospital and 

the SNF. Our analyses found that hospital and SNF revenues together covered 

the combined direct costs of these patients. In addition, we learned about 

three distinct models of hospital-based SNFs, with various patient and facility 

characteristics and financial performances. These models reflect the different 

roles SNFs play in the overall provision of inpatient and post-acute care. 

Refinements to the inpatient hospital PPS and the SNF PPS may help to 

narrow the differences in financial performance between hospital-based and 

freestanding SNFs. Adjusting the inpatient PPS for the patient’s severity of 

illness will improve the accuracy of payments for the inpatient portion of the 

stay. Reforming the SNF PPS to better capture differences in use of NTA 

services and adopting an outlier policy would also improve the financial 

situation for hospital-based SNFs. 

In our March 2007 report, we noted that two measures of SNF quality—

risk-adjusted rates of discharge to the community and avoidable hospital 

readmissions—indicated that quality had worsened between 2000 and 2004. 

After adjusting for case-mix differences, factors most strongly associated 

with the two quality measures included whether the facility was present only 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 189

in 2000 (indicating facilities that later closed), geographic region, staffing 

levels, ownership, and whether the facility was hospital based. Hospitalbased facilities, which made up a larger share of facilities in 2000 than in 

2004, had higher quality measures and higher staffing levels, while forprofit facilities had worse quality measures. There were also large regional 

differences, with facilities in the West having better quality measures than 

facilities in other parts of the country. However, we may not have controlled 

for all of the factors that contribute to differences in the quality measures 

among facilities.

SNFs that appeared to provide good quality of care using these two measures 

appeared to be poor-quality facilities using the publicly reported post-acute 

measures. This inverse relationship, combined with our previous concerns 

about the publicly reported measures, leads us to urge CMS to report 

community discharge rates and rehospitalization rates for Medicare patients 

and to reconsider our recommendation to change the timing of required 

assessments so that changes in health status are gathered for all patients. 

Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 191

Options for reforming the skilled nursing 

facility prospective payment system 

In July 1998, CMS implemented a per day prospective 

payment system (PPS) for skilled nursing facilities 

(SNFs), as required by the Balanced Budget Act of 1997. 

Shortly thereafter, two concerns were raised about its 

design. First, the system does not properly distribute 

payments for nontherapy ancillary (NTA) services such 

as drugs, intravenous (IV) medications, and respiratory 

therapy. As a result, some beneficiaries who require those 

services have difficulty accessing SNF care. Second, the 

PPS encourages SNFs to furnish therapy services, even 

those of little or no value. As a result, beneficiaries may 

receive some therapy that provides no benefit, and the 

program is purchasing unnecessary care. 

In 2000, the Congress directed the Secretary to study 

different systems for categorizing patients that account 

for variation in resource use across patients. Some 

of this research was already under way. Beginning in 

1998 and spanning five years, CMS contracted first 

with Abt Associates and, more recently, with a team of 

researchers directed by the Urban Institute to evaluate 

alternative designs. This team included researchers 

from the University of Colorado at Denver and Health 

Sciences Center, the University of Michigan, and Harvard 

University. In the rest of this chapter, we refer to the work 

this team conducted as “CMS’s research.” Although CMS 

refined the SNF PPS in fiscal year 2006, the Commission 

believes the changes do not correct key problems in the 

payment system.

This section outlines CMS’s research that could form 

the basis of further SNF reforms, organized by issue: 

the inaccurate payments for NTA services, the incentive 

to furnish therapy services, and the lack of an outlier 

policy to defray the costs of exceptionally high-cost 

stays. Some options build on the current system’s design; 

others explore alternative patient classification systems to 

explain cost differences across patients. We summarize the 

research findings and evaluate the options in terms of their 

incentives, the ease of implementation for CMS, and the 

amount of change required of providers. Several options 

are better able to predict cost differences across patients 

than the current PPS design but would require additional 

resources to implement. This tension between improved 

accuracy and ease of implementation makes it difficult 

to choose among the options. In addition, some options 

may create new provider incentives. The reforms should 

minimize undesirable behavioral responses. 

How Medicare pays for SNF care 

Medicare covers up to 100 days of SNF care when a 

beneficiary requires skilled nursing and therapy after a 

hospitalization of at least 3 days in the preceding month. 

In 2003, Medicare paid $14.3 billion to SNFs. The average 

SNF stay is 25 days; stays in hospital-based facilities are 

typically shorter than those in freestanding facilities (Liu 

and Black 2003). 

SNFs receive a daily rate to cover nursing, ancillary, 

and capital costs. A base payment rate is adjusted for 

case mix with the resource utilization group (RUG) 

classification system.1 Patients are classified into a RUG 

based on the number and type of minutes of therapy they 

use or are expected to use, the need for certain services 

(e.g., respiratory therapy and specialized feeding), the 

presence of certain clinical conditions (e.g., pneumonia 

and dehydration), the ability to perform activities of daily 

living (e.g., eating and toileting), and, in some cases, the 

presence of signs of depression. The original PPS had 44 

RUGs; currently, the system has 53 groups. 

Information used to classify patients is gathered from 

patient assessments conducted on a set schedule 

throughout the patient’s stay (the first one is conducted 

on day 5 of the stay). The assessments must be conducted 

with the Minimum Data Set (MDS). MedPAC previously 

raised concerns about the timing of the MDS assessments 

(MedPAC 2006b, 2005b). Many patients are not assessed 

shortly after admission (only 4 percent of patients are 

assessed within three days of being admitted to the SNF) 

and assessments are not conducted at discharge, making it 

impossible to gauge changes in patient function (MedPAC 

2006b). In addition, some of the assessment questions ask 

about care furnished before the SNF stay. 

Each payment has three components: a nursing component 

to reflect the intensity of nursing care and NTA services 

that patients are expected to require, a therapy component 

to reflect the physical and occupational therapy and 

speech–language pathology services provided or expected 

to be provided, and an “other” component to cover room 

and board and other capital-related costs. The nursing 

and therapy components are case-mix adjusted to reflect 

the patient’s relative resource requirements; the other 

component is a fixed amount for all patient groups. 

In 2007, the daily nursing base rate for urban SNFs is 

$142.04, the therapy base rate is $106.99, and the other 

component is $72.49. For each day’s payment, the three 

components are summed.
192 Skilled nursing facilities: The need for refor m

Problems with the SNF PPS

CMS, researchers, and the SNF industry have identified 

and discussed two key problems with the SNF PPS: 

(1) patients who need expensive NTA services may have 

difficulty accessing care, and (2) providers are encouraged 

to furnish therapy even when the services are of little or no 

value (MedPAC 2005a). 

First, the RUG classification system does not adequately 

address the variation in providers’ costs for NTA services. 

NTA services make up a sizable share (16 percent on 

average) of total costs but payments are not higher for 

patients who use these services (White et al. 2002, GAO 

1999). Instead, NTA costs are included in the nursing 

component and payments are adjusted according to 

differences in nursing time. Thus, for example, payments 

are the same for patients with and without respiratory 

therapy (which includes tracheotomy and ventilator care), 

as long as nursing costs are the same. 

Research indicates that NTA costs are highly variable 

across stays (White et al. 2002). In addition, CMS 

found that they vary considerably more than nursing 

costs—18-fold compared with 2-fold (CMS 2006). 

CMS has acknowledged that nursing costs are only a 

modest predictor of the variation in NTA use, explaining 

less than 10 percent of the variation (CMS 2006). As a 

result, payments are too low for beneficiaries who need 

above-average amounts of these services, and patients 

can experience access problems. The Office of Inspector 

General (OIG) found that, while access was good (and had 

improved since 2001) for most beneficiaries, especially 

those requiring rehabilitation therapies, hospital discharge 

planners had problems placing patients who needed IV 

antibiotics, expensive drugs, ventilator care, or dialysis 

(OIG 2006). These placement problems were consistent 

with previous findings of OIG studies (OIG 2001, 2000, 

1999). Last year during our site visits, hospital and SNF 

administrators echoed these concerns (MedPAC 2007). 

The second key problem with the SNF PPS is that it 

encourages SNFs to furnish therapy, even when it is of 

little or no benefit. This is because payment is based on 

the amount of therapy services a patient receives or is 

expected to receive, rather than on patient characteristics 

and care needs. As a result, over time the number of 

beneficiaries receiving therapy has increased, as has the 

amount of therapy each beneficiary has received (MedPAC 

2007). Evaluating the benefit of this additional therapy 

is difficult because patients are not routinely assessed at 

discharge. Further, patients often receive the minimum 

number of minutes to qualify them for a payment group 

or do not receive even the minimum, because to qualify 

for some groups the number of minutes can be estimated 

(GAO 2002b). These patterns raise questions about the 

medical appropriateness of some of the therapy furnished. 

For example, some patients may have received medically 

unnecessary services that qualified them for a higher 

payment group, while other patients could have benefited 

from services they were assessed as needing but did not 

receive. Although fewer patients receive the minimum 

qualifying number of minutes now than when the PPS was 

first implemented, the pattern persists (CMS 2006). 

Recent SNF PPS refinements 

CMS implemented refinements to the SNF PPS in 

fiscal year 2006, adding nine groups at the top of the 

classification hierarchy for patients who qualify for 

both rehabilitation and extensive services.2 Early work 

had found that Medicare beneficiaries who qualified 

for high therapy and extensive services categories had 

higher NTA costs (Abt 2000). With the additional groups, 

the classification better explained NTA cost variations 

without requiring additional data from the SNF or the 

prior hospitalization. CMS noted that the refinement 

represented an incremental improvement and did not add 

undue complexity (CMS 2005b). 

CMS also added an across-the-board increase to the 

nursing component for all RUGs. CMS stated that the 

large variability in NTA costs across stays made the 

adjustments for all RUGs appropriate (CMS 2005a). The 

Commission disagreed with this conclusion. In a comment 

letter to CMS when the refinements were proposed, the 

Commission noted that both changes were inadequate 

(MedPAC 2005b). Although RUGs were added for 

patients who typically have above-average NTA costs, 

NTA payments continue to be tied to a component that is 

poorly related to the variation in NTA costs. As a result, 

the expanded set of RUGs only marginally improves the 

accuracy of payments for patients with high use of NTA 

services. We also stated that the across-the-board increase 

was not a good proxy for better targeted payments for 

NTA costs. 

CMS did not adopt any refinements that would dampen 

the incentives to furnish therapy services of little or no 

value. In its comment letter to CMS, the Commission 

stated that the proposed refinements did not correct the 

incentives of the PPS to provide therapy to maximize 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 193

payments (MedPAC 2005b). CMS continues to base a 

substantial portion of the SNF per diem on the amount of 

therapy provided rather than on patient characteristics or 

care needs. 

Further SNF reforms

CMS stated that the RUG refinements it adopted 

represented the “first of an ongoing series of analyses” 

and that the agency would continue to investigate an 

outlier program and alternatives to the RUG system 

(CMS 2005b). The Commission agrees that additional 

changes are needed. In 2006, MedPAC repeated 

its recommendation to modify the PPS, noting that 

CMS’s refinements did not address the Commission’s 

longstanding concerns about the payment system 

(MedPAC 2006b). 

In this section, we consider the research CMS conducted 

that could form the basis of further reforms, taking the 

current design (the RUG–53) as the starting point. The 

reform options address: 

• the poor targeting of payments for NTA services, 

• the incentive to furnish therapy services of little or no 

benefit, and 

• the lack of an outlier policy for exceptionally costly 

stays. 

Several criteria can be used to evaluate each option, 

including: the ability to explain cost differences across 

patients, the incentives to furnish inappropriate care, 

whether the option makes clinical sense, the burden 

placed on the industry, and the ease of implementation. 

The redistributive impacts of each option have not been 

reported and therefore are not included. 

CMS’s research was generally promising. It found options 

that could better target payments for NTA services than 

the current PPS design. Options to predict therapy costs by 

using patient characteristics had more mixed success, most 

likely because the current incentives have distorted the 

use of physical therapy and occupational therapy. While 

decoupling therapy payments from service provision is 

attractive, policymakers would need to be careful not to 

encourage providers to stint on services. Therefore, CMS 

would have to monitor outcomes for all patients or require 

that the therapy furnished be within a specified range of 

the predicted amount. Research also found that an outlier 

policy is likely to benefit hospital-based SNFs more than 

freestanding facilities. Each option varies in the changes it 

would impose on CMS and providers, the ability to predict 

costs, and the time frames needed to implement them. 

Reforms to accurately pay for NTA 

services 

In this set of reforms, CMS would remove the costs of 

NTA services from the nursing component and use a 

new component to calculate a separate NTA payment, 

in addition to the payments established by the other 

three components (nursing, therapy, and other). CMS’s 

researchers found that two predictive models considerably 

increased the ability to explain differences in NTA costs 

across patients: the RUG–58 + service index model (SIM) 

and the new profiles (NP)–NTA model.3 Although the 

researchers considered using hospital diagnosis related 

groups (DRGs) for explaining differences in NTA costs, 

preliminary work did not show promise and the idea was 

set aside. They also explored an outlier policy that would 

make additional payments for stays with unusually high 

NTA costs (see outlier discussion on p. 200). 

The RUG–58 + Service Index Model 

The RUG–58 + SIM starts with the current RUG 

classification system and improves its explanatory power 

by adding MDS variables associated with NTA costs. 

These variables include patient age, use of IV medication 

or respiratory therapy, the presence of respiratory disease, 

and absence of infection during the SNF stay. The model 

also checks to see if IV medications and respiratory 

therapy were used during the patient’s SNF stay, as 

opposed to during the preceding hospitalization.4 This 

check is necessary because the MDS questions about NTA 

services refer to services patients received in the past 14 

days. Depending on when the assessment is conducted, 

this “look-back period” could include services provided at 

the hospital (see NTA data discussion on p. 201). 

Results: CMS reports that the combined RUG–58 + SIM 

more accurately predicts NTA costs than the RUG–58 

without the SIM (Table 8-1, p. 194). A collapsed version 

of the RUGs, called the grouped RUG–58 + SIM (in 

which just the eight broad categories such as rehabilitation, 

rehabilitation plus extensive services, and special care 

were used), did almost as well as the RUG–58 + SIM 

using all the individual RUGs.5

CMS’s researchers also examined how well the alternatives 

correctly predicted high-cost cases (those in the top 10 

percent of NTA costs). The grouped RUG–58 + SIM 
194 Skilled nursing facilities: The need for refor m

outperformed the RUG-only model in accurately predicting 

cases with high NTA costs (RUG–58 + SIM correctly 

identified 46 percent, compared with 38 percent for RUGonly). 

MedPAC contracted with the Urban Institute to run 

these models on more recent data. The models had 

similar results with 2003 data (Urban Institute 2006b). 

The RUG–58 model did a modestly better job than the 

RUG–44 model in predicting NTA costs but was no better 

at accurately predicting high-cost cases. The RUG + 

SIM had more than double the explanatory power of the 

RUG–58 without SIM model and was considerably better 

at accurately predicting high-cost cases. 

Evaluation: Although the SIM models add complexity 

to the RUG model, they double the variation in NTA 

costs explained. The SIM does not require CMS to collect 

any new data but it does require CMS to make systems 

changes to add the NTA component to the payment 

calculations and the billing and cost reporting systems. 

CMS would also need to make systems changes to check 

that use of NTA services occurred during the SNF stay 

and not during the preceding hospitalization. CMS could 

modify the MDS to inquire about IV medication and 

respiratory therapy services furnished during the SNF stay. 

If the SIM were adopted, CMS would need resources to 

educate providers about the NTA component and changes 

to the MDS (if implemented). 

Providers would need to learn about the new NTA 

component and, if the MDS was changed, would need to 

train assessors about the modifications to the questions. 

We do not know how long it would take assessors to 

incorporate the new definitions into practice. 

With regard to the service incentives, clinicians might 

disagree about whether the model creates incentives 

for providers to furnish IV medications and respiratory 

therapy (the high-cost NTA services) because service use 

would raise payments. The model may need to specify 

which IV medication use and respiratory therapies are 

considered to ensure that clinically unnecessary services 

are not furnished to increase payments. For example, 

paying only for oxygen use related to specific medical 

conditions could discourage indiscriminate provision of 

respiratory therapy services. 

NP–NTA model

CMS also reported on the ability of an alternative 

classification system, the NP groupings, to explain 

differences in NTA costs. This classification system 

groups patients into clinically meaningful categories—

rehabilitation, acute, and chronic—using patient clinical 

and functional characteristics and hospitalization history 

from available administrative data (see text box). The 

NP–NTA model starts with the NP classification system 

and adds variables that help explain differences in NTA 

resource use across patients. These factors include: 

• demographic information (e.g., age and gender) from 

the MDS,

• clinical diagnoses gathered from the SNF and 

qualifying hospital stay,

• service indicators from the SNF (e.g., indications of 

use of NTA services from MDS and SNF claims) and 

the qualifying hospital stay (e.g., radiology and drug 

charges), 

• functional status (calculated from the MDS), and

• facility characteristics (e.g., whether the SNF was 

hospital based). 

CMS’s researchers explored models to predict drug, 

respiratory therapy, and other NTA costs—as well as a 

combined model. 

T A B L E

8 –1 RUG + SIMs are better 

predictors of 2001 NTA costs

 than RUG-only models 

Model

Percent of 

NTA cost 

variation 

explained

Percent of

high-cost cases 

accurately 

predicted

RUG–44 6.4% 31%

RUG–58 9.5 38

RUG–58 + SIM 21.9 46

Grouped RUG–58 + SIM 21.2 45

Note:	 RUG (resource utilization group), SIM (service index model), NTA

(nontherapy ancillary [service]). RUG–58 includes some groups that CMS

later collapsed into 53 groups because there were few or no patients in

them. The grouped RUG–58 + SIM categorizes patients into the eight

broad groups used in the RUG system: rehabilitation plus extensive

services, rehabilitation, extensive services, special care, clinically

complex, impaired cognition, behavior only, and reduced physical

function. Percent of high-cost cases accurately predicted is the share of

cases in the top 10 percent of NTA costs accurately predicted to be high

cost.

Source: 	Urban Institute 2006a.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 195

New profiles classification system

Using administrative data, researchers at the 

University of Colorado at Denver and Health 

Sciences Center developed the “new profiles” 

(NP) classification system, which groups skilled 

nursing facility (SNF) patients into three categories—

rehabilitation, acute, and chronic: 

• Rehabilitation patients are admitted primarily for 

rehabilitation services, such as physical therapy and 

occupational therapy, and are defined according 

to their functional status on day 5 of their stay in 

a SNF (calculated from the Minimum Data Set 

using the Barthel index).6 The provision of therapy 

services is not used to group patients. 

• Acute patients are admitted for skilled nursing 

care (e.g., wound care or intravenous medications) 

after an acute medical or surgical event. Diagnosis 

information from the hospital stay identifies 

these patients. 

• Chronic patients are admitted for skilled nursing 

after a hospitalization for a chronic condition or an 

acute flare-up of an underlying or a chronic disease, 

such as chronic obstructive pulmonary disease. To 

distinguish between acute patients and patients with 

an acute manifestation of a chronic condition, claims 

data are used to examine the medical history of the 

patients. Patients with a hospital or SNF stay within 

the past six months are categorized as chronic. 

Starting with this basic classification scheme, CMS’s 

researchers developed separate models to explain 

variations in nontherapy ancillary and therapy 

resource use. 

Results: Researchers found that use of NTA services 

varied considerably across the three patient groups. Some 

factors had a large effect on ancillary use for one or two 

patient groups but not for all three. For example, patients 

in the acute group who had undergone a solid organ 

transplant had costs that were $70 a day higher than those 

for patients without a transplant (Urban Institute 2006a). 

Other factors were associated with higher NTA costs for 

all three patient groups but by very different amounts. For 

example, the use of IV medications increased daily costs 

by $68 for acute cases but only by $39 for patients in the 

chronic group (CMS 2006). Tracheotomy care increased 

daily costs by $36 for chronic patients but only by $15 for 

rehabilitation patients (Urban Institute 2006a). Given this 

variation, the researchers developed separate models for 

each patient group. 

The NP–NTA classifications were better at explaining the 

variation in NTA costs than the RUG–58 groupings. The 

individual NP–NTA models were best able to explain the 

variation in the NTA costs of acute patients and least able 

to explain the variation in NTA costs of chronic patients 

(Table 8-2, p. 196). A combined model predicting all NTA 

costs for all patient groups explained 25 percent of the 

NTA cost variation and accurately predicted 46 percent of 

the high-cost cases (Urban Institute 2006a). Researchers 

recommended using these analyses to develop an NTA 

payment component.

Evaluation: The NP–NTA model starts with a clinically 

meaningful classification system that considers in broad 

terms the reason for the SNF stay. It does a better job than 

the current system of explaining the variation in NTA costs 

but implementing it would require additional resources. 

CMS would need to make several changes such as adding 

the NTA component to the payment calculations, billing, 

and cost-reporting systems. It would also need to install 

the NP classification system, calculate Barthel functional 

status measures for each patient from the five-day MDS 

assessment, and, like the RUG–58 + SIM, confirm that 

the use of NTA services occurred during the SNF stay (or 

modify the MDS to ask about use of NTA services during 

the SNF stay). 

Information about a patient’s preceding hospitalization 

would need to be transferred to the SNF before a provider 

could know the payment group to which a patient 

would be assigned. While this information is currently 

communicated between many hospitals and SNFs as a 

way to facilitate care coordination, such communication 

does not always occur. One benefit of this alternative is 

that all SNFs would receive this information about every 
196 Skilled nursing facilities: The need for refor m

patient, thus facilitating their care planning. CMS could 

explore the possibility of modifying the common working 

file (CWF)—a database CMS contractors maintain 

that includes merged information about beneficiary 

entitlement, utilization, and payment history—to make 

hospital utilization information available to SNFs through 

their fiscal intermediaries. Although the development of a 

common assessment tool and an electronic health record 

would facilitate the flow of information between the 

hospital and the SNF, neither is near implementation. 

As with the RUG–58 + SIM, the NP–NTA model could 

create incentives for providers to furnish IV medications 

and respiratory therapy because using these services would 

raise payments. Refinements to the service use categories 

could reduce these incentives. 

Comparing the NTA alternatives 

The RUG–58 + SIM and the NP–NTA alternatives are 

better predictors of NTA costs than the current PPS design. 

The NP–NTA is the best predictor but more resources 

would be required to implement it than a RUG–58 + 

SIM because it uses a different classification system and 

requires data about the prior hospitalization (Table 8-3). 

The RUG + SIM option would be easier to implement than 

the NP–NTA option but its explanatory power is somewhat 

more limited.

Removing incentives to furnish therapy of 

little or no value 

CMS’s researchers explored models to accurately predict 

therapy costs without including incentives to furnish 

services. They examined two classification systems 

to group patients with similar therapy needs—NPs 

and DRGs—and compared them with the RUG–44 

system previously used in the SNF PPS. The alternative 

classification systems predict therapy care needs using 

patient characteristics likely to be associated with needing 

more or less therapy, rather than therapy minutes. CMS 

could base the therapy portion of the payment on either 

classification system, replacing the current therapy 

component. However, because either model would base 

payments on predicted need, providers would have an 

incentive to furnish fewer services yet receive the same 

payment. The potential for underprovision is a particular 

concern because we do not have good information about 

how much therapy patients can benefit from or what 

outcomes they achieve from the therapy they receive. The 

incentive to stint could be dampened if CMS gathered 

patient assessment information at discharge and used it 

to monitor the amount of therapy furnished, compared 

outcome measures, and implemented pay for performance. 

Alternatively, CMS could require providers to furnish 

therapy services within a specified range of the amount 

predicted. 

NP therapy model

The NP therapy model starts with the NP classification 

system and adds variables that help explain differences in 

therapy costs across patients. Factors include the functional 

and cognitive status of the patient gathered from the MDS 

and information from the prior hospitalization indicating a 

patient’s probable need for therapy services (e.g., diagnosis 

and previous therapy use). Because the patterns of use for 

physical therapy (PT) and occupational therapy (OT) are 

T A B L E

8 –2 Ability of the NP–NTA model to accurately predict 

2001 NTA costs varies by patient group and type of NTA

Percent of NTA cost variation explained, by patient type

Type of NTA Acute Chronic Rehabilitation All

Drugs 17% 10% 13% 12%

Respiratory therapy 47 48 45 48

Other NTA 31 15 26 25

All NTAs 25 23 24 25

Note:	 NP (new profiles), NTA (nontherapy ancillary [service]). Other NTA includes lab tests, basic radiology procedures, and parenteral feeding.

Source:	 Urban Institute 2006a.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 197

estimate the costs of needed care. The NP model 

performed better than the RUG–44 model in predicting 

SLP costs and the NP model was equally proficient at 

predicting PT and OT costs and SLP costs. In comparison, 

the RUG–44 model is considerably better at predicting PT 

and OT costs than at predicting SLP service costs. 

Evaluation: The NP therapy models have one clear 

advantage over the current RUG system. They establish 

payments on the basis of patient characteristics, not service 

use. Like the NP–NTA model, the NP therapy model 

would require CMS to make several systems changes 

so different from those for speech–language pathology 

(SLP) services, CMS’s researchers examined them 

separately. 

Results: CMS reported mixed success of the NP models 

in predicting therapy costs compared with the RUG system 

(Table 8-4, p. 198). Researchers found that the NP model 

for PT and OT did not predict these therapy costs as well 

as the RUG–44 model did. This result is not surprising 

given that the RUG design encourages providing therapy 

that may be unrelated to the patient’s characteristics and 

care needs. Given the distortions in the amount of therapy 

currently furnished, it may be difficult to accurately 

T A B L E

8 –3 Comparison of classification systems to predict NTA costs

Characteristic RUG–58 + SIM NP–NTA

Basic design • RUGs

• SNF service use variables

• New classification system

• Patient and service use variables from prior hospital

stay and SNF stay

Amount of variation in NTA

costs explained

22% 25%

Clinical meaning of the

classification system

Slightly more clinically meaningful than

RUG-only system because SIM variables

point to clinical conditions that lead to

higher NTA costs.

Moderate. Three broad groups make clinical sense.

CMS burden • Requires no new data.

• Systems changes to add new component,

revise the billing and cost reporting,

and verify NTA use during SNF stay (or

modify MDS questions).

• Educate providers about new NTA

component and MDS changes (if made).

• Requires no new data.

• Systems changes to add new component, revise the

billing and cost reporting, verify NTA use during

SNF stay (or modify MDS questions), add new

classification system, calculate new functional status

scores from MDS data, and merge hospital and SNF

stay information.

• Educate providers about new NTA component, new

classification system, MDS changes (if made), and

method of transferring information from hospital.

Provider burden Educate staff about new NTA component.

If MDS is revised, train assessors on

revisions.

Educate staff about new NTA component, method of

getting information from hospital, and classification

system. If MDS is revised, train assessors on

revisions.

Incentive to furnish

inappropriate NTAs

Possible. Same incentive as NP–NTA. Possible. Same incentive as RUG–58 + SIM.

Note: 	 NTA (nontherapy ancillary [service]), RUG (resource utilization group), SIM (service index model), NP (new profiles), SNF (skilled nursing facility), MDS (Minimum

Data Set). 

Source:	 MedPAC analysis of options outlined in CMS 2006 report to the Congress. 
198 Skilled nursing facilities: The need for refor m

such as adding a new classification system, replacing the 

current therapy component, calculating functional status 

measures from the five-day MDS assessment to classify 

each patient into an NP patient group, and revising the 

cost reports and billing. CMS would also need to use 

information from the prior hospitalization (for which 

the CWF may be useful) and verify use of NTA services 

during the SNF stay. 

