Diagnosis Related Groups (DRGs) and the

Medicare Program: Implications for

Medical Technology

July 1983

NTIS order #PB84-111525
Library of Congress Catalog Card Number 83-600560

For sale by the Superintendent of Documents,

U.S. Government Printing Office, Washington, D.C. 20402
Preface

The increase in the cost of hospital care has been a persistent and growing problem

for both the Medicare program and the general public for more than iS years. A substantial portion of the increase in hospital costs has been attributed to an increase in the

use of new and existing medical technologies.

Congress recently legislated a new prospective per-case payment system for the

Medicare program. Hospitals will be paid a specific, predetermined amount for each

patient treated, regardless of the number or types of services provided. The amount

paid will depend primarily on the Diagnosis Related Group (DRG) into which the patient is classified. The implementation of per-case payment has rested on the availability of an acceptable method of measuring a hospital’s case mix. DRGs are just one of

several approaches to measuring hospital case mix. Their importance is heightened by

their adoption in the new national Medicare prospective payment system.

The House Committee on Energy and Commerce and its Subcommittee on Health

and the Environment requested that OTA examine DRGs and their implications for use

in the Medicare program as part of a larger assessment on Medical Technology and

Costs of the Medicare Program.

This technical memorandum presents the results of that examination. It reviews

the development of DRGs and compares them to alternative case-mix measures. It examines the validity and reliability of the DRG classification system and the accuracy

of DRG coding. It provides examples of proposed and actual uses of DRGs in hospital

payment. Finally, and most important, the technical memorandum includes a thorough

analysis of the implications for medical technology use and adoption of using DRGs

as an integral part of a per-case payment system.

A principal finding is that while DRGs are ready for use in a per case payment

system, their implementation needs to be closely monitored, because there is little experience with their use in this context. In the long run, the success of DRG payment

will rest on its flexibility and aptability to changing costs and technologies. Four findings concern this need for periodic adjustment:

1. the requirement for regular reestimation of relative DRG prices implies a need for

continued collection of hospital cost and charge data;

2. procedures allowing for the adjustment of DRG rates conditional on a hospital’s

adoption of a technology maybe important to stimulate adoption of desirable but

cost-raising technologies;

3. the DRG adjustment process requires supporting evidence about the effectiveness,

risks, and costs of new technologies; and

4. the adjustment process should guard against proliferation of DRGs.
Medical Technology and Costs of the Medicare Program Advisory Panel

Stuart Altman, Panel Chair

Dean, Florence Heller School, Brandeis University

Frank Baker

Vice President

Washington State Hospital Association

Robert Blendon

Senior Vice President

The Robert Wood Johnson Foundation

Jerry Cromwell

President

Health Economics Research

Chestnut Hill, Mass.

Karen Davis

Johns Hopkins University

School of Hygiene and Public Health

Robert Derzon

Vice President

Lewin & Associates

Washington, D. C’.

Howard Frazier

Center for the Analysis of Health Practices

Harvard School of Public Health

Clifton Gaus

Center for Health Policy Studies

Washington, D.C.

Jack Hadley

Urban Institute

Washington, D. C.

Kate Ireland

Chairman, Board of Governors

Frontier Nursing Service

Wendover, Ky.

Judith Lave

Graduate School of Public Health

University of Pittsburgh

Mary Marshall

Delegate

Virginia House of Delegates

Walter McNerney

Northwestern University

Morton Miller

President

National Health Council

New York, N. Y.

James Mongan

Executive Director

Truman Medical Center

Kansas City, Mo.

Seymour Perry

Institute for Health Policy Analysis

Georgetown University Medical Center

Robert Sigmond

Blue Cross of Greater Philadelphia

Anne Somers

Department of Environment and

Community and Family Medicine

Rutgers University

Paul Torrens

UCLA School of Public Health

Keith Weikel

Group Vice President

AMI

McLean, Va.

iv
OTA Project Staff—Diagnosis Related Groups and the Medicare Program

H. David Banta, Assistant Director, OTA

Health and Life Sciences Division

Clyde J. Behney, Health Program Manager

Anne Kesselman Bums, Project Director,

Medical Technology and Costs of the Medicare Program

Judith L. Wagner, Senior Analyst

Cynthia P. King, Analyst

Pamela Simerly, Research Assistant

Virginia Cwalina, Administrative Assistant

Jennifer Nelson, Secretary

Mary Walls, Secretary

Other Contributing Staff

Gloria Ruby, Analyst

Contractors

OTA Publishing Staff

John C. Holmes, Publishing Officer

John Bergling Kathie S. Boss Debra M. Datcher Joe Henson

Glenda Lawing Linda Leahy Donna Young

v
Contents

Page

Glossary of Acronyms and Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

vii
Contents (continued)

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

List of Tables

Table No. Page

I. Malpractice Claims Paid, 1978 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2. Ratesetting Regression Coefficients, Selected Facilities and Services . . . . . . . . . . . . . . . . 38

3. Four Types of Technological Innovations and Effects on Capital Cost,

Operating Cost, and Total Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

4. Impact of Technological Innovation on Per-Case Costs . . . . . . . . . . . . . . . . . . . . . . . . . . 42

B-1. Summary of Similarities and Differences Between the Original and

Modified Digs ....,.... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

viii
Glossary of Acronyms and Terms

Glossary of Acronyms

AHA — American Hospital Association

CPD – cost per discharge

CPHA — Commission on Professional and

Hospital Activities

CT – computed tomography

DHHS — Department of Health and Human

Services

DRGs — Diagnosis Related Groups

EEG – electroencephalogram

GIR — Guaranteed Inpatient Revenue

HCFA — Health Care Financing Administration

IOM — Institute of Medicine

IPPB – intermittent positive pressure breathing

Los — length of stay

MDCS — Major Diagnostic Categories

NCHS — National Center for Health Statistics

OTA — Office of Technology Assessment

PAS — Professional Activity Study

PCB – preliminary cost base

PMCS — Patient Management Categories

PROS — Peer Review Organizations

PSROS — Professional Standards Review

Organizations

SMSA — Standard Metropolitan Statistical Area

TEFRA — Tax Equity and Fiscal Responsibility

Act of 1982

Glossary of Terms

Capital costs: Costs associated with the use of capital

facilities and equipment, including depreciation and

interest expenses.

Case mix: The relative frequency of admissions of

various types of patients, reflecting different needs

for hospital resources. There are many ways of

measuring case mix, some based on patients’ diagnoses or the severity of their illnesses, some on the

utilization of services, and some on the characteristics of the hospital or area in which it is located.

Coinsurance: A form of cost-sharing whereby the insured pays a percentage of total cost. (Also see copayment. )

Copayment: A form of cost-sharing whereby the insured pays a specific amount at the point of consumption, e.g., $10 per visit. (Also see coinsurance. )

Cost-sharing: The general set of financing arrangements whereby the consumer must pay some

out-of-pocket cost to receive care, either at the time

of initiation of care, or during the time of the provision of health care services, or both.

Deductible: A form of cost-sharing in which the insured incurs an initial expense of a specified amount

within a given time period (e.g., $250 per year)

before the insurer assumes liability for any additional costs of covered services.

Diagnosis Related Groups (DRGs): A classification

system that groups patients according to principal

diagnosis, presence of a surgical procedure, age,

presence or absence of significant comorbidities or

complications, and other relevant criteria.

DRG inflation: An increase over time in the number

of separately identified case-mix classification

groups.

DRG payment: Any per-case hospital payment method in which differences in case mix are taken into

account using DRGs to classify case types.

Effectiveness: Same as efficacy (see below) except that

it refers to “ . . . average or actual conditions of

use. ”

Efficacy: The probability of benefit to individuals in

a defined population from a medical technology applied for a given medical problem under ideal conditions of use.

Fee-for-service: A method of paying for medical care

on a retrospective basis by which each service actually received by an individual bears a related

charge.

Length of stay (LOS): The number of days a patient

remains in the hospital from admission to discharge.

Medicaid: A Federal program that is administered and

operated individually by each participating State

government that provides medical benefits to certain low-income persons in need of health and medical care.

Medical technology: The drugs, devices, and medical

and surgical procedures used in medical care, and

the organizational and supportive systems within

which such care is provided.

Medicare: A nationwide, federally administered health

insurance program, authorized in 1965 to cover the

cost of hospitalization, medical care, and some related services for eligible persons over age 65, persons receiving Social Security Disability Insurance

payments for 2 years, and persons with end-stage

renal disease. Medicare consists of two separate but

coordinated programs–hospital insurance (part A)

and supplementary medical insurance (part B).

Health insurance protection is available to insured

persons without regard to income.

Pass-throughs: In a per-case payment system, passthroughs are elements of hospital cost that are paid

on the basis of cost-based reimbursement.

Per-case payment: Any prospective hospital payment

system with fixed rates of payment based on the hos98-823 0 - 83 - 2 ix
—

pital admission, not on the number and types of

services or number of days of care provided.

Professional Standards Review Organizations

(PSROs): Community-based, physician-directed,

nonprofit agencies established under the Social Security Amendments of 1972 to monitor the quality

and appropriateness of institutional health care provided to Medicare and Medicaid beneficiaries.

Prospective payment: Hospital payment programs

where rates are set prior to the period during which

they apply and where the hospital incurs at least

some financial risk.

Recalibration: Periodic changes in relative DRG prices,

including assignment of prices to new DRGs.

Reliability: A measure of the consistency of a method

in producing results. A reliable test gives the same

results when applied more than once under the same

conditions. Also called “precision. ”

Retrospective cost-based reimbursement: A payment

method in which hospitals are paid their incurred

costs of treating patients after the treatment has occurred.

Risk: A measure of the probability of an adverse or

untoward outcome and the severity of the resultant

harm to health of individuals in a defined population associated with use of a medical technology applied for a given medical problem under specified

conditions of use.

Safety: A judgment of the acceptability of risk (see

above) in a specified situation.

Technology: The application of organized knowledge

to practical ends.

Technology assessment: A comprehensive form of

policy research that examines the technical, economic, and social consequences of technological applications. It is especially concerned with unintended, indirect, or delayed social impacts. In health

policy, the term has also come to mean any form

of policy analysis concerned with medical technology, especially the evaluation of efficacy and safety. The comprehensive form of technology assessment is then termed “comprehensive technology assessment .“

Utilization and quality control peer review organizations (PROS): Physician organizations established

by the Tax Equity and Fiscal Responsibility Act of

1982 (Public Law 97-248) to replace PSROs (see definition). Hospitals are mandated to contract with

PROS to review quality of care and appropriateness

of admissions and readmissions.

Validity: A measure of the extent to which an observed

situation reflects the “true” situation. Internal validity is a measure of the extent to which study results

reflect the true relationship of a “risk factor” (e.g.,

treatment or technology) to the outcome of interest

in study subjects. External validity is a measure of

the extent to which study results can be generalized

beyond the study sample.
Chapter 1

Introduction and Summary
Chapter 1

Introduction and Summary

BACKGROUND

The increase in the cost of hospital care has been

a persistent and growing problem for both the

Medicare program and the general public for more

than 15 years. A substantial portion of the increase in hospital costs has been attributed to an

increase in the use of new and existing medical

technologies. * Medicare expenditures for inpatient

hospital services have increased more than tenfold since its inception—from about $3 billion in

1967 to more than $33 billion in 1982. From 1979

to 1982, the average cost of a day of hospital care

increased at an annual rate of almost 18 percent,

and Medicare expenditures for hospital services

increased at a rate of over 19 percent. In 1982,

hospital costs increased by 15.5 percent, three

times the rate of inflation in the economy as a

whole.

While the fiscal health of the program suffers

as a result of hospital cost inflation, Medicare has

contributed to the problem through its traditional

hospital payment policy. Until October 1982,

Medicare employed a retrospective cost-based reimbursement approach whereby hospitals could

recover from Medicare most of what they spent

for Medicare beneficiaries. Consequently, hospitals had little incentive to control costs. Hospitals have thus been encouraged to acquire and

use more technology and to expand their capacity to produce a wider scope of more complex

services.

The Medicare program has historically provided leadership for other hospital payment policies. Other third-party payers, including State

Medicaid programs and private insurers, have

also generally used cost-based reimbursement as

their approach to hospital payment. In fact, until

1981, State Medicaid programs were required to

follow Medicare’s principles of reimbursement for

● OTA has defined medical technology as the drugs, devices, and

medical and surgical procedures used in medical care, and the organizational and supportive systems within which such care is delivered. In this technical memorandum, the focus is on drugs, devices,

and procedures, but many of the points apply to the system technologies.

hospitals unless they applied for and received a

waiver from the Federal Government for an alternative system.

However, as early as the late 1960’s, some of

these other payers began the search for alternatives to retrospective cost-based reimbursement. State-legislated and voluntary programs

using alternative payment schemes have appeared

throughout the 1970’s. These programs have been

broadly termed “prospective payment,” where

rates are set prior to the period during which they

apply and the hospital incurs at least some financial risk. They have varied widely in design. For

example, some control the amounts hospitals

could charge for specific services; others pay

hospitals an all-inclusive rate per day of hospital

care. But paying by the day sets up incentives for

hospitals to increase lengths of stay and admissions, and controlled charges also encourage hospitals to increase the number of services they

provide.

Recently, a new kind of prospective payment

has emerged: per-case payment. Under this form

of payment, the hospital is paid a specific amount

for each patient treated, regardless of the number

or types of services provided. Thus, the hospital

is rewarded for reducing the cost of treating a patient over the entire course of the hospital stay.

Per-case payment removes the incentive to provide more technologies and encourages the hospital and its physicians to consider explicitly the

benefits of additional services against their added

costs.

Per-case payment cannot survive for long without a method to adjust for differences in the kinds

of patients that hospitals treat. If hospitals were

paid the same amount for each admission regardless of its clinical characteristics, over time they

would be encouraged to treat patients who are

less ill and to avoid the cases that require more

resources. Thus, the implementation of per-case

payment has rested on the availability of an acceptable method of measuring the hospital’s case

mix.

3
4

Case mix has been defined in various ways. In

this technical memorandum, it refers to the relative frequency of admissions of various types of

patients, reflecting different needs for hospital

resources. There are many ways of measuring case

mix, some based on patients’ diagnoses or the

severity of their illnesses, some on the utilization

of services, and some on the characteristics of the

hospital or area in which it is located.

Diagnosis Related Groups (DRGs) are just one

of several approaches to measuring hospital case

mix. Their importance is heightened by their recent adoption for use in the new national Medicare prospective payment system. Beginning in

October 1983, Medicare will phase in a per-case

payment system using DRGs as the case-mix

measure. As part of a larger assessment on Med

ical Technology and Costs of the Medicare Program, the Office of Technology Assessment

(OTA) was requested by the House Committee

on Energy and Commerce and its Subcommittee

on Health and the Environment to examine DRGs

and their implications for use in the Medicare

program.

This technical memorandum, Diagnosis Rekted

Groups (DRGs) and the Medicare Program: Implications for Medical Technology presents the

results of that examination. As with all OTA technical memoranda, it contains no policy options

for congressional consideration. It is intended to

be a comprehensive and independent assessment

of DRGs in the context of a per-case payment sysSUMMARY

Alternative Case-Mix Measures

An examination of several case-mix measures

for their validity and acceptability in a per-case

payment system reveals DRGs to be the best available measure. The Disease Staging and the Severity of Illness Index methods of measuring case mix

provide more information about the severity of

the condition of the patients, but both require

more data than are generally available and both

are based on subjective methods. Neither measure

has reached the stage of development where it is

suitable for widespread implementation in a paytem. In its study of DRGs as a case-mix measure,

it reviews their development and compares them

to alternative case-mix measures. It examines the

validity and reliability of the DRG classification

system, the accuracy of DRG coding, and the administrative feasibility of administering a DRGbased payment system. It provides examples of

proposed and actual uses of DRGs in hospital paymerit. ’ Finally, the technical memorandum includes a thorough analysis of the implications for

medical technology use and adoption of using

DRGs as an integral part of a per-case payment

system. This analysis includes a review of the key

features of design of DRG payment systems that

affect medical technology and a discussion of the

implications of technological change for the administration of a DRG payment system over time.

Two issues of concern to policymakers are not

included in this technical memorandum. First, it

does not address the effect of DRG payment on

the costs of the Medicare program; rather, it

discusses the incentives that will be established by

such a system. Second, it does not discuss whether

and to what extent DRG payment under Medicare

will lead to savings in Medicare program costs at

the expense of other payers. These critical issues

are presently under debate by other agencies and

organizations.

● A detailed account of one actual use—the experimental use of

DRGs for hospital payment in New Jersey-is presented in a separate

working paper.

ment context. Nevertheless,

alternative approaches does

the existence of these

reinforce the concern

of some health providers and policymakers regarding the adequacy of DRGs in distinguishing

differences in the relative severity of patients’ conditions in any given DRG.

Another case-mix measure, Patient Management Categories (PMCS), is also in the developmental stage and will be tested soon. PMCs differ from other case-mix measures, including

DRGs, in that they are normative. Physicians

specify an optimal set of clinical care components
5

based on a patient’s clinical characteristics, including both final diagnosis and reason for admission. This set of clinical care components is

the basis for the relative cost weights of PMCs.

This system appears unique in that it recognizes

that optimal patient management should be the

focus of a system that seeks to encourage efficiency. Thus, the further development of PMCs

should be encouraged.

The use of DRGs in the Medicare per-case payment system is appropriate since they are more

refined than the alternative case-mix measures.

Both statistical and clinical considerations support

this conclusion. Since DRGs can be assigned based

on the information already processed on the discharge abstracts of patients’ medical records, it

is superior to the other measures in its administrative feasibility. However, empirical evidence

must still be collected on all of the alternative

measures to compare them in the context of payment.

DRG Payment and the Use of

Medical Technology

There are two general incentives inherent in any

per-case payment system: 1) to reduce the cost

to the hospital of each inpatient case stay, and

2) to increase the number of inpatient admissions.

Cost per case can be reduced by using fewer technological services, including ancillary services,

reducing the number of inpatient days, or both.

This incentive may result in specialization among

hospitals for services that require a minimum

number of patients to maintain profitability. This

specialization may imply lower access to care for

some Medicare patients. There are built-in constraints of unknown magnitude on the possibility of adverse effects on access and quality. One

constraint is the fact that physicians are the decisionmakers, and they continue to have financial,

ethical, and legal reasons to practice high-quality

medicine.

The direction and strength of general incentives

for any particular hospital are altered by key

features of the DRG payment system, including:

1) the proportion of the hospital’s case load

covered by DRG payment, 2) the treatment of

costs as pass-throughs, * 3) the methods of DRG

rate construction, 4) the methods of updating

DRG rates, and 5) the level of risk and reward

built into the payment system. Thus, a DRG payment system may include a variety of specific approaches to payment with some predictable effects on medical technology.

DRG payment incentives may be expected to

affect technology use in the following ways:

Overall, the number and intensity of ancillary procedures provided to inpatients can

be expected to decrease, but the use of procedures that can be shown to lower the cost

per case will increase.

The settings of technology use are likely to

be influenced by DRG payment, but the incentives work in conflicting directions and

are sensitive to the key features of program

design. It remains to be seen which incentive

will dominate for which procedures. DRG

payment will encourage the movement of

technologies into the home, particularly those

for post-hospital care.

DRG payment is likely to influence the specialization of services, but the magnitude and

direction of these effects is unknown. The incentives to reduce costs encourage concentration of capital-intensive technologies in

fewer institutions. Conversely, the increasing competition among hospitals for physicians and patients will create incentives for

the widespread acquisition of some technologies.

A change in technology product mix is likely to result from downward pressure on the

price and quantity of supplies and, if capital

is included in the DRG rate, capital equipment. Greater product standardization can

be expected as more expensive models and

procedures are eased out of the market

through competition.

*“Pass-throughs” are elements of hospital cost that are not controlled by the per-case payment system. Cost-based reimbursement,

as a whole, can be interpreted as a payment method in which all

cost categories are passed through.
6

Effects of DRG Payment on

Technological Change in Medicine

Perhaps even more important than how DRG

payment affects the use of presently available

medical technologies is how DRG payment will

affect technological change in medicine—the

adoption of new technologies and discarding of

old ones. DRG payment will influence hospitals’

decisions to adopt new medical technologies and

may therefore alter the rate and direction of technological change in medicine.