The NP therapy models would also impose changes on 

providers. Providers would need to learn about the NP 

classification system and they may be confused by having 

different classification systems for the therapy and nursing 

components. Information about a patient’s preceding 

hospitalization would need to be transferred to the SNF 

before a provider could know the payment group to which 

a day would be assigned. The CWF and inquiries to the 

fiscal intermediaries might be a feasible approach for 

providers to gather this information. 

Because payments would be based on predicted need 

for therapy, providers may underfurnish services. CMS 

would need to monitor patients’ outcomes to ensure that 

beneficiaries continue to receive the therapy they need. 

Monitoring service use underscores the need for CMS to 

require patient assessments at discharge (see discussion on 

p. 202). 

DRG + functional status model 

An alternative therapy model starts with the DRG to which 

the patient was assigned during the prior hospitalization. 

Because DRGs do not distinguish among patients with 

different functional dependencies, the researchers also 

added MDS-based measures of functional and cognitive 

status to the model. 

Results: The DRG + functional status model explained 

12.5 percent of the variation in per day therapy costs, 

suggesting that the model is worse than the current RUG 

system at predicting PT and OT service costs but slightly 

better at predicting SLP service costs. The DRG model 

accurately predicted 24 percent of the high-cost cases 

(Urban Institute 2006a). It is possible that expanding the 

factors included in the model, such as specific indicators 

for clinically complex patients, would increase its 

explanatory power.

Evaluation: Although this model does not consistently 

improve on the RUG classification system, it does have 

two advantages over the RUG system: It does not include 

incentives to furnish therapy services and, because DRGs 

have a clinical logic to them, it has considerable clinical 

appeal. By using hospital diagnoses and functional status 

measures during the SNF stay, the model uses a fair 

amount of available clinical information. 

This option does not require any new information 

beyond what SNFs and hospitals currently gather. It 

does require systems changes to replace the therapy 

component and revise the billing and cost reporting. It 

would also need to add a new classification system and 

merge hospital and SNF stay information. SNF providers, 

particularly freestanding facilities, are unlikely to have 

detailed knowledge of the DRG system, so CMS would 

need to train them. SNFs will also need a way to obtain 

information about the preceding hospitalization. 

Some interest in a DRG-based classification system 

stems from the lack of reliable diagnosis information for 

SNF stays. While using hospital diagnosis information 

is a reasonable way to obtain such information, a better 

long-term strategy would be to require International 

Classification of Diseases, Ninth Revision, Clinical 

Modification (ICD–9–CM) codes on SNF claims (see SNF 

diagnoses data discussion on p. 201). The claims currently 

have space for these codes but facilities, particularly 

freestanding facilities, often do not use them. More accurate 

SNF coding is likely to increase the explanatory power 

of any model and would greatly improve our ability to 

compare the costs, resource use, and outcomes of patients. 

T A B L E

8 –4 Ability of NP therapy to accurately

 predict 2001 therapy costs is mixed 

Percent of therapy 

cost variation 

explained Percent of 

high-cost 

cases 

accurately 

Type of therapy RUG–44 predicted

NP

therapy 

model

Physical and

occupational therapy 39% 19% 28%

Speech–language

pathology services 11 19 42

Note:	 NP (new profiles), RUG (resource utilization group). Percent of high-cost

cases accurately predicted is the share of cases in the top 10 percent of

nontherapy ancillary costs accurately predicted to be high cost.

Source:	 Urban Institute 2006a.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 199

Comparing the therapy alternatives 

Moving away from basing payments on providing therapy 

is likely to involve sacrificing explanatory power (for 

PT and OT services) and will require CMS resources to 

implement a different therapy component design (Table 

8-5). Indeed, it is difficult to predict current therapy 

costs given the distortions in the payment system. Both 

alternatives (NP therapy and DRG + functional status) 

were better than the RUG–44 model at predicting SLP 

costs. NP therapy models did a better job of predicting the 

costs of PT and OT combined and SLP services than the 

DRG + functional status alternative. 

Because both models use patient characteristics to predict 

resource use, they do not include incentives to furnish 

unneeded therapy services. The DRG + functional status 

model may have more clinical meaning than the NP 

therapy model because it uses more information from 

the preceding hospital stay, but both models have more 

clinical meaning than the current RUG-based component. 

A predictive model may encourage facilities to stint on 

services; therefore, CMS would need ways to ensure 

that beneficiaries receive the services they need, such 

as evaluating patient outcomes or requiring that the 

amount of therapy provided is within a specified range 

of the predicted amount. Gathering patient assessment 

T A B L E

8 –5 Comparison of classification systems to predict therapy costs

Characteristic NP therapy DRG + functional status

Basic design • New classification system

• Patient and service use variables from prior

hospital stay and SNF

• DRG

• Functional status measures from SNF

Amount of variation in NTA

costs explained

• PT and OT: 19%

• SLP: 19%

12.5% for all therapies

Clinical meaning of the

classification system

Moderate. Three broad groups make

clinical sense.

Considerable.

CMS burden • Requires no new data.

• Systems changes to replace therapy

component, add new classification system,

calculate new functional status scores from

MDS data, merge hospital and SNF stay

information, and revise billing and cost

reporting.

• Educate providers about new component,

classification system, and mechanism to get

information from hospital. 

• Requires no new data.

• Systems changes to replace therapy component,

add new classification system, merge hospital

and SNF stay information, and revise billing

and cost reporting.

• Educate providers about new component,

classification system, and mechanism to get

information from hospital. 

Provider burden Training on a new classification system

and mechanism for getting information

from hospital.

Training on a new classification system

and mechanism for getting information

from hospital.

Incentive to furnish

inappropriate NTAs

None None

Note:	 NP (new profiles), DRG (diagnosis related group), NTA (nontherapy ancillary), PT (physical therapy), OT (occupational therapy), SLP (speech–language pathology

services), MDS (Minimum Data Set), SNF (skilled nursing facility). 

Source:	 MedPAC analysis of options outlined in CMS 2006 report to the Congress. 
200 Skilled nursing facilities: The need for refor m

information at admission and discharge is essential to 

monitoring patient outcomes. 

Paying for exceptionally costly care 

CMS’s researchers also considered outlier payment 

policies for stays with unusually high total costs or NTA 

costs. By defraying some of the costs of treating unusually 

expensive cases, outlier policies protect providers from 

extreme financial losses. Medicare has outlier policies for 

most of its PPSs except SNFs. Most other PPSs pay for 

services on a per stay or per episode basis. By comparison, 

Medicare pays SNFs on a per diem basis, so payments 

increase as a patient’s stay increases. The SNF payment 

system thus provides some built-in protection against 

extreme financial losses for patients with long stays. In 

addition, the SNF PPS excludes the costs of several highcost, infrequently provided services (e.g., ambulatory 

surgery performed in an operating room, chemotherapy 

agents, and customized prosthetic devices) and pays for 

them separately under Part B.7 This policy may help 

reduce the number of outlier cases that otherwise might 

occur if the costs of such services were included in the 

daily rate. 

To retain incentives for providers to be efficient, 

Medicare’s outlier payments to providers cover only a 

portion of costs above a fixed loss amount. Before outlier 

payments are made, providers incur the costs covered by 

the PPS payment and the fixed loss amount. Then, outlier 

payments compensate providers for a portion of the losses 

beyond the fixed loss. Outlier payments are typically 

financed by lowering the base rate for all cases. Base rates 

are reduced by 2 percent to 8 percent in the other PPSs 

with outlier payments. 

Although the SNF PPS is a per diem payment system, 

outlier policies typically consider a patient’s costs during 

the entire stay. Given the large differences in per day 

costs between freestanding and hospital-based providers 

(hospital-based providers have much higher daily costs but 

comparable per stay costs), an outlier policy that focuses 

on per stay costs would be more neutral toward facility 

type than a per day outlier policy. Outlier policies also 

generally consider the total costs of care and not specific 

categories of costs. But because NTA costs are a specific 

concern in the SNF PPS, CMS’s researchers investigated 

separate outlier policies for stays with extremely high total 

costs as well as outlier policies for stays with extremely 

high NTA costs. 

Results: CMS’s researchers found that total and NTA 

cost outlier policies are likely to have different effects by 

facility type because the cost distributions differ by facility 

type. While the median total and NTA costs are relatively 

similar, the costs at the 99th percentile vary considerably 

(Table 8-6). 

CMS’s research found that total cost and NTA cost 

outlier policies would improve the financial condition 

for SNFs that are hospital based, government owned, or 

small facilities as well as those that have a large share of 

Medicare patients. The estimated impact on freestanding 

facilities was more variable. The aggregate financial 

condition of freestanding SNFs remained the same under 

an NTA cost outlier policy, but it declined under a total 

cost outlier policy. That is, under a total cost outlier policy, 

the freestanding facilities would pay more into the outlier 

pool (in the form of lower base payments) than they would 

receive in outlier payments (Urban Institute 2006a). 

Evaluation: In separate work, the Government 

Accountability Office found that hospital-based 

facilities had higher routine costs than freestanding 

facilities because of differences in case-mix severity, 

cost inefficiencies, and cost accounting practices (GAO 

2002a). Some of these reasons clearly do not warrant 

higher Medicare payments. However, an outlier policy 

is a promising avenue to explore as a way to cushion the 

financial impact of extremely costly care that is beyond 

the control of the provider. Outlier policies do not require 

additional data but they would require CMS to make 

systems changes to calculate payments. An outlier policy 

could target stays with unusually high total or NTA costs, 

although outlier policies typically are not used to correct 

known systematic problems with a classification system. 

The Commission has previously discussed outlier policies 

for SNFs and noted that changes to the classification 

system—rather than an outlier policy—may better address 

a consistent bias in the PPS, such as the poor targeting of 

payments for NTA services (MedPAC 2005a). However, an 

NTA outlier policy would be relatively easy to implement 

and could be an interim solution until more fundamental 

reforms are made to the classification system. Such 

reforms would not eliminate the rationale for an outlier 

policy—to compensate providers for some of the costs of 

exceptionally high-cost cases. 

CMS plans to continue its investigation of an outlier 

policy. It will evaluate total cost and NTA cost outlier 

models in addition to the basic components of an outlier 

policy—the share of SNF payments set aside for outlier 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 201

payments, the fixed loss amount, and the portion of the 

costs the outlier payment will cover above the fixed loss 

(CMS 2006). 

Better data are needed to develop reform 

options 

The research efforts to develop alternatives to the 

current SNF PPS underscore several problems with 

the information collected about SNF patients. Better 

information—about use of NTA services in SNFs, SNF 

patients’ diagnoses, SNF nursing costs, and patients’ 

functional status at admission and discharge—would help 

explain differences in resource needs across patients and 

the relationship between costs and outcomes. 

Accurate information about use of NTA services 

in SNFs

To accurately predict the costs of SNF care, the payment 

system should closely track the costs of the NTA 

services that SNF patients need. However, under current 

assessment requirements, it is difficult to know which 

NTA services the SNF furnished as opposed to those 

furnished during the preceding hospital stay. The MDS 

asks about NTA services received in the past 14 days. 

At the day 5 assessment, this “look-back period” covers 

days spent in the hospital; thus, the recorded use of NTA 

services will include services the hospital provided. CMS’s 

researchers found that the MDS alone is an unreliable 

indicator of use of NTA services in a SNF. In comparing 

information from the day 5 assessment and SNF claims, 

CMS’s researchers found that about half the stays indicated 

IV medication use in the MDS; yet, few had SNF charges 

for the NTA services. The researchers concluded that the 

NTA services were most likely furnished during the prior 

hospital stay. 

To correctly identify NTA services furnished while the 

patient was in the SNF, CMS needs to revise the MDS to 

ask about services furnished only during the SNF stay. 

This revision could take the form of additional questions 

or changes to the definition used in the existing questions. 

Some providers prefer that questions be added to the MDS 

because they use the current information for care planning. 

CMS plans to evaluate potential modifications to the MDS 

so that only services furnished after admission to the SNF 

are reported (CMS 2006). 

SNF diagnosis information

To correctly classify patients with similar resource needs, 

CMS needs accurate information about diagnoses and 

comorbidities. CMS noted that its researchers found 

incomplete or missing diagnosis information on SNF 

claims. As proxies, the researchers used diagnoses 

from the prior hospital stay. Because the SNF stay is a 

continuation of the hospital stay, for many patients this 

information will accurately project the care needs during 

the SNF stay. However, information about some patients’ 

hospital stays (e.g., those whose conditions have changed 

or those with chronic conditions unrelated to the hospital 

stay) may not accurately represent their clinical condition 

in the SNF. 

The Commission urges CMS to require that SNFs include 

accurate and complete diagnosis codes on their claims. 

Claims have fields for this information but the fields are 

not required for payment. Even when codes are recorded, 

it is common for SNFs to use generic codes that do not 

provide much information. SNFs should be required 

to use full five-digit ICD–9–CM codes to describe the 

principal diagnosis and comorbidities of each patient stay. 

If CMS instructed the fiscal intermediaries to reject claims 

without this information, providers would quickly supply 

it. For example, when CMS needed revenue codes from 

outpatient therapy providers to operationalize the therapy 

caps, its contractors rejected claims without the revenue 

codes. Within a year, the vast majority of claims included 

this information. 

Alternatively, the MDS could gather improved diagnosis 

information. CMS indicated that it will consider including 

T A B L E

8 –6 Hospital-based SNFs had higher costs

 per stay than freestanding SNFs 

Type of per 

stay cost,

by SNF type

Percentile

Median 95th 99th

Total

Freestanding $5,609 $20,913 $29,567

Hospital based 6,272 20,977 36,800

NTA

Freestanding 735 3,968 8,177

Hospital based 738 4,997 10,800

Note:	 SNF (skilled nursing facility), NTA (nontherapy ancillary). Costs are

adjusted for geographic differences in labor costs.

Source: 	MedPAC analysis of 2003 SNF stay costs prepared by the Urban Institute. 
202 Skilled nursing facilities: The need for refor m

variables that increase the accuracy of the diagnosis 

information in developing the next version of the MDS 

(CMS 2006). 

SNF nursing costs

Accurate nursing cost information at the patient level is 

fundamental to measuring differences in care needs across 

patients, especially in SNFs where nursing care represents 

a large portion of total resource use. CMS uses staff time 

measurement (STM) studies to gather staff time data 

on individual patients. These STM studies are costly to 

administer and therefore are undertaken only periodically 

in a sample of facilities. In 2006, CMS undertook the 

first STM survey since the PPS was implemented in 

1998. CMS will use results from this survey to update the 

relative weights of the nursing component. 

CMS needs a timely and less expensive way to gather 

patient-level nursing cost data. In 2004, MedPAC 

recommended that the Secretary direct SNFs to report 

nursing costs separately from routine costs when 

completing the SNF Medicare cost reports (MedPAC 

2004). It would be useful for these costs to be categorized 

by type of nurse (RN, licensed practical nurse (LPN), 

and nurse aide). While not a substitute for patient-level 

information, this facility-level information would allow 

us to examine the relationship between staffing, case mix, 

quality, and costs, especially for facilities that treat a large 

share of Medicare patients. In these facilities, the nursing 

costs are likely to be reasonably accurate for beneficiaries. 

However, for SNFs that treat few Medicare patients, 

facility-level cost information may not accurately reflect 

the costs of treating beneficiaries. 

SNF patient assessment information at 

admission and discharge

The lack of information about patients’ conditions at 

admission and discharge hinders CMS’s ability to measure 

patient changes during the SNF stay and to compare 

patient outcomes across post-acute settings. While CMS 

requires SNFs to assess patients on day 5 of the SNF 

stay, there is variation in when these assessments are 

completed. Only a small share of patients (4 percent) are 

assessed within three days of being admitted (MedPAC 

2006b). As a result, differences in patients’ conditions 

can be the result of actual patient differences or of the 

timing of the assessment. In addition, CMS does not 

require SNFs to assess patients at discharge, so we cannot 

know how patients’ conditions changed during their 

stays. Assessments are required on day 14, but many 

beneficiaries (45 percent) do not stay that long. In 2005 

and 2006, the Commission recommended that CMS 

collect information about activities of daily living at 

admission and discharge (MedPAC 2006b, 2005c). CMS 

is conducting a demonstration on a patient assessment 

instrument to be administered at hospital discharge and 

at discharge from post-acute care (PAC) settings, but the 

findings will not be available until 2011. 

Next steps

The payment system requires reforms to accurately pay 

for SNF services without creating incentives to furnish 

unnecessary care. Building on CMS’s research, options 

can better target payments for NTA and therapy services 

and for stays with unusually high costs. Many of the 

options will require trade-offs between their predictive 

abilities and the burdens they impose on CMS and 

providers. The options differ in the time frames needed 

for implementation. Some options, such as an outlier 

policy and the NTA option using RUG + SIM, could be 

implemented in a relatively short time. Other options 

would require additional resources and time because 

they would involve modifying the MDS and transferring 

data between the SNF and the hospital. In the long term, 

CMS may want to consider developing a payment for an 

entire PAC episode of care or bundling hospital and SNF 

payments. 

The options also differ in whether they facilitate future 

comparisons of costs, payments, and outcomes across 

PAC settings. Some options have an advantage in 

requiring fewer changes but do not create a foundation 

for future refinements. For example, options for 

improving payments for NTA services that build on the 

RUG classification system will be limited in their ability 

to improve the accuracy of payments because NTA costs 

are not closely linked to these patient groups. As we 

learn how to more accurately predict SNF costs, we can 

consider how this information can be used in other PAC 

payment systems. The Commission and CMS have stated 

their interest in putting PAC on a common metric at some 

future time. While the development of a payment system 

to use across all PAC sites is a longer term goal, it is years 

from implementation. Meanwhile, SNF payments need 

to be more accurate than they are now, thus warranting 

interim reforms. 

Over the next year, MedPAC plans to further explore 

alternative ways to reform the PPS. The Commission has 

contracted with the Urban Institute to improve the NTA 

and predictive therapy models it developed for CMS and 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 203

to consider new ones, such as models that explain per 

stay costs. A per stay unit of payment would create more 

incentives for providers to be efficient than a per day unit, 

but it could encourage providers to stint on services. Given 

the considerable variation in the SNF population, per day 

models may be able to explain more of the variation in 

costs across patients than per stay models. Researchers 

will refine previously developed per stay models that 

might represent viable alternatives to a per diem PPS. 

In addition to evaluating individual alternatives, we will 

assess combinations of options. For example, a reform 

might replace the current therapy component with a 

predictive one, add a separate payment component for 

NTA services, and establish an outlier policy. Our intent is 

to contribute to the development of a PPS that accurately 

pays for SNF services, including NTA services, while 

discouraging providers from furnishing therapy services 

that may be of no value to beneficiaries. In this way, the 

program will be more likely to purchase services of value 

while helping to ensure access for all beneficiaries.

Hospital-based SNFs: Analysis from the 

hospital perspective

Hospital-based SNFs have had much poorer financial 

performance under Medicare than their freestanding 

counterparts; in 2005, Medicare margins for hospitalbased SNFs were −85 percent compared with a 13 percent 

margin for freestanding SNFs. Since 1998, one-third of 

hospital-based SNFs have closed, many as a result of their 

poor financial condition. These closings raise questions 

about why some hospitals keep their SNFs open in the face 

of what appears to be their poor financial performance 

under Medicare and what factors other than financial 

performance might play a role in the decisions to retain or 

close them. 

To better understand these issues, the Commission 

undertook qualitative and quantitative analyses of hospitalbased SNFs. We interviewed hospitals that have or recently 

had hospital-based SNFs to gain insight about why the 

facilities remained opened or closed. We also conducted 

detailed analysis of the similarities and differences 

between hospital-based and freestanding SNFs, including 

their facility characteristics, the mix of patients they treat, 

and their patterns of care. In addition, we looked at the 

profitability of hospital-based SNFs setting aside overhead 

and capital costs for both the hospital and the SNF stay. 

We identified three models of hospital-based SNFs that 

we further examined to help us understand the variation 

among them and the roles they play in their hospitals. 

From the early 1990s to 1998, the number of hospitalbased SNFs increased 62 percent (Figure 8-1, p. 204). 

After the SNF PPS was implemented in 1998, however, 

more than one-third of hospital-based SNFs closed. 

Currently, hospital-based SNFs account for about 8 

percent of the facilities offering skilled nursing services 

and 16 percent of Medicare cases using SNF services after 

discharge from the hospital. 

How do hospital-based and freestanding 

SNFs differ?

In this section, we look at how select facility and patient 

characteristics and patterns of care differ between hospitalbased and freestanding SNFs. We also examine the large 

differences in financial performance between the two 

types of facilities. We find that hospital-based SNFs 

tend to be smaller and have a higher concentration of 

Medicare patients. The patients appear similar in many 

respects, but hospital-based SNFs see a higher proportion 

of patients with certain conditions, such as hip and knee 

replacements. We also see differences in patterns of care—

the use of other PAC services and the SNF lengths of stay. 

Hospital-based SNF patients have shorter stays but they 

use another PAC service more frequently than patients 

discharged from freestanding facilities. 

Differences in facility characteristics

Hospital-based and freestanding facilities differ in size 

and payer mix. Hospital-based SNFs are generally much 

smaller than their freestanding counterparts. The median 

hospital-based facility has 26 beds, whereas the median 

freestanding facility is almost four times as large with 98 

beds (Table 8-7, p. 204). Medicare accounts for 73 percent 

of patients in hospital-based facilities, compared with 12 

percent in freestanding facilities. In addition, the average 

stay in a hospital-based SNF is about half the length of 

stay in a freestanding SNF. 

We also see differences in staffing between the two types 

of facilities. Hospital-based SNFs have more staff per bed 

and per patient day than freestanding SNFs. Hospitalbased SNFs also have a more skilled staff, with more 

licensed nursing personnel (RNs and LPNs) per bed than 

freestanding facilities (Liu and Black 2003). This higher 

level of staffing contributes to much higher routine costs 

per day in hospital-based units (see cost discussion, 
204 Skilled nursing facilities: The need for refor m

p. 207). Even though we observe rather substantial 

differences in the characteristics of freestanding and 

hospital-based SNFs, some hospital-based facilities look 

much more like freestanding SNFs—with more beds, low 

Medicare shares, and longer SNF stays (see discussion on 

how hospital-based SNFs differ, p. 209).

Characteristics of hospital inpatients bound 

for SNFs 

Inpatients who go on to use hospital-based SNFs differ 

slightly from those who go on to use freestanding SNFs. 

The average beneficiaries using hospital-based SNFs tend 

to be slightly younger and have lower severity-of-illness 

(SOI) scores as measured by all patient refined DRGs 

for the inpatient care preceding their SNF stay (Table 

8-8). Although relative SOI scores are lower, the share 

of inpatient hospital days spent in an intensive care unit 

before the SNF stay is higher for patients discharged to 

hospital-based SNFs.

Hospital-based SNFs also see a higher concentration 

of certain types of patients. For example, 27 percent 

of hospital-based SNF patients had been treated in the 

hospital for musculoskeletal conditions, such as hip 

and knee replacements, compared with 18 percent of 

patients in freestanding SNFs. A disproportionate share 

of inpatients also go to hospital-based SNFs after major 

The supply of hospital-based skilled nursing facilities increased

 before and declined after the prospective payment system

 for skilled nursing facilities was implemented in 1998

Source:	 MedPAC analysis of CMS Online Survey, Certification, and Reporting system data.

The supply of hospital-based SNFs increased before and declined after the prospective

payment system for skilled nursing facilities was implemented in 1998

FIGURE

8-1

Note and Source in InDesign Number of facilities

2,500

1,000

1,500

2,000

500

0

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

F i g ure

8 –1

T A B L E

8 –7 Differences between hospital-based

 and freestanding SNFs 

SNF characteristic

Hospital 

based Freestanding

Beds 26 98

Medicare patient share 73% 12%

Average length of stay (in days) 13 27

Staffing per bed (in FTEs) 1.00 0.82

Note:	 SNF (skilled nursing facility), FTE (full-time equivalent). Median values are

shown.

Source:	 MedPAC analysis of 2004 hospital cost reports and claims files

from CMS.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 205

small and large bowel procedures and cellulitus, a serious 

bacterial infection of the skin. 

Inpatients who come from a nursing home tend to be 

discharged from the hospital back to a freestanding SNF 

rather than to a hospital-based facility; 2.4 percent of 

hospital-based SNF patients were nursing home residents, 

compared with 5.2 percent of patients in freestanding 

SNFs.8

Patterns of care in hospitals with and 

without SNFs

Although hospitals with and without SNFs discharge 

patients with similar frequency to PAC, they use different 

PAC services. Hospitals with a SNF send their patients to 

SNF care more often than do hospitals without a SNF. In 

hospitals with SNF units, about 17 percent of patients are 

discharged to a SNF, compared with about 14 percent of 

patients in hospitals without a SNF (Table 8-9). However, 

hospitals with a SNF use their own SNF for only about 

a third of the patients utilizing SNF services, raising the 

question of how hospitals decide which patients will use 

their SNF or another SNF. 

Patients in hospitals with SNF services are less likely 

to use other types of PAC services immediately after 

discharge than patients discharged from a hospital without 

a SNF. For example, 9.7 percent of them use home health 

care compared with 11.2 percent of patients discharged 

from hospitals without a SNF. 

Patterns of care in hospital-based and 

freestanding SNFs

Among the patients using SNF care, those who use 

hospital-based SNFs had slightly shorter preceding 

hospital stays than patients discharged to freestanding 

SNFs. In some DRGs (major joint procedures, stroke, 

major small and large bowel procedures), the stays are 

shorter by a day or more. However, in a few DRGs 

(miscellaneous digestive disorders, gastrointestinal 

hemorrhage, and circulatory disorders with acute 

myocardial infarction and major complications), the 

average acute inpatient hospital stay is longer for patients 

discharged to hospital-based SNFs than to freestanding 

SNFs. For those DRGs, the average SOI score for the 

patient is also higher, indicating that hospital-based SNFs 

may be taking the more complicated patients with these 

conditions.9

The average stay in a hospital-based SNF is about half as 

long as in a freestanding SNF. This difference holds across 

all inpatient diagnoses with high use of SNFs. The shorter 

stays in hospital-based SNFs may be related to the types 

of patients treated but they also may be due to hospitalbased SNFs’ tendency to discharge patients to another 

PAC setting. Overall, 9 percent of patients discharged 

from a hospital-based SNF are discharged to another SNF, 

compared with fewer than 2 percent of patients using 

freestanding SNFs (Figure 8-2, p. 206). Hospital-based 

SNFs are also twice as likely to discharge patients to home 

T A B L E

8 –8 Characteristics of hospital

 patients who go to SNFs 

Patient characteristic

Hospital 

based Freestanding

Average age 78.8 80.4

Percent SOI 3 or 4 42.1% 46.6%

Share of inpatient days in ICU 27.0 23.4

Percent in MDC8 (musculoskeletal) 27.0 18.3

Percent nursing home residents 2.4 5.2

Note:	 SNF (skilled nursing facility), SOI (severity of illness), ICU (intensive care

unit), MDC (major diagnostic category). SOI is measured using all patient

refined diagnosis related groups from 3M Health Information Systems.

Values range from 1 to 4, with 4 being the most severely ill. Values shown

are patient-level averages.

Source: 	MedPAC analysis of 2004 Medicare Provider Analysis and Review file

from CMS.