Although no empirical studies on the effect of

DRG payment on adoption of technologies are

available, studies of other kinds of prospective

payment systems suggest that hospitals can and

do respond to changes in financial incentives in

these decisions. In general, technologies that are

cost-reducing to hospitals will be encouraged;

cost-raising technologies will be discouraged.

However, much depends on the strength and design of the program. In particular, the methods

of providing rewards for cost reductions, treating

capital costs, and updating DRG prices have important implications for technological change.

Though DRG payment does not imply that technological change will approach a standstill, its

directions are likely to be altered, and the adoption of technologies that are cost-raising to the

hospital is likely to decline by an unknown quantity.

The longrun viability of any DRG payment system depends on its ability to both adapt to and

encourage appropriate technological change in

medicine. The methods and procedures used to

adjust the average payment level, relative DRG

rates, and the DRG categories themselves are critical to the survival of the system. The objectives

of the adjustment process are to maintain equality across DRGs in the ratios of price to cost of

efficient care and to promote the adoption of appropriate new technologies. There are at least five

potential processes of adjustment. They vary according to whether the adjustment is conditional

on hospitals’ actual adoption of new technology,

who requests the adjustment, and when in the

stage of a new technology’s diffusion the adjustment is made. None of these processes alone is

sufficient to adjust the system adequately for technological change.

Implementation Issues

Other considerations for the feasibility of using

DRGs as the case-mix measure in the Medicare

payment system include two important aspects

of implementation of this new system: 1) data and

coding issues, and 2) hospital administrative issues. Accurate and timely patient-level data are

clearly important to the efficient and effective

operation of the DRG system. In the past, evaluations of patient discharge data have found them

to be unreliable. However, it is important to note

that these abstracts had not been produced for

payment purposes. When payment depends on

the accuracy and timeliness of discharge abstracts,

their importance increases, and data reliability

should improve. Monitoring by utilization and

quality control peer review organizations as mandated by the new Medicare law, should give hospitals added incentive to improve their data collection and coding.

Such improvements in information quality implies a need for several education programs for

medical staff, hospital administrators, and medical records personnel. Error detection, feedback,

and training would be important parts of data

programs. It should be noted that these types of

improvements are likely to be more expensive and

time-consuming. Some of these costs will vary

among the individual hospitals depending on their

current practices. For example, some hospitals

might need to adopt computer capability for medical records, while others might need to add to

their medical records staff.

Findings and Conclusions

● Although the new Medicare law provides for

a DRG-based per-case payment system, DRGs

have been inadequately evaluated for their

validity as an indicator of patient resource

needs and for their impact on medical technology in per-case payment. Thus, it is critical that

the new system’s implementation be carefully

monitored.
7

●

●

●

●

Programs of quality assurance and utilization

review will be required to counter the incentives of the per-case payment system to underprovide services and inappropriately admit and

discharge patients.

The treatment of capital costs will affect the use

of medical technology. The diversity in hospitals’ ages, debt structures, and future needs

for expansion and closure argue for hospitalspecific determinations of capital payment levels, probably at the State level.

Periodic reestimation of relative DRG rates to

reflect changes in the costs of various DRGs is

essential to a workable program. This implies

a need for a continuing source of cost and

charge data to support the process.

Methods for updating DRG rates that are conditional on technology adoption maybe important to stimulate desirable but cost-raising technologies, Frequent creation of new technologyspecific DRGs, however, can ultimately undermine the incentives of per-case payment.

●

●

●

The DRG adjustment process requires supporting evidence about the effectiveness, risks, and

costs of new technologies. Resources must be

adequate for necessary research and the activities of groups such as the Prospective Payment Assessment Commission.

It is fortunate that the new Medicare law does

not discourage individual States from establishing alternative prospective payment systems.

These alternative systems will allow experimentation with different payment system configurations, including the use of other case-mix measures as they become more refined.

The reliance of the DRG classification system

on accurate and timely data collection and coding will necessitate improvement of hospitals’

medical records procedures and performance.

Educational programs for physicians, nurses,

hospital administrators, and medical records

personnel could be initiated.

ORGANIZATION OF THE TECHNICAL MEMORANDUM

This technical memorandum is organized in six

chapters. Chapter 2 reviews and evaluates several

alternative approaches to case-mix measurement

for payment purposes. In addition to DRGs, Disease Staging, Severity of Illness Index, and PMCs

are briefly examined. Chapter 3 analyzes the incentives for hospitals to use medical technologies

under a prospective per-case payment system

based on DRGs. The effects of DRG payment on

technological change in medicine is the focus of

chapter 4. Implementation issues regarding data

and administration are briefly described in chapter

5. Chapter 6 provides an overview and expansion

of the conclusions reached in previous chapters.

Appendix A includes a list of the Health Program Advisory Committee and acknowledgments

of assistance in the preparation of this technical

memorandum. Appendix B provides a descriptive

overview of the development of DRGs, and appendix C contains brief descriptions of per-case

payment systems that have been designed by the

States.

A separate working paper is entitled “Using

Diagnosis Related Groups (DRGs) in Hospital

Payment: The New Jersey Experience” was written by Joanne Finley under contract to OTA. It

is a detailed description of the New Jersey experience with an all-payer prospective payment

system based on DRGs. *

*Available from the National Technical Information Service

(NTIS).

98-823 0 - 83 - 3
Chapter 2

A Brief Review and Evaluation of

Alternative Approaches to

Case - Mix Measurement
Chapter 2

A Brief Review and Evaluation of

Alternative Approaches to

Case-Mix Measurement*

INTRODUCTION

Policymakers, hospital administrators, and

health services researchers have long recognized

the diversity of hospitals’ outputs. Efforts to

analyze hospital behavior and to establish effective and equitable reimbursement systems have

been complicated by this diversity of hospital

products, which include education and research

as well as patient care.

For two decades, researchers have grappled

with the measurement of hospitals’ patient care

output. While enormous progress has been made

during that period, there remains no consensus

about the most appropriate method of case-mix

measurement. Failure to reach such a consensus

is not surprising. Several substantial barriers have

stood in the way, including the variety of purposes case-mix measures have been designed to

serve and the significant data requirements associated with any but the most rudimentary measures. The variation in the purposes to be served

has been a barrier, because it appears that the “optimal” measure may be different if it is to be used

for reimbursement, quality assurance, management, or for some other purpose. Data requireEARLY APPROACHES

The earliest measures of patient care output

were developed at the institutional level. That is,

the initial measures represented one or more indices, developed for the hospital as a whole, that

were designed to reflect a dimension of hospital

performance assumed to be associated with case

mix. These measures included average length of

stay (LOS), surgery rates, relative volume of outpatient visits, number of births, and other similar

● This chapter is based on a paper prepared for OTA by Nancy

L. Kelly, Diane E. Hamilton, and Ralph E. Berry of Policy Analysis, Inc.

ments stem from the need, irrespective of the

specific measure, to obtain detailed information

about the patients for which the case-mix measure

is to be defined. Even in the smallest hospitals,

admissions total about 2,000 annually; in the

largest institutions, more than 40,000 patients are

admitted each year. Even in this age of computers,

the national number of admissions—3l million*—

is formidable.

This chapter presents a brief overview of the

development of case-mix measures, from the early

rudimentary techniques to the most recent advances. It is intended to provide a frame of

reference against which Diagnosis Related Groups

(DRGs) can be assessed. It is not intended to provide a detailed review of past or current approaches to case-mix measurement. Such detail

can be found in the references cited throughout

the chapter or, alternatively, in an excellent

review article by Hornbrook (38).

—.— —

“Total admissions to non-Federal short-term general hospitals in

1980.

indicators. Data to construct such measures were

readily available from published sources, such as

the American Hospital Association’s annual survey of hospitals.

It was recognized that simple summary indices

such as these were no better than crude surrogates

for case mix. However, including such measures

in an analysis of interhospital cost variation, for

example, seemed preferable to excluding any output characteristics, and for a number of years

11
12

there were no alternatives. But the empirical evidence indicated that these measures did not explain very much of the interhospital variation

in costs (see, e.g., 44). Clearly, the low explanatory power resulted in part from the unidimensionality of the measures. The ratio of surgical

operations to admissions, for example, fails to distinguish hospitals performing a great deal of complicated, high-risk surgery from those performing equal numbers of simpler, more common procedures. Similarly, a long LOS may be experienced by patients with acute and severe illnesses

or by patients with chronic conditions awaiting

discharge to a nursing home.

Often, additional surrogate measures were used

along with those described above. The added

measures described characteristics of the hospital,

rather than the patient population, but they were

considered to be highly correlated with case mix.

The earliest surrogate was the absolute size of the

hospital, measured in terms of numbers of beds

or admissions. Case mix was assumed to be more

complex in larger hospitals than in smaller facilities. Teaching status is another commonly used

surrogate; it is widely believed that teaching hospitals treat a more severely ill mix of patients than

nonteaching hospitals. Similarly, physician staff

characteristics have occasionally been identified

as useful surrogates. Measures of the scope of

facilities and services have also been used as indicators of the case mix of patients treated, under

the assumption that hospitals are equipped to treat

a particular array of illnesses. While size, teaching

status, physician staff composition, and scope of

services undoubtedly have some predictive power

with respect to case mix, the evidence suggests that

this power is far from perfect (see, e.g., 4s).

A later development among the early approaches involved the linkage of demographic and

economic characteristics of the hospital’s “market

area” (from sources such as the U.S. Bureau of

Census) to the hospital-specific information. This

was based on the further assumption that unmeasured dimensions of case mix, not contained in the

institutional measures, could be obtained from the

characteristics of the area in which the hospital

was located. Examples of the characteristics

thought to be important were the age distribution

of the population (especially the proportion over

age 65), median income and education levels, and

the numbers of Medicaid recipients. Even such indirect measures as urban versus rural location

and/or population density were considered. Associated with each of these characteristics was an

underlying hypothesis about its effect on the case

mix of area hospitals. For example, a high proportion of poor and/or elderly people in the surrounding area was thought to be more closely associated with severe illnesses than a high proportion of well-educated, moderate income households (70). Broad descriptors of the area, such as

population density, have often been considered

as surrogates for differences in lifestyle, which in

turn, lead to differences in health status.

While it can be argued that they add important information to the limited hospital-specific

indices, the market area characteristics also must

be viewed as particularly crude surrogates for case

mix. The chief drawback of these measures lies

in their imprecision. Given current data sources,

it is not possible to accurately identify, on a national scale, the precise market area from which

the hospital draws its patients. This is especially

difficult in urban areas, where many hospitals are

clustered in a small geographic area, and where

some of the hospitals are referral centers for a

much larger region. Except for the relatively few

States in which patient origin data are collected,

counties or Standard Metropolitan Statistical

Areas are typically the only geographic units for

which data can be obtained. At best, these will

be rough approximations of the true market areas

for most of the Nation’s hospitals.

It should be noted that the measures that have

been labeled “early” are, in fact, still widely in

use in research on hospital costs and behavior today. Although, as mentioned previously, considerable advances have been made in the area of

case-mix measurement, none of the more recent

developments is yet widely available, nor are the

requisite input data. As the next section describes,

the data requirements of the principal alternatives

have placed new demands on traditional recordkeeping and data collection procedures. Gradually, however, alternative measures are likely to become more widely available as existing systems

respond to these demands.
13

RECENT DEVELOPMENTS: DESCRIPTION AND EVALUATION

OF NEW APPROACHES

Dimensions of Case Mix

Recognition that case-mix measurement was an

important problem—and probably a necessary

tool for developing solutions to such longstanding

dilemmas as hospital cost inflation-aroused considerable interest as well as funding for a number

of research and development efforts. As a result,

several important advances have been made. Before they are described, however, a conceptual

framework for viewing the problem of case-mix

measurement will be presented as background.

Perhaps the first “advance” that motivated the

new developments was the recognition that the

patient, not the institution, was the appropriate

unit of analysis. No matter what the hospital was

equipped to do, where it was located, or who

served on its physician staff, the types of patients

it treated during the course of a given year were

the true determinants of the hospital’s patient care

output. The “condition” of the patients in that

population was believed to dictate treatment patterns and, consequently, resource use within the

hospital. Further, it is useful to consider “condition” as having two key dimensions: the nature

of theproldem underlying hospitalization (usually

indicated by the diagnosis or diagnoses), and the

relative severity of that problem.

A major difficulty facing evaluators of case-mix

measures lies in choosing the appropriate frame

of reference for such an evaluation. For instance,

some measures, such as DRGs, were developed

specifically (though not exclusively) to account

for differences in resource use. Other approaches

do not explicitly address resource use, although

they may in fact be highly correlated with this

indicator. An evaluation of how well a given

measure explains variation in resource use, therefore, should in fairness recognize the purpose that

the measure was designed to serve (though this

may not alter the conclusions reached).

Related to this issue are the different perspectives of potential users of case-mix measures and

their implications for the validity and acceptability

of a given measure. For example, measures that

seem intuitively reasonable, exhibit high explanatory power in statistical analysis, and can

be constructed from readily available data sources

may be perfectly acceptable to officials of reimbursement agencies, but they may have no meaning or legitimacy for clinicians. Conversely, measures that are acceptable to clinicians may be infeasible to employ in a large-scale, national program. It seems unlikely that a single measure will

satisfy the requirements of all potential users,

though some approaches will come closer than

others. The likelihood that different conclusions

will be reached by different groups of users is not

necessarily a problem, however, since alternate

approaches may productively be used in tandem.

Against this background, two broad categories

of approaches will be described. The first consists

of institutional measures, which (to distinguish

them from the early, traditional approaches) are

derived from data describing the diagnostic composition of the hospital’s patients. Included in this

category are the “ad hoc” grouping methods (45),

the Resource Need Index (100), and information

theory measures (17,33). The second category

consists of patient-level measures. Of these, the

most well known is DRGs. The other approaches

reviewed in this chapter include two major efforts

to measure severity of illness within a disease

entity: Disease Staging (24) and the Severity of

Illness Index (30,31). Finally, the discussion includes a new approach to defining patient categories and assessing treatment patterns, known

as Patient Management Categories (PMCs) (107).

These last three approaches should be viewed as

potential alternatives to DRGs, although the

Severity of Illness Index can also be viewed as a

complement to them.

Hospital-Level Measures

The institutional-level measures referred to

above all represent aggregations of patient data

designed to capture the overall case-mix complexity of, and the resource implications for, a particular hospital. Of these, the most rudimentary

can be termed the “ad hoc” grouping techniques.
14

These involve the collection of diagnostic information for patients from a sample of hospitals and

aggregating those data in ways that appear to be

analytically meaningful. For example, Lave and

Lave (45) used this approach to define a set of

characteristics for each hospital they studied. In

their study, a set consisted of proportions of patients in broad diagnostic categories, such as cardiovascular diseases. The categories selected were

assumed to distinguish groups of patients that

have different resource requirements, although no

data were available to directly evaluate that hypothesis.

A measure that takes resource use directly into

account is the Resource Need Index (RNI), developed by the Commission on Professional and

Hospital Activities (100). The construction of RNI

is a two-step process. The first step involves the

development of “relative need units” for each

diagnosis (or diagnostic category). The units,

which are based entirely on charge information,

represent the ratio of average charges for a particular diagnosis to overall average charges. RNI

is then constructed as the average number of relative need units for a given hospital. RNI thus

makes a start at the simultaneous evaluation of

diagnosis and resource use, though a possible

drawback lies in its reliance on charges as the sole

indicators of resource requirements. To the extent that differences between charges and costs differ by type of service, hospitals’ charges may not

reflect actual resource use very precisely.

Finally, information theory measures have also

been derived for the institution as a whole. Studies

by Evans and Walker (17) and Horn and Schumacher (33) have employed this approach to casemix measurement. Information theory measures

are based on the assumptions that rare conditions

are complex and tend to be treated by a few specialized hospitals. In contrast, common conditions

are assumed to be less complex and likely to be

uniformly distributed across hospitals. The measure is derived mathematically from the proportions of cases in each diagnostic category. The

highest scores for this measure will therefore go

to the “most complex” hospitals—i.e., those that

treat the uncommon diseases. The specific resource requirements of any of the diseases, including the rare ones, are not explicitly considered

in the information theory approach. Instead, its

validity in measuring case-mix complexity rests

on the validity of underlying assumptions.

The only common threads in these divergent

techniques are their use of diagnostic data and

their assumptions about the appropriateness of

aggregating the information to the hospital level.

While this aggregation is undoubtedly convenient,

it involves considerable simplification and, as a

consequence, loss of information. Some, such as

Klastorin and Watts (44), have considered the

issue of hospital-level case-mix indices and have

concluded that summary indices may not be comparable across hospitals. However, the thrust of

most recent research in the area of case-mix

measurement has been toward the development

of patient-level measures. Though it is possible

to aggregate these measures into a hospitalwide

index (and indeed several such indices have already been constructed), such aggregation has not

been a principal focus of the development process.

Patient-Level Measures

Diagnosis Related Groups

DRGs are undoubtedly the most well known

of the patient classification systems that have been

introduced during recent years. As this section indicates, however, several major alternatives now

exist that differ from DRGs both conceptually and

in practice. All of the prominent systems are described briefly in this chapter. A more complete

description of DRG development methods can be

found in appendix B.

The development of DRGs has been ongoing

since the late 1960’s, and it is appropriate to view

the concept as one that is continuously evolving.

The evolution of DRGs has involved both conceptual refinements and technical improvements,

spurred by the availability of more and better

quality input data and by feedback from a wide

variety of observers and users of DRGs. It is likely

that the evolution will continue as relevant data

increase in availability and improve in quality and

as the concept is subjected to more and more scrutiny.

The first version of DRGs to be widely disseminated was a set of 383 categories, described by
their developers in 1980 (19). Subsequently, in

early 1982, a second and much revised set of 467

categories was issued (103). This revised set bore

little resemblance to the “original” 383, as it was

based on different definitional procedures and a

different coding convention. Both sets had several

common objectives. Both were designed to identify patients with similar expected resource use,

measured by length of hospital stay. Both versions

were defined so as to be medically meaningful to

physicians, the key decisionmakers within the

hospital with respect to patient care, though the

operationalization of this objective varied significantly between the two. Finally, both sets of

DRGs were deliberately based on data that are

commonly available, and both sets sought to be

limited to “manageable” numbers of groups.

In general, the broad outlines for the construction of both sets of DRGs were the same for each

version. Actual patient stays in a sample of hospitals were the units of analysis. Each patient’s

principal diagnosis—i.e., the principal reason

(after study) for his/her hospitalization—was

coded using a detailed coding system that allowed

for many thousands of possibilities. The first step,

therefore, was to collapse the detailed diagnosis

codes into meaningful, but broad, subcategories

called “Major Diagnostic Categories (MDCS). ”

These MDCs were then further subdivided, using

a combination of statistical analysis and medical

judgment, according to other characteristics that

accounted for differences in resource use within

each MDC. (The remainder of this discussion will

focus on the current set; a comparison of the two

can be found in app. B.)

The major differences, however, appear to outweigh the similarities. Significantly modified procedures were used to develop the 467 DRGs.

These differences included the involvement of a

far greater number of participants, many of them

clinicians, which accompanied a shift in the fundamental orientation of the development process.

Whereas the development of the 383 DRGs had

involved both statistical analysis and expert

clinical judgment, the balance between the two

components was relatively more even than it became in the revised method, in which the balance

was shifted in favor of clinical judgment.

The current set of 467 DRGs was derived from

23 MDCs, most of which were defined around

organ systems of the body (in conformance with

the organization of medical practice). Subdividing

MDCs into DRGs was performed by expert panels

comprising physicians and others in the health industry. Their decisions were guided by several

criteria that had been established. For example,

the guidelines required that the initial partition

(when possible) be based on the presence or absence of a surgical procedure performed in an operating room. Panels were also instructed to rank

order surgical procedures according to resource

intensity and to assign patients with multiple procedures to the procedure involving the greatest

intensity. In addition to considering the type of

surgery performed, the nature of coexisting conditions and complications were explicitly evaluated. “Substantial” conditions and complications

were distinguished from those less significant. Surgery, coexisting conditions, and complications

were all viewed as indicators of severity of illness.

Finally, other variables were taken into account

when the experts determined that they were relevant. Examples of other factors used for subdivision include death and “left hospital against

medical advice. ”

Though clinical judgments dominated the decisionmaking process, statistical analysis was used

to aid those judgments. Patient-level data were

made available by several organizations, principally the Professional Activity Study of the

Commission for Professional and Hospital Activities. These data were viewed to be representative of national treatment patterns. Reduction in

variance for LOS was examined for possible partitioning variables, but the fact that variance may

have been significantly reduced by a particular

variable did not guarantee that that variable

would be included in the DRG definition. The

need to group patients with clinically related

diseases, above all, dictated which measures

would be used.