T A B L E

8 –9 Hospitals with SNFs are more likely

 to send their patients to SNFs 

Type of PAC setting

Percent of hospital 

discharges using PAC

Hospital 

with SNF

Hospital 

without SNF

Hospital’s own SNF 5.5% 0.0%

Other SNF 11.4 13.9

Home health agency 9.7 11.2

Inpatient rehabilitation facility 3.2 4.0

Long-term care hospital 0.7 0.9

Total 30.6 30.0

Note:	 SNF (skilled nursing facility), PAC (post-acute care). Values shown are

aggregate averages.

Source:	 MedPAC analysis of 2004 claims files from CMS.
206 Skilled nursing facilities: The need for refor m

health care, with 25 percent of patients discharged to home 

health care compared with 12 percent of freestanding 

discharges. (A large share of these patients (44 percent) 

use the hospitals’ home health agency.) Use of inpatient 

rehabilitation hospitals and long-term care hospitals is also 

higher for patients discharged from hospital-based SNFs. 

Thus, some hospital-based SNFs may focus on providing 

care at the less intensive end of the hospital stay and the 

most intensive part of the SNF stay. This is consistent 

with a smaller proportion of hospital-based SNF patients 

(41 percent) being discharged directly home without any 

additional PAC services compared with freestanding SNF 

patients (48 percent).

A larger proportion of freestanding SNF patients are 

discharged to a nursing home after their SNF stay (7 

percent) compared with 5 percent of hospital-based SNF 

patients. This could reflect differences in patient selection 

consistent with other research showing that hospitals tend 

to not use their hospital-based SNF for patients who are 

unlikely to be discharged home (Stearns et al. 2006). 

Hospital-based and freestanding SNFs also differ in their 

readmission rates to hospitals. Of the patients discharged 

to a freestanding SNF, 23 percent are readmitted to the 

hospital within 30 days of discharge from the hospital, 

compared with 19 percent of inpatients discharged to a 

hospital-based SNF.10 Differences in readmission rates 

could be due to differences in the complexity and mix 

of the patients. In addition, hospital-based SNFs have a 

larger proportion of patients recovering from hip and joint 

replacements, who have a much lower readmission rate 

than patients with other conditions frequently treated with 

SNF care, such as pneumonia and heart failure. Hospitalbased SNFs may also have fewer readmissions because 

of their close proximity to the hospital (which makes 

physician visits more common) and their higher staffing 

and greater use of RNs. With its more immediately 

Percent of SNF cases discharged to different PAC settings

Note: 	 SNF (skilled nursing facility), PAC (post-acute care). Subsequent use of a second PAC provider is determined using matched claims files for the different PAC

services. Discharge to a nursing home is based on a different source. It is determined based on the discharge destination field on the claim and not on a matched

claim. Total percent of cases discharged from hospital-based SNFs to other PAC settings was 43.8 percent; total percent of cases discharged from freestanding

SNFs to other PAC settings was 23.1 percent. Patient-level averages are shown.

Source: MedPAC analysis of 2004 claims files from CMS.

Percent of SNF cases discharged to different PAC settings FIGURE

8-X

Note and Source in InDesign Percent of discharges from SNF

30

20

10

5

25

15

0

Inpatient 

rehabilitation 

facility

Home health 

care

Psychiatric 

hospital

or unit

Other SNF Long-term care 

hospital

Hospice Nursing

home

Hospital-based SNF

Discharged from:

Freestanding SNF

0

5

10

15

20

25

30

F i g ure

8 –2
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 207

available resources, the hospital-based SNF may be able 

to handle more resource-intensive patients, who otherwise 

might need to be readmitted. 

Differences in financial performance

The financial performance of hospital-based and 

freestanding SNFs measured using Medicare margins 

is very different. The aggregate Medicare margin 

for hospital-based facilities was −85 percent in 2005 

compared with an aggregate of 13 percent for freestanding 

facilities. A large difference in margins has persisted 

since the SNF PPS began. This is somewhat expected 

because the Congress directed that the payment system not 

reflect all the higher costs of hospital-based facilities and 

provided no payment adjustments that would specifically 

pay hospital-based facilities more than freestanding 

facilities.11

Differences in cost of the SNF day

The differences in margins for SNFs are largely due 

to differences in per diem costs which, on average, are 

more than twice as high in hospital-based SNFs as in 

freestanding SNFs (Figure 8-3). Routine costs (including 

room and board and nursing costs) are also more than 

twice as high in hospital-based SNFs as in freestanding 

facilities.12 The higher routine costs may be due in part 

to higher staffing levels and a higher mix of licensed 

professional nursing staff. Hospital-based SNFs also 

generally pay their staff the same as equivalent hospital 

employees. These rates tend to be higher than what 

freestanding facilities pay in the same market. Moreover, 

because hospital-based SNFs tend to be smaller, certain 

administrative costs are spread over fewer patients, which 

may also raise hospital-based units’ costs relative to those 

of freestanding facilities. 

Differences between hospital-based and freestanding 

SNFs in the cost of ancillaries vary by type of service. The 

costs of therapy services are similar but the costs for NTA 

services (drugs, supplies, lab, and respiratory therapy) 

are considerably higher in hospital-based SNFs than in 

freestanding facilities. For example, the average drug cost 

per day in hospital-based SNFs is $48 compared with $38 

Costs per day are higher in hospital-based SNFs

Note: 	 SNF (skilled nursing facility). Costs include associated overhead and capital expenses. Costs were not standardized for wages or case-mix differences.

Source: 	Analysis of 2004 Medicare Provider Analysis and Review file and cost report data from CMS.

Costs per day are higher in hospital-based SNFs FIGURE

8-3

Note and Source in InDesign Cost per 

day

$500

200

300

400

100

0

Routine Therapy Drugs Supplies Lab Respiratory Other ancillary

Type of cost

Hospital based

Freestanding

$395

$176

$78 $80

$48 $38 $18 $5 $14 $3 $10 $4 $11 $1

F i g ure

8 –3
208 Skilled nursing facilities: The need for refor m

in freestanding facilities. According to our interviews, 

hospitals often had difficulty placing patients who required 

very expensive drugs in freestanding SNFs, especially if 

they required certain IV medications. This may explain 

the difference we observe in drug costs between the two 

types of facilities. We see even larger differences for 

other NTA services, averaging $53 in hospital-based 

facilities compared with $13 in freestanding facilities. The 

higher nontherapy costs may be due to differences in the 

complexity of some patients. In addition, our interviews 

with hospital-based SNFs indicated that some physicians 

tend to treat hospital-based SNF patients as if they are still 

hospital inpatients, ordering ancillary tests—which are 

more readily available in this setting. 

Why did hospital-based SNFs close?

The high closure rate of hospital-based SNFs raises 

questions about the reasons for, and the consequences 

of, hospitals’ decisions to close or keep open their SNF 

units. In 2006, we interviewed officials at 15 acute care 

hospitals that operated Medicare SNF units in 1998, 

some of which have since closed (Liu and Jones 2007). 

We selected a sample of hospitals in several urban and 

rural geographic areas for this qualitative study. We also 

interviewed administrators of three freestanding SNFs that 

are geographically near some of those hospitals. 

Hospitals that kept SNF units open noted that the units 

fostered savings on the acute care side by providing an 

easily accessible source of PAC, which helped them 

shorten their inpatient stays and free up acute care beds 

for other patients. In some areas, hospitals continued to 

operate their units in part because few PAC alternatives 

were available locally, particularly for medically complex 

patients. Other hospitals reported that keeping the SNF 

open was important to maintain continuity of care or 

good relationships with physicians in the community or to 

provide resources for teaching health care professionals.

Hospitals that closed their SNF units mentioned various 

reasons. Financial losses associated with operating the 

SNF were cited as a major reason for closing the unit. The 

need for additional acute beds, or other more profitable 

uses for the space the SNF unit occupied, was cited 

frequently as another important reason. Hospitals noted 

other contributing factors that added to their operating 

costs, such as burdensome state SNF regulations, 

particularly the survey and certification process, and 

difficulties staffing the unit with RNs.

The consequences of SNF closures varied among the 

hospitals we interviewed. In some cases, especially 

for metropolitan hospitals, there were so many other 

local PAC options that closure of hospital-based SNF 

units did not affect the ability to place patients in PAC 

after discharge from hospitals. In other areas, however, 

discharge from hospitals was more problematic because 

of the limited capacity of alternative PAC providers or the 

ability or willingness of freestanding SNFs to take certain 

complex patients. In such situations, some patients had 

longer acute hospital lengths of stay after the hospital’s 

SNF unit closed. 

Regardless of the presence of other PAC options, hospitals 

told us that some categories of patients were hard to place. 

Medically complex patients, such as those requiring 

vacuum-assisted closure care of wounds, ventilator care, or 

intensive IV antibiotic care, can be hard to place because 

many freestanding SNFs are not staffed with the requisite 

RNs or respiratory specialists. Some hospitals reported 

that placement of such patients could be improved if the 

SNF PPS were refined to more accurately pay for the care 

these patients need. Long-term care hospitals accepted 

some of these difficult cases. Extended stays in acute care 

inpatient units were another option. 

Consequences of SNF operations on hospitals’ 

margins

A hospital’s decision to retain or close a SNF was often 

multifaceted. The large negative SNF margin has to be 

considered along with the impact of the SNF on the 

Medicare inpatient margin, the inpatient length of stay, 

and the potential for freeing up inpatient capacity for 

additional acute care patients. Medicare inpatient margins 

were slightly higher in facilities with a hospital-based SNF, 

−0.4 percent compared with −1.1 percent for hospitals 

without a SNF in 2005. On average, hospital stays for 

patients discharged to a hospital-based SNF were a little 

shorter than for patients discharged to a freestanding 

facility. The slightly shorter inpatient hospital stays could 

contribute to the somewhat higher Medicare inpatient 

margin for hospitals that have SNF units. 

The poor financial performance of hospital-based SNFs, 

however, affects the overall Medicare margin, which 

reflects the six largest services’ lines of business provided 

to Medicare patients by hospitals plus graduate medical 

education. In 2005, overall Medicare margins were lower 

in hospitals with a SNF than in hospitals without one 

(−3.9 percent compared with −3.0 percent), an indication 

that the losses from SNF services were bringing down 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 209

the overall Medicare margin. However, the approximate 

1 percentage point difference in overall margins in 2005 

is the smallest difference that has been observed since the 

SNF PPS was implemented in fiscal year 1998. This may 

be an indication that hospital-based SNFs have closed in 

facilities where they had the greatest negative impact on 

overall Medicare margins. 

Costs and profitability of inpatient stays 

discharged to SNFs

Inpatient hospital costs for beneficiaries who use SNFs 

are generally much higher than costs for beneficiaries with 

the same condition who are discharged home with no PAC 

provider use, reflecting differences in severity not captured 

under the current inpatient hospital payment system. In 

2004, the average payment-to-cost ratio for patients who 

did not use PAC was 1.13 compared with payment-to-cost 

ratios less than 0.90 for patients discharged to a SNF. This 

relationship was fairly consistent across DRGs. Thus, on a 

fully allocated cost basis (including capital and overhead), 

the hospital portion of care for inpatients discharged 

to a SNF was unprofitable. However, on a direct cost 

basis, with overhead and capital costs removed, the cases 

were profitable for inpatient care, although the relative 

profitability varies by DRG. Refinement of the hospital 

inpatient PPS to more accurately capture differences in 

inpatient severity should help to narrow or eliminate this 

difference. 

Direct costs and profitability of a hospital and 

SNF stay combined 

Hospital administrators told us that that they looked at the 

direct costs of the SNF unit operations when viewing a 

unit’s profitability. Hospitals also considered the potential 

savings they achieved from reducing inpatient length of 

stay. Because hospitals with SNFs provide both the acute 

and post-acute care, costs and payments for both sets of 

services should be considered in evaluating the financial 

viability of the SNF operations. If Medicare payments 

cover the combined direct costs of inpatient and SNF 

care for a patient, then hospitals will have an incentive 

to provide care to such patients as hospitals are covering 

the individual expenses those patients incur. On a fully 

allocated cost basis, the combined Medicare payments 

for the hospital stay and the SNF stay in 2004 did not 

cover the cost of care if overhead and capital costs (the 

fixed costs) were included. However, if we look only at 

direct costs (excluding overhead and capital) for both the 

hospital and the SNF stays, we find that Medicare hospital 

and SNF payments covered slightly more than the direct 

costs of care. The payment-to-direct-cost ratio for these 

cases was 1.05. At a minimum, the payment system needs 

to cover the direct costs of hospital and SNF services 

combined: The system did so in 2004.13

How do hospital-based SNFs differ? 

Our interviews with hospital-based SNFs revealed three 

different models of hospital-based SNF operations: 

• The long-term care model looks very much like 

freestanding SNFs: The facilities have a large number 

of beds and treat a predominantly long-stay nursing 

home population. These facilities are often located in a 

separate building from the acute care hospital.

• The rehabilitation model concentrates on patients, 

mostly Medicare beneficiaries, who require large 

amounts of therapy services, such as patients 

recovering from joint replacement. 

• The complex medical model focuses on providing care 

to medically complex patients who might stay in the 

hospital a little longer if a SNF unit were not available. 

These units, sometimes referred to as transitional 

care units, often act as step-down units, providing 

just a slightly lower level of nursing intensity than 

general medical–surgical units in the hospital. In this 

model, hospitals attempt to shorten the inpatient stay, 

essentially substituting SNF days for inpatient hospital 

days. Hospitals benefit because they receive the same 

inpatient payment (since the hospital is paid on a per 

discharge basis with the exception of cases that are 

paid as PAC transfers, which are paid a per diem) 

and they receive a separate SNF payment that they 

would not have received had the patient remained in 

the hospital. In addition, the hospital has freed up the 

inpatient bed for a new patient. 

Using Medicare claims files and Medicare cost reports, 

we classified hospital-based SNFs into one of these three 

models.14 We found that about 16 percent of hospitalbased SNFs fit into the long-term care model, 47 percent 

fit into the rehabilitation model, and 17 percent fit into 

the complex medical model. The other 20 percent of 

hospital-based SNFs did not fit neatly into one of these 

three models.15 These distinct models have different 

facility characteristics that may shed light on differences 

in Medicare financial performance of freestanding and 

hospital-based facilities. 
210 Skilled nursing facilities: The need for refor m

Patient and facility characteristics

Across most characteristics, the long-term care model 

of hospital-based SNFs is similar to freestanding SNFs 

(Table 8-10). They are larger, have longer SNF stays, and 

have lower staffing ratios than other hospital-based SNFs. 

Medicare also accounts for a small share of these facilities’ 

patient days. In looking at patient characteristics, we see 

other similarities to freestanding SNFs, including the small 

share of SNF admissions that are for musculoskeletal 

conditions (MDC8), which includes hip and knee 

replacements. The portion of SNF patients’ preceding 

inpatient hospital days that were spent in the intensive 

care unit is also similar, as is the small percentage of 

patients discharged to a second SNF. This model is the 

predominant model of hospital-based SNFs in New York 

state, where 23 percent of these facilities are found. They 

also make up a large share of the hospital-based SNFs in 

Minnesota. Despite the concentration in some states, the 

long-term care model can be found across the country in 

34 states. 

By comparison, hospital-based SNFs following the 

rehabilitation model are much smaller than hospitalbased SNFs following the long-term care model, with 

shorter SNF stays and a higher level of staffing. These 

facilities concentrate more on patients who will require 

therapy services, as 32 percent of their patients have 

musculoskeletal conditions. Compared with freestanding 

SNFs and the long-term care model of hospital-based 

SNFs, a larger share of patients are discharged to another 

SNF or to home health care. 

Compared with other hospital-based SNFs, the complex 

medical model SNFs have the shortest SNF stays, the 

highest SNF staffing, and a very high share of preceding 

hospital inpatient days spent in the intensive care 

unit. They also have the largest proportion of patients 

continuing SNF care in another facility and the largest 

share using home health care after discharge. This use of 

SNF and home health care is very similar to that found 

among patients discharged from hospitals without a SNF. 

T A B L E

8 –10 Characteristics of freestanding SNFs and different hospital-based SNF models

Characteristic Freestanding

Hospital-based SNF model

Long-term care* Rehabilitation

Complex 

medical

Number of facilities 13,129 183 537 197

Share of hospital-based SNFs** N/A 16% 47% 17%

Beds 98 80 24 20

SNF LOS (in days) 26.9 26.5 12.8 10.7

Staff per bed (in FTEs) 0.82 0.83 1.03 1.22

Medicare share 12% 9% 79% 83%

MDC8 (musculoskeletal) case share 15 17 32 22

Percent ICU days 13 13 15 32

Percent SOI 3 or 4 45 47 39 45

Percent of cases:

Discharged to another SNF 1 0 7 14

Discharged to home health care 6 7 23 27

Note:	 SNF (skilled nursing facility), N/A (not applicable), LOS (length of stay), FTE (full-time equivalent), MDC (major diagnostic category), ICU (intensive care unit), SOI

(severity of illness). Long-term care SNFs treat predominantly long-stay nursing home patients. Rehabilitation SNFs treat predominantly Medicare patients requiring

rehabilitation services. Complex medical SNFs treat predominantly patients who are medically complex. SOI is measured using all patient refined diagnosis related

groups from 3M Health Information Systems, with values ranging from 1 to 4 (4 being the most severely ill). Table shows median values.

	 *23 percent of these facilities are in New York.

	 **20 percent of hospital-based SNFs did not fit neatly into one of these three models.

Source:	 Analysis of 2004 Medicare Provider Analysis and Review file and cost report data from CMS.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 211

Differences in profitability of different types of 

hospital-based SNFs

There are considerable differences in costs among these 

three models of hospital-based SNFs (Table 8-11). The 

long-term care model has the lowest per diem costs, 

while the complex medical model has the highest. These 

differences can also be observed for routine and ancillary 

costs. The daily costs for the complex medical model 

patients are 86 percent higher than for the patients in the 

long-term care model. Given the greater use of intensive 

care units by the complex model patients during their 

preceding hospital stays, we might expect a lower paymentto-cost ratio for their hospital stays, but we actually see 

a slightly higher ratio (0.89 on average) than that for 

inpatients who go on to use other types of hospital-based 

SNFs (0.87) (data not shown). This indicates that hospitals 

may use these units to substitute for the later days of an 

inpatient stay. 

To evaluate the combined financial performance of 

hospitals with their hospital-based SNFs, we considered 

the costs and payments of both the hospital and SNF stays. 

Our analysis shows that in 2004 the ratio of payments to 

direct costs for hospital and SNF services combined for 

the long-term care model SNF patients was 1.25. Thus, 

patients in these facilities contributed to the bottom line 

operations of the hospital by more than covering their 

direct costs. For hospitals with rehabilitation and complex 

models of hospital-based SNFs, the combined payment-todirect-cost ratios for the hospital and SNF stays were both 

a little above 1.0, indicating that—on average—hospitals 

received payments that covered the direct costs of their 

patients. 

Conclusion

To evaluate the performance of hospital-based SNFs, we 

need to consider both the hospital and the SNF portions 

of care. We found that hospitals with hospital-based 

SNFs covered the direct costs (costs less overhead and 

capital) of inpatient acute care and SNF care. We also 

need to consider the cost of an efficient provider: Despite 

the higher costs in hospital-based SNFs compared with 

freestanding facilities, it is not clear that the Medicare 

program should recognize their higher costs. Yet, we report 

in the next section that hospital-based SNFs appear to 

provide higher quality of care than freestanding facilities, 

though factors unaccounted for in the analysis may explain 

some of these differences. The provision of better care, 

not facility type, using these or other measures would 

warrant higher payments if Medicare paid on the basis of 

performance. 

The Commission believes the best way to address the 

financial circumstances of hospital-based SNFs is to 

reform the applicable payment systems so that they more 

accurately account for cost differences attributable to 

patient characteristics rather than differences attributable 

to facility characteristics. Adjusting the inpatient hospital 

PPS for severity, as the Commission has recommended, 

T A B L E

8 –11 Hospital-based SNFs have differing financial performance

Characteristic

SNF model

Long-term care Rehabilitation Complex medical

SNF payment per day $322 $314 $319

Cost per day 367 594 686

Routine cost 276 413 472

Ancillary cost 91 181 214

Direct cost per day 250 397 461

Ratio of hospital and SNF payments to

direct costs of hospital and SNF 1.25 1.04 1.03

Note:	 SNF (skilled nursing facility). Direct costs include all patient care costs less overhead and capital expenses. Costs were not standardized for differences in wages or

case mix. Values shown are aggregate averages.

Source:	 Preliminary analysis of 2004 Medicare Provider Analysis and Review file and cost report data from CMS.
212 Skilled nursing facilities: The need for refor m

would likely result in an increase in Medicare inpatient 

payments for patients who subsequently use hospital-based 

SNFs. Moreover, refinements to the SNF PPS discussed 

in the first section of this chapter that better recognize 

differences in use of NTA services should also result in 

more accurate payments for SNF care, regardless of the 

type of facility. 

Understanding the declines in

SNF quality

In addition to focusing on payment issues, the 

Commission has examined the quality of care SNFs 

furnish and the measures used to gauge it. In the March 

2007 report, we noted that two risk-adjusted quality 

measures for Medicare SNFs—facility rates of discharge 

to the community and potentially avoidable hospital 

readmissions—indicated that quality worsened between 

2000 and 2004 (MedPAC 2007). To better understand 

these trends, we contracted with researchers from the 

University of Colorado at Denver and Health Sciences 

Center to identify the factors associated with the changes 

over time, such as differences in case mix, facility mix, 

staffing, and regional practice patterns. We also examined 

the relationship between these two measures and the 

CMS publicly reported Nursing Home Compare shortstay quality measures. 

Measures of SNF quality of care 

To assess the quality of care furnished in SNFs, the 

Commission has examined facility rates of community 

discharge and potentially avoidable rehospitalizations for 

any of five conditions (congestive heart failure, respiratory 

infection, urinary tract infection, sepsis, and electrolyte 

imbalance). We use these measures for two reasons. First, 

they relate to major goals of SNF care. Regaining physical 

function and being discharged to the community are the 

goals for many SNF patients recovering from acute events, 

surgery, or debilitating medical problems. About 80 

percent of SNF patients received rehabilitation services. 

Methodology used to examine factors associated with changes in 

outcome measures

Researchers from the University of Colorado 

at Denver and Health Sciences Center linked 

data on Medicare-covered stays in skilled 

nursing facilities (SNFs), the preceding qualifying 

hospitalization, patient assessment information from 

the Minimum Data Set (MDS), facility characteristics, 

and staffing from the Online Survey Certification 

and Reporting System and community factors from 

the Area Resource File for 2000–2004. Data on the 

MDS-based post-acute care measures were added 

from the CMS database. Resident characteristics were 

aggregated to the facility level and the community 

discharge and rehospitalization outcome measures 

were risk-adjusted using measures of functional 

and cognitive performance, presence of advance 

directives, comorbidities, length of stay of the 

qualifying hospitalization, and other patient assessment 

information. To ensure that the quality measures were 

stable, only facilities with more than 25 discharges 

(excluding deaths) were included in the analysis 

(Donelan-McCall et al. 2006). About 13,000 facilities 

were included from each year (more than 80 percent 

of the industry and 99 percent of the SNF stays). For 

the subset of facilities present each year, differences in 

outcomes over time were calculated. 

Community discharge was defined as a discharge to 

the community or to assisted living facility within 30 

days and excluded patients who were rehospitalized 

(they were included in the rehospitalization measure). 

Rehospitalizations included direct hospital transfers 

within 100 days to an acute care hospital that were 

considered potentially avoidable—that is, due to heart 

failure, electrolyte imbalance, respiratory infection, 

sepsis, or urinary tract infection. 

Researchers conducted descriptive and multivariate 

analyses to examine the case mix, facility, and 

community characteristics associated with the 

outcomes and the extent to which these factors 

explained temporal changes in the outcomes. For each 

outcome measure, regression models were estimated 

that included year indicators and measures of case mix, 

facility, and community characteristics. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 213

Stabilizing patients after acute care and avoiding costly 

and harmful hospital readmissions are goals for many 

SNFs. Second, the measures overcome the data limitations 

of the publicly reported Nursing Home Compare PAC 

measures (facility rates of delirium, pain, and pressure 

ulcers for short-stay patients), including the timing of 

patient assessments, sample bias, and questionable validity 

(Donelan-McCall et al. 2006; MedPAC 2006b, 2005b; Abt 

2005). In this work, we report the results for community 

discharge within 30 days of admission to the SNF and 

rehospitalizations within 100 days—the two measures that 

changed the most over time. The text box describes the 

methodology used to examine the factors associated with 

changes in the outcome measures.

Factors associated with community 

discharge and potentially avoidable 

rehospitalization rates 

Two outcome measures—observed rates (unadjusted for 

differences in case mix) of discharges to the community 

within 30 days and hospital readmissions within 100 days 

of the SNF admission—got worse between 2000 and 2004. 

At the facility level, the average decline in the community 

discharge rates was 1.8 percentage points (from 23.7 

percent in 2000 to 21.0 percent in 2004) and the rate of 

potentially avoidable rehospitalizations increased 2.8 

percentage points (from 14.7 percent in 2000 to 17.5 

percent in 2004).16 The observed rates varied considerably 

by facility characteristic, especially for community 

discharge rates. Most rehospitalizations (85 percent) 

occurred at least 3 days after SNF admission, suggesting 

that they were not attributable to admitting SNF patients 

who were too unstable to be discharged from the hospital.

Controlling for differences in case mix, facility 

characteristics, and other factors reduced—but did not 

eliminate—the differences in the quality measures between 

2000 and 2004 (Figure 8-4).17 Adjusting for case mix 

eliminated about one-third of the change in rates over the 

period. After accounting for many additional differences—

including staffing levels, length of stay for the qualifying 

hospital stay, SNF location, facility type, and market 

characteristics—the quality measures, particularly the 

average rehospitalization rate, still declined. Unmeasured 

case-mix changes could possibly explain some of the 

differences. For example, the availability of patients’ social 

support could influence a facility’s ability to discharge 

them to the community. 

Key factors associated with community discharge and 

rehospitalization rates included the mix of facilities 

present only in 2000 (specifically, hospital-based facilities 

that closed after 2000), SNF location, staffing levels, 

whether the SNF was hospital based, and ownership (Table 

8-12, p. 214). On average, facilities present only in 2000 

had community discharge rates 17.5 percent higher and 

rehospitalization rates 4.0 percent lower than facilities 

present in both 2000 and 2004 or facilities present only 

in 2004. The impact of higher staffing ratios, particularly 

for RNs and total licensed nurses, was also large. There 

were significant differences in community discharge 

and potentially avoidable rehospitalization rates across 

the regions. Facilities in the Northeast, Midwest, and 

South had lower community discharge rates and higher 

potentially avoidable rehospitalization rates than facilities 

in the West. Hospital-based SNFs had much better 

quality measures (higher community discharge rates and 

lower potentially avoidable rehospitalization rates) than 

freestanding facilities, while for-profit SNFs had worse 

F i g ure

8 –4 After adjusting for case mix and 

other factors, SNF quality measures

 declined between 2000 and 2004

Note: 	 SNF (skilled nursing facility). Table shows mean facility rates. Community

discharges occurred within 30 days of the SNF admission. Potentially

avoidable rehospitalizations include hospitalizations within 100 days

to an acute care hospital for heart failure, electrolyte imbalance,

respiratory infection, sepsis, and urinary tract infection. Fully adjusted

includes adjustments for differences in case mix, staffing levels, length

of the qualifying hospital stay, SNF location, facility type, and market

characteristics.

Source:	 Kramer et al. 2007.

After adjusting for case-mix and

other factors, SNF quality measures

declined between 2000 and 2004

FIGURE

8–4

Note and Source are in InDesign.