The outcome of this process was a mutually exclusive and exhaustive set of 467 DRGs. * By de-

*In operation, there are 468 DRGs, the last for patients who have

received an operating room procedure unrelated to their MDC.

98-823 0 - 83 - 4
16

sign, DRGs can be determined from discharge abstract data, which are commonly available in

computerized form. A computer algorithm is

available to classify each patient into the appropriate DRG. As a consequence, use of this

classification system poses few administrative

burdens. As the following chapter will describe,

little empirical research has been conducted to

date on the current set of DRGs. Some recent evidence indicates that there still remains substantial within-DRG variation in resource use. For example, one analysis of a random sample of cases

in 12 high-volume DRGs applicable to older patients in New Jersey hospitals found that 13.6 percent of discharges had a LOS more than two times

the average of cases in that category (75). Analysis

of Medicare discharges for 1979 also showed a

wide range of within-DRG variation around the

mean cost per discharge. For both the old 383

DRG and the new 467 DRG classification systems,

it was found that DRGs had coefficients of variation ranging from about 0.2 to 1.5 in the Medicare

population (93). A coefficient of variation of 0.2

can be interpreted as indicating that roughly twothirds of patients in the DRG have costs within

20 percent above or below the mean cost of the

DRG. A coefficient of 1.5 indicates that about

two-thirds of patients in the DRG lie within ISO

percent of the mean cost. The new DRGs do not

appear to increase the homogeneity of the groups

with respect to their actual consumption of resources. Finally, the extent to which DRGs are

acceptable to clinicians is unclear.

New Jersey and several other States have used

DRGs in hospital payment systems with varyingly

degrees of success. Appendix C contains brief

descriptions of several State per-case payment

systems, some of which used DRGs.

Disease Staging

Apart from DRGs, the most prominent patientlevel measure in the literature is Disease Staging.

Both Disease Staging and the Severity of Illness

Index (to be described subsequently) were developed to address the perceived need to measure the

severity of the patient’s illness as well as his or

her diagnosis. Severity has been defined as the risk

of death or permanent impairment resulting from

the illness (38).

Staging consists of the specification of progressive levels of severity for disease in terms of

the events and pathophysiological observations

that characterize each stage (24). As described in

Hornbrook (38), staging involves the segmentation of a disease entity into five primary stages,

which are defined as follows:

● Stage O: No disease present, or diagnosis

unknown.

● Stage 1: Disease is certain and no complications are present either local or systemic.

● Stage 2: Disease process is limited to an organ

or system; significantly increased risk of complications.

● Stage 3: Significantly greater problem than

stage 2: multiple site involvement; generalized systemic involvement; poor prognosis.

● Stage 4: Death or most severe stage possible

(i.e., the final event of the illness).

Six, rather than five, primary stages are used for

cancers, to maintain consistency with previous

work. (In fact, staging was first used in oncology

in clinical trials of new treatments to incorporate

illness severity into experimental design and evaluation (38). ) Substages have also been defined for

the cancers.

Stage assignments can be made by a computer

algorithm, based on data recorded on discharge

abstracts. Computer-assigned stages, however,

may represent underestimates of the stages that

technicians would derive manually from medical

records. Again, according to Hornbrook (38), underestimates may occur for two reasons: 1) the

primary diagnostic coding systems used for discharge abstracts are not sufficiently precise, and

2) insufficient data are included on the discharge

abstracts.

Staging is the product of physicians’ judgments

about the biological progression of a given

disease. First and foremost, it was developed to

be a clinically meaningful concept. The extent to

which costs, charges, or LOS varied within stages

was not considered during the development process. Although some limited evaluation has indicated that the stages of a disease are indeed systematically related to differences in those other

measures (6,23), the relationship between the

stages of a disease and resource consumption has
17

not yet been investigated in depth. Further, it has

not been demonstrated that the individual stages

are homogeneous with respect to resource use

(23). In addition, the stages are not necessarily

comparable across diseases, as each disease entity is staged separately. Consequently, stage 2 of

a surgical condition may be much more serious

than stage 2 of a medical condition and thus require more resources during treatment.

The primary advantages of Disease Staging lie

in its apparent meaningfulness to clinicians, as

well as in the direct way in which the stages capture the biological severity of illness within a given

diagnosis. Staging has the added advantage of requiring only information commonly available on

computerized discharge abstract data (although

some precision is sacrificed when computerized

methods are used). A significant drawback lies in

the likelihood that certain diseases cannot be

staged. (In general, medical conditions are more

difficult to stage than surgical.) The fact that

resource consumption was not an explicit consideration in developing the stages (and as a result

may or may not be captured by them) may be

a serious drawback if stages were used in a reimbursement context. Moreover, since stages are

based on single diseases and on the prognosis for

each patient, they ignore concurrent conditions

and other patient characteristics which affect

resource use, such as social, economic, and psychological factors (38).

Severity of Illness Index

Still more recent than staging is a measure that

was developed to reflect the overall severity of

illness of the patient, and not just the severity of

the principal diagnosis. The Severity of Illness Index, developed at Johns Hopkins University (31),

classifies patients into four severity levels. Unlike

the staging procedure, this Severity of Illness Index is not disease-specific but instead was designed

to apply to all conditions treated in the medicine,

surgery, obstetrics/gynecology, and pediatrics departments of hospitals. Developed in conjunction

with a panel of physicians and nurses, the index

is built from seven criteria deemed to be the best

indicators of overall illness severity. These

include:

●

●

●

●

●

●

●

stage of the principal diagnosis;

complications of the principal condition;

concurrent, interacting conditions that affect

the course of hospital treatment;

dependency on the hospital staff;

extent of nonoperating room procedures;

rate of response to therapy, or rate of recovery; and

impairment remaining after therapy for the

acute aspect of the hospitalization.

Data relevant to each of the above criteria are

obtained from the patient’s medical record. Abstraction of data is performed manually, by a

trained rater. Based on the combined pattern of

severity levels within each of the seven criteria,

the rater makes a judgment about the overall

severity of the patient’s condition. The overall index can range from 1 (least severe) to 4 (most

severe).

The Severity of Illness Index may be used as

an adjunct to other patient classification systems,

such as DRGs. In that context, refined categories

of severity can be developed within categories of

patients. Current research, however, suggests that

a preferred use of the Severity of Illness Index

would be in conjunction with a very broad classification system, such as the 23 MDCsdescribed

earlier (30).

The major advantage of the Severity of Illness

Index, particularly for payment purposes, would

appear to be the extent to which it explains variation in resource use. In a comparative analysis involving six disease conditions, Horn and colleagues (36) found that the Severity of Illness Index produced groups that were more homogeneous than DRGs, Disease Staging, or PMCS (to be

discussed subsequently). Homogeneity was assessed with respect to total charges, LOS, laboratory charges, routine charges, and often

radiology charges. The index has also been shown

to be a good predictor of resource use (35). This

explanatory power may, in part, result from the

method used to ascribe a severity level to a given

patient. Although the seven criteria do not explicitly address resource use, some of the criteria

(e.g., extent of nonoperating room procedures)

are clearly correlated with it, and there may be
18

a tendency on the part of raters to take it into account when forming a judgment about overall severity (38).

A drawback of the Severity of Illness Index is

its reliance on manual abstraction of data from

the medical record and on the judgments of individual raters. Although recordkeeping systems

could be modified in such a way that the data necessary to construct the Severity of Illness Index

would be available on computerized discharge abstracts, in general, discharge abstract systems are

currently inadequate for this purpose. Thus, assignment of severity levels by this method is relatively costly. Also, so long as subjective judgments are employed in assigning the index values,

there are likely to be problems with the reliability and acceptability of the measure.

The acceptability of the Severity of Illness of

Index to clinicians is currently unclear. Although

the development of the index was in conjunction

with physicians and nurses, there is as yet no indication of how meaningful the index is to clinicians around the country. At present, the chief

advantage of this approach seems to be its success in accounting statistically for variation in

resource use.

Patient Management Categories

A criticism that has been leveled at all case-mix

measures based on discharge diagnosis is that the

diagnosis at discharge is not the only relevant

diagnostic information (106). Instead, it is argued

that diagnosis at the time of admission determines

the course of treatment that the physician will employ. In other words, not only the diagnosis of

the patient, but also the reason for admission, will

affect the ultimate LOS and total costs/charges.

Reasons underlying admission can range from observation to chemotherapy to major surgery, all

of which have vastly different implications for

resource use. Young and colleagues (106) also

have argued for the development of a measurement system that avoids building in actual treatment patterns, regardless of their appropriateness.

They favor a method that is more normative—

i.e., one that views patient characteristics and

management without regard to current treatment

patterns that may result from discretionary decisions, differences in the availability of particular

facilities and services, inefficiencies, etc.

As a consequence, Young and colleagues (105)

at Blue Cross of Western Pennsylvania have developed an alternative patient classification system, whereby patients are classified into PMCs *

PMCsdiffer from most of the other systems, including DRGs, in that they are based primarily

on the patient’s clinical characteristics. The definitions of PMCsdo not hinge on how the patient

was treated while in the hospital. PMCsdiffer in

other ways as well, as will be discussed subsequently.

Like the other case-mix measures (all to varying degrees), PMCshave been developed in consultation with physicians so that they represent

clinically meaningful entities. Both final diagnosis

and reason for admission are considered

simultaneously in defining the categories. PMCs

have been designed to take levels of severity into

account, again from a clinical perspective. For

each PMC, physicians have specified components

of care (i.e., diagnostic services, treatment procedures, and expected LOS) that, in their view,

are required for the effective management of the

typical patient. Thus, a “patient management

path” has been associated with each PMC. These

components of care then form the basis for the

derivation of relative cost weights for each PMC.

Weights are based on actual cost data from six

participating hospitals that have been adjusted

and allocated to the components of care. The identification of patient management paths and relative costs during the development process is

another distinguishing feature of the PMC approach.

Currently, PMCs are still being defined, although it is anticipated that the process will be

completed by the end of the summer of 1983.

Computer software is also being developed that

will enable the automated mapping of patients

into PMCs. Although discharge abstract data typically do not include information on reason for

admission, Hornbrook (38) reports that preliminary research indicates that valid mapping into

● Much of the substance of this discussion was drawn from unpublished documents provided by Wanda W. Young (ICM,IOS).
19

PMCS may be possible without collecting additional data.

The principal advantage of PMCs over either

DRGs or staging would appear to derive from the

joint consideration, during the development process, of payment and patient management. While

the system was designed for use in a payment context, actual patterns of use (as has been noted)

were not directly considered in defining the categories. However, the relative cost of each PMC

is calculated as part of the development process.

This can presumably be incorporated into a payment procedure. What is most unique to this system, however, is its recognition that patient manSUMMARY

Until recent years, case mix has been measured

by hospital-level surrogates for patient care output. These measures have been derived from

readily available sources of data and generally

represent crude volume and performance measures along with relevant characteristics of the

hospital (e.g., teaching status) and location.

The early measures have been useful in explaining some of the observed interhospital variations,

but it is apparent that these measures do not contain the amount of information necessary to accurately capture interhospital case mix differences.

As a result, considerable effort has been devoted

in the past decade to developing new and improved measures of patient care output. The most

well known of these advances, DRGs, are the subject of this technical memorandum. Other advances, including both substitutes for and complements to DRGs, have also been reviewed briefly in this chapter.

The major advances in measuring health care

output have been in the area of severity of illness

measurement. Disease Staging and the Severity

of Illness Index were both designed to provide a

framework for classifying diseases according to

the relative severity of the patient’s condition.

Both have required extensive developmental work

and testing, which are still underway. Use of either

measure would require more data than are genagement should be the focus of any system that

seeks to encourage efficiency and the deliberate

attempt on the part of the developers to produce

a system that would simultaneously be meaningful

to physicians and facilitate efficiency improvements in the management of patient care.

Because the system is not yet completed, it is

premature to make comparisons between it and

other alternatives. For the same reason, no empirical evaluations have yet been performed.

Clearly, however, PMCs represent an interesting

innovation in the area of case-mix measurement

that has considerable potential.

erally available at the present time, though the

staging approach can be employed using data that

are normally included in hospitals’ computerized

records. Neither measure has reached the point

where it is suitable for widespread implementation in a reimbursement context. However, the

existence of such measures, at a minimum, serves

as a reminder that the relative severity of patients’

illnesses is important to consider when measuring case mix.

Patient Management Categories represent the

newest of the major advances in case-mix measurement. It will still be some time before the

system is fully operational and adequate testing

can be performed. In the short term, however,

this method of patient classification again represents a reminder that currently used methods

may not be appropriately targeted.

This review of alternative methods of measuring hospital case mix has revealed that early

methods are lacking in precision and that the new

approaches (with the exception of DRGs) are not

yet ready for widespread use. CIearly, any “refined” system that tackles patient-level case-mix

information will require considerably more data

than has been employed (or even available) in the

past. Feasibility considerations, therefore, should

include the relative administrative burdens

associated with each measure as well as the stage
20

of development each measure has reached. Most statistical evidence) in order to assess the apimportantly, the conceptual differences among the propriateness of each for the purposes it is to

alternatives should be evaluated (as well as the serve.
Chapter 3

DRG Payment and the Use

of Medical Technology
Chapter 3

DRG Payment and the Use

of Medical Technology

INTRODUCTION

The use of Diagnosis Related Groups (DRGs)

in hospital payment has grown from an experiment in a handful of hospitals to national Medicare policy in just 3 years. At the time of passage

of the 1983 Social Security Amendments (Public

Law 98-21), which established a national Medicare

prospective payment system using DRGs, the

Medicare program was planning to use DRGs to

implement the hospital expenditure control provisions of the Tax Equity and Fiscal Responsibility

Act of 1982 (TEFRA). Before that, DRG payment

had been used for only 2 years in 26 hospitals in

New Jersey and even fewer in Maryland. *

*See app. C for detailed descriptions of selected DRG payment

systems.

DRG PAYMENT: AN OVERVIEW

Theoretically, DRGs could be used in any hospital payment method, including retrospective

cost-based reimbursement, but their importance

in payment comes from their use as part of prospective per-case payment systems. Per-case payment refers to any prospective hospital payment

system with fixed rates of payment based on the

hospital admission, not on the number and types

of services or number of days of care provided.

Per-case payment is a radical departure from traditional cost-based reimbursement and even from

other kinds of prospective payment. One of the

unique features of a per-case payment system is

that it cannot survive for long without a way to

adjust payment for differences in case mix; otherwise serious inequities among hospitals would be

likely to develop, and selective admitting strategies would be encouraged.

DRGs represent only one possible approach to

characterizing hospital case mix; but as the

The rapid acceptance of DRG payment in the

absence of much experience argues for a careful

look at its implications, both good and bad, for

medical technology. DRG payment methods establish incentives for the use of medical technologies both within and outside of hospitals that

differ markedly from those of retrospective costbased reimbursement and other kinds of prospective payment. These new incentives have implications for the efficiency and quality of care

delivered to Medicare beneficiaries. This chapter

will examine the implications of DRG payment

for the amount, characteristics, prices, and settings of medical technology use.

previous chapters demonstrate, the DRG system

is the only explicit case-mix measurement approach that is now ready for use in a payment

system. It is not surprising, then, that the search

for a case-mix adjuster has led to DRGs. DRG

payment is defined here as any per-case hospital

payment method in which differences in case mix

are taken into account using DRGs to classify case

types. Appendix C provides examples of various

types of per-case payment methods that have been

applied or suggested for use by third-party payers.

Per-case payment is possible without the use

of DRGs, but any such method must somehow

adjust for case mix, if only implicitly. One frequently used approach to per-case payment that

does not use DRGs or any other explicit case-mix

measure is to tie each hospital’s future rate per

case to its own costs per case in a fixed base year

(2). The base year cost is presumably a reflection

98-823 0 - 83 - 5

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2 4

of the mix of cases treated by the hospital in that

year. So long as it can be assumed that the hospital’s case mix is stable and not subject to

manipulation, this is a reasonable, though imprecise, implicit case-mix adjustment method. As

time passes, however, the assumptions of stability

and nonmanipulation of case mix become more

and more tenuous, requiring ever more cumbersome appeals processes or revisions than in a

system with explicit case-mix adjustment. *

There are two general approaches to the use of

DRGs in per-case payment: 1) DRG-specific prices

per case; and 2) a single rate per case that reflects

the hospital’s case mix determined by a DRGbased case-mix category. The first approach,

DRG-specific prices per case, requires the payer

to issue a separate rate for each DRG. The payer

may pay a unique rate for each DRG in each hospital, the same amount for each DRG regardless

of the hospital in which care is rendered, or different amounts for any given DRG depending on

the location or particular characteristics of the

hospital.

The case-mix category approach requires that

hospitals be classified into a number of groups

● Other criticisms can be lodged against this implicit case-mix adjustment method. The most important is that it rewards past inefficiency. Hospitals which have been relatively efficient in treating patients will have a lower cost base than those which have been less

so prior to initiation of the per-case system. This criticism can also

be made of some DRG payment systems, particularly those in which

rate per DRG is based totally or partially on the hospital’s own cost

per DRG.

based on similarities in their case mixes. All

hospitals in a group would be paid a uniform rate

per case. A DRG case-mix index is a categorization method in which each hospital is assigned a

unique index value reflecting the relative resource

requirements of its particular patients. The index

value is determined by a formula using DRGs.

The two approaches to DRG payment–DRGspecific prices and DRG-based case-mix adjusters—do not differ much from one another. The

principal difference is in the time period on which

case-mix measurement is based. A DRG index

must be constructed on the basis of case mix in

some prior time period, perhaps the most recently completed fiscal year. A DRG-specific pricing

system adjusts for changes in case mix as they occur. Thus, any fluctuations in case mix that occur either by chance or by a hospital’s actions,

such as the introduction of a new service, would

be reflected immediately in a DRG-specific pricing system but would enter a DRG case-mix adjustment system only as time passes.

Both kinds of DRG payment—DRG prices and

DRG case-mix indexes—have two essential components: the average level of payment per case;

and the relative weights applied to each DRG. The

average payment level determines how stringent

or generous the payment system is as a whole,

while the relative DRG weights or prices determine the profitability of one DRG relative to

another. The financial incentives of a DRG payment system depend on both the average level and

the relative weights.

DRG PAYMENT AND THE USE OF MEDICAL TECHNOLOGY

Appendix C describes eight per-case payment

systems, five of which use DRGs. Three DRG

payment systems have already been implemented,

and one was recently enacted for the Medicare

program. This section presents an analysis of the

expected effects of per-case payment, and specifically DRG payment, on access to and use of

medical technologies. For the purposes of this

technical memorandum, medical technology is

defined as the drugs, devices, and medical and

surgical procedures used in medical care and the

organizational and supportive systems within

which such care is provided. In this technical

memorandum, the focus is on drugs, devices, and

procedures, but many of the points apply to the

system technologies.

DRG payment establishes a new set of financial incentives for hospital behavior that differs

from those found under both cost-based reimbursement and other kinds of prospective hospital

payment. These incentives are rooted in per-case
25

payment itself, but their effects differ with the particular case-mix measure adopted. For example,

while financial incentives facing hospitals are

generally the same under the old 383 DRGs and

the new 467 DRGs, their strength and the ability

of hospitals to respond to them may differ. This

discussion concentrates on the new DRGs on the

assumption that they are more refined than the

old DRGs and, at present, are more practical than

any alternative case-mix measurement approach

(see ch. 2). It should be understood that the basic

incentives are the result of paying by the case and

will remain to some extent regardless of the casemix measurement approach taken.

Despite the fact that DRG payment has been

embraced by Congress and the administration in

the past 9 months, there is no empirical evidence

available on its effect on access to or use of medical technologies. Evaluations of New Jersey’s and

Maryland’s DRG systems on the use of services

within or outside of hospitals are not yet available. These programs are themselves so new or

of such limited scope that they cannot offer empirical evidence on which to draw conclusions.

Evidence does exist on the effect of other types

of prospective hospital payment on the use of

medical technologies. As part of a comprehensive

study of nine State-legislated hospital ratesetting

systems, Worthington and Piro (102) found that

programs that pay hospitals on the basis of a perdiem rate all produced an increase in hospitals’

average lengths of stay (LOS) and occupancy

rates. This result would be expected from a perdiem ratesetting system in which the longer patients stay, the more revenue the hospital receives.