Average percentage point change

4

0

–1

–2

3

2

1

Discharge to

community rate

Potentially avoidable 

rehospitalization rate

Observed

Case-mix adjusted

Fully adjusted

–1.8

–1.3

–0.4

2.80

2.05 2.03
214 Skilled nursing facilities: The need for refor m

quality measures (lower community discharge rates and 

higher potentially avoidable rehospitalization rates) than 

nonprofit facilities. The length of the preceding hospital 

stay was not a factor in predicting either rate. However, as 

we discuss later, we may not have controlled for all of the 

factors that contributed to differences between facilities. 

Facilities with the largest observed changes in the two 

quality measures had the greatest changes in the severity 

of the cases they admitted. SNFs with the largest declines 

in community discharge rates treated patients with worse 

functional status; fewer rehabilitation patients; and more 

patients with dementia, genitourinary disease, and donot-resuscitate orders than other facilities. SNFs with the 

largest increases in potentially avoidable rehospitalizations 

treated patients with worse functional status and more 

patients with catheters, pressure ulcers, genitourinary 

disease, respiratory disease, or musculoskeletal disease 

than other facilities. For example, an increase of 10 

points in the average functional status score increased the 

community discharge rate by 2.5 percent and decreased 

the rehospitalization rate by 1 percent. Researchers 

adjusted for these case-mix changes in their multivariate 

analyses.

Mix of facilities contributed to declines in the 

two quality measures 

Hospital-based facilities made up slightly more than 13 

percent of SNFs in 2000 but only 9 percent in 2004. This 

is because 50 percent of the facilities that were in business 

in 2000 and no longer in business by 2004 were hospital 

based, whereas only 5 percent of the new facilities in 2004 

were hospital based. Because freestanding facilities treated 

more complex patients (reporting greater complexity for 

16 of the 22 case-mix measures), some of the decline in 

the two observed quality measures reflects a shift in the 

mix of facilities. However, after adjusting for case-mix 

T A B L E

8 –12 Facilities with certain characteristics had higher or lower SNF quality measures 

Characteristic (facility average)

Change in:

Discharge 

to community rate 

Potentially avoidable 

rehospitalization rate 

SNF present only in 2000 (compared with present only in 2004 and

present in 2000 and 2004) 17.5% –4.0%

Each additional hour per patient day

Registered nurse 8.0 –1.9

Licensed nurse 5.0 –1.2

Certified nurse aide 1.6 –0.4

SNF location (compared with West)

Northeast –5.3 2.8

Midwest –7.5 2.3

South –4.3 1.9

Hospital based (compared with freestanding) 19.0 –5.7

For profit (compared with nonprofit and government) –3.6 2.3

Average acute hospital length of stay of preceding hospitalization 0.0 0.0

Note: 	 SNF (skilled nursing facility). Rates are adjusted for case mix, time, and presence in 2000 only and 2004 only. Community discharges occurred within 30 days

of the SNF admission. Potentially avoidable rehospitalizations include hospitalizations within 100 days to an acute care hospital for heart failure, electrolyte

imbalance, respiratory infection, sepsis, and urinary tract infection.

Source:	 Kramer et al. 2007.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 215

differences, hospital-based facilities still had higher riskadjusted community discharge rates (19 percent higher) 

and lower risk-adjusted rehospitalization rates (5.7 percent 

lower) than freestanding facilities. With the closing of 

many hospital-based SNFs during the study period, the 

mix of facilities that reported only in 2000 had higher 

average community discharge rates and lower average 

rehospitalization rates than the SNFs that reported in both 

periods or only in 2004. 

Although the researchers controlled for many casemix factors, there still could be unmeasured selection 

differences between hospital-based and freestanding 

facilities, such as the availability of community support. 

In addition, as discussed earlier (p. 205), we found that 

patients treated in hospital-based SNFs were more likely to 

use other PAC services, including inpatient rehabilitation 

facilities, long-term care hospitals, and home health care. 

Staffing levels explained some of the declines in 

the two quality measures 

After controlling for differences in case mix, one 

additional hour of RN time per resident day was associated 

with an 8 percent increase in the community discharge 

rate and a 1.9 percent decrease in the rehospitalization 

rate. Different staffing levels also partly explained some 

of the differences in the rates between hospital-based and 

freestanding facilities. Hospital-based facilities had much 

higher staffing levels than freestanding facilities—five 

times the RN hours per resident day and more than twice 

the licensed nurse hours per resident day—even though 

they treat a less complex mix of patients.18 Facilities that 

were present only in 2000 had four times more RN hours 

per resident day and two times more licensed nurse hours 

per resident day relative to facilities that were new in 2004, 

and their closing contributed to the declines in the two 

quality measures in 2004. 

Other factors may help explain differences in 

quality 

Other factors may help explain the differences across 

facilities and over the study period. These unaccounted 

factors may include unmeasured differences in staffing 

(e.g., staff turnover and experience) and case mix, the 

availability of community support, market characteristics 

(e.g., the availability of inpatient rehabilitation facilities 

and long-term care hospitals), and facility practice 

patterns, such as the frequency of physician visits.

Relationships between different quality 

measures 

In general, facilities that had good community discharge 

scores (where higher rates reflect better quality) also had 

good rehospitalization scores (where lower rates reflect 

better quality). In 2004, 50 percent of facilities with the 

highest community discharge rates (the top 25 percent) 

also had the lowest potentially avoidable rehospitalization 

rates (the bottom 25 percent). Facilities that performed 

poorly on one quality measure generally also performed 

poorly on the other—43 percent of facilities with the 

highest rehospitalization rates also had the lowest 

community discharge rates. 

Quality based on the risk-adjusted rates of community 

discharge and rehospitalization was inversely related to 

quality for the same facilities based on CMS’s publicly 

reported post-acute quality measures (rates of delirium, 

pain, and pressure sores for short-stay patients measured 

on day 14 of their stay). That is, SNFs that appeared to 

provide good quality of care using community discharge 

and rehospitalization rates appeared to provide poor 

quality using CMS’s measures. One possible explanation 

of the inverse relationship is that the indicators measure 

patients at different points in time and, as a result, can 

include different mixes of patients. Almost half the SNF 

admissions were not present on day 14 of their stays 

(because they were discharged, they were readmitted to a 

hospital, or they died) and are not included in the publicly 

reported measures but are counted in the community 

discharge and rehospitalization rates. As a result, for 

example, facilities with high community discharge rates 

(indicating good quality) may discharge their healthiest 

patients, leaving the sickest patients to be included in the 

publicly reported quality measures. 

The inverse relationship between quality based on 

the publicly reported measures and quality based on 

community discharge and avoidable rehospitalization 

rates is of concern. We previously reported on the 

shortcomings of the publicly reported measures, including 

that they do not reflect the goals of most SNF patients 

and data accuracy problems (MedPAC 2006b, 2005b). 

In addition, the timing of the patient assessments may 

not accurately capture changes in patients’ conditions. 

Although assessments are required at admission, there is 

some flexibility in when the assessments are conducted; as 

a result, they are completed within three days of admission 

for only 4 percent of patients, which may understate the 

improvements patients achieve during their stay (MedPAC 
216 Skilled nursing facilities: The need for refor m

2006b). Because assessments are not required at discharge, 

the publicly reported measures capture information about 

only those patients with stays of at least 14 days, which 

may penalize SNFs that treat patients with short stays, 

discharge their healthiest patients, or elect to treat their 

sickest patients rather than send them to the hospital. We 

previously recommended that CMS gather assessment 

information at patient discharge. 

Other problems with the publicly reported information 

center on the measures. While pain is an important 

dimension to capture, SNF quality experts told us that the 

current measure is too narrow and confusing (MedPAC 

2006b). For example, assessors may differ in how to code 

a patient with considerable pain that was successfully 

managed. Because pressure ulcers take time to develop, 

experts thought that process measures (e.g., whether a 

facility follows well-established guidelines to prevent, 

identify, and treat the sores) would be valuable measures. 

The delirium measure is neither specific to delirium nor 

sensitive relative to reported literature on rates of delirium.

Conclusions 

The declines in the two quality measures—the community 

discharge rates and potentially avoidable rehospitalization 

rates—are of concern to the Commission and we will 

continue to monitor them. In light of the extensive 

problems of the publicly reported measures and the fact 

that they do not reflect the goals for most SNF patients, 

the Commission urges CMS to consider adding the 

community discharge and rehospitalization measures 

to the publicly reported measures. We also ask that it 

reconsider our previous recommendation to gather patient 

assessment information at admission and discharge so that 

changes in health status are known for all patients. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 217

1 Urban and rural SNFs have separate base rates. The base rates 

are adjusted for differences in labor costs. For a complete 

description of the SNF PPS, see MedPAC’s Payment Basics 

series (MedPAC 2006a).

2 In work for CMS, the researchers evaluated a 58-group 

version of the RUG classification system. In the refinements 

it adopted in fiscal year 2006, CMS collapsed some of the 

new groups because there were no patients in them, resulting 

in nine new groups. 

3 The researchers did not evaluate a 53-group version of the 

RUGs. Conversations with researchers at the Urban Institute 

indicate they do not think the results would vary significantly 

from the RUG–58 results included in the CMS report.

4 The check includes matching use of NTA services reported 

in the MDS with use of NTA services reported in the SNF 

claims. 

5 The eight groups are rehabilitation plus extensive services, 

rehabilitation, extensive services, special care, clinically 

complex, impaired cognition, behavior only, and reduced 

physical function. 

6 The Barthel index is a measure of a patient’s independence. 

It scores a patient’s time and assistance needed to perform 

activities of daily living (Mahoney and Barthel 1965).

7 The costs of certain services provided during a stay are 

paid for separately under Part B. Excluded services include 

certain chemotherapy and dialysis-related items, cardiac 

catheterizations, computed tomography scans, MRIs, 

ambulatory surgery that requires an operating room, radiation 

therapy, angiography, lymphatic and venous procedures, 

emergency services, radioisotope services, customized 

prosthetic devices, and ambulance transportation for dialysis. 

8 These results are based on an indicator on the hospital claim 

file, which shows where the patient came from before the 

hospital stay. 

9 In looking at the top 20 DRGs discharged to hospitalbased SNFs, this occurs for esophagitis, gastroenteritis, and 

miscellaneous digestive disorders (DRG 182); gastrointestinal 

hemorrhage (DRG 174); and circulatory disorders with acute 

myocardial infarction and major complications (DRG 121).

10 Although we report differences in 30-day readmission rates, 

the actual difference in discharge from the SNF back to the 

hospital is bigger, in large part because of differences in 

the length of the SNF stay; 24 percent of freestanding SNF 

patients are discharged from the SNF directly to the hospital 

compared with 12 percent of patients discharged from 

hospital-based SNFs. 

11 Because of policymakers’ concerns, the calculation of the PPS 

base rates explicitly did not recognize the full amount of the 

higher costs of hospital-based SNFs. The base rates for the 

SNF PPS were set at a weighted average of the freestanding 

SNF average cost plus 50 percent of the difference between 

the freestanding average and a weighted average of all 

facilities’ (freestanding and hospital-based) costs. In addition, 

the base rate did not include the costs of SNFs that were 

exempt from Medicare cost limits.

12 These costs include associated overhead and capital costs. 

13 This is likely a lower bound estimate for the relationship, as 

our measure of direct costs is based on the average cost of 

providing variable cost services such as nursing, food, tests, 

drugs, and supplies. However, the additional cost the hospital 

must incur is likely less than the average cost of providing 

many of the services the patient requires. 

14 We did this by developing three composite scores for each 

facility as to how well they fit into each of the three hospitalbased SNF models. The composites were based on a number 

of factors that help to differentiate the different types of 

hospital-based SNFs, such as SNF length of stay and inpatient 

use of the intensive care unit. Facilities were identified as 

fitting into a particular model based on which one produced 

the highest composite score. 

15 We found that 8 percent appear to be a mixture between the 

rehabilitation model and the complex medical model and 

11 percent appear to be similar to the long-term care model, 

except the Medicare patients tend to have shorter SNF stays. 

16 In aggregate, unadjusted rates of discharges to the community 

declined 5.1 percentage points, from 34.4 per 100 residents 

in 2000 to 29.2 per 100 residents in 2004, indicating that 

facilities treating the most SNF patients had the largest 

declines. In aggregate, rehospitalizations increased from 13.5 

to 17.4 per 100 residents during the study period.

Endnotes
218 Skilled nursing facilities: The need for refor m

17 Factors associated with community discharge rates explained 

70 percent of the variation across facilities; factors associated 

with potentially avoidable rehospitalization rates explained 54 

percent of the differences in rates across facilities. The model 

controlled for market characteristics including the Medicare 

managed care penetration rate; the number of hospitals and 

hospital admissions per 100,000 residents; the number of 

SNFs and beds per 100,000 residents; the number of nursing 

facility beds per 100,000 residents; and the number of home 

health agencies. Researchers controlled for the availability of 

home health care but not inpatient rehabilitation facilities or 

long-term care hospitals.

18 On average, there were 1.72 RN hours and 3.72 licensed nurse 

hours per patient day in hospital-based SNFs compared with 

0.35 RN hour and 1.44 licensed nurse hours per patient day in 

freestanding SNFs.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 219

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for SNFs for FY 2006. July 8. http://www.medpac.gov./.

Medicare Payment Advisory Commission. 2005c. Report to the 

Congress: Medicare payment policy. Washington, DC: MedPAC. 

Medicare Payment Advisory Commission. 2004. Report to the 

Congress: Medicare payment policy. Washington, DC: MedPAC. 

Office of Inspector General, Department of Health and Human 

Services. 2006. Medicare beneficiary access to skilled nursing 

facilities: 2004. OEI–02–04–00270. Washington, DC: OIG. 

Office of Inspector General, Department of Health and Human 

Services. 2001. Medicare beneficiary access to skilled nursing 

facilities. OEI–02–01–00160. Washington, DC: OIG. 

Office of Inspector General, Department of Health and Human 

Services. 2000. Medicare beneficiary access to skilled nursing 

facilities. OEI–02–00–00330. Washington, DC: OIG. 

References
220 Skilled nursing facilities: The need for refor m

Office of Inspector General, Department of Health and Human 

Services. 1999. Early effects of the prospective payment system 

on access to skilled nursing facilities. OEI–02–99–00400. 

Washington, DC: OIG. 

Stearns, S., K. Dalton, G. Holmes, et al. 2006. Using propensity 

stratification to compare patient outcomes in hospital-based 

versus freestanding skilled-nursing facilities. Medical Care 

Research and Review 63 (October): 599–622.

Urban Institute. 2006a. Options for improving Medicare payment 

for SNFs. Report prepared for CMS under contract no. 500–00–

0025. Washington, DC: Urban Institute. http://www.cms.hhs.gov/.

Urban Institute. 2006b. Unpublished analysis of RUG 

assignments in first and second MDS assessments conducted for 

MedPAC. 

White, C., S. D. Pizer, and A. J. White. 2002. Assessing the RUG–

III resident classification system for skilled nursing facilities. 

Health Care Financing Review 24, no. 1: 7–15.
Analysis of changes 

to physicians’ practice 

expense payments

C h a p t er 9

Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 223

Chapter summary

The physician fee schedule has three components: physician work, 

practice expense (PE), and professional liability insurance. The PE 

component pays for the direct costs (nonphysician clinical staff, medical 

equipment, and medical supplies) and indirect costs (administrative 

staff, office rent, and other expenses) of operating a physician practice. 

In this chapter, the Commission examines how CMS determines PE 

payment rates, because PE payments are substantial, accounting for 

close to half of the $58 billion Medicare spent under the physician 

fee schedule in 2005. The goal of this chapter is to help policymakers 

understand this complex payment methodology and focus attention on 

major changes that CMS has recently made to improve its accuracy. 

We describe these changes and their impacts, examine CMS’s method 

for allocating indirect costs to specific services, and explore how the 

agency adjusts PE payment rates to account for geographic differences 

in input prices. Although the Commission does not recommend changes 

to the PE methodology in this chapter, we have previously suggested 

In this chapter

• CMS’s methods and data 

changes redistributed 

practice expense payments 

across services

• Allocating indirect practice 

expenses

• Adjusting for geographic 

differences in practice 

expenses

• Conclusion

Analysis of changes 

to physicians’ practice 

expense payments

9 C H A PT E R 
224 Analysis of changes to physicians’ practice expense payments

ways for CMS to improve the accuracy of the survey data, direct cost 

estimates, and equipment prices used in the methodology (MedPAC 2006).

Ensuring the accuracy of payments under the physician fee schedule is 

important for several reasons. First, inaccurate payment rates can distort the 

market for physician services. Overvalued services may be overprovided, 

because they are more profitable than other services. Undervalued services 

may prompt providers to increase volume to maintain their overall level 

of payment. Conversely, some providers may not furnish services that are 

undervalued, which can threaten beneficiaries’ access to care. Second, 

if certain types of services become undervalued relative to others, the 

specialties that perform them may become less financially attractive, which 

can affect the supply of physicians. Finally, when services are misvalued, 

Medicare is paying too much for some services and not enough for others 

and therefore is not spending taxpayers’ and beneficiaries’ money wisely. 

Because Medicare spends a great deal of money on PE, those payments 

could be a tool for achieving important policy goals such as rewarding 

providers for efficient use of resources. 

Beginning in 2007, CMS is using:

• new methods to calculate direct and indirect PE relative value units 

(RVUs), 

• the same approach to calculate PE RVUs for services that do and do not 

involve physician work, and

• more current practice cost data to calculate indirect PE RVUs for eight 

specialty groups. 

In addition, CMS adopted significant changes to physician work RVUs, 

which affect both the physician work and the PE components of the fee 

schedule. Collectively, these changes represent the biggest revision to the 

methods and data used to calculate PE RVUs since the agency implemented 

resource-based PE payments in 1999. CMS will phase in these changes over 

a four-year period.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 225

The new PE methods and data redistribute PE payments across services. 

When CMS fully implements the changes in 2010, PE RVUs will increase 

by 7 percent for evaluation and management (E&M) services and by 3 

percent for other (nonmajor) procedures and tests. By contrast, PE RVUs 

will decrease by 8 percent for major procedures and by 9 percent for imaging 

services. 

To better understand these impacts, we isolated the separate effects of the 

new methods and the new data on PE RVUs. Our analysis shows that E&M 

services, other (nonmajor) procedures, and tests, on average, benefited from 

the change in the methods, while major procedures and imaging services 

did not. In some instances, the new methods and new data CMS used—

more current practice cost data from some (but not all) specialties and the 

2007 work RVUs (which include the budget-neutrality adjustment)—had 

offsetting effects. On average, other procedures, imaging services, and tests 

benefited from using more current practice cost data because the specialties 

that submitted the data account for a substantial share of the total volume of 

these services. Finally, using the 2007 work RVUs to calculate indirect costs 

had a downstream benefit for services, such as E&M services, whose work 

values increased because of the third five-year review. 

Because indirect costs represent about two-thirds of total practice costs, we 

examine CMS’s new method for calculating indirect PE RVUs, explore other 

methods to pay physicians’ indirect practice costs, and analyze the sensitivity 

of PE RVUs to changes in the indirect method. We find that indirect PE 

RVUs are quite sensitive to changes in the methodology. For example, under 

an alternative approach, allocating indirect costs using nonphysician clinical 

labor plus physician work for the service would shift PE RVUs from imaging 

services, tests, and other procedures to E&M services and major procedures. 

Finally, we examine how CMS adjusts PE payment rates to account for 

geographic differences in the price of inputs used in operating a physician 

practice. The PE geographic practice cost index (GPCI) includes three 

components that correspond to three types of practice costs: nonphysician 
226 Analysis of changes to physicians’ practice expense payments

staff wages; office rent; and medical equipment, supplies, and other 

expenses. The current PE GPCI does not recognize that individual services 

have different shares of inputs for which prices vary geographically (e.g., 

nonphysician staff and office space) and for which prices are uniform (e.g., 

equipment and supplies). Thus, for services with below-average shares of 

equipment and supplies, the index does not adjust a large enough portion 

of the PE RVU; for services with above-average shares of equipment and 

supplies, it adjusts too large a portion of the RVU. This distorts prices, which 

may alter the mix of services provided within a high- or low-cost area.

PE payments might be more accurate if CMS excluded the equipment and 

supplies component from the GPCI and applied the GPCI only to the portion 

of the PE RVU related to nonphysician clinical labor and indirect costs. 

This alternative would better recognize that services have different shares of 

inputs for which prices vary geographically. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 227

CMS’s methods and data changes 

redistributed practice expense payments 

across services 

The physician fee schedule has three components: 

physician work, practice expense (PE), and professional 

liability insurance. PE payments cover the direct and 

indirect costs of operating a physician practice. Direct 

expenses include costs for nonphysician clinical labor, 

medical equipment, and supplies. Indirect expenses 

include costs for administrative labor, office expenses 

(e.g., rent and utilities), and all other expenses. CMS bases 

PE payments on the relative resources needed to provide a 

service, known as relative value units (RVUs). PE accounts 

for close to half of the $58 billion Medicare spent under 

the physician fee schedule in 2005.

In the final rule for the 2007 physician fee schedule, CMS:

• implemented new methods to calculate direct and 

indirect PE RVUs; 

• calculated PE RVUs using the same method for 

services that do and do not involve physician work 

(i.e., eliminated the nonphysician work pool);

• used more current practice cost data to calculate 

indirect PE RVUs for eight specialty groups; and 

• adopted significant changes to physician work RVUs, 

which affected both the physician work and the PE 

components of the fee schedule.

To mitigate the impact on providers, the agency will 

phase in the changes over a four-year period beginning in 

January 2007. The text box (pp. 228–229) describes the 

changes in calculating PE RVUs.

Implementing new methods and data will redistribute PE 

RVUs across services because CMS is implementing the 

changes in a budget-neutral manner, as required by statute 

(Table 9-1, column C, p. 230). On average, PE RVUs will 

fall for major procedures and imaging by about 8 percent 

and 9 percent, respectively. PE RVUs for evaluation 

and management (E&M) services, other (nonmajor) 

procedures, and tests will increase on average by about 

7 percent, 3 percent, and 3 percent, respectively, when 

CMS phases in all of the changes. 

More specific types of services experience fairly large 

changes under the new PE method and data. The largest 

reductions in PE RVUs occur in echography of the heart, 

standard chest X-ray, and electrocardiograms. For these 

types of services, the average reductions in PE RVUs 

range from about 20 percent to 28 percent. The largest 

gains in PE RVUs go to echography–other, coronary 

angioplasty, ambulatory skin procedures, colonoscopy, and 

upper gastrointestinal endoscopy, with increases ranging 

from 24 percent to about 37 percent. 

We isolated the effect of using new methods, more current 

utilization data, more recent practice cost data for some 

specialties, and 2007 work RVUs to better understand their 

impact on PE RVUs. We found substantial redistributions of 

PE RVUs across some types of services.

First, we isolated and examined the effect of changing the 

methods—moving to a bottom-up method to calculate 

direct PE RVUs, refining the methods used to calculate 

indirect PE RVUs, and eliminating the nonphysician 

work pool—and using more current utilization data on 

PE RVUs (Table 9-1, column A, p. 230). We compared 

2006 RVUs (the baseline) with an alternative in which 

we calculated RVUs with the new 2007 methods and new 

(2005) utilization data. Both the baseline and alternative 

approach use old practice cost data for all specialties and 

2006 work RVUs. 

Next, we quantified the effect of using more current 

practice cost data on PE RVUs (Table 9-1, column B, p. 

230). We compared 2006 RVUs with an alternative in 

which we calculated RVUs using the new 2007 methods, 

new utilization data, and more current data for the eight 

specialties. Both the baseline and alternative approach use 

2006 work RVUs.

Last, we quantified the impact of all changes CMS made 

to PE RVUs (Table 9-1, column C, p. 230). This analysis 

compares 2006 PE RVUs with 2007 PE RVUs (assuming 

full implementation of the changes). The 2006 PE RVUs 

reflect the old methods, old practice cost data for the eight 

specialties, 2006 work RVUs, and old utilization data. The 

2007 PE RVUs reflect the new methods, more current 

practice cost data for the eight specialties, 2007 work 

RVUs, and 2005 utilization data. 

Changing the methods and using more 

current utilization data

The combined effects of moving to a bottom-up method, 

refining the indirect methods, eliminating the nonphysician 

work pool, and using more current utilization data increase 

payments for E&M services, other (nonmajor) procedures, 

and tests and decrease them for imaging and major 
228 Analysis of changes to physicians’ practice expense payments

CMS is using new methods and data to calculate practice expense

payment rates

For 2007, CMS made significant changes to both 

the methods and data used to determine practice 

expense (PE) relative value units (RVUs); we 

discuss each type of change separately. 

CMS implemented three major changes to 

the methods

CMS’s new method to calculate direct PE RVUs sums 

the costs of the direct resources—nonphysician clinical 

staff time, medical supplies, and equipment—required to 

furnish each service.1 Stakeholders refer to this method 

as “bottom-up.” Prior to 2007, CMS calculated direct PE 

RVUs by starting with total direct cost pools and then 

allocating practice costs to individual services using 

direct resource estimates—referred to as the “top-down” 

method. The bottom-up method does not use specialtyspecific cost pools, which makes it more understandable 

and transparent than the top-down method. 

Indirect practice costs, which include office rent, 

utilities, and administrative staff, are important because 

they represent about two-thirds of total practice costs. 

Because it is difficult to link indirect costs to specific 

services, CMS had to come up with a way to allocate 

them to services based on some other metric. The 

agency developed a method in which it allocates 

specialty-specific indirect cost pools to individual 

codes. Prior to 2007, CMS allocated indirect costs 

to individual services based on the sum of the direct 

practice cost and physician work RVU for each service. 

Beginning in 2007, the agency made two changes in 

how it allocates costs to specific services:2

• It adjusts the direct practice cost of a service based 

on the ratio of total indirect to total direct costs for 

the specialties that perform the service. 

• For services with low or no physician work RVUs, 

CMS uses the clinical labor RVU (e.g., the cost of a 

nurse’s time) instead of the physician work RVU in 

the allocation method (CMS 2006).

In its third major change, CMS began using the 

same direct and indirect methods to calculate PE 

RVUs for services that do not involve physician 

work—nonphysician work pool services—as for 

other services. The major specialties composing the 

nonphysician work pool are radiology, cardiology, and 

internal medicine. Prior to 2007, the agency used a 

different method to calculate PE RVUs for services 

that did not involve physician work. 

Now that CMS is using a bottom-up method, it 

is important to make sure that the direct resource 

estimates are as accurate as possible. The Commission 

discussed several ways to improve the accuracy of 

these estimates in our June 2006 report. Because 

the agency has limited administrative resources, we 

suggested that CMS focus on areas where the estimates 

are most out of date and the impact on relative weights 

is likely to be greatest. The Congress should provide 

CMS with the financial resources and administrative 

flexibility to undertake this effort as it will improve the 

accuracy of Medicare’s payments. We summarize each 

of these four areas in order of priority.

First, CMS should revisit how it estimates the per 

service price of medical equipment, in particular the 

assumption that all equipment is operated half the time 

that practices are open for business. If this assumption 

is an underestimate, Medicare’s per unit price is too 

high. Our survey of imaging providers in six markets 

indicated that providers in those markets use MRI 

machines more than 90 percent of the time they are 

open for business and use computed tomography (CT) 

machines more than 70 percent of the time (MedPAC 

2006). CMS also assumes that practitioners pay an 

interest rate of 11 percent per year when borrowing 

money to buy equipment, but more recent data 

suggest a lower interest rate may be more appropriate. 