However, a per-diem ratesetting program should

also encourage increases in rates of inpatient admission, but no such admission effects were

found. These findings suggest that manipulating

admission rates may be more difficult than increasing the length of hospital stay for those

already admitted. Taken as a whole, however, the

results do suggest that decisionmakers in hospitals

respond in predictable ways to financial incentives

for the use of hospital services. Consequently, in

the absence of empirical evidence on the effects

of DRG payment on medical technology use, an

assessment of the direction and strength of its

financial incentives is reasonable.

General Incentives of DRG Payment

To understand how DRG payment affects incentives to use particular medical technologies,

it is helpful first to examine incentives affecting

the use of hospital and other health services in

general. These general incentives ultimately translate into specific demands for medical technologies.

DRG payment creates two fundamental incentives: to reduce the cost to the hospital of each

inpatient hospital stay; and to increase the number

of inpatient admissions.

Incentives To Reduce Cost Per Case

The incentive to reduce cost per case is the

motivation for per-case payment in the first place.

Per-case payment is predicated on the belief that

hospitals have many opportunities to save money

by operating more efficiently and offering a more

cost-effective mix of services, Per-case payment

rewards hospitals that take advantage of these opportunities.

Reductions in cost per admission can be

achieved by reducing LOS, the number or mix of

services provided during the stay, or the prices

paid for inputs into the production of hospital

services. Reductions in LOS are likely to have the

greatest immediate effects on per-case costs, although such savings would be lower for hospitals

already operating at low occupancy rates. A reduction in occupancy rate does not result in a proportional reduction in operating costs, because

many of these (e.g., utilities, housekeeping, administration) may be largely fixed. Thus, in hospitals with low occupancy, the incentive to reduce

LOS, though present, will be less than in hospitals

with a high daily census and a backlog of potential admissions. Recent studies have demonstrated

that the well-known regional differences in average LOS in the United States persist even when

diagnosis and severity of illness are taken into account (37,89). Thus, there may be substantial

room for reduction of LOS in some areas of the

country.
26

Shorter LOS could have positive or negative

effects on patients’ health. * On the one hand, hospitalization itself carries the risk of iatrogenic illness; shorter lengths of stay reduce this risk. Psychological factors associated with hospitalization

may also be important in adversely affecting outcomes. On the other hand, too early discharge

could place patients at risk of inadequate care and

threaten recovery. For example, patients with

serious infections have often remained hospitalized simply to receive long-term intravenous antibiotic therapy. There is suggestive evidence that

hospital-sponsored home antibiotic therapy programs can save total hospital costs and be safe if

accompanied by adequate patient training and

monitoring (43,82). However, the potential for

inadequate education and followup by hospital

personnel exists. While financial incentives under

DRG payment would encourage home intravenous antibiotic therapy, they would also discourage the expenditure of resources to educate and

monitor patients adequately.

The incentives inherent in DRG payment regarding the use of particular ancillary services are

complex. The cost of ancillary services whose use

would, on the average, shorten hospital LOS,

would be weighed against the savings from reductions in LOS. The effect on any particular ancillary service would depend on the nature of these

cost tradeoffs. For example, hospitals might provide more high-cost antibiotics prophylactically

if these were shown to substantially reduce the

average LOS through reductions in hospital-acquired infection rates. Or, as another example,

liaison psychiatric services, which appear to

shorten LOS of postoperative elderly patients (47),

might be provided more frequently under DRG

payment than under cost-based reimbursement.

A probable byproduct of DRG payment will be

an increase in the demand for and supply of information on such cost tradeoffs. Nevertheless,

if the consensus is correct that ancillary services,

particularly diagnostic tests, have been provided

in the past without adequate consideration for

their impact on total hospital costs (1,26,52,56)

then the net effect of per-case payment would be

*This topic is the subject of OTA’s Health Technology Case Study

#24, “Variations in Hospital Length of Stay: Their Relationship to

Health Outcomes. ”

to reduce the intensity or amount of these services

per stay.

The incentive to reduce the price of technologies

such as drugs and medical supplies is obvious. In

the past 10 years, hospitals have increasingly embraced membership in group purchasing plans and

generic substitution programs. For example, hospital membership in pharmacy purchasing groups

grew from 40 to 88 percent between 1975 and 1981

(16,83). Generic substitution–the automatic substitution of a less costly but chemically equivalent

generic drug for a prescribed brand-name drug—

has become commonplace in U.S. hospitals: 96

percent of hospitals responding to a national

survey in 1981 reported having such programs

(16). The pressure to find new ways to save on

the purchase of drugs and supplies should continue. A logical outcome of this trend is a decline

in product variation as hospitals and their purchasing groups seek further price reductions and

strengthen the competitive position of products

with high sales volumes.

Incentives To Increase Admissions

DRG payment encourages hospitals to increase

admissions selectively. Whereas cost- and chargebased reimbursement gave the hospital an incentive to keep occupancy rates high by increasing

either admissions or LOS, only admissions produce or increase revenue under DRG payment.

Every new admission generates new revenue (in

the amount of the DRG price) and new costs.

Serving patients in some DRGs will be more profitable than in others, because those DRGs will

have higher ratios of price to cost. The hospital

would naturally want to encourage the more profitable admissions. If the average level of payment

is high enough that all DRGs are profitable, then

the hospital has an incentive to increase admissions in general, but the most profitable admissions should still be sought more vigorously.

A variety of mechanisms is available to increase

admissions selectively, including recruitment of

physicians in key specialties, adoption of services

useful in certain DRGs, and marketing campaigns

targeted to preferred patients or their physicians.

These strategies may be called “competitive” in

that they are designed to draw patients from other

hospitals.
27

As competition for admissions increases under

per-case payment, some specialization in service

delivery can be expected (5). Since the per-unit

costs of major services often decline as service

volumes increase, hospitals with high service volumes in specific DRGs will find them more profitable, and those with low volumes less. When

a hospital finds that a service is unprofitable and

when the prospects for more efficient operation

or increases in volume are dim, it may abandon

the service. For example, a hospital in New Jersey

recently closed its hyperbaric chamber because it

was found to be unprofitable under DRG payment. Those in need of hyperbaric services (primarily divers) will be referred to a hospital in New

York City (64). However, competition for admissions can also drive hospitals to maintain unprofitable services if their existence is important to the

maintenance of the hospital’s position with physicians or patients.

Specialization in service delivery may have desirable effects on quality as well as cost, since for

many services there is a positive relationship between quality and volume (5). However, these

gains in quality and cost could be partially or

totally offset by reductions in patient access to

services. Since it is difficult to predict the kinds

of services that will be subject to specialization

under DRG payment, the desirability of future

patterns of service availability is unknown. As

DRG payment is implemented nationwide, patterns of service specialization among hospitals

should be carefully monitored.

Hospitals may turn to noncompetitive strategies

to increase admissions and lower per-case costs.

For example, physicians or staff might be encouraged directly or indirectly to hospitalize

marginally ill patients and to discharge and readmit patients at a later date for deferrable procedures that might otherwise be performed as part

of a single stay. This “revolving door” incentive

is a new phenomenon, unique to per-case payment. For example, a patient under treatment for

pneumonia might be found during the course of

the hospital stay to have a urological condition

requiring a deferrable therapeutic procedure.

Rather than initiate therapy during the first stay,

the physician might discharge the patient for readmission at a later date. This strategy is both

easy for physicians to implement and difficult for

third-party payers to control.

The incentive to increase admissions selectively has its counterpart in an incentive to avoid admitting unprofitable patients. Patient selection

strategies could conceivably be used to exclude

patients in unprofitable DRGs or unprofitable patients within a DRG. But there are important restrictions on the potential for direct manipulation

of case loads. Although hospitals may be able to

avoid admissions in some unprofitable DRGs by

not offering the necessary facilities or services, for

many patients the DRG is unknown at the time

of admission. Moreover, to discriminate against

the less profitable (i.e., more costly) patients

within a specific DRG, two conditions would have

to hold. First, the physician would have to be able

to predict with reasonable accuracy the relative

costliness of different patients within the same

DRG at the time of admission; and second, the

physician would have to be induced not to admit his or her more costly (and presumably sicker)

patients. These conditions are simply unlikely to

be met frequently.

Of course, a hospital could simply choose not

to participate in the DRG payment system by refusing all such patients. While this response is infeasible in an all-payer system, it might be attractive to some hospitals in a Medicare-only system.

Total nonparticipation would be financially attractive to a hospital if the average DRG payment

level were to lie below the additional (marginal)

costs of serving patients in any DRG, but it is

unlikely in the foreseeable future that the payment

level, which is calculated on the basis of fully

allocated average operating costs, will be less than

marginal costs for all DRGs in most hospitals. A

hospital could decide that the losses in some DRGs

outweigh the surplus available in others, but with

Medicare accounting for about 30 percent of hospitals’ revenues, this situation would also be rare.

Thus, the probability that many hospitals will

refuse to serve any DRG patients at all is low.

Constraints on Financial Incentives

Whether the financial incentives to reduce LOS

and the cost per case and to increase admissions

will lead hospitals to overadmit patients and
28

underprovide services is an empirical question.

The potential is real, but the possibility of adverse

effects on access and quality of care under DRG

payment is moderated by several built-in constraints whose strength is unknown at present.

First, the physician, not the hospital administrator, makes the decision to admit and discharge patients and order procedures. The physician’s income often is dependent on hospitalization, as in the case of surgical admissions. Physician visits to hospitalized patients may be more

lucrative relative to their time requirements than

are office visits (28). Perhaps most important, the

physician’s professional and ethical standards protect the patient from the withholding of needed

care. And, in a DRG payment system not covering all payers, the physician would still be likely

to engage in a uniform style of practice for all

patients.

It is often asserted that defensive medicine—

practices that are employed directly in response

to fears of malpractice lawsuits—would limit the

willingness of physicians and hospitals to engage

in practices that threaten the outcome of care (91).

The strength of the influence of malpractice on

physician behavior is arguable. There are no direct

objective data on how much defensive medicine

is practiced today or how much it costs. Physicians have claimed in some surveys that they perform more tests than they otherwise would (67,

85); in other surveys that they perform fewer tests

(29) due to malpractice lawsuits.

Hospitals themselves are subject to malpractice

suits, which have risen dramatically since the first

Table 1 .—Malpractice

lawsuit was decided against a hospital in 1961

(60,66). Approximately 75 to 80 percent of all

malpractice claims arise from medical care provided in hospitals (60). An Institute of Medicine

(IOM) study found in 1978 that a relatively small

number of institutions had formal programs for

managing such risks (60), but their frequency and

importance is growing (71). Even if objective estimates were available on the extent of defensive

medicine and risk management under present conditions, it would be dangerous to generalize these

results to a DRG payment system, where the financial incentives conflict with the incentives to

practice defensive medicine. Thus, at this time,

one can only conjecture about the potential

strength of defensive medicine.

To the extent that it does function as a deterrent to the underprovision of services, defensive

medicine may be less effective in protecting the

elderly or disabled. There is a general consensus

among experts that these patients are less “litigious, ” in that they are less likely to sue physicians if they are harmed. A commonly cited reason for this is the fact that malpractice lawyers

work on a contingency basis and rarely accept

cases in which the claimant would not receive a

large compensation award. Most elderly and disabled persons would be awarded less money than

younger patients, because part of the compensation award is based on lost wages (60), and the

elderly and disabled generally have lower income

potential. Table 1 shows that the elderly received

less money in closed malpractice cases in 1978 regardless of the severity of the injury suffered (61).

Claims Paid, 1978

Age of injured person
29

The incentive to increase admissions could conceivably be limited by the reluctance of patients

to be hospitalized for marginal indications or be

subjected to the “revolving door. ” The Medicare

beneficiary is currently responsible for a deductible of $304 upon hospitalization (18). It might be

argued that this financial disincentive to hospitalization would moderate the incentive to admit Medicare patients. Yet the deductible is not

likely to act as an effective deterrent to hospital

admission. First, approximately 65 percent of

Medicare beneficiaries have private supplementary insurance (“Medigap” coverage) which often

pays for part or all of the deductible. Second, the

elderly patient is unlikely to question “doctor’s

orders” in a decision involving hospitalization.

Third, the deductible will not adequately discourage readmission because the beneficiary is liable

for the deductible upon a readmission only if it

occurs more than 60 days later than the previous

episode of hospitalization.1

In summary, natural limits do exist on the inclination or ability of hospitals to overadmit,

discharge too rapidly, and underprovide services.

Yet, the magnitude of these constraints is unknown, and the protection of the elderly in particular may be relatively weak. Programs to monitor hospital performance may be necessary to

identify behavior that is ultimately costly or harmful resulting from the economic incentives inherent

in DRG payment.

Key Features of DRG Payment Systems

That Affect Hospital Incentives

The effects of any DRG payment system on decisions in hospitals and, hence, on medical technology use are influenced by five critical elements

of program design:

1. the proportion of the hospital’s case load

covered by DRG payment,

Z. the treatment of costs as pass-throughs,

3. the methods of DRG rate construction,

4. the methods of updating DRG rates, and

5. the level of risk and reward built into the

payment system.

The Proportion of the Hospital’s Case Load

Covered by DRG Payment

Every case excluded or exempted from the DRG

payment system will weaken its incentives. Exclusion of major payer categories from the system,

for example, will limit its leverage on hospitals. *

Under the new Medicare law, about 32 percent

of the revenues of non-Federal short-term hospitals will be subject to DRG payment (27), except in the few States with DRG payment systems

covering other payers as well. DRG payment

could become even less important if States develop alternative prospective payment systems, as

the law allows them to do. Yet, a payment system

with control over about one-third of hospitals’

revenues is not inconsequential. To the extent that

it does force changes in hospitals’ behavior and

does not merely shift costs to other payers, Medicare’s DRG payment system will influence the use

of medical technologies by all kinds of patients.

Many changes in physicians’ practice patterns or

hospitals’ purchasing decisions will probably be

applied broadly across all patients. And, if a

hospital decides to eliminate a service to discourage unprofitable DRGs, the service would be unavailable to all patients.

The leverage of a DRG payment system can be

reduced by exclusions built into the system itself.

For example, recognition of “outliers,” cases with

unusually high or low resource use, may reduce

the strength of DRG incentives. Approximately

20 percent of all cases in New Jersey’s all-payer

system, comprising 35 percent of hospitals’ costs,

fall into the system’s outlier category (15). And

the State’s criteria for declaring a case an outlier

have become more generous over time (97).

The treatment of outliers complicates hospitals’

incentives. Exclusion of low-cost cases from DRG

payment is an important strategy for discouraging potentially unnecessary hospitalizations, particularly for surgery that could be performed on

an outpatient basis. Otherwise, in DRGs containing both simple and complicated procedures, the

hospital will have an incentive to admit the simple surgeries as inpatients. At the high end of the

‘Social Security Act, sees, 1861(a) and 1813(a).

*Failure to cover all payers has important implications for equity among payers, but that topic is beyond the scope of this memorandum.
30

cost distribution, there may be incentives to increase LOS in order to qualify a patient as an

outlier. The strength of these incentives depends

on the location of the cutoff points-whether they

involve only a few or many patients—and the

payment method for outliers. New Jersey’s DRG

system, which pays both high and low outliers

on the basis of controlled charges, provides an

incentive to manipulate LOS in high-cost patients.

The Medicare system, which as currently legislated has no low-cost outliers, may encourage

potentially unnecessary admissions.

DRG payment systems could conceivably exclude certain types of cases or DRG categories on

the rationale that these categories or services need

to be treated in a special way. Patients treated in

burn care centers or psychiatric services, for example, could be excluded from DRG payment on

the grounds that these patients present unique

medical and social problems. However justified

such exclusions are, they would nevertheless

weaken the impact of DRG payment and, depending on how they are paid, could encourage admissions in these categories.

Treatment of Costs as Pass-Throughs

“Pass-throughs” are elements of hospital cost

that are not controlled by the per-case payment

system. Cost-based reimbursement, as a whole,

can be interpreted as a payment method in which

all cost categories are passed through. Per-case

payment systems that directly link a hospital’s percase rates in a given year to its own previous

year’s costs are only minor departures from passthrough payment. Effective removal of passthroughs requires a break in the link between the

hospital’s own current costs and its future rate of

payment.

Individual cost categories are treated as passthroughs to varying degrees under different DRG

payment systems. During its first 3 years of operation, Medicare will treat capital costs (depreciation and interest payments) as complete passthroughs: the hospital will be reimbursed for

whatever capital costs are incurred. New Jersey

has established a capital facilities allowance for

buildings and fixed plant and equipment that is

designed to meet the hospital’s need for cash to

pay off existing debt and to fund the downpayment for replacement or additions approved by

the health planning agency. For major movable

equipment, such as beds and laboratory instruments, the State allows a depreciation rate that

is adjusted for inflation in replacement costs.

Thus, except to the extent that the State’s health

planning agency limits bed expansion or the acquisition of equipment, the New Jersey system

passes through capital costs.

Like New Jersey, Maryland has specific capital

allowances, but in the case of major movable

equipment, the hospital’s asset value is calculated

in a base year and adjusted in subsequent years

with inflation factors. The allowance for movable

equipment is unaffected by the hospital’s subsequent capital expenditure decisions except for

special cases in which the ratesetting commission

may make exceptions (42).

Other common pass-through categories under

per-case payment are the costs of medical education (i.e., stipends of interns and residents, and

teaching faculty costs), malpractice premiums,

and utility expenses. Treatment of one or more

categories of cost as pass-throughs under DRG

payment renders these inputs to patient care free

to the hospital at the same time that the effective

price of all other inputs has been increased because

of their inclusion in a per-case prospective system

(46). In the absence of other effective controls,

this change in the relative price of inputs gives

hospitals an incentive to expand pass-through

inputs.

The Medicare law also excludes from per-case

payment an important product of hospitals: outpatient services. These services will continue to

be reimbursed on a retrospective cost basis (and

the patient is responsible for 20 percent coinsurance). Consequently, hospitals have a strong

incentive to increase outpatient service volumes

as a way of shifting fixed and overhead costs from

inpatient to outpatient categories. Ancillary departments, such as radiology, clinical laboratory,

physical therapy, and occupational therapy, will

be encouraged to compete for business with independent providers of these services. New hospital-based home health services, which also

escape the DRG system for now, are strongly en-
31

couraged both for their contribution to profitability and their prospects for reducing inpatient LOS

(7,39,51,54).

Methods of DRG Rate Construction

The methods used to construct the relative

weights or prices of each DRG can affect hospitals’

incentives. The important issue is how the ratio

of cost to price varies among the patients served

by the hospital. This ratio of cost to price should

be constant across all patients; if not, incentives

will exist to manipulate case load (i.e., to encourage low-cost or discourage high-cost admissions). Though it is virtually infeasible to devise

a per-case payment system that does not have

some variation in the ratio of cost to price, the

method of rate construction determines how great

the variation is and which patients are paid according to relatively high and low rates.

There are two sources of variation across patients in the ratio of cost to price: within-DRG

variation and across-DRG variation. Within-DRG

variation stems from the inherent heterogeneity

of patients’ resource needs in a particular DRG.

Any per-case payment method that establishes a

single price (or weight) for all patients in a casemix category will result in some within-group differences in the ratio of cost to price. This cannot

be avoided, but the extent of the problem may

depend on the case-mix classification system. The

relative performance of DRGs and other case-mix

classification systems with respect to within-group

variation has been discussed in chapter 2. The

method of DRG price or weight construction does

not alter this kind of variation. Policies regarding

the handling of “outlier” cases, discussed earlier,

are more germane to this issue.

Conversely, across-DRG variation is determined largely by the method of construction of

relative DRG prices or weights. In theory, relative

DRG prices should reflect the relative costs of efficient and clinically optimal patient care across

DRGs (5,69). This would encourage hospitals to

specialize in those services that they can provide

efficiently and to search for ways to further reduce

costs. In practice: however, efficient care is difficult to identify and even harder to measure, and

at present, all DRG rates are constructed from empirical estimates of DRG costs. The DRG casemix index under TEFRA and the DRG prices of

the new Medicare system are estimated from the

average operating costs in a national sample of

Medicare hospital claims. Maryland uses the hospital’s own average revenue per case in a fixed

base year to develop relative DRG weights specific

to the hospital. New Jersey combines average

statewide costs with the hospital’s own average

cost of treating each DRG to arrive at a hospitalspecific price. *

None of these methods assures that the relative

weights reflect efficient relative costs. Suppose

that patients in one DRG are treated relatively efficiently and uniformly throughout all hospitals

while those in another are subject to a great deal

of inefficient care. By Medicare’s average cost

calculation, the inefficient DRG would be assigned

a higher rate than it should be relative to the efficiently produced DRG. It is important to recognize both the reality of this problem and the opportunity for mitigating it over time. As hospitals

respond to the incentives of DRG payment, increases in their efficiency can be expected. Over

time, as DRG relative prices are recalibrated

using hospitals’ updated cost data, the disparities

in cost-to-price ratios should diminish. Without

recalibration, whatever disparities in cost-to-price

ratios existed at the beginning will remain.