Increasing the assumption about equipment use and 

lowering the interest rate estimate would reduce PE 

payments for CT and MRI services. Because changes 

to PE relative values are budget neutral, these savings 

would be redistributed among other physician services. 

The American Medical Association (AMA)/Specialty 

Society Relative Value Scale Update Committee 

recommended that CMS use a competitive market 

interest rate and an equipment use rate higher than 50 

percent, while allowing specialty societies to present 

evidence supporting lower rates for specific equipment 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 229

CMS is using new methods and data to calculate practice expense

payment rates (cont.)

(CMS 2006). In the final rule on the physician fee 

schedule for 2007, CMS said that it did not have 

sufficient evidence to change the interest rate and 

equipment use rate assumptions for 2007 but expressed 

interest in potentially revising these assumptions in a 

future rule (CMS 2006).

Second, CMS should set a reasonable schedule for 

periodically updating the prices it assigns to the direct 

cost inputs (clinical staff, supplies, and equipment). The 

agency could also review the prices of expensive supply 

and equipment items more frequently than other items. 

Third, to ensure that the types and quantities of direct 

cost inputs are accurate and complete, CMS—with the 

assistance of the medical community—could check the 

consistency of values across similar services and obtain 

current estimates for services that have no information. 

Fourth, CMS should set a reasonable schedule for 

reviewing PE relative weights at least every five years 

(as required by statute) and more often for services 

experiencing rapid changes.

Data on practice costs and work RVUs affect 

the value of indirect PE RVUs 

CMS uses four sources of data to calculate indirect 

PE RVUs: (1) estimates of the types, quantities, and 

prices of clinical labor, medical equipment, and medical 

supplies; (2) estimates of each specialty’s hourly practice 

costs; (3) physician work RVUs; and (4) Medicare 

utilization data. We focus on the latter three data sources, 

which CMS updated between 2006 and 2007. 

CMS multiplies each specialty’s average hourly indirect 

practice cost by the total volume of services it furnishes 

to derive the specialty’s indirect cost pool (i.e., total 

indirect dollars). The agency allocates this pool to each 

service the specialty performs (see p. 235). 

Beginning in 2007, CMS determines indirect PE 

RVUs by using more current hourly practice cost data 

submitted by eight specialties (allergy/immunology, 

cardiology, dermatology, gastroenterology, urology, 

radiology, radiation oncology, and independent 

diagnostic testing facilities). Prior to 2007, CMS 

had begun using more current cost data from five 

specialties. The Balanced Budget Refinement Act of 

1999 mandated that the agency establish a process to 

consider more current practice cost data submitted by 

specialties when updating the physician fee schedule. 

For most other specialties, CMS uses practice cost data 

that the AMA collected between 1995 and 1999.3

Using more current data for the eight specialties: 

• increases their hourly practice costs relative to all 

other specialties,

• increases their total indirect cost pools relative to all 

other specialties, and therefore

• distributes a larger share of indirect costs to the 

services these eight specialties perform relative to all 

other groups. 

The AMA and specialty societies are in the process of 

fielding a survey to collect more current practice cost 

data from nearly all specialty groups. CMS supports the 

AMA’s effort to field a new survey and will consider 

using the data once they are available (CMS 2006). 

The agency anticipates that the data will be available to 

incorporate in the fee schedule no earlier than 2009. 

CMS uses physician work RVUs to calculate indirect 

PE RVUs. The larger a service’s work RVU, the more 

indirect costs it will be allocated, all other factors 

being equal. Using the 2007 work RVUs benefits 

those services whose work values increased due to the 

third five-year review, such as some evaluation and 

management services.

CMS uses Medicare volume data to calculate the 

indirect cost pools (by multiplying each specialty’s 

hourly practice costs and the total volume of services 

that each specialty performed). For the 2007 PE RVUs, 

CMS used 2005 volume data, the most current available. 

Before 2007, the agency used older (1997–2000) 

volume data for services that existed during that time 

period and newer data for services that were introduced 

after 2000. Using more current volume data increases 

the PE RVUs of those services with high growth rates 

between 1997 and 2005, such as imaging services. 
230 Analysis of changes to physicians’ practice expense payments

T A B L E

9 –1 CMS’s changes had a large effect on PE RVUs

Type of service

Impact on PE RVUs due to changes in:

Change in 

volume per 

beneficiary 

2004–2005

Percent

of total 

volume

Methods and 

use data only

(column A)

Methods and 

use data, 

plus practice costs

(column B)

All 

changes*

(column C)

Evaluation and management 7.5% 0.9% 6.5% 2.9% 40.0%

Office visit—established patient 6.2 2.9 7.2 2.5 17.1

Hospital visit—subsequent 9.0 –2.9 13.0 2.4 7.8

Consultation 5.9 3.8 9.4 3.6 5.7

Emergency room visit 12.7 –11.7 –4.6 5.0 2.7

Hospital visit—initial 7.8 –8.0 4.3 1.2 1.9

Office visit—new patient 9.0 1.7 0.2 1.9 1.8

Nursing home visit 20.9 2.2 –4.9 1.3 1.8

Imaging –19.5 –5.8 –9.0 8.7 16.3

Standard—nuclear medicine –27.2 –12.6 –16.5 7.1 2.4

Echography—heart –38.5 –26.0 –28.2 8.2 2.2

Advanced—CT: other –11.3 6.8 0.7 14.7 2.2

Advanced—MRI: other –17.0 –3.6 –7.8 14.2 1.9

Standard—musculoskeletal 2.4 6.7 1.7 4.9 1.2

Advanced—MRI: brain –25.6 –12.3 –16.5 7.1 1.1

Echography—other 10.5 24.0 18.8 12.5 0.8

Standard—chest –30.8 –14.0 –19.9 3.0 0.7

Standard—breast –88.9 –82.7 –2.3 4.3 0.7

Imaging/procedure—other –28.6 –15.8 –19.3 12.8 0.6

Major procedures –6.1 –9.9 –7.6 3.5 8.9

Cardiovascular—other –19.9 –11.4 –3.4 0.4 2.0

Orthopedic—other 4.9 –13.2 –13.0 7.7 1.1

Knee replacement 4.0 –14.8 –13.0 11.1 0.7

Coronary artery bypass graft –5.5 –20.2 –14.3 –8.6 0.6

Coronary angioplasty –6.3 43.9 36.9 –0.8 0.5

Hip fracture repair 4.6 –13.8 –12.1 0.5 0.4

Hip replacement 6.0 –13.3 –11.3 2.0 0.4

Other procedures 5.0 5.4 2.9 8.5 22.3

Minor—other, including outpatient rehab

and drug administration

11.0 7.8 6.0 15.6 4.8

Ambulatory—skin 20.5 30.5 24.9 4.9 2.1

Cataract removal/lens insertion 12.7 –6.4 –9.7 7.8 1.8

Colonoscopy 15.6 33.9 23.6 2.9 1.1

Upper gastrointestinal endoscopy 18.1 40.0 29.3 1.2 0.6

Cystoscopy –26.0 0.8 –1.5 13.9 0.5

Tests 1.9 4.7 2.6 6.2 5.2

Other tests—other 4.6 6.0 2.4 11.1 2.1

Lab test—other (physician fee schedule) 18.0 14.0 12.9 3.5 1.5

Electrocardiogram –31.5 –20.4 –25.0 0.8 0.7

Cardiovascular stress test –6.2 17.4 13.9 4.7 0.6

Note:	 PE (practice expense), RVU (relative value unit), CT (computed tomography). This analysis does not include the effect of the Deficit Reduction Act cap on imaging

services. Some low-volume categories and services are not shown in the table but are included in the summary calculations. Column A models the impact on PE RVUs

due to changing the methods and using more current volume data. The impacts in column A are based on old practice cost data for all specialties and 2006 work

RVUs. Column B models the impact on PE RVUs due to changing the methods and using current volume data and current practice cost data for eight specialties. The

impacts in column B are calculated using 2006 work RVUs. Column C compares the fully phased in new PE RVUs to 2006 PE RVUs and shows the impact of all method

and data changes, including using the 2007 work RVUs for calculating PE. Volume is measured as units of service multiplied by each service’s RVU from the physician

fee schedule. The estimated impact assumes that CMS has phased in all PE changes, which will occur in 2010.

* All changes include using the 2007 work RVUs for calculating PE.

Source:	 MedPAC and NORC analysis of physician RVU and utilization files, direct practice cost data, Medicare claims, and specialty practice cost-per-hour file from CMS.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 231

procedures (Table 9-1, column A). Each of these changes 

has a different effect on PE RVUs. 

Moving to a bottom-up method increases the PE RVUs 

of services that are office based and use costly medical 

equipment and supplies, such as some imaging services, 

tests, and other procedures. By contrast, moving to a 

bottom-up method has a negative effect on office-based 

services that do not use costly equipment and supplies 

(e.g., E&M services) and services furnished in facilities 

(e.g., major procedures). Changing the indirect methods 

has a stronger effect on PE RVUs than changing the direct 

methods because indirect PE RVUs account for at least 

two-thirds of total PE RVUs, on average, for the broad 

service categories. In the case of E&M services, the 

positive effect of modifying the indirect methods offsets 

the negative effect of moving to a bottom-up method for 

direct costs. 

Eliminating the nonphysician work pool affects PE RVUs 

of services administered by nonphysician staff, such as 

imaging services. Before 2007, the PE RVUs of services 

administered by nonphysicians (e.g., performing an Xray) were not resource based. Rather, the services—called 

nonphysician work pool services—were valued using 1998 

charges. The Government Accountability Office noted 

that some nonphysician work services were overvalued 

and some were undervalued (GAO 2001). Moving to a 

resource-based method causes some nonphysician work 

services to increase and some to decrease depending 

on the relationship between charges and estimates of 

resources.

Finally, using more current volume data benefits those 

services whose volume grows more rapidly than other 

services. Between 2000 and 2004, imaging services, other 

procedures, and tests grew 10 percent, 6 percent, and 8 

percent per year, respectively, while E&M services and 

major procedures each grew 4 percent per year (MedPAC 

2007).

Changing the methods and using more 

current utilization and practice cost data

The impact of the new methods is somewhat offset 

when we use more current practice cost data from eight 

specialties to determine PE RVUs (Table 9-1, column B). 

Using more current practice cost data benefits imaging 

services, other procedures, and tests because specialties 

that submitted more current data account for a substantial 

share of the total volume of these services: 

• Radiology and cardiology together provide about 60 

percent of the total volume of imaging services. 

• Dermatology, gastroenterology, urology, and radiation 

oncology together account for about 35 percent of the 

total volume of other procedures. 

• Cardiology, dermatology, and independent diagnostic 

testing facilities together account for about onequarter of the volume of tests.

By contrast, for E&M services, using more current 

practice cost data offsets some of the increases realized 

from the changes in the methods. The leading two 

specialties that provide E&M services—internal medicine 

and family practice—did not submit more current practice 

cost data. In aggregate, using more current practice cost 

data for some specialties decreases PE RVUs for major 

procedures except those services performed primarily by 

cardiology (e.g., coronary angioplasty). 

The American Medical Association (AMA) and specialty 

societies are in the process of fielding a survey to collect 

more current practice cost data from nearly all specialty 

groups. CMS, which supports this effort, anticipates that 

the new data will be available for use in the fee schedule 

no earlier than 2009 (CMS 2006). 

CMS uses the survey data to estimate average hourly 

practice costs for each specialty, which are used to 

calculate indirect PE RVUs. Consistent with the 

Commission’s position that Medicare should pay for costs 

incurred by efficient providers, it is worth asking whether 

cost data used to set indirect PE RVUs should reflect the 

costs of efficient practices rather than the costs of average 

practices. For example, CMS could explore using the 

hourly cost of a practice at the lowest 25th percentile of 

costs rather than the mean.4 In calculating the cost of an 

efficient practice, CMS would need to adjust for practices’ 

service mix and geographic location, because differences 

in the type of services provided and input prices probably 

affect practice cost variations. Unfortunately, the survey 

data that CMS currently uses to calculate PE RVUs do not 

contain information on practices’ service mix, nor does the 

survey that the AMA and specialty societies are currently 

fielding. Thus, in future surveys, CMS might want to 

consider collecting the data necessary to identify efficient 

practices, controlling for service mix and other factors. 

Basing RVUs on the cost of efficient practices might 

require a statutory change.
232 Analysis of changes to physicians’ practice expense payments

Implementing all methods and data 

changes: Comparing 2006 and 2007 

PE RVUs

Finally, we look at all the changes—including using the 

2007 work values to calculate indirect PE RVUs—by 

comparing the values of 2006 and 2007 PE RVUs (Table 

9-1, column C) (p. 230). The comparisons between 2006 

and 2007 RVUs assume that all the PE changes have 

been fully phased in, which will not occur until 2010. 

The impact of using the 2007 work RVUs, refined during 

the third five-year review, is apparent when comparing 

columns B and C of Table 9-1 (p. 230). The values of 

many E&M services increase due to the third five-year 

review. Using the 2007 work RVUs to calculate indirect 

PE RVUs benefits those categories of E&M services 

whose work values increased on average due to the fiveyear review, such as office visits for established patients 

and hospital visits. By contrast, using the 2007 work RVUs 

does not help those categories of E&M services whose 

work values remain unchanged due to the five-year review, 

such as nursing home visits. To maintain budget neutrality, 

CMS cut the work value of all services by 10.1 percent 

when it implemented the five-year review changes.5

For major procedures, the use of the 2007 work RVUs to 

calculate indirect PE RVUs offsets some of the negative 

impacts from the change in the methods and the use of 

more current practice-cost data. The third five-year review 

increases the work value of several major procedures. 

Nonetheless, when CMS phases in all the methods and 

data changes, PE RVUs will decline by 8 percent for major 

procedures.

Changes in PE RVUs vary by type and place of service

Note:	 PE (practice expense), RVU (relative value unit), E&M (evaluation and management). Changes include new methods, new volume data, new practice cost data for eight

specialties, and the use of 2007 work RVUs in PE calculations. The estimated impacts assume that CMS has phased in all the PE changes, which will not occur until 2010.

Source:	 MedPAC analysis of physician RVU and utilization files from CMS.

Change in PE RVUs due to all methods and data changes varies by place of service FIGURE

9-1

Source: MedPAC analysis of Provider of Services file from CMS. Percent

20

–10

10

0

–20

–30

–40

x-axis label

Total

Nonfacility

Facility

E&M Imaging Major procedures Other procedures Tests

F i g ure

9 –1
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 233

For other procedures and tests, using the 2007 work RVUs 

to calculate indirect PE RVUs offsets some (but not all) 

of the positive effect of the methods changes and the use 

of more current practice cost data. Even so, PE RVUs will 

increase by 3 percent for other procedures and tests when 

considering all the methods and data changes. 

Using the 2007 work RVUs to calculate indirect PE RVUs 

offsets some of the positive effects that imaging services 

realize from the use of more current practice cost data. 

Considering all the changes, including the new methods’ 

negative effect on their PE RVUs, imaging PE RVUs will 

fall by 9 percent. 

Our analysis also shows that the changes to the methods 

and the data can affect PE RVUs of services differently 

depending on whether providers most frequently furnished 

them in a facility or in an office (nonfacility) setting 

(Figure 9-1). For example, PE RVUs for E&M services will 

increase for services furnished in both settings. By contrast, 

PE RVUs will decrease substantially for major procedures 

performed in nonfacility settings (35 percent), while the 

decrease in PE RVUs will be more modest for facility-based 

care (4 percent) when CMS phases in all the changes. 

Allocating indirect practice expenses

Indirect costs, which include office rent, utilities, and 

administrative staff, represent about two-thirds of total 

practice costs. Because it is difficult to link these costs 

to specific services, CMS has to allocate them based on 

some other measure of resource use. CMS uses a complex 

method to allocate specialty-specific indirect cost pools to 

individual services based primarily on the direct practice 

cost and physician work value for each service. In this 

section, we explain CMS’s current approach, review some 

alternatives to this approach, and illustrate that changes to 

the method can significantly affect the distribution of PE 

payments across services. 

In general cost-accounting systems, indirect costs are 

allocated to specific items or services by a two-step 

process (Hawkins and Cohen 2004). First, cost pools

that incorporate a grouping of indirect costs (e.g., office 

rent) are created. Second, each cost pool is assigned to 

individual items by using a cost allocation basis, which 

ideally should reflect a cause-and-effect relationship 

between the cost pool and the item. For example, in a 

manufacturing process, direct labor hours might be used 

to allocate indirect labor costs (e.g., management) to a 

product; the demand for indirect labor is assumed to be 

a function of the direct labor hours worked. Likewise, 

CMS creates cost pools of indirect practice costs and 

allocates them to specific services based on the amount 

of physician work and direct practice costs (medical 

equipment, medical supplies, and nonphysician clinical 

labor) that each service requires. The underlying 

assumption of this method is that the use of indirect 

practice resources is a function of a service’s physician 

work and direct practice costs.

It is important to recognize that, given the variety of 

physician practices and services and the very nature 

of overhead costs, there is no single best method for 

allocating indirect practice costs (CMS 2006, 1998). 

Since the 1990s, researchers and CMS have examined 

various ways to allocate indirect costs, some of which 

are described below. Policymakers may wish to use the 

following four broad principles to guide any allocation 

approach: 

• The allocation should be based on the factors that 

influence indirect practice costs.

• It should avoid creating financial incentives that favor 

certain sites of care over others (PPRC 1992).

• It should limit the administrative burden on CMS and 

providers.

• It should be understandable.

Most indirect costs are fixed in the 

short run

Practice expenses include costs that are fixed in the short 

run (e.g., office rent, utilities, equipment) and variable 

costs (e.g., medical supplies and clinical labor). CMS 

classifies most fixed costs as indirect costs and most 

variable costs as direct costs. Some researchers have 

proposed that Medicare should pay for a practice’s fixed 

costs only until those costs have been covered, perhaps 

by making a periodic, lump-sum payment that would be 

related to practice characteristics such as size and location 

(Latimer and Becker 1992). Because practices incur 

variable costs for each additional service, Medicare would 

pay for variable costs on a per service basis. 

This approach is conceptually appealing but it would be 

quite difficult to implement. CMS would need to collect 

very detailed information about practice characteristics 

that could affect the level of fixed costs, such as: size, 

number of individual offices, site of practice, service 
234 Analysis of changes to physicians’ practice expense payments

mix and volume, Medicare’s share of overall volume, 

and specialty. CMS and other organizations do not 

currently collect nationally representative data on 

these characteristics at the practice level. Even if CMS 

collected this information, developing homogeneous 

payment groups would be difficult. There could also be 

opportunities for physicians to increase their payments 

by changing their practice characteristics. For example, 

physician practices might increase their number of offices 

to gain more payments if the additional payments exceed 

their actual indirect costs. Given the difficulty of paying 

practices for their indirect costs on a lump-sum basis, the 

more practical alternative is to pay physicians for their 

indirect costs on a per service basis, which is CMS’s 

current approach.

CMS’s method for assigning indirect costs

CMS uses a complicated, two-step process to calculate 

indirect PE RVUs per service, as described in the 

following sections.

How CMS calculates indirect practice expense for a specific service

Note:	 All numbers are for illustrative purposes. In this illustration of service A, the physician work value is greater than the nonphysician clinical labor value. Hence, the

physician work value is used in the allocator.

	 *The sum of indirect allocators for all services performed by specialty A is derived by (1) multiplying each service’s indirect allocator by the Medicare volume of

each service, and (2) summing the products across all services.

	 **CMS applies a budget-neutrality adjustment to this value.

How CMS calculates indirect practice expense for a specific service FIGURE

9-2

Note and Source in InDesign.

Administrative labor

$150,000,000

Office expenses

$200,000,000

Step 1: Estimate total indirect costs for specialty A

+ Other expenses

$100,000,000

Total indirect costs

$450,000,000 + =

Direct cost

service A

$11.80

Ratio of indirect

 to direct costs

specialty A

1.2

Step 2: Allocate total indirect costs to specific services

2A: Calculate indirect allocator for service A

x

Physician work

service A

$33

+ = $47.16

Total indirect costs

Sum of indirect allocators for all

services performed by specialty A*

2B: Calculate scaling factor for specialty A

= = 0.35

$450,000,000

$1,290,000,000**

$47.16

2C: Multiply indirect allocator by scaling factor

x 0.35 = $16.51**

F i g ure

9 –2
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 235

Creating indirect cost pools

First, CMS creates separate indirect cost pools for each 

specialty by:

• estimating the specialty’s average hourly costs 

for indirect expenses (administrative labor, office 

expenses, and other expenses), as derived from an 

AMA survey or supplemental surveys submitted by 

specialties; and

• multiplying the average hourly indirect cost by the 

total Medicare volume of services the specialty 

furnishes and the amount of time it takes to provide 

each service.6

An illustrative indirect cost pool is shown in Figure 9-2, 

step 1. Services performed by specialties with indirect 

cost pools that are larger than average (e.g., cardiology 

and dermatology) receive more indirect RVUs, while 

services performed by specialties with indirect cost pools 

that are smaller than average (e.g., internal medicine and 

emergency medicine) receive fewer indirect RVUs. 

Allocating cost pools to specific services

Until 2007, CMS allocated specialty-specific indirect 

cost pools to individual services based on the sum of the 

direct cost RVU (which includes nonphysician clinical 

labor, medical equipment, and medical supplies) and 

physician work RVU for each service. The agency used 

both physician work and direct practice costs in the 

allocator so that indirect costs could be spread across 

a broad range of inputs that are traceable to specific 

services. Although some researchers and stakeholders have 

argued that only physician work or physician time should 

be used to allocate indirect costs (see text box, p. 236), 

using both physician work and direct costs helps balance 

services performed by office-based and hospital-based 

specialties. Hospital-based specialties, such as general 

surgery, incur few direct costs but perform services with 

higher physician work RVUs. Thus, if the allocator were 

based only on direct costs, it would be difficult to allocate 

indirect costs for hospital-based physicians. On the other 

hand, office-based specialties, such as internal medicine 

and dermatology, generally perform services with lower 

work RVUs but higher direct costs. 

For 2007, CMS made two changes to the indirect cost 

allocator (the formula for the current indirect allocator 

appears in Figure 9-3, p. 237):

• CMS decided to adjust direct costs by the ratio of 

indirect to direct costs for the specialty that performs 

the service. To use the example in Figure 9-2 (step 

2A), direct costs for service A ($11.80) are multiplied 

by the ratio of overall indirect to overall direct costs 

for specialty A (1.2). Because most specialties have 

higher indirect costs than direct costs (i.e., their ratio 

of indirect costs to direct costs is greater than 1.0), 

this adjustment increases the weight of direct costs 

in the allocator. Because of this change, direct costs 

now account for roughly one-third of the allocator (on 

average), compared with one-quarter of the allocator 

previously (Thompson 2007). This change benefits 

services whose direct cost RVU is higher than the 

physician work RVU, such as the technical component 

of diagnostic tests. Previously, CMS expressed 

concern that using physician work to allocate indirect 

costs may disproportionately benefit hospital-based 

services (CMS 1998).

• Because certain services, such as the technical 

component of imaging studies and radiation therapy, 

have no physician work RVUs, CMS decided to use 

the higher of a service’s physician work RVU or 

nonphysician clinical labor RVU (e.g., the cost of 

a radiology technologist’s time). For example, the 

technical component of MRI of the brain (without 

contrast followed by contrast) has no work RVU, 

but it has an estimated cost of $42.30 for the MRI 

technologist’s time. This cost is converted to an RVU 

and used twice in the indirect allocator, first as part 

of the direct cost RVU (nonphysician clinical labor is 

part of direct costs) and then in place of the physician 

work RVU. This change increases indirect payments 

for services with low or no work RVUs. 

If a service is performed by multiple specialties, its 

indirect cost equals the average of each specialty-specific 

indirect cost for that service. In calculating the average, 

each specialty’s indirect cost is weighted by its Medicare 

volume for that service. 

Issues with CMS’s allocation method

Some observers have raised important issues about the 

allocator, including that it: 

• double counts nonphysician clinical labor for services 

with little or no physician work;

• includes medical supplies and equipment, even though 

the relationship between indirect costs and equipment 

and supplies may not be linear; and
236 Analysis of changes to physicians’ practice expense payments

• is calculated using new data for some specialties, 

which creates distortions among RVUs.

The current allocator double counts nonphysician clinical 

labor for services with low or no physician work RVUs 

(e.g., the technical component of imaging studies). This 

adjustment means that such services are allocated more 

indirect costs—and receive higher indirect PE RVUs—

than they would otherwise, which may create a stronger 

financial incentive for providers to perform them. Some 

stakeholders have asked why these services should be 

treated differently (CMS 2006). Although they have low 

Allocating indirect costs using only physician work or time

Some researchers and stakeholders favor 

allocating indirect costs based solely on the 

physician time or work involved in a service 

(Lewin Group 2000, Latimer and Becker 1992). 

Physician work includes both the time and intensity 

(mental effort, technical skill, stress, and risk) involved 

in performing a service. Proponents of this approach 

argue that physicians should receive the same indirect 

payment per unit of physician “involvement,” defined 

as the work or time they expend in providing the 

service (Latimer and Becker 1992). If the indirect 

payment amount is proportional to the physician’s 

involvement in the service, physicians should have no 

financial incentive to provide one service over another. 

An important concern about using only physician time 

or work as the allocator is that indirect costs would not 

be assigned to services that are performed primarily 

in physician offices by nonphysician staff, such as 

diagnostic tests (CMS 1997).

The Lewin Group simulated the effects of using only 

physician work or physician time in the allocator. They 

found that using only physician work would shift about 

12 percent of indirect payments to facility-based (e.g., 

hospital) services and using only physician time would 

shift about 8 percent of indirect payments to facilitybased services (Lewin Group 2000). These effects 

occur because facility services are more likely to have 

higher work and time values than office-based services. 

Some specialties have favored using physician time over 

physician work because they argue that indirect costs 

should vary by the time, but not the intensity, related to 

a service (CMS 2006, 1998). One study claimed that 

allocating indirect expenses based on physician time, 

rather than physician work, is more likely to create 

neutral financial incentives across services (Latimer and 

Becker 1992). According to this study, time is a better 

measure of physician involvement than work because 

physicians are more constrained by the time they have 

available for practice than by the number of work 

relative value units (RVUs) they can bill. (It is easier 

to substitute a high-work RVU service for a low-work 

RVU service than to practice more hours.) Allocating 

indirect costs on the basis of time instead of physician 

work would reduce practice expense (PE) payments for 

services that require more work per unit of time (e.g., 

surgery) and would increase payments for services that 

require less work per unit of time (e.g., office visits). 

For many services, however, the physician time 

estimates have not been as rigorously validated as 

the physician work values, which raises concerns 

about using physician time to allocate indirect costs 

(Rich 2007, Lewin Group 2000). The physician 

time data come primarily from: (1) the American 

Medical Association/Specialty Society Relative Value 

Scale Update Committee (RUC), and (2) surveys 

conducted by William Hsiao and his colleagues during 

development of the original fee schedule—known as 

the Harvard time data (Becker et al. 1988). When CMS 

and the RUC reviewed physician work RVUs during 

the early 1990s, time estimates did not receive the 

same scrutiny as the work values because time is one 

of several components of work (other factors include 

technical skill, mental effort, and psychological stress) 

(Rich 2007). However, physician time became more 

important when CMS implemented resource-based 

PE RVUs in 1999 because physician time was used in 

the methodology. For example, CMS uses the time per 

service to determine the specialty cost pools that are 

used to calculate indirect PE RVUs. Thus, since 1999, 

CMS and the RUC have scrutinized physician time 

estimates more closely. 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 237

or no work RVUs, these services can still be allocated 

indirect costs based on their direct costs. Indeed, the 

average direct cost of services with no physician work 

is nearly twice as high as the direct cost of services with 

physician work.7 In addition, multiplying the service’s 

direct cost by the ratio of total indirect to total direct 

costs for the specialties that perform the service benefits 

many services with no physician work. This adjustment 

increases the weight of direct costs and decreases the 

weight of physician work in the allocator for services 

performed by specialties with a ratio of indirect to direct 

costs greater than 1.0. The three specialties that account 

for the largest share of spending on nonphysician work 

services (radiology, cardiology, and internal medicine) 

have ratios greater than 1.0.8 On the other hand, CMS 

contends that services with low or no physician work 

RVUs would be undervalued unless the agency used a 

proxy for physician work, such as nonphysician clinical 

labor, when allocating indirect costs to these codes (CMS 

2006). 

The indirect allocator includes all three components 

of direct costs: nonphysician clinical labor, medical 

equipment, and medical supplies. Practices that use 

expensive medical equipment probably incur additional 

costs for office space, utilities, and—in the case of 

certain imaging machines—radiation shielding. The 

question is whether the relationship between equipment 

or supply costs and indirect costs is linear; do overhead 

costs increase in direct proportion to equipment and 

supply costs? For example, a $1,000 supply should not 

require 10 times as much office rent or administrative 

staff as a $100 supply. Similarly, a $1 million imaging 

machine may not require overhead costs 10 times as high 

as a $100,000 machine. CMS’s current method allocates 

indirect costs in proportion to equipment and supply 

costs and thus may overvalue services that use expensive 

supplies and equipment. Medicare pays for the direct costs 

of equipment and supplies through the direct PE method; 

at issue here is the magnitude of indirect costs associated 

with equipment and supplies. 

CMS’s new method for allocating indirect costs and two alternatives 

Note:	 PE (practice expense), RVU (relative value unit). Direct PE RVU includes nonphysician clinical labor, medical equipment, and medical supplies. CMS’s current

method and alternative 1 use specialty-specific indirect cost pools; alternative 2 uses a single indirect cost pool across all specialties.

Source:	 CMS 2006 and MedPAC.

CMS’s new method for allocating indirect costs and two alternatives FIGURE

9-3

Note and Source in InDesign.

CMS’s new method

Allocator = Direct PE RVU of service

% indirect cost for specialty

% direct cost for specialty

Physician work RVU of service

(or clinical labor RVU if clinical labor > work)

x +

Alternative 1

Allocator Nonphysician clinical

labor RVU

= % indirect cost for specialty

% direct cost for specialty

x + Physician work RVU of service

Alternative 2

Allocator = Direct PE RVU of service

% indirect cost for specialty

% direct cost for specialty

x + Physician work RVU of service

F i g ure

9 –3
238 Analysis of changes to physicians’ practice expense payments

By contrast, it seems more likely that a service’s overhead 

costs vary in proportion to the amount of time it takes to 

perform the service. In other industries, the amount of time 

required to produce an item is considered a reasonable 

allocator for overhead costs (Hawkins and Cohen 2004, 

Bruns 1993). Nonphysician clinical labor and physician 

work are better proxies of service time than equipment and 

supplies. Neither, however, is a pure measure of service 

time; nonphysician clinical labor is based on both time 

and wage rates and physician work is based on both time 

and intensity. In addition, using both nonphysician labor 

and physician work in the allocator may overestimate the 

time required for a service because nonphysician staff 

and physicians might be involved in performing a service 

at the same time (e.g., when a nurse assists a physician 

during an in-office procedure). 

CMS creates indirect cost pools for each specialty using 

a specialty’s average hourly costs for indirect expenses. 

For most specialties, the agency derives these hourly 

costs from an AMA survey of physicians, which reflects 

practice costs from 1995 through 1999. However, CMS 

uses more recent practice cost data for 13 specialties to 

estimate their hourly costs. The use of older cost data for 

many specialties, and newer cost data for others, creates 

distortions in the indirect cost pools. In addition, the 

use of separate cost pools for each specialty makes the 

methodology more complex and difficult to understand. 

An alternative would be to use a single indirect cost 

pool across all specialties, which was the approach CMS 

proposed in 1997 (GAO 1998). A single cost pool could 

be based on the amount of indirect PE RVUs in the current 

payment system. On the other hand, some stakeholders 

contend that, for the indirect method to be resource based, 

it should use specialty cost pools based on survey data, 

including the more recent survey data for the 13 specialties 

(CMS 2006). If CMS eventually uses the survey data that 

the AMA and specialty societies are currently collecting 

on practice costs for all specialties, this would address 

concerns about the distortions caused by using more recent 

data for some, but not all, specialties. 

Sensitivity of PE RVUs to changes in the 

indirect method

Based on the four principles outlined earlier (p. 233), we 

modeled two alternatives to show that choices made in 

designing the indirect method can significantly affect the 

distribution of payments (Figure 9-3, p. 237). Neither of 

these alternatives should be viewed as a proposal or even 

the best way to allocate indirect costs; other allocators 

would also be consistent with the four principles. 

Our contractor, NORC, estimated the impact of each 

alternative on PE RVUs by type of service and place of 

service (facility or nonfacility). Their model uses the new 

methodology for direct PE RVUs, 2005 utilization data, 

new practice cost data for eight specialties, and the new 

work RVUs that CMS adopted for 2007. In addition, each 

model applies an overall budget-neutrality adjustment 

to ensure that total indirect payments do not exceed the 

current level. The text box (pp. 240–241) and Table 9-2 

describe the impact of each alternative by type of service. 

Alternative 1

Alternative 1 is based on the nonphysician clinical labor 

RVU (adjusted by the ratio of indirect to direct costs for 

the specialties that perform the service) plus the physician 

work RVU for the service. This approach is based on a 

relatively simple assumption: Indirect costs are related to 

the amount of labor involved in a service, whether it is 

provided by a physician or nonphysician clinical staff. It 

does not allocate indirect costs to services based on their 

use of equipment and supplies and does not double count 

nonphysician clinical labor for services with low or no 

physician work RVUs (e.g., the technical component of 

imaging studies). 

Although this alternative does not allocate indirect costs 

to individual services on the basis of medical supplies 

and equipment, it does take into account that some 

specialties have higher overall indirect costs related to 

their use of supplies and equipment. The relationship 

between equipment and indirect costs may not be linear, 

but practices that use expensive equipment probably incur 

some additional overhead costs. Like CMS’s allocator, 

this alternative uses the specialty-specific indirect cost 

pools, which include overhead costs related to medical 

equipment and supplies. The use of specialty-specific cost 

pools means that services performed by specialties with 

large cost pools (e.g., radiology, radiation oncology, and 

cardiology) will receive higher indirect PE RVUs than if 

specialty cost pools were not used. 

Removing equipment and supplies from the indirect 

allocator increases the weight of physician work in the 

allocator from about two-thirds to five-sixths of the total 

(nonphysician clinical labor accounts for the remaining 

one-sixth). As described in the text box (pp. 240–241), 

increasing the weight of physician work would shift PE 

RVUs from office-based to facility-based services.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 239

T A B L E

9 –2 Alternative indirect cost allocation methods can change PE RVUs significantly

Type of service

Impact on PE RVUs (relative to current method) Percent

of total 

Alternative 1 Alternative 2 volume

Evaluation and management 10.4% 6.8% 40.0%

Office visit—established patient 6.6 3.7 17.1

Hospital visit—subsequent 18.4 9.4 7.8

Consultation 17.6 –6.6 5.7

Emergency room visit 5.9 80.4 2.7

Hospital visit—initial 18.1 16.1 1.9

Office visit—new patient 9.4 2.8 1.8

Nursing home visit 9.8 21.6 1.8

Imaging –13.5 –7.2 16.3

Standard—nuclear medicine –19.0 –21.7 2.4

Echography—heart –16.6 –25.4 2.2

Advanced—CT: other –6.9 1.2 2.2

Advanced—MRI: other –20.3 0.8 1.9

Standard—musculoskeletal –12.6 –4.3 1.2

Advanced—MRI: brain –17.4 1.6 1.1

Echography—other –17.1 –2.3 0.8

Standard—chest 20.8 2.8 0.7

Standard—breast 90.8 7.1 0.7

Imaging/procedure—other 1.7 –7.1 0.6

Major procedures 11.0 –6.6 8.9

Cardiovascular—other 12.0 –5.9 2.0

Orthopedic—other 8.9 –10.2 1.1

Knee replacement 12.4 –13.9 0.7

Coronary artery bypass graft 7.5 8.7 0.6

Coronary angioplasty 34.7 –39.1 0.5

Hip fracture repair 11.6 –14.1 0.4

Hip replacement 12.5 –14.0 0.4

Other procedures –1.9 –2.7 22.3

Minor—other, including outpatient rehab

and drug administration

–4.0 7.5 4.8

Ambulatory—skin –1.1 –14.4 2.1

Cataract removal/lens insertion 11.1 –9.8 1.8

Colonoscopy 2.9 –22.7 1.1

Upper gastrointestinal endoscopy 3.4 –25.5 0.6

Cystoscopy –3.5 –12.7 0.5

Tests –8.5 –5.3 5.2

Other tests—other –12.4 –2.3 2.1

Lab test—other (physician fee schedule) –6.9 2.7 1.5

Electrocardiogram 0.4 –22.2 0.7

Cardiovascular stress test –5.8 –32.6 0.6

Note:	 PE (practice expense), RVU (relative value unit), CT (computed tomography). This analysis does not include the effect of the Deficit Reduction Act cap on imaging

services. Some low-volume categories and services are not shown in the table but are included in the summary calculations. Alternative 1 uses an indirect cost

allocator that is based on nonphysician clinical labor (adjusted by the ratio of indirect to direct costs for the specialties that perform the service) plus physician

work for the service. Alternative 2 uses an indirect cost allocator that is based on the direct practice costs (equipment, supplies, and nonphysician clinical labor),

adjusted by the ratio of indirect to direct costs for the specialties that perform the service, plus physician work for the service. Alternative 1 uses specialty-specific

indirect cost pools. Alternative 2, however, uses a single indirect cost pool for all specialties that is based on the amount of indirect PE RVUs in the current payment

system. Both alternatives 1 and 2 use current practice cost data for the eight specialties that recently submitted such data, 2007 work RVUs, and 2005 Medicare

utilization data. Volume is measured as units of service multiplied by each service’s RVU from the physician fee schedule. 

Source:	 NORC analysis of physician RVU files, direct practice cost data, Medicare claims, and specialty practice cost-per-hour file from CMS.
240 Analysis of changes to physicians’ practice expense payments

Alternative 2

Alternative 2 uses all the direct practice costs (equipment, 

supplies, and nonphysician clinical labor), adjusted by 

the ratio of indirect to direct costs for the specialties that 

perform the service, plus physician work for the service. 

However, it does not double count nonphysician clinical 

labor for services with low or no physician work RVUs. 

In addition, this alternative does not use specialty-specific 

indirect cost pools. Instead, it uses a single indirect cost 

pool across all specialties that is based on the amount 

of indirect PE RVUs in the current payment system. In 

CMS’s current method, services performed by specialties 

with larger-than-average indirect cost pools receive 

more indirect RVUs than other services. In alternative 2, 

services are neither advantaged nor disadvantaged by the 

relative size of specialty-specific cost pools. 

Eliminating specialty-specific cost pools makes the 

allocator less complex and eliminates the distortions 

caused by using more recent data for some, but not all, 

Impact of two alternative indirect cost allocation methods 

Under indirect alternative 1, practice expense 

(PE) relative value units (RVUs) would shift 

from imaging services (13.5 percent lower 

than under CMS’s current method), tests (–8.5 percent), 

and other procedures (–1.9 percent) to evaluation 

and management (E&M) services (+10.4 percent) 

and major procedures (+11 percent) (Table 9-2, p. 

239). Because services that use expensive equipment 

and supplies are not allocated more indirect costs, 

PE RVUs would be lower for office-based services 

that use costly equipment and supplies (e.g., nuclear 

medicine, MRI, radiation therapy, and certain lab tests). 

Because work RVUs account for a larger portion of the 

allocator than under the current method, services with 

high work RVUs relative to their direct costs would be 

assigned more indirect expenses. Thus, PE RVUs would 

increase for hospital visit–subsequent (18.4 percent), 

cardiovascular procedures–other (12 percent), knee 

replacement (12.4 percent), and cataract removal and 

lens insertion (11.1 percent). PE RVUs for some officebased services, such as office visit–established patient 

and office visit–new patient, would increase because 

these codes have relatively small equipment and supply 

costs. Thus, these services would receive some of the 

indirect expenses no longer allocated to codes that use 

costly equipment and supplies. Many services would 

experience fairly large changes in PE RVUs; codes 

accounting for 40 percent of overall volume would 

change by more than 15 percent. 

PE RVUs for services performed in facilities (e.g., 

hospitals) would increase by 22 percent, on average. 

Conversely, PE RVUs for codes provided in physician 

offices and other nonfacility settings would decline by 

7 percent, on average. This shift would occur because 

physician work RVUs account for a larger share of the 

allocator, and services with high work RVUs are more 

likely to be provided in facilities. 

Alternative 2 would result in large changes in PE RVUs 

for several categories of services, but the changes 

are not as extreme as under alternative 1 (Table 9-2, 

p. 239). PE RVUs for E&M services would increase 

by 6.8 percent, while they would decline for other 

procedures (–2.7 percent), major procedures (–6.6 

percent), imaging (–7.2 percent), and tests (–5.3 

percent). Imaging and tests would decline for two 

primary reasons:

• Alternative 2 does not substitute nonphysician 

clinical labor for physician work if the nonphysician 

labor RVU is higher than the physician work RVU. 

This change would result in fewer indirect costs 

being allocated to imaging services and tests that 

have no work RVUs (e.g., the technical component 

of an MRI study). 

• Cardiology, which performs about one-quarter 

of imaging studies and one-fifth of tests, has an 

indirect cost pool that is much larger than average. 

Because alternative 2 does not use specialty-specific 

cost pools, services performed by cardiology would 

be assigned fewer indirect costs. 

(continued next page)
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 241

specialties to construct the cost pools. On the other hand, 

some stakeholders could argue that, by not using specialty 

cost pools based on survey data, this approach is not 

resource based. Eliminating the specialty cost pools would 

shift payments from services performed by specialties 

with relatively high practice costs to services performed by 

specialties with relatively low practice costs, which may 

be undesirable. Finally, if CMS eventually uses the survey 

data that the AMA and specialty societies are currently 

collecting on practice costs for all specialties, this would 

address concerns about the distortions caused by using 

more recent data for some, but not all, specialties. 

Although the relative size of specialty-specific cost pools 

does not matter under alternative 2, each specialty’s ratio 

of indirect to direct costs plays a role because the direct 

cost for a service is adjusted by the ratio of indirect to 

direct costs for the specialties that perform the service. 

Consequently, services performed by specialties with a 

higher ratio of indirect to direct costs will be assigned 

higher indirect costs. CMS would still need to use 

physician survey data to estimate this ratio.

Adjusting for geographic differences in 

practice expenses

Under the physician fee schedule, Medicare adjusts 

payment rates to account for geographic differences in 

the price of inputs used in furnishing physician services. 

Three separate geographic practice cost indexes (GPCIs) 

Impact of two alternative indirect cost allocation methods (cont.)

Within the major procedures category, PE RVUs would 

decline for services frequently performed by specialties 

with indirect cost pools that are larger than average. 

For example, coronary angioplasty (often performed 

by cardiology) would decline by 39 percent and hip 

replacement (often provided by orthopedic surgery) 

would drop by 14 percent. Conversely, PE RVUs would 

increase for major procedures generally performed by 

specialties with indirect cost pools that are smaller than 

average. For example, heart bypass surgery (performed 

by cardiac surgery) would increase by 9 percent. 

There would be a large effect on PE RVUs for many 

individual codes: services that account for about onethird of overall volume would change by more than 15 

percent. However, shifts of PE RVUs from nonfacility 

to facility services would be minimal: Facility services 

would increase by 3 percent, on average, and services 

provided in physician offices and other nonfacility 

settings would decline by 1 percent, on average. 

Alternative 2 would result in higher PE RVUs 

for outpatient therapy services (physical therapy, 

occupational therapy, and speech–language pathology 

services), which would lead to higher overall Part B 

spending. This would occur because physician fee 

schedule rates apply to outpatient therapy services 

provided in hospital outpatient departments and nursing 

homes outside of a Part A stay.9 Thus, when physician 

fee schedule rates for outpatient therapy increase, 

this increase affects these other settings. When CMS 

changes RVUs for services paid under the physician 

fee schedule, the agency applies a budget-neutrality 

adjustment to ensure that overall physician spending 

does not change significantly. However, this adjustment 

does not consider the impact of RVU changes on 

spending for other providers, such as outpatient 

departments and nursing homes. The outpatient 

prospective payment system (PPS) has its own budgetneutrality adjustment, but it does not include services 

whose rates are set by the physician fee schedule (e.g., 

mammography and outpatient therapy). Thus, when 

changes to the physician fee schedule lead to higher 

rates for therapy, the additional Part B spending for 

outpatient department and nursing home services is not 

offset by a budget-neutrality adjustment.10 CMS would 

probably require a change in statute to consider this 

additional spending when applying budget-neutrality 

adjustments to the outpatient PPS. 
242 Analysis of changes to physicians’ practice expense payments

correspond to each of the three components of physician 

payment: physician work, PE, and professional liability 

insurance.11 The current PE GPCI does not recognize 

that individual services have different shares of inputs 

for which prices vary geographically (e.g., nonphysician 

staff and office space) and for which prices are uniform 

(e.g., equipment and supplies). Thus, for services with 

below average shares of equipment and supplies, the 

index does not adjust a large enough portion of the PE 

RVU; for services with above average shares of equipment 

and supplies, it adjusts too large a portion of the RVU. 

This distorts prices, which may alter the mix of services 

provided within a high- or low-cost area. 

We developed an alternative GPCI, which better 

recognizes that services have different shares of inputs for 

which prices vary geographically. This alternative—which 

excludes equipment and supplies—would be applied to 

the portion of the PE RVU related to indirect costs and 

nonphysician clinical labor but not to the portion related to 

equipment and supplies. It would produce more accurate 

prices for specific services among different markets, 

thus reducing financial incentives to provide one service 

over another. CMS is required to review and revise the 

PE GPCI every three years and to phase in any changes 

over two years.12 The next review is under way and CMS 

expects to implement any changes in 2008 (CMS 2006). 

We believe that CMS could adopt the alternative GPCI 

described here within its current statutory authority. 

CMS’s current PE GPCI

The PE GPCI includes three components that correspond 

to three types of practice costs: nonphysician staff 

wages; office rent (which includes utilities); and medical 

equipment, supplies, and other expenses. Other expenses 

include legal, office management, and accounting 

services; professional association memberships; journals; 

continuing education; and other professional expenses 

(CMS 2005).13 CMS assumes that nonphysician staff 

wages and office rent vary geographically, while the cost 

of equipment, supplies, and other expenses is uniform 

nationally because these inputs are generally purchased 

in national, rather than local, markets. The portion of the 

index related to equipment, supplies, and other expenses 

is set to 1.0 for each market. This assumption is based on 

a study the Urban Institute conducted for CMS, which 

found no evidence that prices for medical equipment 

and supplies vary geographically (Zuckerman et al. 

1990).14 When CMS last updated the GPCI for the 2005 

fee schedule, it also was unable to find evidence that 

demonstrated geographic price differences for these inputs 

(CMS 2004). CMS concluded that some price differences 

exist among providers but are more likely based on 

volume discounts than on geographic variations. 

Each component of the GPCI is assigned a weight based 

on the share of each input in the Medicare Economic 

Index (MEI), which is based on the AMA’s 2001 Patient 

Care Physician Survey (CMS 2005). Staff wages account 

for 43 percent of the GPCI; equipment, supplies, and other 

expenses account for 29 percent; and office rent accounts 

for 28 percent.15 The national average of the GPCI is 1.0. 

Geographic areas with input prices above the national 

average have GPCI values above 1.0. Areas with belowaverage input prices have index values under 1.0. 

The current GPCI uses the average share of equipment, 

supplies, and other expenses across all services, even 

though the share of practice expense related to equipment 

and supplies varies among services. Based on the MEI, 

CMS assumes that, on average, 14.6 percent of practice 

costs are related to medical equipment and supplies. As 

Figure 9-4 demonstrates, the portion of PE attributable to 

equipment and supplies varies greatly by service (other 

expenses are not shown because we lack data on these 

costs by service). Services performed in facilities (e.g., 

hospitals) tend to have very low equipment and supply 

costs because Medicare makes a separate payment to 

hospitals to cover these costs.16 For example, for cataract 

surgery and total knee arthroplasty (both facility-based 

services), equipment and supply shares are well below 

average (Figure 9-4). Some office-based (nonfacility) 

services, such as office/outpatient visit (established) and 

electrocardiogram (complete) also have relatively low 

equipment and supply shares. Assuming that medical 

equipment and supplies are indeed purchased in a national 

market, the GPCI should adjust for a larger share of inputs 

for these services than it currently does. Consequently, PE 

payments for these services are too low (relative to input 

prices) in high-cost areas, and too high (relative to input 

prices) in low-cost areas. 

On the other hand, some high-technology services 

performed in physician offices have much higher than 

average shares of PE related to equipment and supplies. 

For example, equipment and supplies account for 60 

percent of the PE rate for the technical component of MRI 

of the brain, without contrast followed by contrast, and 

for 67 percent of intensity-modulated radiation therapy 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 243

(Figure 9-4). Assuming that medical equipment and 

supplies are indeed purchased in a national market, the 

GPCI should adjust for a smaller share of inputs for these 

services than it currently does. Consequently, PE payments 

for these services are too high (relative to input prices) 

in high-cost areas, and too low (relative to input prices) 

in low-cost areas. These pricing distortions may alter the 

mix of services provided within a high- or low-cost area. 

For example, there may be financial incentives to provide 

more imaging services and fewer office visits in high-cost 

regions.

An alternative PE GPCI

We developed an alternative PE GPCI that takes into 

account that individual services have different shares 

of inputs for which prices vary geographically (e.g., 

nonphysician staff and office space). This alternative 

excludes equipment and supplies; it is composed of 

nonphysician staff wages (50 percent of the index), office 

rent (33 percent), and other expenses (17 percent). The 

weights for each component are based on the relative size 

of each component in the MEI. Staff wages and office 

rent vary geographically and, according to CMS, input 

prices for other expenses do not; thus, this portion of 

the index is uniform across all areas. Even though other 

Share of practice expenses related to equipment and supplies varies by service, 2007

Note:	 IMRT (intensity-modulated radiation therapy). The facility (e.g., hospital) values are shown for total knee arthroplasty and cataract surgery. The nonfacility (e.g.,

physician office) values are shown for the other services. The global values are shown for MRI, brain; heart image (3D), multiple; and echo exam, heart. CMS’s

geographic practice cost index assumes that 14.6 percent of practice expenses are related to equipment and supplies.

Source:	 NORC analysis of physician relative value unit file and direct practice cost data from CMS.

Share of practice expenses related to equipment and supplies varies by service, 2007 FIGURE

9-4

Note and Source in InDesign Percent

80

50

60

70

40

30

20

10

0

Total knee

arthroplasty

Cataract

surgery

MRI,

brain

Radiation 

therapy,

IMRT

Heart 

image (3D),

multiple

Electrocardiogram,

complete

Echo

exam,

heart

Office/

outpatient visit,

established

Average share

assumed by CMS,

all services

F i g ure

9 –4
244 Analysis of changes to physicians’ practice expense payments

expenses do not vary geographically, we included them 

in the alternative GPCI because there are no data on the 

share of other expenses by service.17 We can exclude 

equipment and supplies from the alternative GPCI because 

we have data on their shares by service from the direct 

cost database used to calculate direct PE RVUs. Compared 

with the current GPCI, a larger share of the alternative 

GPCI has inputs that vary geographically (83 percent vs. 

71 percent). Thus, the alternative index has more extreme 

values than the current index. The highest payment area, 

San Francisco, has a value of 1.55 under the current GPCI 

and 1.65 under the alternative. The lowest payment area, 

Puerto Rico, has a value of 0.70 under the current GPCI 

and 0.65 under the alternative. 

Although the alternative GPCI has more extreme values, 

it is balanced by not applying it to the entire PE RVU. 

We apply the alternative GPCI to the portion of the RVU 

related to indirect costs and nonphysician clinical labor 

but not to the portion related to equipment and supplies. 

The reason is because the alternative GPCI includes inputs 

related to indirect costs and nonphysician clinical labor 

but not equipment and supplies. To determine the full 

PE RVU, the portion of the RVU adjusted by the GPCI is 

added to the unadjusted portion (representing equipment 

and supplies). 

Our contractor, NORC, modeled the impacts of the 

alternative GPCI on PE payments compared with 

payments under the current GPCI. The alternative would 

reduce PE payments for services with below-average 

shares of equipment and supplies (e.g., office/outpatient 

visit) in areas where input costs are low and increase them 

in areas where input costs are high. It has the reverse effect 

on services with above-average shares of equipment and 

supplies (e.g., MRI of the brain).

Although moving to the alternative GPCI would cause PE 

payments for individual services to shift geographically, 

NORC found that the net impact on PE payments by 

type of service (imaging, E&M, major procedures, other 

procedures, and tests) across all payment areas would be 

minimal; aggregate payments for each category would 

change by less than 1 percent. Increases to payment rates 

for a given type of service in some geographic areas 

would be almost fully offset by decreases in other areas. 

In addition, the alternative GPCI would cause very small 

shifts in total PE payments among geographic areas; 

almost all areas would experience total payment changes 

of less than 1 percent. Within a payment area, payment 

increases for some types of services would generally be 

balanced by decreases for others. 

Conclusion

This chapter assesses how CMS determines PE payment 

rates because PE payments are substantial, accounting 

for close to half of the $58 billion Medicare spent under 

the physician fee schedule in 2005. This chapter aims 

to help policymakers understand this complex payment 

methodology and focus attention on major changes that 

CMS has recently made to improve its accuracy. Although 

the Commission does not recommend changes to the PE 

methodology in this chapter, we have previously suggested 

ways for CMS to improve the accuracy of the survey data, 

direct cost estimates, and equipment prices used in the 

methodology (summarized on p. 228) (MedPAC 2006).

CMS’s changes represent the biggest revision to the 

methods and data used to calculate PE RVUs since the 

agency implemented resource-based PE payments in 1999. 

Our analysis showed that CMS’s recent changes to the 

PE methods and data will redistribute PE RVUs across 

services when they are fully phased in. PE RVUs will fall 

for major procedures and imaging and increase for E&M 

services, other procedures, and tests. 

CMS uses a complex process to calculate indirect PE 

RVUs. This method involves creating separate indirect 

cost pools for each specialty and allocating the cost 

pools to individual services based primarily on the 

direct practice cost (nonphysician clinical staff, medical 

equipment, and supplies) and physician work value for 

each service. If a service has little or no physician work 

(e.g., the technical component of imaging studies), CMS 

counts the nonphysician clinical labor cost twice. Some 

observers have expressed concern that CMS double counts 

nonphysician clinical labor for services with little or no 

physician work, includes medical supplies and equipment 

in the allocator, and uses more recent cost data for some 

specialties, which creates distortions among RVUs. 

We discussed some alternatives to CMS’s indirect allocation 

method and illustrated two specific approaches based 

on four principles. Neither alternative should be viewed 

as a proposal or even the best way to allocate indirect 

costs; other allocators would also be consistent with these 

principles. Our modeling of two alternatives demonstrates 

that choices made in designing the indirect method can 

significantly affect the distribution of payments.