The method used to allocate hospitals’ costs to

particular DRGs presents a more enduring problem for relative prices. The Medicare method relies

on hospitals’ charges to reflect average costs,

where the DRG weight construction method is

based on the hospitals’ charges for services. These

charges are deflated by hospital-and-departmentspecific cost-to-charge ratios calculated from the

Medicare cost reports. While this deflator reduces

some of the distortions created by interdepartmental subsidies, there remains a residual cross-subsidy of procedures and cases within departments.

Cohen has claimed that this method compresses

the relative weight scale by underestimating the

true cost of complex cases and overestimating the

true cost of simple cases (9). Routine care is

charged at a flat rate per day, regardless of case

*See app. C for details.

98-823 0 - 83 - 6
-..——

32

severity, and some ancillary services, such as the

use of operating rooms, are billed on the basis of

time, not on the basis of resources needed to conduct more complex procedures. Though the extent of the bias is unknown, it implies that the

charge-based cost weights are likely to penalize

the more complex DRGs.

New Jersey intends to improve on this method

by directly observing the use of nursing time by

patients in various DRGs in selected hospitals (22).

Direct observation of resource use is costly and

has some methodological problems (94), but the

results of these studies should provide valuable

information on the magnitude of this problem.

Methods of Updating Relative DRG Prices

As the cost of efficient care in each DRG

changes over time, so, too, should the relative

DRG price. If it were reasonable to expect that

costs would increase or decrease uniformly across

all DRGs, then the only issue would be whether

the average payment level is sufficiently high to

cover the costs of efficient operation. But, uniform

cost increases are highly unlikely: From year to

year, some DRGs will experience cost-saving technological innovations; others will experience costraising ones. The relative prices of inputs (personnel, supplies, energy, etc. ) also change, with

consequences for relative DRG costs. In the absence of any changes in DRG prices, the ratio of

DRG price to efficient cost would show increasing divergence across DRG categories. As these

ratios diverge, certain DRGs will become more

profitable, others less so, and hospitals will have

greater incentives to engage in patient selection

strategies. Therefore, the mechanisms employed

to update, or recalibrate, relative DRG prices influence the Iongrun incentives of the system. Recalibration must depend on information if it is to

avoid being completely arbitrary; thus, these updating mechanisms must include specification of

the data and information systems available to support them.

There are three basic approaches to recalibrating relative DRG payment rates: empirical cost

estimation techniques, central policy decision adjustments, and provider appeals.

All DRG pricing systems have originally been

established with empirical estimates of the relative

cost of various DRGs. Periodic reestimation of

relative costs based on updated data merely repeats the process at reasonable intervals. New

Jersey employs a ratesetting method that, at least

in theory, annually reestimates relative DRG

costs. The Medicare law calls for changes in DRG

relative rates at least every 4 years, but the

methods to be used to recalibrate DRGs are unspecified. The law establishes an independent

panel of experts—the Prospective Payment Assessment Commission—to recommend changes in

relative prices to the Secretary of the Department

of Health and Human Services (DHHS) who will

authorize the changes. Presumably, the methods

used by the Commission will include reestimation

of DRG costs.

Central policy adjustments in DRG rates occur

when those in charge of ratesetting determine that

certain changes in relative prices are justified to

take account of new technology or changes in clinical practice. The Prospective Payment Assessment Commission is specifically charged with

making recommendations about such adjustments. The Commission, and DHHS, will therefore require an information base that exceeds that

needed for empirical cost estimation. Data on the

cost and clinical effectiveness of new technology

will also have to be collected and synthesized.

Provider-initiated appeals or petitions for

changes in relative rates represent the third avenue

for relative DRG rate adjustments. Like policy adjustments, provider appeals can be used to adjust

rates for changing technology, but this approach

allows more flexibility in responding to the needs

of particular hospitals. The burden of producing

data to justify changes in DRG prices rests to a

greater degree on the appealing institution. New

Jersey has instituted a DRG appeals mechanism

to specifically account for changing technology.

The new Medicare system prohibits appeals of

rates per se, but it does permit hospitals to appeal for additional payments for “outlier” cases

whose estimated per-case costs are extraordinarily

high. The effective price of DRGs containing new

technologies (e.g., organ transplants), may be

altered through this process.
33

Risk and Reward

The degree to which the hospital is able to generate surplus revenues and appropriate them to

its own use will influence the strength of incentives to provide technologies more efficiently and

can also affect the hospital’s access to sources of

capital. The ability to generate surplus depends

on both the average level of payment and the rules

governing hospitals’ ability to keep surplus and

liability for deficits. One program may emphasize

the risk side, putting hospitals entirely at risk for

losses without allowing them to keep surplus,

while another may offer both substantial risks and

rewards.

Traditional cost-based reimbursement is essentially a “no risk/no reward” system. DRG payment systems vary widely in this regard. Hospitals

in New Jersey and Maryland can keep any surpluses attained from cutting costs per case and

must bear the full burden of cost increases. However, both systems limit the revenue gains or losses

attributable to changes in admissions to their estimated marginal costs or savings. In New Jersey,

the potential for continued surplus-building in

subsequent years is reduced somewhat by periodic

recalibration of DRG prices reflecting changes in

costs. In Maryland, however, the benefits of cost

reductions (and the penalties for cost increases)

are maintained in subsequent years, because DRG

weights are not updated. Under the temporary

provisions of TEFRA, the hospital reaps little

reward for keeping its per-case costs low (a maximum of 5 percent of its per-case rate) but bears

the full penalty of exceeding the per-case limit.

Under the new Medicare law, the hospital bears

the full burden of a loss and reaps the full rewards

of a surplus, regardless of their source. The hospital keeps the full portion of any surplus due to

increases in admissions. Thus, under the Medicare

law, hospitals will have strong incentives both to

reduce costs and increase profitable admissions.

Technology-Specific Effects of

DRG Payment

How do the general incentives of DRG payment

translate into specific effects on the use of medical

technologies? The previous sections demonstrate

both the complexity of the underlying incentives

and the impact of program design on their direction and strength. DRG payment will not have

a uniform effect on medical technologies and in

some instances technologies will be subject to conflicting incentives. From the discussions above it

can

●

●

●

●

b; concluded that:

Overall, the number and intensity of ancillary procedures provided to inpatients can

be expected to decrease, but the use of procedures that can be shown to lower the cost

per case will increase.

The settings of technology use are likely to

be influenced by DRG payment, but the incentives work in conflicting directions and

are sensitive to the key features of program

design. In the absence of an outlier policy for

low-cost patients, DRG payment encourages

inpatient admissions for simple procedures.

On the other hand, the exclusion of outpatient costs gives hospitals an incentive to offer outpatient procedures. It remains to be

seen which incentive will dominate for which

procedures. DRG payment will encourage

the movement of technologies, particularly

those for posthospital care, into the home

and other nonhospital sites of care.

DRG payment is likely to influence the specialization of services, but the magnitude and

direction of these effects is unknown. The incentives to reduce costs encourage concentration of capital-intensive technologies in

fewer institutions. Conversely, the increasing competition among hospitals for physicians and patients will create incentives for

the widespread acquisition of some technologies.

A change in technology product mix is likely to result from downward pressure on the

price and quantity of supplies and, if capital

is included in the DRG rate, capital equipment. Greater product standardization can

be expected as more expensive models and

procedures are eased out of the market

through competition.

Implications for Utilization Review and

Quality Assurance

Per-case payment introduces much needed incentives for cost control in hospitals, but it also
—

34

has potential negative implications for quality of

care, access to care, and systemwide costs. The

incentives in DRG payment for hospitals to potentially manipulate case load, overadmit patients,

discharge patients too early, and underprovide ancillary technologies argue for safeguards in the

form of quality and utilization review.

Review functions under Medicare have always

had two partially conflicting objectives: quality

assurance and cost containment. Under DRG payment, these dual objectives remain. Utilization

review will be necessary both to avoid costly increases in admissions and readmissions, and quality audits will be necessary to protect inpatients

from the underprovision of technologies and from

too early discharge.

Both types of review overlap because of the

tradeoff between quality and cost that becomes

more explicit with per-case payment. For example, physicians may become more selective in their

ordering of diagnostic tests. Some tests may add

to the cost per case but give better patient outcomes. Other tests can be avoided with little consequence for outcomes. Review processes that recognize the balance between cost and quality become critical under DRG payment.

Historically, the responsibility for quality assurance and utilization review has been shared by

hospitals, intermediaries, and Professional Standards Review Organizations (PSROs). Hospitals

have been required to have programs of quality

assurance and utilization review as conditions of

participation in the Medicare program2 as well as

for accreditation by the Joint Commission on Accreditation of Hospitals. Before the PSRO law was

implemented, Medicare fiscal intermediaries were

required to perform independent utilization reviews and thereafter remained the reviewers of

last resort in areas without active PSROs (68).

Congress instituted the PSRO program in the 1972

Social Security Act amendments (Public Law 92-

’42 CFR 405J.

603), establishing independent physician review

organizations with the dual objectives of quality

assurance and cost containment.

In 1982, Congress replaced the PSRO program

with utilization and quality control peer review

organizations (PROS) (Public Law 97-248). PROS

will be physician organizations whose performance will be evaluated by the degree to which they

meet objectives for quality assurance and cost

containment specified in 2-year contracts with

DHHS. Under the new Medicare DRG payment

system, hospitals must enter into agreements with

PROS for review of the quality of care and the

appropriateness of admissions and readmissions.

The integration of cost-containment and quality-assurance objectives in a single physician-run

independent review organization such as a PRO

is both necessary and troublesome. Because the

inherent tradeoff between cost and quality is

bound up in every review decision, it would be

impossible to separate the two. Yet, it is difficult

for those responsible for conducting review and

for those funding such efforts to maintain a balance between the two objectives. The history of

PSROs is instructive. Although the original intent of Congress was that PSROs were to both

contain costs and assure quality, Federal evaluations of the program focused largely on the costcontainment objectives (86,87,88,91). The difficulty of specifying and measuring criteria for quality of care added to the relative obscurity of this

objective. The critical question to Federal policymakers was whether PSROs were cost saving to

the Medicare program—i. e., did they reduce inpatient hospital utilization sufficiently to cover

the program costs? On the other hand, at the local

level, PSROs emphasized quality assurance (25,

59).

Whether PROS can strike an appropriate balance between the cost containment and quality

assurance objectives remains to be seen. It is important that at the Federal level the real need for

quality assurance presented by DRG payment be

recognized.
Chapter 4

Effects of DRG Payment on

Technological Change

in Medicine
Chapter 4

Effects of DRG Payment on

Technological Change in Medicine

INTRODUCTION

Perhaps even more important than how Diagnosis Related Group (DRG) payment affects the

use of presently available medical technologies is

how DRG payment will affect technological

change in medicine—the adoption of new technologies and discarding of old ones. As a society, we value technological progress, the “introduction to practice of new and more useful ways of

serving human purposes” (73). Technological

change should act as a filtering process, continually winnowing out the less useful in favor of more

useful technologies. To what extent can DRG payment be expected to improve or hinder this process? Since DRG payment will influence hospitals’

incentives to adopt new medical technologies, it

may therefore ultimately alter the rate and direction of technological change in medicine. This

chapter will examine the implications of DRG

payment for technological change.

Conversely, technological change occurring due

to other forces will inevitably require adjustments

in a DRG payment system itself, if the system is

to continue to function effectively. These adjustments range from changes in the relative rates

of payment across DRGs to changes in the definitions of DRGs themselves. Thus, a DRG payment

system must encompass procedures for timely adjustment to new knowledge and technological

conditions as they arise. This chapter will explore

the reasons for this conclusion and will also examine the kinds of information and procedures

that will be needed to monitor the performance

of a DRG payment system over time.

EVIDENCE OF EFFECT ON TECHNOLOGICAL CHANGE

Empirical evidence on the effect of DRG payment on the adoption of new technologies is unavailable, but studies of other kinds of prospective payment systems suggest that hospitals can

and do respond to changes in financial incentives

in their decisions regarding the adoption of new

technologies. Three studies of the impact of hospital prospective payment programs on the adoption of new services and technologies provide

evidence of the response of hospitals to changes

in financial incentives.

Joskow (41) analyzed the effect of ratesetting

programs on the availability of computed tomography (CT) scanning in hospitals. The number of

CT scanners in a State in 1980 was found to be

negatively related to the number of years that

ratesetting had been in effect in the State. Hospital

ratesetting also led to a shift in the location of CT

scanners to physicians’ offices.

Cromwell and Kanak (11) recently analyzed the

impact of 15 State ratesetting programs on the

availability of 13 different services in the hospital

between 1969 and 1978. Table 2 summarizes the

results. New York had the most consistently negative effects on the availability of all types of services. New Jersey’s DRG ratesetting program also

appeared to generally reduce the availability of

complex services, with the exception of electroencephalogram (EEG) services. Other States’

programs showed no consistent impact on service

adoption. It is interesting to note that the service

upon which ratesetting had the highest and most

widespread negative effect is social work, a laborintensive, not equipment-intensive, hospital

service.

In still another study of hospital payment and

technology diffusion, Wagner, et al. (95), investigated the impact of prospective payment in

37
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39

three States (New York, Maryland, * and Indiana)

on the adoption of five new pieces of capital

equipment: electronic fetal monitoring, gastroendoscopy, volumetric infusion pumps, automated

bacterial susceptibility testing, and computerized

energy management systems. The first three technologies probably raise the daily cost of care,

although their effect on the average cost per case

is unknown. The latter two are investments in

cost-saving equipment. The New York ratesetting

system was found consistently to lead to adoption of fewer units of the cost-raising technologies

and to increase the probability of large hospitals’

*The test of the Maryland system did not discriminate between

hospitals paid by the case and those on the State’s conventional ratesetting system.

adopting the cost-saving equipment. However,

the prospective payment programs in Maryland

and Indiana showed no such consistent effects on

hospitals’ adoption behavior.

Together these studies imply that prospective

payment does affect the adoption of new technology in predictable ways, but that much depends

on the strength and design of the program. New

York’s system, the oldest and most restrictive

ratesetting program, has clearly altered the extent

of availability of new technology. Since its inception in 1970, New York’s system has put a heavy

emphasis on financial risk to the hospital while

at the same time offering little financial reward.

Other systems may be too new, too small, or too

weak to show longrun consequences.

CLASSIFICATION OF NEW HOSPITAL TECHNOLOGIES

DRG payment incentives do not affect the introduction of new technologies uniformly. This

is partly because of variation in the cost implications of medical technologies. But it is also because

medical technologies may be adopted (or not

adopted) according to their clinical benefits (or

risks). These clinical implications naturally vary

widely. However, for the purpose of analysis, it

is necessary to classify new hospital technologies

according to criteria that highlight the likely effect of DRG payment on their diffusion into the

practice of medicine. The most basic distinction

is due to the per-case nature of DRG payment.

Thus, hospital medical technologies can be viewed

as those whose adoption decreases the hospital’s

total cost per case—cost-saving technology-and

those whose adoption increases the hospital’s total

cost per case—cost-raising technology.

Cost-saving technology would include a new

technology that provides a particular service less

expensively, or one that reduces the required number of services or length of stay (LOS) sufficiently to justify the investment. A cost-saving technology in one hospital may be cost-raising in

another. The expected volume of use is often an

important factor in determining whether a technology will be cost-saving. For example, capital

equipment that replaces other procedures may be

cost-saving in a large hospital but cost-raising in

a small one. Had CT head scanning been introduced in an era of DRG payment, it may well

have been justified on the basis of per-case cost

reductions in hospitals with large neurology or

trauma services (40,84). Yet its introduction into

hospitals with lower neurological case loads

would probably have required justification on

other grounds, such as improvements in patient

access or outcome.

Many new procedures and therapies introduced

in the past have raised the cost of hospital stays,

even in high-volume institutions. Presumably,

these cost-raising technologies improve patients’

health outcomes or reduce patients’ medical care

expenditures outside of the hospital. Of course,

some new cost-raising technologies have neither

improved patient outcomes nor saved systemwide

medical care costs. Gastric freezing, a technology

of the mid-1960’s is a good example (21). Some

have claimed that intermittent positive pressure

breathing (IPPB), a respiratory therapy technology, provides another example (74).

Hospital technologies can be further classified

according to their effects on capital and operating

costs per case. Table 3 describes four kinds of

technological innovation categorized by their ex-
40

Table 3.—Four Types of Technological Innovations and Effects

on Capital Cost, Operating Cost, and Total Cost

Direction of effect on:

Capital cost Operating cost Total cost

Type of innovation per case per case per case

1. Cost-raising, quality-enhancing

new technology . . . . . . . . . . . . . . . . . . . . . . . . . . + + +

Il. Operating cost-saving innovations

A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . + — +

B. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . + —

Ill. Capital cost-saving innovations

—

A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . - + +

B. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . - +

IV. Service/procedure disadoption . . . . . . . . . . . . . -

—

—

SOURCE: Office of Technology Assessment.

pected effects on capital, operating, and total costs

per case. Type technology, which raises all components of hospital cost per case, represents the

classic cost-raising, quality-enhancing technology

that would be introduced for its presumed patient

benefits. Intensive care monitoring is an example

of such a technology. Type II represents a broad

range of capital investments that would save operating costs during the patient’s stay. Automation technologies fall into this category, but so,

too, do new diagnostic or therapeutic procedures

that reduce LOS or intensity of care. These “operating cost-saving” technologies may or may not

lower the total per-case cost of care, depending

on the relationship between capital and operating

costs. Type III technologies involve new, simpler

approaches to care, in which expendable labor,

materials, or supplies are substituted for capital

equipment. The abandonment of a capital costsaving technology falls into this category, as does

a new labor-intensive test that replaces less effective but automated laboratory procedures. Finally, Type IVrepresents the disadoption of a service or procedure resulting from new knowledge

of its lack of clinical efficacy or safety. The rapid

decline in recent years in the use of IPPB therapy

following publication of evidence on its lack of

efficacy in many clinical settings is a good

example.

GENERAL INCENTIVES FOR TECHNOLOGICAL CHANGE

IN DRG PAYMENT

Although there is no empirical evidence, it is

possible to describe the incentives regarding technological change under DRG payment. Many observers have pointed out that per-case payment

systems, in which future levels of payment are

largely independent of the hospital’s own historical costs, create incentives for hospitals to

adopt cost-saving technologies. Yet because technologies are neither cost-saving nor cost-raising

independent of the context in which they are used,

all hospitals cannot be expected to adopt the same

technologies. The introduction of new capitalintensive cost-saving technologies in a DRG payment environment is likely to speed up the process of specialization as small hospitals become

unable to reap the cost-saving benefits of some

investments. Some technologies that depend on

high volume to be cost-saving could be provided

to smaller hospitals on a contract basis by large

hospitals or independent laboratories. The feasibility of such arrangements would vary with the

specific uses of a technology and the geographical

and competitive environment in which the hospitals operate.

The introduction of new “cost-raising” technologies is discouraged, but not eliminated, under

DRG payment compared to cost-based payment.
—

41

Under cost-based payment, the additional hospital

costs of new technologies are fully covered; hospitals therefore have no financial incentives not

to adopt such innovations. Under DRG payment,

the adoption of new cost-raising technology is not

met with an automatic increase in revenues to

cover the additional cost. New technology will

have to compete with alternative uses of funds,

such as employee wage and benefit increases, additional nursing staff, etc. New technology may

beat an additional disadvantage relative to other

uses of funds because of the relative uncertainty

about its benefits in the early stages of diffusion

(72). The implications are obvious: with limited

resources, hospitals will need to assess new technologies more closely and ration resources more

carefully.

Nevertheless, the introduction of promising

new technologies, even those that are cost-raising

to the hospital, will be attractive to hospitals as

they compete for physician loyalties and, ultimately, the admissions they represent. For example, despite its high capital and operating cost,

nuclear magnetic resonance imaging, a new medical imaging technology still in the investigational

phase, may be highly desirable to hospitals that

seek to protect their admissions base from encroachment by other hospitals. The importance

of this incentive as a constraining force to the

previous incentive is unknown. Thus, though

DRG payment, in general, does not imply that

technological change will aproach a standstill, its

directions are likely to be altered, and the adoption of technologies that are cost-raising to the

hospitals is likely to decline by an unknown quantity.

KEY FEATURES OF PROGRAM DESIGN THAT AFFECT

TECHNOLOGICAL CHANGE

As with its impact on technology use, the impact of DRG payment on technological change

will be influenced by the key features of program

design. In particular, the methods of providing

rewards for cost reductions, treating capital costs,

and updating DRG prices have important implications for technological change.

Risk and Reward

The ability of hospitals to generate and keep

surplus sufficient to fund investments in new technology depends to a large extent on the average

payment level of the payment system as a whole.

A DRG payment system can be either restrictive

or generous. A restrictive payment policy will encourage rapid adoption of cost-saving technologies but will discourage investments in cost-raising

technologies. A more generous payment level may

reduce the pressure for the adoption of cost-saving

technology but will also provide surplus for costraising investments.

Treatment of Capital Costs

The exclusion of capital costs in the DRG payment rates will change hospitals’ incentives to introduce certain kinds of new technologies. * Table

4 describes how the incentives provided by DRG

payment are influenced by the capital payment

mechanism. Capital payment methods do not

alter the direction of such incentives as long as

the effect of a new technology on total cost per

case is in the same direction as its effect on

operating costs. For example, DRG payment provides a disincentive to adopt most cost-raising,

quality-enhancing (Type I) technologies regardless

of the way in which capital is handled. Capital

cost pass-throughs weaken the disincentive not

to adopt this kind of technology, but they do not

*Exclusion of costs from DRG payment rates is referred to as treating those costs as “pass-throughs. ” Under some DRG payment systems, pass-throughs have been subjected to different kinds of controls. (See app. C for a description of New Jersey’s and Maryland’s

systems. )
. — —

42

Table 4.—impact of Technological Innovation on Per-Case Costs

Direction of effect on: Incentives for adoption

Capital cost Operating cost Total cost With capital without capital

Type of innovation per case per case per case in rate in rate

1. Cost-raising, quality-enhancing

new technology . . . . . . . . . . . . . . . . . . . . . . . . . + + + 1 1

Il. Operating cost-saving innovations

A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . + — + 1 !

B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . + — — f f

Ill. Capital cost-saving innovations

A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . – + + 1 1

B. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . – + — I 1

IV. Service/procedure disadoption . . . . . . . . . . . . – — t 1

SOURCE: Office of Technology Assessment.

remove it. New technologies with high capital

costs, but only small increases in operating costs,

would be affected less by DRG payment with a

capital cost pass-through than by DRG payment

with capital built into the rate. Similarly, capital

disinvestments (Type IV), occasioned perhaps by

the introduction of a simple procedure to replace

a capital-intensive one or simply by the abandonment of an ineffective technology, are encouraged

by DRG payment regardless of the way in which

capital costs are treated.

Operating cost-saving (Type II) and capital

cost-saving (Type III) technologies can lead to

situations where the incentives for hospitals to

adopt may actually be reversed by the policy regarding payment of capital. Of particular concern

is the incentive under a pass-through to adopt expensive capital equipment that reduces operating

costs but raises total cost per case. Like regulated

utilities, where allowed profits are computed as

a percentage of the amount of equity capital, hospitals can be expected over time to become too

capital-intensive (3).

The Social Security Amendments of 1983 (Public Law 98-21) provide for capital to be treated

as a pass-through under the new DRG payment

system. Capital technologies will continue to be

paid retrospectively on the basis of reasonable

costs. However, the inadequacy of this method

was recognized by Congress in the new law. The

law requires the Department of Health and Human Services (DHHS) to submit a report to Congress by October 20, 1984 on payment for hospital capital. The intention of the law is that at the

end of 3 years, Congress will have legislated a new

capital policy that will deviate from present costbased reimbursement. Although the issues in

capital formation policy are complex and beyond

the scope of this technical memorandum, any

policy that eliminates the pass-through will have

desirable effects on the consistency of hospital

technology acquisition decisions.

Methods of Updating DRG Prices

and Categories

The longrun viability of any DRG payment system depends on its ability to both adapt to and

encourage appropriate technological change in

medicine. A payment system that is rigid in the

face of medical progress will become unacceptable

to providers, patients, and the public. Consequently, the methods and procedures used to adjust the average payment level, relative DRG

rates, and the DRG categories themselves are critical to the longrun survival of the system.

The primary objective of a DRG price adjustment process is to maintain equality across DRGs

in the ratios of price to the cost of efficient care.

This objective implies that as new cost-saving

technology becomes available for use in certain

DRGs, the relative prices of these DRGs should

be adjusted downward to reflect the new efficiencies. Alternatively, the development of new costraising technologies that improve patient outcomes enough to justify their use should be met

with increases in the prices of relevant DRGs. Adjusting relative prices to reflect technological

change as it occurs provides appropriate incentives for efficiency and specialization in the

delivery of hospital care.
43

But to simply adjust prices in reaction to technological change is insufficient. A second objective of the price adjustment process is to encourage

hospitals to adopt appropriate new technology.

The previous sections concluded that while DRG

payment encourages the adoption of cost-saving

technology, it reduces hospitals’ incentives to

adopt new cost-raising technologies. Depending

on the general restrictiveness of the payment

system, the effects may be to discourage diffusion

and perhaps even the development of costly but

effective new hospital technologies. The DRG

price adjustment process can be used to counteract

this incentive for deserving technologies.

There are two general methods of DRG price

adjustment: conditional adjustments and unconditional adjustments. Conditional DRG rate adjustments are those granted only to hospitals actually adopting a new technology; unconditional

rate adjustments are those that apply to all hospitals (or to a class of hospitals) whether or not

they use the new technology. There are three possible unconditional adjustment processes:

Periodic empirical reestimation of relative

DRG costs. —This method is statistical and

reactive in nature; as technological change

alters the costs of serving specific DRGs, the

calculated rates change. The process is also

gradual, because estimated relative weights

based on average costs across both adopters

and nonadopters of a new technology would

only partially reflect the effects of the new

technology on the efficient cost of accepted

care. Only in the final stages of diffusion

when the new technology is uniformly applied across all hospitals would the estimated

relative costs reflect the technology’s full

effect.

Application of a technology factor. —This

method employs an annual percentage increase in the average rate of payment for all

DRGs to provide funds for the adoption of

cost-raising technology. * For example, the

new Medicare law requires that the annual

increase in the average payment level reflect

technological change as well as general in-

*In theory, a negative technology factor could be used to reduce

funds in anticipation of cost reductions, but it has not been applied

in this manner in any State system.

●

flation, but the amount of increase is not

statutorily specified. Inclusion of such a factor in the annual rate increase gives hospitals

funds to use for any purpose, including technology adoption.

Central policy decision to change relative

DRG rates. —-A designated State or national

agency can make purposeful adjustments in

relative DRG rates to reflect a change in technological conditions. The new Medicare law

specifies that the Prospective Payment Assessment Commission make recommendations regarding periodic adjustments in relative DRG rates to reflect changing technology. This Commission could recommend an

increase in the rate paid for a particular DRG

relative to other DRGs as a means of funding the acquisition of a cost-raising technology. Of course, hospitals treating patients in

the DRG with the increased rate would be

free to use the additional revenue for any

purpose.

Conditional adjustments in DRG prices can be

accomplished by two general mechanisms:

●

●

Provider-initiated appeal. —Individual hospitals contemplating adopting a new technology can request an adjustment of rates in specific DRGs to fund its acquisition. Presumably, hospitals would not request reductions

in rates due to cost-saving innovations. Instead, this approach is potentially useful for

case-by-case exceptions to DRG rates to pay

for new cost-raising technology. The new

Medicare law specifically prohibits hospitals

from appealing DRG rates, but appeals are

allowed for very high cost “outlier” patients

on a case-by-case basis. The State of New

Jersey has a DRG appeals mechanism that is

intended to bring to the surface new technologies in need of extra payment. To qualify

as a DRG appeal, the new technology must

be shown to affect one or more DRGs accounting for at least 10 percent of the hospital’s costs or admissions and to affect one

or more hospitals other than the applicant

(63).

Creation of new DRGs. —New DRGs, differentiated from preexisting ones by the use

of a specific technology, can be created as
— —

44

a way of paying a hospital only if it actually

adopts and uses the technology. The new

DRG would be assigned a price reflecting the

higher or lower resource costs associated with

the use of the new technology. For example,

liver transplantation might become its own

DRG, although the Medicare program does

not yet pay for such procedures. When and

if the new procedure is considered ready for

payment, a DRG price will be assigned that

can be obtained by any hospital performing

the procedure. Those hospitals not performing transplant operations will not receive the

additional revenue, because they will have

no patients in that DRG.

The first objective of DRG rate adjustment—

the maintenance of equal ratios of price to the cost

of efficient care among DRGs—depends largely

on unconditional rate adjustments. Although periodic reestimation of DRG costs represents a gradual adjustment to new technology, it is a reasonable method for adapting to cost-saving technological imovation. Because cost reductions would

be reflected only gradually in relative rates, hospitals adopting such innovations would reap a

positive, albeit declining, return on their investments over a long period. Hospitals lagging

in adoption of cost-saving innovation would gradually be subjected to greater penalties. Thus, the

reestimation process is a gentle adjustment method

which nevertheless embodies strong incentives for

hospital efficiency.

Central policy decisions to force more rapid adjustment of payment rates to new cost-saving

technologies are also feasible and may be useful

from time to time. Yet, it maybe difficult early

in the availability of a new technology to predict

how large a saving to expect and how such savings are likely to vary among hospitals. Since the

incentives to adopt new cost-saving technology

are already strong in DRG payment, radical reductions in specific DRG rates are likely to be

more disruptive than useful.

The second objective of a DRG adjustment

process-encouraging the adoption of appropriate

technologies, particularly those that are costraising to the hospital-may use either conditional

or unconditional adjustment methods. But no one

method alone is completely satisfactory for this

purpose. Periodic reestimation of DRG costs is

not likely to be sufficient to induce hospitals to

adopt desirable cost-increasing technologies, especially very expensive ones. Early adopters

would bear the full extra costs of such a new technology, but the updated weights based on averaging costs across both adopters and nonadopters

would not reflect the full increase in per-case costs.

Hence, the initial stages of the diffusion process

would be underfinanced.

Inclusion of a general technology factor in the

annual average rate of increase does provide funds

for the adoption of new cost-raising technology.

However, this kind of across-the-board increase

rewards innovative and noninnovative hospitals

alike and may even cushion some hospitals against

the need to become more efficient, because they

are free to spend the additional revenue however

they choose. Consequently, a technology factor

is an inefficient approach to encouraging the

adoption of cost-raising technology, since it is

likely to fund less of such innovation than the cost

of the approach to third-party payem.

The same criticism can be made of central policy decisions to increase the relative rates of certain DRGs. The hospital would be free to use the

extra payment from an adjusted DRG price for

any purpose. Thus, centrally mandated rate adjustments would give equal reward to adopters

and nonadopters.

Yet, conditional adjustment mechanisms, such

as creation of new DRGs or individual provider

appeals, have their own shortcomings. Creation

of new DRGs may appear on the surface to be

an ideal approach, but it has serious deficiencies

as the major tool. The prospect of DRG inflation—the gradual increase in the number of categories-has severe implications for management

of data and information. If the experience with

medical procedure nomenclature is any guide, the

rate of increase in the number of categories can

be expected to be high (55). The first revision in

DRG nomenclature increased the number of categories by 22 percent. The additional data required on hospitals’ claims to make fine distinctions in DRG assignments would certainly add an

administrative burden on hospitals and payers.
—.

45

Second, and more important, over time DRG

payment would come to look more and more like

fee-for-service medicine, where the amount of

payment is inextricably linked to the procedures

performed. Financial incentives to perform profitable combinations of procedures could develop.

In addition, substitution of a new cost-saving procedure for a more expensive one would be discouraged if it were to bump a patient out of the

higher priced category into a lower priced one.

Thus, the uncontrolled expansion of categories

can create a more rigid, less cost-effective health

care delivery system.

Reliance on case-by-case hospital appeals of

DRG rates is likely to be administratively costly

and cumbersome compared to other methods. Unfortunately, there is little experience to date with

New Jersey’s DRG appeals process, and even if

there were, it is unclear whether the experience

of a single State is germane to operation of a national appeals process. Nevertheless, the New

Jersey appeals process bears watching as a potential model for adoption in a more general DRG

payment system.

It is worth noting that any approach to updating relative DRGs to account for new costraising technology requires information on which

to base conclusions about the readiness of a new

medical technology for payment. In effect, technology assessments are needed to support the decision process. For example, should the relative

rates of those DRGs using hyperalimentation be

adjusted to reflect the additional costs of this technology? Such questions would of necessity transcend case-by-case appeals processes. A mechanism and adequate resources for conducting integrated and comprehensive assessments of such

questions are important for supporting the DRG

adjustment process. The Prospective Payment

Assessment Commission established by the Medicare law is empowered to conduct or fund such

assessments, but other research resources may also

be usefully employed in providing information to

support such critical decisions.

COVERAGE POLICY, DRG PAYMENT, AND TECHNOLOGICAL CHANGE

Since its inception, the Medicare law has mandated that a specific technology or service must

be a “covered” benefit, i.e., a benefit eligible for

payment, in order for providers to be reimbursed

for its provision. Although Medicare specifies

broad categories of covered benefits, specific technologies, particularly new ones, require individual

coverage determinations. Such coverage decisions

are governed by section 1862 of the Social Security Act, which excludes payment for items and

services that are “not reasonable and necessary”

for diagnosis, treatment, or improved services.

“Reasonable and necessary” lacks precise definition: the Medicare program applies the terms to

technologies that are generally accepted by the

medical community as being safe and effective and

are perceived to have moved beyond experimental status to clinical application.

These coverage requirements have acted as a

barrier to adopting new, untested technologies

and, in a passive way, have discouraged the abandonment of outmoded technologies. Although the

extent to which coverage policy has been an effective “technology watchdog” varies among particular technologies, it is generally accepted as influencing the diffusion of technologies. Under the

new Medicare law, the provisions of section 1862

remain. Since DRG payment will also affect the

rate and direction of technological change, the

relation of the two, coverage policy and DRG

payment, has implications for medical technology in the Medicare program.

At this time, the interactions between Medicare

coverage policy and DRG payment as mandated

in Public Law 98-21 are limited to the hospital portion of inpatient services. As noted previously,

outpatient services and physician services-provided in or out of the hospital—are not included

in the DRG system. Instead, they are paid for as

before the law’s enactment: outpatient services are
46

reimbursed on a reasonable cost basis, and physician services are reimbursed on a reasonable

charge basis.

The ability of coverage decisions to assist in appropriate technological change depends in part on

the identification of new technologies and new

uses of established technologies. If a technology

that is part of the hospital portion of inpatient

services (now paid by DRG payment) comes to

the Medicare program’s attention as “new,” a

determination of its safety and efficacy will still

be required. Although DRGs are generally constructed according to diagnosis (and not treatment), there are several ways to identify new

technologies under the newly mandated DRG

payment system. A few still-to-be-covered technologies, the most obvious being heart transplant,

are specific DRGs. Program administrators also

will have the potential to identify established

surgical procedures in reviewing claims for DRG

payment, but will not be able to identify new

surgical procedures by claims review.

Another opportunity for identifying new technologies will occur when hospitals appeal their

payment levels for “outlier” cases. Many of these

cases will be high-cost outliers precisely because

new and costly technologies will have been employed. If a new procedure is not covered, denial

of an outlier claim is a likely prospect.

Finally, new technologies and new uses of established technologies will be recognized during

the process of adjusting DRG rates. Indeed, updating DRG prices appears to offer the most significant opportunity of identifying such technologies for coverage purposes. The decision to adjust DRG rates can therefore be considered a

quasi-coverage decision itself.

Under current Medicare coverage policy, once

a new technology is identified and brought to the

attention of program administrators, the Health

Care Financing Administration (HCFA) arranges

for the Public Health Service to conduct an assessment to ascertain the safety and efficacy of the

technology. HCFA subsequently determines the

coverage status of the technology based, in part,

on the results of the technology assessment.

For the DRG payment system, changes in DRG

relative weights or prices will be made, in part,

to reflect technological change. Because this

process* must include identification of new technologies, it is reasonable that some of the techniques, including technology assessments, used to

adjust DRG rates will be similar to those used for

supporting coverage decisions. For example, the

Prospective Payment Assessment Commission has

been given broad powers to assess medical technology and the appropriateness of medical practice patterns in performing this task. It is this

Commission that must make recommendations to

the Secretary of DHHS concerning the appropriate payment rate for hospital services according

to its findings.

Thus, both the coverage process and the process of adjusting DRG rates share a similar “approval for payment” function. They differ in that

a coverage determination focuses on specific technologies while adjusting DRG payment rates focuses on the larger entity of a diagnostic group,

which includes particular technologies. Moreover,

the DRG rate adjustment process must include

issues of cost as an integral issue, while the

coverage process at present does not consider cost

issues. Nonetheless, the technology assessments

performed for both processes no doubt will be

similar. The potential for duplication is not to be

ignored.

Whether technologies will be subject to a double review of safety and efficacy for payment purposes will depend on the approach chosen to update DRG prices. Irrespective of approach, it is

reasonable to assume that hospitals’ adoption of

cost-raising technologies will be made evident to

HCFA for DRG payment and for coverage policy.

However, some approaches to updating DRG

rates, i.e., provider appeals, would not surface

cost-saving technologies. Because specific technologies will not be identified on the DRG hospital

claims form, the adoption by a hospital of a new,

uncovered, cost-saving technology would not

become known to HCFA through hospital claims

*As noted in the previous section, the specific mechanisms of the

process have not yet been determined.
47

review. However, as in the past, HCFA will rely

on physician claims and other sources to provide

information to stimulate the initiation of a technology assessment solely for coverage purposes.

Many new technologies, especially those that

are cost-raising, could conceivably be faced with

the prospect of a double barrier to introduction—

an assessment for coverage purposes and an assessment for DRG rate adjustment. Although the

evaluations may differ for the two purposes, the

processes appear to be sufficiently similar to warrant coordinated Government effort. The unintended confluence of the two administrative processes needs to be examined in order to conserve

Federal resources and to promote the diffusion of

appropriate technologies.
Chapter 5

Selected Implementation Issues:

The Hospital Perspective
Chapter 5

Selected Implementation Issues:

The Hospital Perspective*

The preceding chapters have examined potential hospital behavior toward technology use and

adoption when faced with a prospective per-case

payment system based on Diagnosis Related

Groups (DRGs). As noted earlier, potential behavior toward technology is predicted to vary as

specific features of a system vary. In addition to

the general and specific incentives provided by the

system, a number of issues concerning implementation arise that should be noted by policymakers.

These issues assume even greater importance in

view of the recently legislated Medicare payment

system. Thus, this chapter will briefly examine

issues involving the implementation of a DRG reporting system to support per-case payment. It

will not focus on implementation issues from the

perspectives of the Health Care Financing Administration (HCFA) of the Department of Health

and Human Services or the intermediaries who

will also be involved in the operation of the new

system.

PATlENT DATA ISSUES

Classification and Coding Errors

Because assignment of patients to DRGs requires data from the patients’ discharge abstracts,

the accuracy and timeliness of these data have

come under question. Several studies have been

undertaken under the auspices of the Institute of

Medicine (IOM) of the National Academy of Sciences to determine just how accurate and timely

the data are, and how various procedures might

be employed to ensure data reliability (57,58,59).