Finally, we examined how CMS adjusts PE payment 

rates to account for geographic differences in the price 

of inputs used in operating a physician practice. CMS 

assumes that nonphysician staff wages and office rent vary 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 245

geographically while the cost of equipment, supplies, and 

other expenses is uniform nationally because these inputs 

are generally purchased in national, rather than local, 

markets. 

The PE GPCI assumes an average share of equipment 

and supplies across all services, even though the share 

of PE related to equipment and supplies varies among 

services. Thus, for services with below-average shares of 

equipment and supplies, the index does not adjust a large 

enough portion of the PE RVU; for services with aboveaverage shares of equipment and supplies, it adjusts too 

large a portion of the RVU. This distorts prices, which may 

alter the mix of services provided within a high- or lowcost area. PE payments might be more accurate if CMS 

excluded the equipment and supplies component from the 

GPCI and applied the GPCI only to the portion of the PE 

RVU related to nonphysician clinical labor and indirect 

costs. This alternative would better recognize that services 

have different shares of inputs for which prices vary 

geographically. 
246 Analysis of changes to physicians’ practice expense payments

1 CMS uses a database that contains estimates of the prices, 

types, and quantities of the clinical labor, medical equipment, 

and supplies required to provide each service paid under the 

physician fee schedule.

2 The new formula for the allocator appears in Figure 9-3, 

p. 237. 

3 The AMA did not collect practice cost data from certain 

nonphysician specialties, such as audiology, clinical social 

worker, and nurse practitioner.

4 If all specialties have the same variance between their mean 

hourly cost and their cost at the 25th percentile, then the 

RVUs would not be affected. This is because each specialty’s 

indirect cost pool would be reduced by the same percentage. 

However, if some specialties have a larger variance in practice 

costs than others, using the 25th percentile instead of the 

mean would affect RVUs because indirect cost pools would 

be reduced by different proportions. This change could be 

implemented in a budget-neutral manner.

5 CMS reduced the work RVUs, rather than the conversion 

factor, to maintain budget neutrality because reducing the 

conversion factor would have affected payments for services 

with no physician work, which were outside the scope of the 

five-year review (CMS 2006). After the second five-year 

review in 2002, CMS applied the budget-neutrality adjustment 

to the conversion factor. After the first five-year review in 

1997, the agency applied the budget-neutrality adjustment to 

the work RVUs. 

6 The data on the amount of time it takes to perform a service 

come from the AMA/Specialty Society Relative Value Scale 

Update Committee and from surveys conducted during 

development of the original fee schedule (Becker et al. 1988). 

Utilization data for each service are from Medicare claims. 

7 The average direct cost for services with no physician work is 

$48 (not adjusted for budget neutrality), compared with $25 

for services with physician work RVUs. The average direct 

cost is weighted by the volume of services for each code. 

8 Based on 2000 data, these specialties had the largest share of 

nonphysician work pool dollars (CMS 2002).

9 Outpatient therapy services provided during a Part A-covered 

skilled nursing facility (SNF) stay are included in Medicare’s 

per diem payment to the SNF. Outpatient therapy services 

provided in a nursing home outside of a Part A-covered stay 

are paid separately according to physician fee schedule rates. 

10 The sustainable growth rate formula includes outpatient 

therapy services provided in a nursing home that are paid 

under Part B. This spending, however, is not offset by changes 

to the RVUs for therapy services. 

11 There are 89 geographic payment areas, each with its own 

value for each index. 

12 Social Security Act, section 1848 (e) (1) (C).

13 CMS uses wage data from the decennial census to account for 

geographic wage differences and residential rent data from the 

Department of Housing and Urban Development to account 

for variations in office rent.

14 According to the study, anecdotal evidence suggested 

only minimal price variation for equipment and supplies 

(Zuckerman et al. 1990). 

15 Equipment and supplies combined and other expenses 

account for equal shares of the index (just under 15 percent 

each).

16 For facility-based services such as surgery, the physician 

fee schedule pays for equipment and supply costs related to 

postoperative follow-up visits in the office.

17 Other expenses are a component of indirect costs. Indirect 

costs are allocated as a group, rather than by component, to 

individual codes, so we cannot estimate the share of indirect 

costs related to other expenses at the level of individual 

services.

Endnotes
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 247

Becker, E. A., D. Dunn, and W. Hsaio. 1988. Relative cost 

differences among physicians’ specialty practices. Journal of the 

American Medical Association 260, no. 16 (October): 2397–2402.

Bruns, W. J. 1993. Accounting for indirect costs. No. 9–193–070. 

April 27. Boston, MA: Harvard Business School. 

Centers for Medicare & Medicaid Services, Department of 

Health and Human Services. 2006. Medicare program; revisions 

to payment policies, five-year review of work relative value 

units, changes to the practice expense methodology under the 

physician fee schedule, and other changes to payment under Part 

B; revisions to payment policies of ambulance services under the 

fee schedule for ambulance services; and ambulance inflation 

factor update for CY 2007. Final rule. Federal Register 71, no. 

231 (December 1): 69623–70274.

Centers for Medicare & Medicaid Services, Department of Health 

and Human Services. 2005. Medicare program; revisions to 

payment policies under the physician fee schedule for calendar 

year 2006 and certain provisions related to the competitive 

acquisition program of outpatient drugs and biologicals under 

Part B. Final rule. Federal Register 70, no. 223 (November 21): 

70116–70150.

Centers for Medicare & Medicaid Services, Department of Health 

and Human Services. 2004. Medicare program; revisions to 

payment policies under the physician fee schedule for calendar 

year 2005. Final rule. Federal Register 69, no. 219 (November 

15): 66236–66916. 

Centers for Medicare & Medicaid Services, Department of Health 

and Human Services. 2002. Medicare program; revisions to 

payment policies under the physician fee schedule for calendar 

year 2003 and inclusion of registered nurses in the personnel 

provision of the critical access hospital emergency services 

requirement for frontier areas and remote locations. Final rule. 

Federal Register 67, no. 251 (December 31): 79966–80184.

Centers for Medicare & Medicaid Services, Department of Health 

and Human Services. 1998. Medicare program; revisions to 

payment policies and adjustments to the relative value units under 

the physician fee schedule for calendar year 1999. Final rule and 

notice. Federal Register 63, no. 211 (November 2): 58813–59187.

Centers for Medicare & Medicaid Services, Department of Health 

and Human Services. 1997. Medicare program; revisions to 

payment policies under the physician fee schedule, other Part B 

payment policies, and establishment of the clinical psychologist 

fee schedule for calendar year 1998. Proposed rule. Federal 

Register 62, no. 117 (June 18): 33158–33207.

Government Accountability Office. 2001. Medicare physician fee 

schedule: Practice expense payments to oncologists indicate need 

for overall refinements. GAO–02–53 (October). Washington, DC: 

GAO.

Government Accountability Office. 1998. Medicare: HCFA 

can improve methods for revising physician practice expense 

payments. GAO/HEHS–98–79 (February). Washington, DC: 

GAO.

Hawkins, D. F., and J. Cohen. 2004. Introduction to costaccounting systems. No. 9–105–039. Boston, MA: Harvard 

Business School. 

Latimer, E. A., and E. Becker. 1992. Incorporating practice costs 

into the resource-based relative value scale. Medical Care 30, no. 

11 (November): NS50–NS59. 

Lewin Group. 2000. An evaluation of the Health Care Financing 

Administration’s resource based practice expense methodology. 

Contract no. 500–95–0059/TO #6. Report for the Health Care 

Financing Administration, Department of Health and Human 

Services. Falls Church, VA: Lewin Group. 

Medicare Payment Advisory Commission. 2007. Report to the 

Congress: Medicare payment policy. Washington, DC: MedPAC. 

Medicare Payment Advisory Commission. 2006. Report to the 

Congress: Increasing the value of Medicare. Washington, DC: 

MedPAC. 

Physician Payment Review Commission. 1992. Practice expenses 

under the Medicare fee schedule: A resource-based approach. 

Washington, DC: PPRC. 

Rich, William L., AMA/Relative Value Scale Update Committee. 

2007. E-mail message to author. March 20.

Thompson, Donald, Centers for Medicare & Medicaid Services, 

Department of Health and Human Services. 2007. E-mail 

message to author. February 5.

Zuckerman, S., W. P. Welch, and G. C. Pope. 1990. A geographic 

index of physician practice costs. Journal of Health Economics 9, 

no. 1 (June): 39–69. 

References

A A PP E N D I X

Review of CMS’s preliminary 

estimate of the physician 

update for 2008

Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 251

CMS’s preliminary estimate of the 2008 payment update 

for physician services is –5.1 percent (Gustafson 2007). 

However, when combined with the effect of the Tax Relief 

and Health Care Act of 2006 (TRHCA), CMS estimates 

the net change to the conversion factor from 2007 to 

2008 to be –9.9 percent. Due to continued growth in 

expenditures on physician services and increased spending 

associated with legislative overrides to avert payment cuts 

for physician services, the sustainable growth rate (SGR) 

formula has called for negative updates since 2002. In 

communicating its estimate to MedPAC, CMS states that 

it is embarking on several initiatives to improve the quality 

and efficiency of physician services delivered to Medicare 

beneficiaries.

In a recent report to the Congress, the Commission stated 

that slowing the increase in Medicare outlays is becoming 

urgent (MedPAC 2007a). Medicare’s rising costs, 

particularly when coupled with the projected growth in 

the number of beneficiaries, threaten to place a significant 

burden on taxpayers. Rapid growth in expenditures also 

directly affects beneficiary liability for out-of-pocket 

costs through higher Part B and supplemental insurance 

premiums. 

The Commission’s report also discussed several flaws 

associated with using the SGR formula. For example, it 

neither rewards physicians who restrain volume growth 

nor penalizes those who prescribe unnecessary services. 

Ideally, Medicare’s physician payment system should 

include incentives for physicians to provide better 

quality of care, to coordinate care (across settings and 

medical conditions), and to use resources judiciously. As 

mandated by the Congress, the Commission examined 

alternative approaches to the SGR system in our report, 

many of which included frameworks with expenditure 

targets. Although some disagreement exists within the 

Commission about the utility of expenditure targets, the 

Commission is united in its belief that a major investment 

should be made in Medicare’s capability to develop, 

implement, and refine payment systems to reward quality 

and efficient use of resources while improving payment 

equity. Examples of such reforms include pay-forperformance programs for quality, improving payment 

accuracy, and bundling payments to reduce overutilization.

This appendix fulfills the Commission’s requirement to 

review CMS’s estimate of the 2008 update for physician 

services. In reviewing the technical details involved in 

estimating the update under current law (in accordance 

with the SGR formula), we find that CMS used estimates 

in calculating the update that are consistent with recent 

trends.1 Moreover, the Commission anticipates that no 

alteration in the factors of CMS’s estimates would be large 

enough to eliminate the application of the statutory limit 

the SGR formula imposes. MedPAC concurs with CMS 

that Medicare should be initiating strategies to improve the 

quality and efficiency of services delivered to Medicare 

beneficiaries.

Review of CMS’s preliminary 

estimate of the physician 

update for 2008

a p p endixA
252 Review of CMS’s preliminar y estimate of the physician update for 2008

For further details on how Medicare pays for physician 

services, see MedPAC’s Payment Basics publications, 

available on our website.2

How TRHCA affects 2007 and 2008 

updates for physician services

TRHCA includes several provisions that affect physician 

payments in 2007 and 2008. To avert a cut in the 

conversion factor, it provided for a temporary one-year 

bonus in the fee schedule conversion factor for 2007. This 

increase offset the 5 percent decrease in the conversion 

factor required by the SGR formula. Consequently, the 

conversion factor for 2007 was kept at the same level as 

for 2006. 

TRHCA also requires that the 2008 conversion factor be 

calculated as if the 2007 one-year bonus had never applied. 

Thus, estimations for the 2008 conversion factor first 

assume a 5 percent cut to the 2007 conversion factor and 

then apply the statutorily required cut (5.1 percent) in 2008, 

per the SGR formula. While the implementation of the 

one-time conversion-factor bonus will increase both actual 

and expected expenditures for 2007, its expiration will 

decrease both actual and expected expenditures for 2008.

Another provision in TRHCA allows physicians to be 

eligible to receive a 1.5 percent bonus on all covered 

services they furnished to Medicare beneficiaries between 

July 1 and December 31, 2007, provided they submit to 

CMS data on an adequate number of approved quality 

measures. CMS will pay this quality reporting bonus to 

physicians as a lump sum in 2008. 

TRHCA also established a $1.35 billion fund to be used 

toward physician payments at the Secretary’s discretion in 

2008. Although the law explicitly allows the Secretary to 

direct the fund toward the 2008 conversion factor update, 

the Secretary has not yet allocated this fund. Thus, CMS’s 

estimate of the 2008 conversion factor does not account 

for this fund. Even if the fund were used entirely to update 

the 2008 conversion factor, a legislative change would 

still be needed to avert a cut in 2008, because the amount 

needed to avert a cut exceeds $1.35 billion. CMS plans to 

implement this provision through the rulemaking process 

during the summer.

Also, TRHCA extended through 2007 the work 

geographic practice cost index (GPCI) floor. The floor was 

imposed by the Medicare Prescription Drug, Improvement, 

and Modernization Act of 2003 (MMA) and was originally 

T A B L E

A –1 Preliminary estimate of the 

sustainable growth rate, 2008

Factor Percent

2008 change in:

Input costs for physician services* 2.0%

Real GDP per capita 1.9

Fee-for-service enrollment –0.2

Change due to law or regulation –1.5

Sustainable growth rate 2.2

Note:	 GDP (gross domestic product). Percents are converted to ratios

and multiplied, not added, to produce the update. Estimates shown

are preliminary.

*Input costs are adjusted for productivity and include inflation measures

for services performed by a physician or in a physician’s office.

Source:	 Gustafson 2007.

T A B L E

A –2 Update and conversion factor 

changes for physician services

Updates and update factors Percent

Excluding TRHCA conversion factor bonus for 2007

2007 update per SGR formula –5.0%

2008 update factors per SGR formula:

Change in MEI 2.0

Change in update adjustment factor –7.0

2008 update per SGR formula –5.1

Including TRHCA conversion factor bonus for 2007

2007 update 0.0

2008 update –9.9

Note:	 TRHCA (Tax Relief and Health Care Act of 2006), SGR (sustainable

growth rate), MEI (Medicare Economic Index). Percents are converted to

ratios and multiplied, not added, to produce the update. The MEI—an

estimate of the change in input prices (inflation) for physician services—

includes a productivity adjustment. Estimates shown are preliminary.

Source:	 Gustafson 2007.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 253

slated to expire on December 31, 2006, but TRHCA 

extended it to December 31, 2007. 

Together, these four provisions in the TRHCA—the 

conversion factor bonus, the quality reporting bonus, the 

physician fund, and the GPCI floor extension—account 

for $5 billion, which will be directed toward physician 

payments over the coming years. These spending increases 

will be financed through Medicare’s Supplementary 

Medical Insurance program, which is funded through 

general revenues (75 percent) and beneficiary premiums 

(25 percent).

Calculating the update

Calculating the physician update is a two-step process. 

CMS first estimates the target growth rate—the 

sustainable growth rate (SGR)—then computes the update. 

For the first step, the SGR is the target growth rate in 

spending for physician fees and is a function of projected 

changes in:

• productivity-adjusted input prices for physician fees—

an allowance for inflation,3

• real gross domestic product (GDP) per capita—an 

allowance for growth in the volume of services,4

• enrollment in fee-for-service (FFS) Medicare—an 

allowance for fluctuations in the number of FFS 

beneficiaries, and

• spending attributable to changes in law and 

regulation—an allowance for policy changes that 

affect spending on physician services.

Allowing for these four factors, CMS’s preliminary 

estimate of the SGR is 2.2 percent for 2008 (Table A-1). 

For the second step, CMS calculates the update, which is a 

function of:5

• the change in productivity-adjusted input prices for 

physician services, as measured by the Medicare 

Economic Index (MEI); and

• an update adjustment factor (UAF) that increases 

or decreases the update as needed to align actual 

spending, cumulated over time, with target spending 

determined by the SGR.

The estimate of the change in input prices for use in the 

2008 update is 2.0 percent (Table A-2).6 The part of the 

update calculation that has the bigger effect, however, 

is the UAF, which CMS estimates at –7.0 percent—the 

maximum negative adjustment permitted under current 

law. When we combine this adjustment with the estimated 

change in input prices, the result is an update of −5.1 

percent. (Note that this calculation of the estimate converts 

percentages to ratios, which are multiplied rather than 

being the sum of the two components of the adjustment.)

The UAF is negative because actual spending for physician 

services started to exceed the target in 2001 (Figure A-1). 

Since then, spending has remained above the target. In 

addition, overrides of the formula (in which the Congress 

has changed the law to prevent negative updates) have kept 

payment rates above the level necessary to align actual 

spending and the target. Indeed, the overrides in the MMA 

and the Deficit Reduction Act of 2005 (DRA) added to the 

cumulative growth in physician spending without increases 

in allowed (or target) spending calculated under the SGR. 

In total, the estimated cumulative difference in allowed 

spending versus actual spending results in a UAF that 

would be −27.7 percent, if not for the –7.0 percent limit.

F i g ure

A –1 Beginning in 2001, 

actual spending for physician 

services has exceeded target

Note:	 Estimates for 2006 are preliminary.

Source:	 Office of the Actuary 2007 and Gustafson 2007.

Target

Actual

Beginning in 2001, 

actual spending for physician 

services has exceeded target

FIGURE

A-1

Note and source in InDesign Spending (dollars in billions)

100

80

90

70

60

50

40

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
254 Review of CMS’s preliminar y estimate of the physician update for 2008

Reviewing CMS’s estimate

CMS’s update estimate (–5.1 percent) is unlikely to change 

by a substantial amount because an update adjustment 

factor of −27.7 percent is well beyond the statutory limit 

(–7.0 percent). Thus, the Commission anticipates that 

no alteration in the factors of CMS’s estimates would be 

large enough to bring the UAF within the limit. Even so, 

we review the factors that CMS considers in its update 

estimate for the 2008 SGR. 

Changes that affect the target growth rate 

As mentioned earlier, CMS’s estimate is a function of 

projected changes in four factors: change in physician 

input costs, change in beneficiary enrollment, change in 

real GDP per capita, and change due to law or regulation 

(Table A-1, p. 252). If, for example, the MEI increases, 

then the SGR formula would allow for a proportional 

increase in payments to account for growth in input 

costs. If, on the other hand, the MEI decreases, then the 

SGR formula would require a proportional decrease in 

payments to account for declining input costs. 

For the SGR, CMS’s estimate of the change in input 

prices, as measured by the MEI, is within the range of 

changes in the MEI for the last 15 years—though it is at 

the low end of the range.7 The MEI includes an adjustment 

for productivity. In fact, the recent decline in the MEI is 

due to recent growth in multifactor productivity (MFP) as 

estimated by the Bureau of Labor Statistics. Indeed, the 

two most recent years’ MFP rates were among the highest 

in recent history; as a result, the 10-year moving average 

of the MFP increased.8 Consequently, the MEI for 2008 is 

lower than in previous years because it is reduced by the 

MFP.9

The change in real GDP per capita of 1.9 percent is the 

10-year moving average of real GDP estimates from 

the Bureau of Economic Analysis. These estimates are 

adjusted for population growth (BEA 2007).

The change in FFS enrollment is a little less certain. 

CMS assumes a decrease of 0.2 percent. This figure 

differs by 1 percentage point from the Congressional 

Budget Office’s (CBO’s) enrollment projection, which 

is a decrease in FFS enrollment of 1.2 percent for fiscal 

year 2008 (CBO 2007). Because CMS and CBO project 

similar total Medicare enrollment, differences are likely 

due to difficulties projecting shifts in enrollment from 

Medicare FFS to Medicare Advantage (MA). (CBO 

forecasts an increase in MA enrollment of 14 percent in 

2008.) CMS may be better able to project any such shift 

when MA plans submit bids and identify market areas in 

June 2007. CMS can then revise the enrollment projection, 

if necessary, before the update becomes final in November 

2007. Even then, CMS will have limited information on 

changes in enrollment in 2008, but the agency will have 

another two years to revise the enrollment estimate if 

better data become available, just as the agency does with 

changes in spending due to law and regulation.

CMS’s estimate also allows for anticipated changes in 

payments due to law and regulation. For example, a 

change in current law that might increase total payments, 

such as benefit expansion under Part B, would allow CMS 

to estimate a proportional increase (positive impact) to the 

SGR. In contrast, a law change that requires a payment 

decrease, such as the expiration of a payment bonus, 

would call for a proportional decrease (negative impact) in 

CMS’s estimate of the SGR.

Although some of the statutory and regulatory changes 

will, in fact, increase physician spending, CMS expects 

changes in law and regulation to net a –1.5 percent impact 

on spending in 2008.10 Among the three provisions with 

negative spending effects, two are linked to TRHCA 

provisions discussed earlier. The provision with the largest 

negative impact on the SGR stems from the TRHCA 

conversion factor bonus. Because this bonus applies only 

to 2007, fee schedule rates will experience a relative 

decrease in 2008 to account for the absence of the bonus 

in 2008.11 A second TRHCA provision that will have a 

negative impact on the 2008 SGR is the extension of the 

floor on the work GPCI through 2007. Accordingly, 2008 

payment rates will fall in some geographic areas when the 

floor on the work GPCI expires at the end of 2007. 

An MMA provision—the physician scarcity bonus—is 

also set to expire at the end of 2007. Under this provision, 

services provided by physicians in scarcity areas—

determined separately for primary care physicians and 

specialists—received a 5 percent bonus in Medicare 

payments from 2005 through 2007. Thus, CMS’s estimate 

of the 2008 update accounts for a reduction in some 

physician payments with the elimination of this bonus. 

Despite an overall negative impact on the 2008 SGR due 

to law and regulation, CMS projects that three provisions 

will have a positive impact on spending. First, in 

compliance with TRHCA, $1.35 billion will be allocated 
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 255

toward physician payment in 2008 from an established 

fund. Although the exact nature of the distribution is not 

yet known, the statute requires that the fund be directed 

toward physician payment in 2008. Second, the quality 

reporting bonus instituted by TRHCA will be paid to 

eligible providers in 2008 and therefore will increase 2008 

total spending. 

Finally, CMS expects a positive spending impact on the 

SGR due to the interaction of the DRA and the reduction 

in fee-schedule rates. In accordance with the DRA, for 

certain imaging services, Medicare pays the lesser of 

hospital outpatient department rates and physician fee 

schedule rates. Because hospital outpatient prospective 

payment system (OPPS) services will receive a positive 

update in 2008, while fee-schedule services will receive a 

negative update, CMS estimates total spending increases 

for fee-schedule services as the OPPS and fee-schedule 

rates come closer together. That is, for certain imaging 

services, the ceiling for fee-schedule payments will 

increase consistent with the OPPS update. (Note that, for 

the 2007 SGR estimate, CMS projected initial savings 

from the DRA legislation from those items that moved to 

the OPPS payment level.)

Comparison of target spending with actual 

spending

The remaining issue concerns CMS’s estimates of actual 

spending for 2006 and 2007. In previous years, CMS 

provided MedPAC with helpful, preliminary type-ofservice volume analyses for the current year with its 

update estimates. This year, however, CMS did not include 

such analyses and indicated that it was waiting for more 

complete spending figures. Therefore, CMS’s estimate of 

actual spending (particularly for 2007) may increase or 

decrease somewhat before CMS issues a final rule on the 

update in November 2007.

Summary

Regardless of what happens with the various estimates 

that determine the physician update, it is quite unlikely 

that any change will overcome an update adjustment factor 

of −27.7 percent. For this reason, we anticipate that CMS 

will revise the update calculations this fall, in preparation 

for implementing the 2008 update on January 1, and that, 

barring any overriding statutory provisions, the calculations 

will show the maximum reduction that the statute permits: 

the change in productivity-adjusted input prices (as 

measured by the MEI) minus 7.0 percentage points. 
256 Review of CMS’s preliminar y estimate of the physician update for 2008

1 Note that our purpose in reviewing CMS’s estimate is not 

to assess the adequacy of the update, but rather to evaluate 

the technical details involved in estimating the update under 

current law. For further information on the Commission’s 

analysis of payment adequacy for physician services, see our 

March 2007 report (MedPAC 2007b). 

2 http://www.medpac.gov.

3 For calculating the SGR, physician fees include services 

commonly performed by a physician or in a physician’s 

office. In addition to physician fee schedule services, these 

fees include diagnostic laboratory tests and most of the drugs 

covered under Medicare Part B. 

4 As required by the MMA, the real GDP per capita factor in 

the SGR is a 10-year moving average.

5 For the update, physician services include only those services 

in the physician fee schedule.

6 In MedPAC’s earlier update recommendation for physician 

services, the Commission used an MEI of 1.7 percent, which 

differed from this 2.0 percent estimate primarily because 

it was based on a forecast for the fourth quarter of 2008 

(MedPAC 2007c). For the physician service update, CMS 

is statutorily required to use the MEI for the most recent 

historical quarter for which it has data.

7 Since 1992, the MEI has ranged from 2.0 percent to 3.2 

percent.

8 The Bureau of Labor Statistics recently reclassified some 

industries in its calculations.

9 For MedPAC’s physician service payment adequacy analysis, 

CMS provides us with forecasted input price changes that are 

not adjusted for productivity. We then adjust (reduce) these 

figures for productivity separately (MedPAC 2007c).

10 In earlier years, this component of the SGR has been positive 

to account for spending increases that occur when legislation 

expands benefits under Medicare Part B.

11 Conversion factor overrides in previous legislation (i.e., MMA 

and DRA) explicitly did not require a change in law and 

regulation for purposes of the SGR calculation. In contrast, 

the conversion factor bonus in TRHCA allows a change in law 

and regulation to be a factor in CMS’s update calculation.

Endnotes
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 257

Bureau of Economic Analysis, Department of Commerce. 2007.

Current-dollar and “real” GDP. Washington, DC: BEA. February 

28. http://www.bea.gov/national/gdplev.xls.

Congressional Budget Office. 2007. CBO estimate of Medicare 

proposals in the President’s budget for fiscal year 2008.

Washington, DC: CBO.

Gustafson, T. A., Centers for Medicare & Medicaid Services, 

Department of Health and Human Services. 2007. Letter to 

Glenn M. Hackbarth, Medicare Payment Advisory Commission. 

February 28. http://www.cms.hhs.gov/SustainableGRatesConFact/

Downloads/medpacfinal.pdf

Medicare Payment Advisory Commission. 2007a. Assessing 

alternatives to the sustainable growth rate system. Washington, 

DC: MedPAC.

Medicare Payment Advisory Commission. 2007b. Report to the 

Congress: Medicare payment policy. Washington, DC: MedPAC.

Medicare Payment Advisory Commission. 2007c. Report to the 

Congress: Assessing payment adequacy and updating payments 

in fee-for-service Medicare. Washington, DC: MedPAC.

Office of the Actuary, Centers for Medicare & Medicaid Services, 

Department of Health and Human Services. 2007. Estimated 

sustainable growth rate and conversion factor, for Medicare 

payments to physicians in 2008. Baltimore, MD: CMS. http://

www.cms.hhs.gov/SustainableGRatesConFact/Downloads/

sgr2008p.pdf.

References

A PP E N D I X

Commissioners' voting 

on recommendations

B

Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 261

In the Medicare, Medicaid, and SCHIP Benefits Improvement and Protection Act of 2000, the Congress required 

MedPAC to call for individual Commissioner votes on each recommendation, and to document the voting record in its 

report. The information below satisfies that mandate.