In each study, a sample of patient records was

reabstracted by a trained field team of Registered

Record Administrators and compared with the

original data compiled by either the abstract services (for the first study), HCFA (for the Medicare

data in the second study), or the National Center

● This chapter incorporates work by Diane E. Hamilton, Ralph

E. Berry, and Nancy L. Kelly of Policy Analysis, Inc.

Implementation issues encompass a variety of

problems with varying cost implications. Two important aspects of implementation of the new

Medicare DRG payment system will be described

in this chapter: data and coding issues and hospital

administrative issues. Data issues exist because of

the reliance of the classification process on data

summaries (discharge abstracts) of the patients’

hospital medical records. These issues include

classification and coding errors, and DRG “gaming” or “creep. ” Administrative issues exist because the administrative burden to hospitals obviously increases when any new type of payment

system is introduced, particularly one as complex

as a DRG payment system is likely to be. For example, hospitals will need to institute procedures

to improve the quality of information, including

a reduction in missing data in medical charts.

for Health Statistics (NCHS) (for the third study).

For diagnosis and procedures, two types of data

discrepancies were possible. Ordering discrepancies would reflect problems in determining which

of several diagnoses or procedures should be regarded as principal. Coding discrepancies would

reflect errors in assigning a diagnosis or procedure

code number.

Findings from these studies indicated that hospital data on admission date, discharge date, and

sex were highly reliable; however, this was not

the case when diagnosis and procedure data were

examined. For all diagnoses combined, when

codes were compared (up to four digits), Medicare

data were reliable in only 59.5 to 64.1 percent of

the cases. In the study of abstract service data,

the comparable figures had been 66.8 to 77.5 percent, and in the NCHS study, 63.4 to 86.0 percent. Further, Medicare data concerning the pres51
52

ence of additional diagnoses were reliable in 74.5

percent of the cases, and the reliability level for

Medicare principal procedures was 78.9 percent.

Finally, in the abstract service and NCHS studies,

the field team concluded that in 4.6 percent of the

cases, the correct diagnosis code was a matter of

judgment. This was also true for 1.7 percent of

all procedures in the Medicare study.

It should be noted, however, that the data used

in IOM studies were for 1974 and 1977. A second

and even more important consideration is that the

studies were based on detailed coding of diagnoses

and procedures; therefore, discrepancy rates did

not reflect error rates that might occur when cases

were aggregated into DRGs. In fact, in a study

of coding error at the DRG level, reliability increased to 76.7 percent (69). A third problem with

extrapolating from these studies is that the discharge abstracts studied were not produced for

payment purposes. When payment depends on

the accuracy and timeliness of discharge abstracts,

their importance increases and data reliability

should improve. Monitoring by peer review organizations (PROS) in the new system should give

hospitals added incentive to improve their data

collection and coding procedures.

The dependence of payment on coded diagnoses and procedures in a DRG payment system

raises the possibility of deliberate changes in

coding conventions. Several authors (4,77,91)

have noted that the ability to maximize payment

by changing diagnosis codes could be a serious

problem. “DRG creep” was defined by Simborg

as a deliberate and systematic shift in a hospital’s

reported case mix in order to improve payment

(77). As described in chapter 2, the primary basis

for subdivision of cases into discrete DRGs is the

principal diagnosis. Using the original DRG classification system, Simborg pointed out that by

changing the sequence of discharge diagnoses for

patients with more than one diagnosis, a higher

priced DRG can result. If done systematically,

perhaps using sophisticated computer programs,

a more costly case mix would result.

It should be noted that the potential for “coding

creep” exists with all available case-mix measures

and is potentially even more problematic with

those requiring subjective severity determination.

To some extent, the new DRGs limit the discretion permitted in the assignment of DRGs, reducing but not eliminating the “upcoding” possibilities

that the original DRG system offered. Under the

modified DRG system, only significant, predetermined secondary diagnoses (complications and

comorbidities) or age can lead to a case being included in a higher cost DRG; i.e., sequence no

longer matters. In addition, a surgical procedure

hierarchy is now used to assign patients who had

surgery to DRG categories. Where there are multiple medical and surgical conditions, the one involving the major surgical procedure becomes the

principal one. Thus, surgery takes precedence.

Data processing sophistication should increase

within the hospitals in response to the new Medicare payment system. For example, it would pay

a hospital to use its computer to screen for uncomplicated cases or certain DRGs that the medical records department should review for potential undercoding (77). DRG creep, or deliberate

overcoming, can be controlled in two ways. First,

PROS or other review organizations can screen

certain DRGs for overcoming. This function was

specifically assigned to PROS by the new Medicare

law. Second, the potential gains from DRG creep

would diminish if DRG prices are regularly reestimated. New prices or weights would reflect the

new distribution of patients among DRGs and the

new average costs per DRG. Over time, reestimation of weights would cause the more profitable

DRGs to become less profitable, and the less profitable ones more profitable. Thus, one could expect a gradual decline in the potential for “gaming” via DRG creep with periodic reestimation of

DRG prices.

Clearly, these improvements can be expected

from the hospital industry as administrators, medical records persomel, and particularly physicians

become more aware of the reimbursement implications of inaccurate data. Obviously, improvements of this type have potentially significant

resource implications for the hospital industry,

as well as third party payment agencies. These

costs should be considered when the potential impact of a DRG reimbursement system is assessed.

New Jersey has conducted educational programs

for medical records personnel, physicians, nurses,

and hospital administrators. Data accuracy has

improved subsequently (22,76), though some

DRG creep may exist in New Jersey (14).
53

HOSPITAL ADMINISTRATIVE ISSUES

The foregoing discussion of problems in data

accuracy and timeliness in the use of DRGs is indicative of the need for improvements in the procedures used for data abstraction and coding. Researchers who have examined some of the problems have suggested several areas in which improvements should be made.

First, because a great deal of the error is introduced at the hospital level, programs to improve the quality of the information should be

instituted. These might include additional training for persons abstracting information from the

medical record, routinization of hospital procedures so that activities of billing personnel could

be limited to information transfer (rather than interpretation of the medical record data), and instructional programs for physicians in classifying

diagnoses, determining principal diagnosis, and

completing the medical record* (12). Again, New

Jersey has implemented these suggestions and has

found them to be successful (22,48,49).

The medical record should also be completed

in a timely fashion in order to bill third parties

as soon after discharge as possible. In this case,

physicians must be encouraged to complete the

medical record as soon after discharge as possible. Also, for some hospitals, additional medical

records personnel may be necessary.

A third suggestion for improving data quality

is to establish direct, timely links among error

detection, feedback, and training (10). (In fact,

the New Jersey system has instituted many editing

and educational initiatives throughout that State. )

It is suggested that this error detection should include, as a supplement to data checks by the computer, a regular program for independently reabstracting samples of records. New Jersey Professional Standards Review Organizations have done

this to monitor DRG assignments of patients to

DRGs (14). As stated earlier, however, these types

● Demlo and her colleagues (12) have also suggested that if the

practice of determining principal diagnosis by referring to the firstlisted item on the face sheet of the medical record continues, the

medical record format itself might be revised so that the conditions

are recorded in order of priority with the principal diagnosis listed

first.

of improvements are not without cost or time implications, and there is some evidence to suggest

that these improvements may increase the average

cost of preparing a bill under a DRG-based payment system.

Some preliminary results of the effect of DRG

payment on hospital behavior and performance

are available from the New Jersey DRG payment

experience (98). To assess the effect of the experimental DRG-based payment system on hospital organization and procedures, comparisons

were made between matched samples of participating and nonparticipating hospitals. Based on

that comparison, the following conclusions were

reached:

1.

2.

3.

A

the importance of the medical records departments has increased dramatically in participating hospitals. This was considered to

be the result of the required expansion of the

departments’ functions and personnel, as

well as the need for better trained personnel;

the medical staff in the participating hospitals

has become more directly involved in hospital operations; and

the quantity and type of information collected in DRG hospitals has expanded, allowing for the development of more sophisticated management information systems.

second portion of the New Jersey analysis

examined the quality and timeliness of data before

and after the institution of the new payment system and found that while data accuracy improved, the length of time needed to produce the

data increased. Although the abstract face sheet

incompletion rate decreased, the amount of time

required by the medical records and patient billing departments increased. The author stated that

as hospitals become more experienced with the

system, it is likely that the time needed to process data will decrease (98).

The details of the methods of the New Jersey

evaluation are not yet publicly available, so that

interpretation of the results must be preliminary.

Nevertheless, there is some indication that imposition of a DRG-based reimbursement scheme requires additional administrative resources. The
—

54

magnitude of these additional resources and the in current hospital procedures. It is hoped by proimplications for Medicare payment cannot nec- ponents that the increased administrative burden

essarily be inferred from this preliminary analysis. would be offset by the cost savings attributable

Some hospitals could be expected to incur larger to the imposition of a flat-rate, prospective paycost increases than others as a result of differences ment system.
Chapter 6

Findings and Conclusions
Chapter 6

Findings and Conclusions

DRG EVALUATION ISSUES

Diagnosis Related Groups (DRGs) were developed as a method for characterizing hospitals’ case

mix using data commonly available on hospital

discharge abstracts. Although the motivation behind their original development was unrelated to

their use in hospital payment systems, they were

ready for implementation at a time when a consensus was developing that hospitals should be

paid for their outputs (treated cases) rather than

for their inputs (services or days of care). As the

only system of case-mix classification currently

practical for use with per-case payment, it is understandable that DRGs have been selected for use

in the Medicare program.

DRG PAYMENT ISSUES

Although there exists little empirical evidence

about the effect of DRG payment on the adoption and use of medical technology, potential

problems with various aspects of program design

can be identified.

First, just as cost-based reimbursement has

created inappropriate incentives regarding the use

and adoption of medical technology, so too does

DRG payment, but the incentives are new and different. Whereas overuse of inpatient services and

too lengthy hospitalization were problems under

cost-based reimbursement, underprovision of

services and inappropriate admission and discharges may be a problem under DRG payment.

These incentives will require programs of quality assurance and utilization review designed specifically to deal with them.

Second the incentives affecting the use of medical technology depend on several important aspects of system design. In particular, the way in

which capital costs are treated will affect incentives to use and adopt medical technologies. In

drafting the Medicare law, Congress recognized

that treating capital costs as a pass-through item

is not an optimal longrun approach. Hospitals are

Nevertheless, it is important for those who

would use DRGs to recognize that in their present state of refinement (i.e., the 467 DRG classification) they have been inadequately evaluated

for their validity as an indicator of patient resource needs and for their impact on medical technology in per-case payment. In light of the budget

crisis facing the part A Medicare trust funds in

the upcoming years, to move to DRG payment

is reasonable. But given how little anyone knows

about what to expect from DRG payment, it is

critical that its implementation be carefully

monitored, particularly with respect to its effect

on the use and adoption of medical technology.

likely to become too capital-intensive over time

as a result. The diversity in hospitals’ ages, debt

structures, and future needs for expansion or

closure all argue for hospital-specific determinations of the capital payment levels. These issues

are inevitably intertwined with planning for health

facilities and are therefore most amenable to treatment on a State level.

Third, DRGs must be updated to both reflect

and induce desirable technological change if the

system is to remain responsive to the needs of all

patients. Periodic reestimation of relative DRG

rates to reflect changes in the costs of various

DRGs is essential to a workable program. Reestimation guards against growing divergence in the

ratios of DRG cost to DRG price, and it also

counteracts the potentially deleterious effects of

DRG creep. How frequently such reestimation

should occur is a debatable issue. The new Medicare law mandates recalibration at least every 4

years, but this interval may be too long. More

frequent, perhaps annual, reestimation has disadvantages in increased administrative burden on

program administrators and reporting requirements on hospitals. But these administrative costs

57
58

might be offset by the enhanced ability of the Federal Government to capture cost savings as they

occur and by the strengthened incentives to adopt

cost-saving innovations more quickly. Annual reestimation would also be more effective in controlling the longrun incentives for DRG creep than

would infrequent reestimation. Whatever its interval, reestimating relative DRG costs implies the

need for a continuing source of cost and charge

data to support the process. Plans for altering

Medicare billing forms and cost reporting requirements should proceed with these requirements in mind.

Methods for updating DRG rates that are conditional upon technology adoption may be important to stimulate desirable but cost-raising innovations. The adjustment process should allow

for differentiation in rates between adopters and

nonadopters of new medical technologies whose

diffusion needs to be stimulated. Creation of new

DRGs and provider appeals represent the only

viable conditional adjustment methods, and each

of these has shortcomings. In particular, heavy

reliance on new DRGs runs the risk in the long

run of creating a fee-for-service system in the

hospital, the precise opposite of what DRG payment is intended to do. Provider appeals conjure

up visions of administrative bureaucracy and

delays which detract from the otherwise attractive simplicity of DRG payment. Yet, appeals may

bean important vehicle for encouraging new technologies. New Jersey’s DRG appeals mechanism

should provide some insight into its usefulness in

this regard.

The DRG adjustment process requires supporting mechanisms for identifying and assessing new

hospital cost-raising technologies. Judgments

about the readiness of new technologies for payment under one or more DRGs need to be supported by evidence about their effectiveness, risks,

and costs. While the Medicare law established a

Prospective Payment Assessment Commission

whose purpose, among others, is to collect such

information, adequate research resources are

necessary to support the process.

The reliance of the DRG classification system

on accurate and timely data collection and coding

will necessitate improvement of hospital’s medical

records procedures and performance. Educational

programs for physicians, nurses, hospital administrators, and medical records personnel

should be initiated. Monitoring of information

quality both within hospitals and by the mandated

peer review organizations will be necessary.

IMPLICATIONS FOR RESEARCH AND EVALUATION

Although a conclusion of this memorandum is

that DRGs are ready for use in per-case payment

and that they are currently superior for this purpose to any other measure, the importance of a

good case-mix measure in making per-case payment a viable longrun payment strategy implies

that research on alternative measures must continue. It is too early to consider DRGs the basis

for all future changes in case-mix measurement.

The new Medicare law requires the Department

of Health and Human Services (DHHS) to study

the appropriateness of modifying DRGs to account for illness severity or other factors. This

study should be enlightening, but by itself it is

not enough. DHHS has an excellent record for

support of the development of DRGs and other

case-mix measurement techniques. Continued aggressive Federal support for development and

refinement of promising alternatives is required

to reap real improvements in case-mix measurement techniques.

So little is known about the magnitude of the

effects of DRG payment on the utilization of services and technologies in the health care system that

systematic study of these effects is needed. Studies

need to be designed now to evaluate the effect of

DRG payment on rates of admissions, lengths of

stay, and the use of ancillary, outpatient, and

nonhospital care. The new Medicare law mandates a study of its impact on hospital admissions.

Such a study could be part of a larger investigation of the law’s effects.
59

Since the effects of DRG payment on access to

and quality of care are unknown at present, these

factors should be closely monitored as the Medicare program is implemented. Patterns of service

specialization are likely to change, and while these

results may have benefits, they may leave pockets

of inadequate access in some areas. The hallmark

of the Medicare program has been the great increase in access of the elderly to mainstream

medical care. The effect of DRG payment on access, particularly through hospitals’ decisions to

open and close services, merits close scrutiny in

the coming years.

The importance of pass-throughs in altering the

incentives of hospitals argues for careful study of

ways to expand the scope of DRG payment. The

Medicare law mandates separate studies of two

important elements that are currently passthrough items: capital and teaching costs.

Finally, the remaining questions about the impact of specific elements of program design in

altering general hospital incentives provided by

per-case payment make study of State-level alternative prospective payment systems an attractive

prospect. Although States need not adopt per-case

payment, the law encourages them to design systems that they can be reasonably confident will

do at least as well as the Medicare system in containing hospital costs. Evaluations of such systems, as mandated in the new law, will provide

important information to support future improvements in payment system design.

IMPLICATIONS FOR PAYMENT SYSTEM ORGANIZATION

The relatively untested introduction of a national per-case payment system using DRGs as the

case-mix measure is a bold step toward improving the efficiency of hospital care. But the system

as designed in the new Medicare law is imperfect,

and the details of program administration are still

to be worked out. DRGs are the best case-mix indicators currently ready for use in a payment system, but there are other measures under development with equal or perhaps even greater promise.

Moreover, there are many potential useful approaches to prospective payment and even to percase payment. It is therefore fortunate that the

new Medicare law does not discourage individual

States from establishing alternative prospective

payment systems. These alternative systems will

allow for experimentation with different payment

system configurations, including the use of other

case-mix measures as they become more refined.

By statute, alternative prospective payment systems must cover a high proportion of the State’s

inpatient admissions. The inclusion of payers in

addition to Medicare in prospective payment will

strengthen its incentives. Furthermore, there are

many components of per-case payment that appear to be suited to decentralized administration.

For example, utilization review, provider-initiated

appeals, and decisions regarding payment for capital costs are more amenable to decentralized administrative structures.

When these administrative issues are considered

in conjunction with the potential for sharing administrative costs with other payers, considerable

attention should be given to the possibility that

the future of DRG payment rests on the degree

to which the States join with Medicare to devise

all-payer systems. The incentives analyzed in previous sections would all be strengthened under allpayer systems.
Appendixes
.

Appendix A.— Health Program Advisory Committee

and Acknowledgments

HEALTH PROGRAM ADVISORY COMMITTEE

Sidney S. Lee, Chair,

Michael Reese Hospital and Medical Center

Stuart I-I. Altman*

Florence Heller School

Brandeis University

Carroll L. Estes

Department of Social and Behavioral Sciences

School of Nursing

University of California, San Francisco

Rashi Fein

Department of Social Medicine and Health Policy

Harvard Medical School

Melvin A. Glasser

Health Security Action Council

Committee for National Health Insurance

Patricia King

Georgetown Law Center

Joyce C. Lashof

School of Public Health

University of California, Berkeley

Margaret Mahoney

The Commonwealth Fund

Frederick Mosteller

Department of Health Policy and Management

School of Public Health

Harvard University

Mitchell Rabkin*

Beth Israel Hospital

Boston, Mass.

Dorothy P. Rice

Department of Social and Behavioral Sciences

School of Nursing

University of California, San Francisco

Richard K. Riegelman

George Washington University

School of Medicine

Walter L. Robb

Medical Systems Operations

General Electric

Milwaukee, Wis.

Frederick C. Robbins

Institute of Medicine

Rosemary Stevens

Department of History and Sociology of Science

University of Pennsylvania

● Until April 1983.

63
64

ACKNOWLEDGMENTS

The development of this technical memorandum was greatly aided by the advice and review of a number

of people in addition to the Advisory Panel for the study on Medical Technology and Costs of the Medicare

Program and the Health Program Advisory Committee. OTA staff would like to express their appreciation especially to the following individuals:

Laura Allendorf, American Society of

Internal Medicine

Gerard Anderson, Department of Health and

Human Services

Robert Berenson, Washington, D.C.

Patricia Booth, Health Care Financing

Administration, Department of Health and

Human Services

Alan Cohen, Johns Hopkins Medical Institutions

Harold Cohen, Maryland Health Service Cost

Review Commission

Barbara Cooper, Health Care Financing

Administration, Department of Health and

Human Services

Richard Crout, National Institutes of Health

Helen Darling, Government Research Corp.

W. Palmer Dearing, Blue Cross/Blue Shield

Association

Allen Dobson, Health Care Financing

Administration, Department of Health and

Human Services

Dennis Duffy, Suburban Medical Review

Association, Kenilworth, N.J.

Albert Esposito, Health Care Financing

Administration, Department of Health and

Human Services

Robert Evans, University of British Columbia

Robert Fetter, Yale University

Joanne E. Finley, Maryland State Department

of Health

Faith K. Goldschmidt, New Jersey State

Department of Health

Joseph Gonnella, Jefferson Medical College,

Philadelphia, Pa.

Lyla Hemandez, American Medical Peer Review

Association

Susan Horn, Johns Hopkins Medical Institutions

Mark Hombrook, Kaiser-Permanente Medical Care

Program

Michael J. Kalison, Manger, Kalison, Murphy &

McBride, Morristown, N.J.

Jack Keane, Maryland Health Service Cost Review

Commission

. . .

Irvin Kues, The Johns Hopkins Hospital

Leo Lichtig, Blue Cross of Northeastern New York

Bryan Luce, Health Care Financing

Administration, Department of Health and

Human Services

John E. Marshall, National Center for Health

Services Research, Department of Health and

Human Services

Katherine Means, Health Care Financing

Administration, Department of Health and

Human Services

Mark Ozer, Washington, D.C.

Dale Patterson, Department of Health and

Welfare, Idaho

Steven Pelovitz, Health Care Financing

Administration, Department of Health and

Human Services

Julian Pettengill, Health Care Financing

Administration, Department of Health and

Human Services

Ellen Pryga, American Hospital Association

Marsha Rosenthal, Prudential Insurance Co.