Chapter 1: Medicare in the 21st century: Changing beneficiary profile

No recommendations

Chapter 2:  Producing comparative-effectiveness information

The Congress should charge an independent entity to sponsor credible research on comparative effectiveness of health 

care services and disseminate this information to patients, providers, and public and private payers. 

Yes: Behroozi, Bertko, Burke, Castellanos, Crosson, DeParle, Durenberger, Hackbarth, Hansen, Kane, 

Milstein, Muller, Reischauer, Scanlon, Wolter

Absent: Borman, Holtz-Eakin

Chapter 3: Update on the Medicare Advantage program and implementing 

past recommendations

No recommendations

Chapter 4: Value-based purchasing: Pay for performance in home health care

No recommendations

Chapter 5:  Payment policy for inpatient readmissions

No recommendations

B A PP E N D I X

Commissioners’ voting

on recommendations
262 Commissioners' voting on recommendations

Chapter 6: An alternative method to compute the wage index

6A The Congress should repeal the existing hospital wage index statute, including reclassifications and exceptions, 

and give the Secretary authority to establish new wage index systems.

Yes: Behroozi, Bertko, Burke, Castellanos, Crosson, DeParle, Durenberger, Hackbarth, Hansen, Kane, 

Milstein, Muller, Reischauer, Scanlon, Wolter

Absent: Borman, Holtz-Eakin

6B The Secretary should establish a hospital compensation index that:

• uses wage data from all employers and industry-specific occupational weights,

• is adjusted for geographic differences in the ratio of benefits to wages,

• is adjusted at the county level and smooths large differences between counties, and 

• is implemented so that large changes in wage index values are phased in over a transition period.

Yes: Behroozi, Bertko, Burke, Castellanos, Crosson, DeParle, Durenberger, Hackbarth, Hansen, Kane, 

Milstein, Muller, Reischauer, Scanlon, Wolter

Absent: Borman, Holtz-Eakin

6C The Secretary should use the hospital compensation index described in recommendation 6B for the home health 

and skilled nursing facility prospective payment systems and evaluate its use in the other Medicare fee-for-service 

prospective payment systems.

Yes: Behroozi, Bertko, Burke, Castellanos, Crosson, DeParle, Durenberger, Hackbarth, Hansen, Kane, 

Milstein, Muller, Reischauer, Scanlon, Wolter

Absent: Borman, Holtz-Eakin

Chapter 7: Issues in Medicare coverage of drugs

7A The Congress should direct CMS to identify selected overlap drugs and direct plans to always cover them under 

Part D. Identified drugs should be:

• low cost

• covered under Part D most of the time.

Yes: Behroozi, Bertko, Burke, Castellanos, Crosson, DeParle, Durenberger, Hackbarth, Hansen, Kane, 

Milstein, Muller, Reischauer, Scanlon, Wolter

Absent: Borman, Holtz-Eakin

7B The Congress should allow plans to cover a transitional supply of overlap drugs under Part D under the same 

conditions as the general transition policy applied by CMS.

Yes: Behroozi, Bertko, Burke, Castellanos, Crosson, DeParle, Durenberger, Hackbarth, Hansen, Kane, 

Milstein, Muller, Reischauer, Scanlon, Wolter

Absent: Borman, Holtz-Eakin
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 263

7C The Congress should permit coverage for appropriate preventive vaccines under Medicare Part B instead of Part D.

Yes: Behroozi, Bertko, Burke, Castellanos, Crosson, DeParle, Durenberger, Hackbarth, Hansen, Kane, 

Milstein, Muller, Reischauer, Scanlon, Wolter

Absent: Borman, Holtz-Eakin

Chapter 8:  Skilled nursing facilities: The need for reform

No recommendations

Chapter 9: Analysis of changes to physicians’ practice expense payments

No recommendations

Appendix A: Review of CMS’s preliminary estimate of the physician update for 2008

No recommendations

Acronyms

Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 267

ACIP Advisory Committee on Immunization Practices

ADE adverse drug event

ADL activity of daily living

AHIP America’s Health Insurance Plans

AHRQ Agency for Healthcare Research and Quality

AIDS acquired immunodeficiency syndrome

ALLHAT The Antihypertensive and Lipid-Lowering 

Treatment to Prevent Heart Attack Trial

AMA American Medical Association

AMDA American Medical Directors Association

AMCP Academy of Managed Care Pharmacy

AMI acute myocardial infarction

APR–DRG all patient refined diagnosis related group

BBA Balanced Budget Act of 1997

BEA Bureau of Economic Analysis

BIPA Medicare, Medicaid, and SCHIP Benefits 

Improvement and Protection Act of 2000

BLS Bureau of Labor Statistics

BMI body mass index

C3I Command, Control, Communications & 

Intelligence

CABG coronary artery bypass graft

CAH critical access hospital

CBO Congressional Budget Office

CBSA core-based statistical area

CCP coordinated care plan

CDC Centers for Disease Control and Prevention

CEA cost-effectiveness analysis

CHD coronary heart disease

CHF congestive heart failure

CMS Centers for Medicare & Medicaid Services

COPD chronic obstructive pulmonary disease

CPOE computerized provider order entry

CRS Congressional Research Service

CT computed tomography

CVD cerebrovascular disease

CWF common working file

DeCIDE Developing Evidence to Inform Decisions about 

Effectiveness

DERP Drug Effectiveness Review Project

DME durable medical equipment

DRA Deficit Reduction Act of 2005

DRG diagnosis related group

Acronyms

DXA dual-energy X-ray absorptiometry

E&M evaluation and management 

EHR electronic health record

ER emergency room

ESI employer-sponsored insurance

ESRD end-stage renal disease 

FDA Food and Drug Administration

FFRDC federally funded research and development center

FFS fee-for-service 

FHA Federal Health Architecture

FICA Federal Insurance Contributions Act

FTE full-time equivalent

FY fiscal year

GAO Government Accountability Office

GASB Governmental Accounting Standards Board

GDP gross domestic product

GI gastrointestinal

GPCI geographic practice cost index 

GPO group purchasing organization

HCFA Health Care Financing Administration

HHA home health agency

HHRG home health resource group

HHS Department of Health and Human Services

HMO health maintenance organization

ICD implantable cardioverter defibrillator

ICD–9–CM International Classification of Diseases, Ninth 

Revision, Clinical Modification

ICU intensive care unit

IHI Institute for Healthcare Improvement

IME indirect medical education

IMRT intensity-modulated radiation therapy

IOM Institute of Medicine

IPPS inpatient prospective payment system

IT information technology

IV intravenous

KFF The Henry J. Kaiser Family Foundation

KPS Kindred Pharmacy Services

LIS low-income subsidy

LOS length of stay

LPN licensed practical nurse 

LTCP long-term care pharmacy

MA Medicare Advantage

MA–PD Medicare Advantage–Prescription Drug [plan]
268 Acronyms

MCAC Medicare Coverage Advisory Committee

MCBS Medicare Current Beneficiary Survey 

MDC major diagnostic category

MDS Minimum Data Set

MedCAC Medicare Evidence Development & Coverage 

Advisory Committee

MedPAC Medicare Payment Advisory Commission

MEI Medicare Economic Index

MFP multifactor productivity

MGCRB Medicare Geographic Classification Review 

Board

MHS Medicare Health Support

MMA Medicare Prescription Drug, Improvement, and 

Modernization Act of 2003

MRI magnetic resonance imaging

MSA medical savings account

MSA metropolitan statistical area 

MTMP medication therapy management program 

N/A not applicable

NACDS National Association of Chain Drug Stores

NBER National Bureau of Economic Research

NCHS National Center for Health Statistics

NCQA National Committee for Quality Assurance

NDC national drug code

NECTA New England city and town area

NF nursing facility

NHSC National Horizon Scanning Centre

NICE National Institute for Health and Clinical 

Excellence (United Kingdom)

NIH National Institutes of Health 

NORC (formerly) National Opinion Research Center

NP new profiles

NQF National Quality Forum

NSF National Science Foundation

NTA nontherapy ancillary

OASIS Outcome and Assessment Information Set

OBQI Outcome-Based Quality Improvement

OBRA Omnibus Budget Reconciliation Act

OES Occupational Employment Statistics

OIG Office of Inspector General

OMB Office of Management and Budget

OPPS outpatient prospective payment system

OT occupational therapy

P&T pharmacy and therapeutics

P4P pay for performance

PAC post-acute care

PBGH Pacific Business Group on Health

PBM pharmacy benefit manager

PCCM primary care case management

PCMA Pharmaceutical Care Management Association

PDP prescription drug plan

PE practice expense

PFFS private fee-for-service

PHC4 Pennsylvania Health Care Cost Containment 

Council

PHR personal health record

PPO preferred provider organization

PPS prospective payment system

PT physical therapy

PTCA percutaneous transluminal coronary angioplasty

RN registered nurse

RTI Research Triangle Institute

RUC Relative Value Scale Update Committee

RUG resource utilization group

RUG–53 resource utilization group, 53-group model

RUG–58 resource utilization group, 58-group model

RVU relative value unit

SCHIP State Children’s Health Insurance Program

SES socioeconomic status

SGR sustainable growth rate

SHIP State Health Insurance Assistance Program

SIM service index model

SLP speech–language pathology

SMS Socioeconomic Monitoring System

SNF skilled nursing facility

SNP special needs plan

SOI severity of illness

SQI Standardized Quality Index

SSRI selective serotonin reuptake inhibitor

STM staff time measurement

TEC Technology Evaluation Center

TRHCA Tax Relief and Health Care Act of 2006

UAF update adjustment factor

U.K. United Kingdom

U.S. United States

U.S.C. United States Code

VA Department of Veterans Affairs

WLF Washington Legal Foundation
More about MedPAC

Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 271

Commission members

Glenn M. Hackbarth, J.D., chairman

Bend, OR

Robert D. Reischauer, Ph.D., vice chairman

The Urban Institute

Washington, DC

Term expires April 2009

Mitra Behroozi, J.D.

1199SEIU Benefit and Pension Funds

New York, NY

Karen R. Borman, M.D.

University of Mississippi 

Medical Center

Jackson, MS

Ronald D. Castellanos, M.D.

Southwest Florida 

Urologic Associates

Ft. Myers, FL

Glenn M. Hackbarth, J.D.

Douglas Holtz-Eakin, Ph.D.

John McCain 2008

The Exploratory Committee

Arlington, VA

Robert D. Reischauer, Ph.D.

Term expired April 2007

John M. Bertko, F.S.A., 

M.A.A.A.

Humana Inc.

Louisville, KY

Sheila P. Burke, M.P.A.,

R.N., F.A.A.N.

Smithsonian Institution

Washington, DC

Francis J. Crosson, M.D.

The Permanente Federation, LLC

Oakland, CA

Arnold Milstein, M.D., M.P.H.

Pacific Business Group on Health

San Francisco, CA

Ralph W. Muller, M.A.

University of Pennsylvania

Health System

Philadelphia, PA

William J. Scanlon, Ph.D.

Health policy consultant

Oak Hill, VA

Term expires April 2008

Nancy-Ann DeParle, J.D.

CCMP Capital Advisors, LLC

Chevy Chase, MD

David F. Durenberger, J.D.

National Institute of Health Policy

University of St. Thomas

Minneapolis, MN

Jennie Chin Hansen, R.N., 

M.S.N., F.A.A.N.

AARP

San Francisco, CA

Nancy M. Kane, D.B.A.

Harvard School of Public Health

Boston, MA

Nicholas Wolter, M.D.

Billings Clinic

Billings, MT

Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 273

Commissioners’ biographies

Mitra Behroozi, J.D., is the executive director of 

1199SEIU Benefit and Pension Funds. Ms. Behroozi 

oversees eight major benefit and pension funds for health 

care workers. Collectively, the funds are among the largest 

in the nation. Previously, Ms. Behroozi was a partner 

with Levy, Ratner & Behroozi, PC, representing New 

York City unions in collective bargaining negotiations 

and proceedings. While at the law firm, she also served as 

union counsel to Taft-Hartley benefit and pension funds. 

Ms. Behroozi has a law degree from New York University 

and an undergraduate degree in sociology from Brown 

University.

John M. Bertko, F.S.A., M.A.A.A., is vice president and 

chief actuary for Humana Inc., where he manages the 

corporate actuarial group and directs the coordination 

of work by actuaries in Humana’s major business units, 

including public programs, commercial, individual, and 

TRICARE. Mr. Bertko has extensive experience with 

risk adjustment and has served in several public policy 

advisory roles, including prescription drug benefit design. 

He served the American Academy of Actuaries as a board 

member from 1994 to 1996 and as vice president for the 

health practice area from 1995 to 1996. He was a member 

of the Actuarial Board for Counseling and Discipline from 

1996 through 2002. Mr. Bertko is a fellow of the Society 

of Actuaries and a member of the American Academy 

of Actuaries. He has a B.S. in mathematics from Case 

Western Reserve University.

Karen R. Borman, M.D., is a professor of surgery and 

vice-chair for surgical education at the University of 

Mississippi Medical Center. She is a member of the 

American College of Surgeons’ General Surgery Coding 

& Reimbursement Committee and is on the board of 

directors of the American Board of Surgery. Dr. Borman 

was a member of the executive committee and vice-chair 

of the American Medical Association’s Current Procedural 

Terminology editorial panel. Dr. Borman frequently works 

with the Centers for Medicare & Medicaid Services 

on issues related to physician payment. She also has 

served in various positions at the American Association 

of Endocrine Surgeons, the Association for Academic 

Surgery, the Association of Program Directors in Surgery, 

and the Association for Surgical Education. Dr. Borman 

earned her medical degree from Tulane University. Her 

undergraduate degree in chemistry is from the Georgia 

Institute of Technology.

Sheila P. Burke, M.P.A., R.N., F.A.A.N., is the 

Smithsonian Institution’s deputy secretary and chief 

operating officer. Before joining the Smithsonian, she was 

executive dean and lecturer in public policy at the John 

F. Kennedy School of Government, Harvard University. 

From 1986 to 1996, Ms. Burke was chief of staff for 

former Senate Majority Leader Bob Dole and was elected 

Secretary of the Senate in 1995. She currently serves as 

the chair of the board of the Kaiser Family Foundation and 

is a member of the Kaiser Commission on Medicaid and 

the Uninsured, the American Board of Internal Medicine 

Foundation, WellPoint Health Networks, Chubb Insurance, 

and the University of San Francisco. She is a member 

of the National Academy of Public Administration and 

the Institute of Medicine (IOM) and chairs the IOM 

Committee on the Restructuring of the Food and Drug 

Administration. She is currently an adjunct lecturer in 

public policy at Harvard, a fellow of the Wiener Center, 

and an adjunct faculty member at Georgetown University. 

She has chaired the National Academy of Social 

Insurance’s project on Restructuring Medicare for the 

Long-Term. Ms. Burke holds a B.S. in nursing from the 

University of San Francisco and an M.P.A. from Harvard 

University.

Ronald D. Castellanos, M.D., has practiced urology for 

more than 30 years. Dr. Castellanos has been a member 

and chair of the Practicing Physicians Advisory Council 

on issues related to physician payment. Dr. Castellanos 

was president of the Florida Urologic Society and has 

worked with several other organizations on health policy, 

including the American Urologic Association and the 

American Lithotripsy Society. Dr. Castellanos earned 

his medical degree from Hahnemann Medical College. 

His undergraduate degree is from Pennsylvania State 

University.

Francis J. Crosson, M.D., is executive director of the 

Permanente Federation of medical groups that make up 

the physician component of Kaiser Permanente. He also 

cochairs the Kaiser Permanente Partnership Group, the 

organization’s management committee. He joined Kaiser 

Permanente in 1977. In 1988 he was appointed associate 

executive director of the Permanente Medical Group and 

served in that position until his current appointment. He 

also has experience with prescription drug arrangements 

and has led efforts on comprehensive public report cards 
274 Commissioners' biographies

Glenn M. Hackbarth, J.D., chairman of the Commission, 

lives in Bend, OR. He has experience as a health care 

executive, government official, and policy analyst. He was 

chief executive officer and one of the founders of Harvard 

Vanguard Medical Associates, a multispecialty group 

practice in Boston that serves as a major teaching affiliate 

of Harvard Medical School. Mr. Hackbarth previously 

served as senior vice president of Harvard Community 

Health Plan. From 1981 to 1988, he held positions at the 

U.S. Department of Health and Human Services, including 

deputy administrator of the Health Care Financing 

Administration. Mr. Hackbarth received his B.A. from 

Pennsylvania State University and his M.A. and J.D. from 

Duke University.

Jennie Chin Hansen, R.N., M.S.N., F.A.A.N., of San 

Francisco, is president-elect of AARP; a senior fellow 

at the University of California, San Francisco, chairing 

the Integrated Nurse Leadership Project; and a parttime nursing faculty member at San Francisco State 

University. Ms. Hansen was executive director of On Lok 

Senior Health Services, the prototype for the Program 

of All-Inclusive Care for the Elderly. She has practiced 

nursing in both urban and rural settings and taught 

in undergraduate programs. She currently serves in 

leadership roles with the AARP Foundation, Agency of 

Health Care Research and Quality Effective Healthcare 

Stakeholders group, Lumetra (California’s Quality 

Improvement Organization), the Advisory Board of the 

Institute for the Future of Aging Services, the Robert 

Wood Johnson Executive Nurse Fellows Program, and the 

California HealthCare Foundation Health Care Fellows 

Program. She was a delegate to the 2005 White House 

Conference on Aging. Ms. Hansen received her B.S. from 

Boston College and her M.S.N. from the University of 

California, San Francisco.

Douglas Holtz-Eakin, Ph.D., is the economic policy 

chair of the John McCain 2008 Exploratory Committee. 

Previously, he was the director of the Maurice R. 

Greenberg Center for Geoeconomic Studies at the Council 

on Foreign Relations, where he held the Paul A. Volcker 

Chair in International Economics. He has served as the 

director of the Congressional Budget Office (CBO) and 

was the chief economist for the President’s Council of 

Economic Advisors. He also represented CBO on the 

Federal Accounting Standards Advisory Board. Before 

joining the federal government, Dr. Holtz-Eakin taught at 

Syracuse University’s Maxwell School, where he chaired 

its Department of Economics and was associate director 

on clinical quality, management of a drug formulary, and 

adoption of a state-of-the-art electronic medical record. He 

serves on the Board of the California Medical Association 

Foundation and the Advisory Board of the Mayo Health 

Policy Institute. Dr. Crosson received his undergraduate 

degree in political science from Georgetown University 

and his M.D. degree from Georgetown’s School of 

Medicine.

Nancy-Ann DeParle, J.D., is managing director of CCMP 

Capital Advisors, LLC, and adjunct professor of health 

care systems at the Wharton School of the University 

of Pennsylvania. From 1997 to 2000, she served as 

administrator of the Health Care Financing Administration 

(HCFA), which is now the Centers for Medicare & 

Medicaid Services. Before joining HCFA, Ms. DeParle 

was associate director for health and personnel at the 

White House Office of Management and Budget. From 

1987 to 1989 she served as the Tennessee Commissioner 

of Human Services. She has also worked as a lawyer in 

private practice in Nashville, TN, and Washington, DC. 

She is a trustee of the Robert Wood Johnson Foundation 

and a board member of Cerner Corporation, CareMore 

Health Plan, MedQuest Associates, DaVita, Boston 

Scientific, Triad Hospitals, and the National Quality 

Forum. Ms. DeParle received a B.A. degree from the 

University of Tennessee; B.A. and M.A. degrees from 

Oxford University, where she was a Rhodes Scholar; and a 

J.D. degree from Harvard Law School.

David F. Durenberger, J.D., is president of Policy Insight, 

LLC; senior health policy fellow at the University of 

St. Thomas in Minneapolis, MN; and chairman of the 

National Institute of Health Policy. He is also president 

of the Medical Technology Leadership Forum, a member 

of the Kaiser Foundation Commission on Medicaid and 

the Uninsured, the Board of the National Committee for 

Quality Assurance, and the National Commission for 

Quality Long Term Care. From 1978 to 1995, he served 

as the senior U.S. Senator from Minnesota, as a member 

of the Senate Finance Committee, and chairman of its 

health subcommittee. He was a member of the Senate 

Environment Committee; Government Affairs Committee; 

and the committee now known as the Health, Education, 

Labor, and Pensions Committee. He chaired the Senate 

Select Committee on Intelligence. Senator Durenberger is 

a graduate of St. John’s University, received his J.D. degree 

from the University of Minnesota, and served as an officer 

in the U.S. Army.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 275

Ralph W. Muller, M.A., is chief executive officer of the 

University of Pennsylvania Health System, one of the 

largest academic health systems in the country. Most 

recently he served as managing director of Stockamp & 

Associates, a hospital consulting firm, and as a visiting 

fellow at the King’s Fund in London. From 1985 to 

2001, he was president and chief executive officer of the 

University of Chicago Hospitals and Health Systems. 

Before joining the hospital, he held senior positions with 

the Commonwealth of Massachusetts, including deputy 

commissioner of the Department of Public Welfare. Mr. 

Muller is past chairman of the Association of American 

Medical Colleges, past chairman of the Council of 

Teaching Hospitals and Health Systems, and past vice 

chairman of the University Health System Consortium. He 

is past chairman of the National Opinion Research Center, 

a social service research organization, and serves on the 

board of the National Committee for Quality Assurance. 

Mr. Muller received his B.A. in economics from Syracuse 

University and his M.A. in government from Harvard 

University.

Robert D. Reischauer, Ph.D., is vice chairman of the 

Commission and president of The Urban Institute. 

Previously, he was a senior fellow with the Brookings 

Institution, and from 1989 to 1995 he was the director 

of the Congressional Budget Office. Dr. Reischauer 

currently serves on the boards of the Academy of Political 

Sciences, the Center on Budget and Policy Priorities, 

and the Committee for a Responsible Federal Budget. 

He also is a member of the Institute of Medicine, the 

National Academy of Public Administration, and Harvard 

Corporation. Dr. Reischauer received his A.B. degree from 

Harvard College and his M.I.A. and Ph.D. from Columbia 

University.

William J. Scanlon, Ph.D., is a senior policy advisor 

with Health Policy R&D. He is a consultant to the 

National Health Policy Forum and is a research professor 

with the Institute for Health Care Research and Policy 

at Georgetown University. Dr. Scanlon is a member of 

the National Committee on Vital and Health Statistics, 

the National Commission for Quality Long-Term Care, 

and the White House Conference on Aging Advisory 

Committee. Before his current positions, Dr. Scanlon was 

the managing director of health care issues at the U.S. 

Government Accountability Office. Previously, he was 

codirector of the Center for Health Policy Studies and an 

associate professor in the Department of Family Medicine 

of the Center for Policy Research. Dr. Holtz-Eakin has a 

Ph.D. in economics from Princeton University and a B.A. 

in economics and mathematics from Denison University.

Nancy M. Kane, D.B.A., is professor of management 

in the Department of Health Policy and Management at 

the Harvard School of Public Health. Dr. Kane directs 

the Masters in Healthcare Management Program, an 

executive leadership program for midcareer physicians 

leading health care organizations. She has taught health 

care accounting, payment systems, financial analysis, 

and competitive strategy. Her research interests include 

measuring hospital financial performance, quantifying 

community benefits and the value of tax exemption, the 

competitive structure and performance of hospital and 

insurance industries, and nonprofit hospital governance. 

Professor Kane consults with federal and state agencies 

involved in health system design, oversight, and payment. 

She is an outside director of the Urban Medical Group, 

a nonprofit physician group practice providing care to 

frail elderly in institutional and home settings. Prior to 

obtaining her business training, she practiced as a hospitalbased physical therapist. Dr. Kane earned her master’s and 

doctoral degrees in business administration from Harvard 

Business School.

Arnold Milstein, M.D., M.P.H., is medical director of 

the Pacific Business Group on Health (PBGH) and U.S. 

health care thought leader at Mercer Human Resource 

Consulting. PBGH is the largest employer health care 

purchasing coalition in the U.S. Dr. Milstein focuses on 

health care purchasing strategy, clinical performance 

measurement, and the psychology of clinical performance 

improvement. He cofounded both the Leapfrog Group and 

the Consumer-Purchaser Disclosure Project and heads 

performance measurement activities for both initiatives. 

Dr. Milstein was previously a Rosenthal Lecturer at the 

Institute of Medicine, and the New England Journal 

of Medicine described him as a “pioneer” in efforts to 

advance quality of care. In 2004 and 2005, World-atWork, the largest global organization of human resource 

managers, awarded him its highest annual award and the 

National Business Group on Health recognized him for 

innovation and implementation success in health care cost 

reduction and quality gains. He is an associate clinical 

professor at the University of California at San Francisco. 

Dr. Milstein has a B.A. in economics from Harvard, an 

M.P.H. in health services planning from the University 

of California at Berkeley, and an M.D. degree from Tufts 

University. 
276 Commissioners' biographies

at Georgetown University and was a principal research 

associate in health policy at the Urban Institute. Dr. 

Scanlon has a Ph.D. in economics from the University of 

Wisconsin–Madison.

Nicholas Wolter, M.D., is a pulmonary and critical 

care physician who serves as chief executive officer 

for Billings Clinic in Billings, MT. Billings Clinic is a 

regional, not-for-profit medical foundation consisting of 

a multispecialty group practice, tertiary hospital, critical 

access hospital affiliates, health maintenance organization, 

research division, and long-term care facility serving a 

vast rural area in the northern Rockies. Dr. Wolter began 

his Billings Clinic practice in 1982 and served as medical 

director of the hospital’s intensive care unit from 1987 to 

1993. He began his leadership role with the successful 

merger of the clinic and hospital in 1993. Dr. Wolter is a 

diplomate of the American Board of Internal Medicine and 

serves on the boards of many regional and national health 

care organizations. He has a B.A. degree from Carleton 

College, an M.A. degree from the University of Michigan, 

and an M.D. degree from the University of Michigan 

Medical School.
Repor t to the Congress: Promoting Greater Efficiency in Medicare | June 2007 277

Commission staff

Mark E. Miller, Ph.D.

Executive director

Sarah Thomas, M.S.

Deputy director

Analytic staff

Jack Ashby, M.H.A.

Cristina Boccuti, M.P.P.

Niall Brennan, M.P.P.

Sharon Bee Cheng, M.S.

Evan Christman, M.P.Aff.

David V. Glass, M.S.

Timothy F. Greene, M.B.A.

Scott Harrison, Ph.D.

Kevin J. Hayes, Ph.D.

Craig K. Lisk, M.S.

James E. Mathews, Ph.D.

Anne Mutti, M.P.A.

Jennifer Podulka, M.P.Aff.

Nancy Ray, M.S.

Rachel Schmidt, Ph.D.

Joan Sokolovsky, Ph.D.

Jeffrey Stensland, Ph.D.

Ariel Winter, M.P.P.

Daniel Zabinski, Ph.D.

Research assistants

Sarah Friedman

Megan Moore

Special assistant to the 

executive director

Annissa McDonald

Administrative staff

Reda H. Broadnax, B.S., 

 Executive officer

Wylene Carlyle

Paula Crowell

Diane E. Ellison

Timothy Gulley

Tina Jennings, MTESL

Plinie (Ann) Johnson

Cynthia Wilson

Staff consultants

Carol Carter, Ph.D.

Dana K. Kelley, M.P.A.

Julian Pettengill

Carlos Zarabozo, A.B.

601 New Jersey Avenue, NW • Suite 9000 • Washington, DC 20001

(202) 220-3700 • Fax: (202) 220-3759 • www.medpac.gov