Leonard Saxe, Boston University

Jo A. Surpin, Alpha Health Consultants

James Vertrees, Health Care Financing

Administration, Department of Health and

Human Services

Jeffrey Warren, New Jersey Rate Setting

Commission

Jeffrey Wasserman, New Jersey Health Research

and Education Trust

Carolyn Watts, University of California,

Berkeley

Karen Williams, Health Insurance Association

of America

Donald Woodbury, Georgia Department of

Medical Assistance

Donald Young, Health Care Financing

Administration, Department of Health and

Human Services

Wanda Young, Blue Cross of

Western Pennsylvania
Appendix B.—A Brief Review of the

Development of DRGs*

Overview and Historical Perspective

The development of Diagnosis Related Groups

(DRGs) has been ongoing since the late 1960’s, and it

is appropriate to view the concept as one that is continuously evolving. To this point, the evolution of

DRGs has involved both conceptual refinements and

technical improvements, spurred by the availability

of more and better quality input data and by feedback

from a wide variety of observers and users of DRGs.

It is likely that the evolution will continue as relevant

data increase in availability and improve in quality and

as the concept is subjected to more and more scrutiny.

The first version of DRGs to be widely disseminated

was a set of 383 categories, described by their developers in 1980 (19). Subsequently, in early 1982, a second and much revised set of 467 categories was issued

(103). This revised set bore little resemblance to the

“original” 383, as it was based on different definitional

procedures and a different coding convention. Both

sets had several common objectives. Both were designed to identify patients with similar expected

resource use, measured by length of hospital stay. (The

advantages and disadvantages of the length of stay

criterion will be discussed subsequently. ) Both versions

were defined so as to be medically meaningful to physicians, the key decisionmakers within the hospital with

respect to patient care, though the operationalization

of this objective varied significantly between the two.

Finally, both sets of DRGs were deliberately based on

data that are commonly available, and both sets

sought to be limited to “manageable” numbers of

groups.

In general, the broad outlines for the construction

of both sets of DRGs were the same for each version.

Actual patient stays in a sample of hospitals were the

units of analysis. Each patient’s principal diagnosis—

i.e., the reason (after study) that the patient was

admitted—was coded using a detailed coding system

that allowed for many thousands of possibilities. The

first step, therefore, was to collapse the detailed

diagnosis codes into meaningful, but broad, subcategories called “Major Diagnostic Categories”

(MDCS). MDCs were then further subdivided, using

a combination of statistical analysis and medical judgment, according to other characteristics that accounted

for differences in resource use within the MDC.

The major differences, however, may appear to outweigh the similarities. Significantly modified procedures were used to develop the 467 DRGs. These differences included the involvement of a far greater

number of participants, many of them clinicians,

which accompanied a shift in the fundamental orientation of the development process. Whereas the development of the 383 DRGs had involved both statistical analysis and expert clinical judgment, the

balance between the two components was relatively

more even than it became in the revised method, in

which the balance was shifted in favor of clinical

judgment.

In addition, there were a number of differences in

the specific features of the development process. The

differences were so extensive that there is very little

correspondence between the two sets of DRGs. In the

remainder of this appendix, the procedures used to

create each of the two sets will be summarized and the

similarities and differences among them will be examined.

Development of the “Original”

383 DRGs**

The original 383 DRGs were developed from data

for approximately 500,000 patients. Most of these were

from New Jersey hospitals, though additional data

were also available from a large hospital in Connecticut and for a sample of patients reviewed under the

Federal Professional Standards Review Organization

program. Before the data were analyzed, cases thought

to be misleading or unrepresentative were eliminated

from further consideration. These included deaths,

miscodes, and patients with extremely long lengths of

stay (LOS). The reason for this exclusion was that the

overriding objective of the process was to describe a

“typical” patient. Apparently, aberrant cases were

disregarded.

As a first step, clinicians classified the patient records

into 83 mutually exclusive and exhaustive MDCs.

MDCs were based on both the etiology (or cause) of

the disorder and the organ system involved. The 83

MDCs thus contained a number of categories that were

applicable to the same organ system. For example,

MDCs relating to the respiratory system included malignancies of the respiratory system, pneumonia, acute

upper respiratory infections and influenza, asthma,

bronchitis, and other lung and pleural diseases.

● This appendix is based on a paper prepared for OTA by Nancy L. Kelly,

Diane E. Hamilton, and Ralph E. Berry of Policy Analysis, Inc.

● ● Much of the substance of this section was derived from Fetter and colleagues (19).

65
66

Analysis was then performed to determine if each

MDC should be further subdivided in order to reduce

the variance in length of hospital stay. LOS, as noted

earlier, was selected because it was viewed as a key

indicator of resource use and the best such indicator

for which data were available. Justification for its use

was the close correspondence between LOS, case complexity, and cost that has previously been reported in

the literature (19). Particularly because of the per diem

based rate structure of many of the existing reimbursement systems, LOS was considered to be a reasonably

accurate and accepted measure of resource use. In addition, the consistency with which this measure was

reported was considered a practical advantage. More

direct measures of resource use, such as charges, were

(and are) not only more difficult to obtain, but they

are more difficult to make comparable across areas,

due to wage and price differences, and across hospitals,

due to differing markups. Consequently, LOS became

the focus of the statistical analysis and strongly influenced the final form of the diagnostic categories.

Many have argued that while the number of days

a patient is hospitalized is one indicator of resource

consumption, LOS by itself may not be an accurate

indicator of total patient treatment costs (4,13). This

becomes even more evident when one considers different treatment patterns, including the many different

ancillary procedures possible, that may occur with different patients in the same DRG.

The use of LOS as the primary measure of resource

consumption also contributes to the lack of homogeneity within the original DRGs formed. For instance,

the old DRGs grouped together in a single category

lung cancer patients with a short diagnostic workup,

a lengthy chemotherapy treatment, or a terminal admission (4). Researchers have suggested that clinical

data in addition to those already used in the original

DRG classification system are needed in order to construct groups that are more homogeneous from both

a clinical and resource consumption standpoint (4,13).

For instance, age, socioeconomic status, and type of

admission have been suggested as important elements

in classifying patients into homogeneous groups (4).

Subdivision of MDCs resulted from an iterative

process, during which statistical output was reviewed

by clinicians in order to determine which grouping alternatives best satisfied both medical and statistical criteria. The statistical analysis involved the assessment

of whether within-group variance in LOS was significantly reduced when the patients were subdivided according to secondary diagnosis, primary and secondary surgical procedures, and age. For example, if LOS

among all pneumonia patients was highly variable, but

it was much more similar within groups of elderly and

nonelderly patients, then a decision would be made

to divide the MDC “Pneumonia” further, according

to whether or not the patient was 65 or older. A next

step might involve examining the improvement in LOS

homogeneity (or, more technically, the “reduction in

variance” in LOS) when elderly and nonelderly patients were further subdivided according to the presence or absence of a secondary diagnosis. The process

would continue until it was determined that further

subdivision would not significantly reduce the variance, would not be medically meaningful, would result

in too many groups, or would result in too few cases

contained in a group.

The number of variables investigated was deliberately limited to a small number to reduce the complexity

of the analysis. However, they consisted of the variables that the developers considered to be the key discriminators among the characteristics for which data

could be obtained from hospital records. Others, such

as sex, were tested but did not prove to be important

in explaining variations in LOS.

The process of defining DRGs was therefore a complicated one, involving both subjective and objective

techniques. Although a consistent approach was maintained within each MDC, few firm guidelines were employed during the initial development phase. However,

it appears that, during the formation of the original

DRGs, a slight edge was given to the statistical criteria.

One result of this was that some MDCs, such as acute

myocardial infarction, were not subdivided at all and

that others, such as fractures, were subdivided into as

many as 13 DRGs. Also, this approach resulted in variation across MDCs in the criterion (or criteria) for subdivision. For some, such as appendicitis, secondary

diagnosis was the criterion for subdivision; while for

pneumonia, age, surgery, and secondary diagnosis

were all used. This difference was accepted insofar as

it was seen to reflect variation in relevant patient characteristics.

The result was a set of 383 groups that simultaneously satisfied the criteria established for distinctiveness,

medical meaningfulness, and size. It is worth noting

once again that the 383 DRGs were based on input data

derived from a sample of patients that was mainly limited to the northeast region. The result thus reflected

the composition of cases in that sample, as well as the

medical practice patterns employed in the hospitals

from which these patients were discharged. Also, judgments about the alternative grouping configurations

were made by a small group of clinicians whose views

may not have been representative of physicians nationally. The possibility that the initial set of DRGs
67

● Much of the substance of this section was derived from Yale University

(103).

within the original DRGs. The 38 experts who participated in this study identified and ranked 16 categories of problems that caused inappropriate assignments. Most important to them was that “some DRGs

combine clinically similar patients, who nevertheless

require different treatments” (99). They also identified

37 of the original 383 DRGs as categories likely to contain patients whose clinical status was not appropriately recognized. Physicians and hospital administrators

identified different reasons for the problems, but they

specified the same problematic DRGs (99).

Several of the problems identified by Williams and

colleagues concerned the medical meaningfulness of

DRG-based case-mix measures. Some who have used

the original DRGs argue that they are not medically

meaningful because patients with very different medical problems are grouped together (4). For instance,

old DRG 301 groups together all patients whose principal diagnosis is “replacement of hip with prosthetic

device, biopsy of bone, and spinal fusion.” In addition, DRGs fail to subdivide some broad diagnostic

groups. The original DRG 121, for instance, includes

all patients with acute myocardial infarction.

As a result of such criticisms, significant changes

were made in the organization and orientation of the

development process, the manner in which decisions

were made, and the nature of the input data. Specifically, a more structured organization was used to

administer the classification process and to guide the

decisionmaking. A large number of participants from

the medical profession, as well as other areas within

the health industry (e.g., medical records) were involved. For this later phase, data were made available

by the Commission for Professional and Hospital Activities from the Professional Activity Study (PAS).

This meant that a nationally representative sample of

patients could be selected and analyzed, thus improving the generalizability of the results.

DRG development procedures were substantially altered during this second phase. The major change was

in the basic orientation of the decisionmaking, in that

strong emphasis was placed on the clinical, rather than

statistical, validity of DRGs, The first manifestation

of this change was in the redefinition of MDCs. Rather

than using the 83 MDCs defined previously, the revised approach redefined a total of only 23 MDCs,

most of which were confined to a single organ system.

To return to the example used earlier, diseases of the

respiratory system, which were represented by six

MDCs in the earlier methodology, were represented

by a single MDC in the modified approach. Only a

few MDCs (e.g., burns) remained the same. The purpose of this change was to bring MDCs into conformance with the organization of medical practice, in

which, for the most part, specialties are defined around
68

the various organ systems. This also appeared to facilitate the increased use of expert clinical judgment

in developing MDC subdivisions.

Additional changes were made in the process after

the redefinition of MDCs. An important change in the

analysis was the retention of patients who died.

Another was the development and use of more precise

guidelines for subdividing MDCs than had been used

previously. An outgrowth of this was more consistency in the application of criteria for subdivision. For

example, the guidelines required that the initial partition (when possible) be based on the presence or absence of a surgical procedure performed in an operating room. The need for grouping patients with clinically related diseases was continually stressed in the

guidelines.

Several important modifications were made in the

variables used to subdivide MDCs, primarily to capture severity of illness more precisely. One such modification instructed the expert panels to rank order

surgical procedures according to resource use intensity and to assign patients with multiple procedures to

the procedure involving the greatest intensity. This

meant that the type of surgical procedure became an

important consideration in the new DRG development, whereas in the original grouping procedure, only

the presence or absence of surgery was taken into account. In addition to considering the type of surgery

performed, the nature of existing comorbidity (i.e.,

coexisting conditions) and complications was explicitly

evaluated based on the specific ICD-9-CM codes contained in the patient’s discharge abstract. Again,

“substantial” comorbidity and complications were

distinguished from those considered to be less significant. “Substantial” was defined to include conditions

likely to increase LOS by at least 1 day for at least

75 percent of the cases. In many instances, a composite

variable indicating whether or not the patient was aged

70 or more and/or had substantial comorbidity or

complications proved to be an important determinant

of resource use.

Finally, other variables in addition to diagnosis, procedures, and age were taken into account when the

experts judged that the additional factors were relevant. For example, for the MDC “Pregnancy, Childbirth, and the Puerperium,” the initial division is made

according to whether or not the patient was “delivered

this admission.” With respect to substance abuse, the

initial split is according to whether or not the patient

“left [the hospital] against medical advice.” Death was

also included as a possible criterion for subdivision.

While, as noted earlier, the modified process was

much more dependent on clinical judgment, statistical

analysis again was used to aid decisionmaking. Reduction in variance for LOS was again examined for each

partitioning variable considered, but the fact that

variance was significantly reduced by a particular

variable did not guarantee that that variable would

be included in the modified DRG definition. Clinical

coherence, above all, dictated which measures would

be used.

The modified approach resulted in the definition of

467 DRGs, * which bear little resemblance to the original 383. To the extent that the original groups can be

“mapped” into the revised ones, it is clear that while

some of the original groups were further subdivided

by the new process, others were collapsed into fewer

categories. For example, the original DRG 121 was

“acute myocardial infarction” (AMI), undifferentiated.

In the new configuration, AMI patients are classified

into three DRGs:

● circulatory disorders with AMI and cardiovascular complications, discharged alive;

● circulatory disorders with AMI, without cardiovascular complications, discharged alive; and

. circulator disorders with AMI, expired.

In an example of the opposite effect, bronchitis and

asthma were divided into three bronchitis- and three

asthma-related DRGs under the original system. The

modified set includes only a total of three DRGs for

bronchitis and asthma combined.

Comparison of Alternative Sets of DRGs

Table B-1 presents a summary of the fundamental

similarities and differences between the original 383

DRGs and the modified set of 467. Most of the specific

areas shown in the table have been discussed in the

previous section. This concluding section, therefore,

will focus on the implications of the changes made.

Clearly, the major thrust of all of the methodological

changes was to improve the medical meaningfulness

of DRGs. To the extent that this was accomplished,

it should result in the increased acceptability of the

DRG scheme to physicians, who manage the medical

care provided to hospital inpatients. As a consequence,

the necessary interactions between clinical staff and

hospital administrators should be improved. The development of a medically meaningful grouping scheme

has always been a clear objective of those who originated the concept of DRGs.

The enhancement of the “clinical coherence” of

DRGs was attempted in several important ways:

• by increasing the number of clinician participants

and the role of clinicians in DRG development;

● A 468th DRG has also been defined to account for patients who have

received an operating room procedure unrelated to their MDC. This “outlier”

category is not normally included in descriptions or evaluations of the system.
69

Table B.1.—Summary of Similarities and Differences Between the Original and Modified DRGs

Original DRGs Modified DRGs

Based on data for patients from New Jersey and Based on nationally representative sample of patients

Connecticut, and a sample of Medicare/Medicaid derived from PAS data (deaths retained)

patients (deaths excluded)

Based on ICDA-8 (or HICD-2) diagnostic coding scheme Based on [CD-9-CM diagnostic coding scheme

Result from subdivision of 83 broad subcategories of Result from subdivision of 23 broad subcategories of

diagnoses (MDCs) based on organ system and etiology diagnoses (MDCs) based on organ system only

Subdivision of MDCs based on statistical analysis and Subdivision of MDCs based on clinical judgment and

clinical judgment statistical analysis

Variables used to subdivide MDCs include Variables used to subdivide MDCs include type of

presence/absence of secondary diagnoses and surgery surgery and comorbidity/complications, age, death, and

as well as age other relevant criteria

Subdivisions not uniform across DRGs When possible, first subdivision based upon

presence/absence of operating room procedure;

generally, tighter guidelines for subdivision were

applied

End result: 383 mutually exclusive and exhaustive DRGs End result: 467 mutuallv exclusive and exhaustive DRGs

SOURCE: Office of Technology Assessment.

●

●

●

by tying the initial subdivisions (MDCs) to major

organ systems, in conformance with the delineation of medical specialties;

by taking the specific nature of surgical procedures and comorbidity/complications into account in forming the groups; and

by extending the number of characteristics used

to partition-MDCs when appropriate.

It would appear that significant strides have been made

towards the objective of medical meaningfulness.

These would also seem to be important strides if physician behavior is to be the target of management or cost

control efforts.

It is unclear whether grouping according to clinical

similarities was attained at the expense of statistical

validity, and if so, whether it is important. The deemphasis of statistical analysis as a mechanism for decisionmaking in forming the groups implies that the new

groups may be less internally homogeneous and distinct from each other than the original set. Admittedly, however, the basis for evaluation of within-group

homogeneity and between-group heterogeneity—LOS

—is an imperfect criterion for indicating resource consumption. Preliminary evidence does indicate, however, that the groups achieve similar reductions in

variance for charges as they do for LOS (103). Data

available for about 330,000 New Jersey discharges (including cost data) were analyzed, and the results indicated that the distribution of costs was similar to that

of LOS for most DRGs. In the few instances where

there were significant differences, modifications were

made to the relevant DRGs.
Appendix C.— Examples of Per-Case and

DRG Payment Systems

Diagnosis Related Groups (DRGs) have been used

in three State ratesetting systems, as well as in the

Medicare reimbursement system under the Tax Equity and Fiscal Responsibility Act of 1982 (TEFRA) and

the Social Security Amendments of 1983 (Public Law

98-21). TEFRA was designed as a temporary response

to the problem of hospital cost-containment and specifically called for the Secretary of the Department of

Health and Human Services (DHHS) to develop a proposal for a permanent system of prospective payment

under Medicare. A December 1982 proposal by the

former Secretary of DHHS, Richard Schweiker, called

for the creation of a national DRG-specific payment

system for Medicare beneficiaries (91). In April 1983,

a Medicare DRG payment system was enacted into law

with features similar to those suggested in the

Schweiker proposal. The new system will be phased

in over a 3-year period beginning in October 1983.

Theoretically, DRGs could be used in any hospital

payment method, including retrospective cost-based

reimbursement, but their importance in payment

derives from their use as part of prospective per-case

payment systems. Per-case payment refers to any prospective hospital payment system with fixed rates of

payment based on the hospital admission, not on the

bundle of services or number of days of care provided.

DRG payment is defined here as any per-case hospital

payment method in which differences in case mix are

taken into account using DRGs to classify case types.

New Jersey is currently the only State in which all

patients and all hospitals are subject to DRG-specific

rates per case. * Maryland currently uses a case-mix

index approach in about 30 of its 51 hospitals, and the

current Medicare hospital reimbursement system established by TEFRA sets maximum limits on per-case

payment using a DRG case-mix index. Georgia has experimented with the use of DRGs to define hospital

groups for per-case payment but is no longer using the

system.

New Jersey began using the old 383 DRGs and is

now using the modified 467 DRGs in its payment system. The new Medicare system bases payment on the

new DRGs. A Medicare case-mix index was developed

for TEFRA using the 467 DRGs. Georgia’s experiment

with hospital groupings was based on the old 383

DRGs. These programs are described in this section.

*The State of New York currently uses a DRG case-mix index in its ratesetting program, but there the unit of payment is the inpatient day, not the case.

Payment of per-diem rates creates incentives that are quite different from percase payment. Consequently, the use of DRGs in New York is not discussed

here.

70

Several other States are using, and the American

Hospital Association (AI-IA) once proposed as a model

system, per-case payment systems that do not explicitly adjust for case mix. Descriptions of selected per-case

systems are presented below as well.

DRG-Specific Rates Per Case:

New Jersey*

In 1978, New Jersey passed a law mandating the

gradual implementation of a per-case payment system

covering all payers. A Hospital Rate-Setting Commission was given the power to adopt an approach that

ties payment rates directly to the patient’s DRG. (Much

of the developmental work for this ratesetting method

was funded by a $3 million Federal Health Care Financing Administration (HCFA) grant to the New

Jersey Department of Health.) In May 1980, 26

hospitals began billing patients on a DRG-specific rate

per case. By October 1982, all New Jersey hospitals

had been brought into the DRG system (22).

The DRG payment system works as follows: each

patient is assigned to a specific DRG on discharge, and

the hospital is paid a previously specified rate for that

DRG. All classes of payers must pay the assigned rate

to the hospital regardless of the actual amount of resources consumed in treating the patient, with the exception of these “outlier” cases: patients for whom the

length of stay is unusually short or long relative to the

mean stay in the DRG; cases where the hospital stay

ends with death; when the DRG is a low-volume category; or when discharge is against medical advice.

These outlier cases are paid according to the hospital’s

charges, which are themselves controlled under a preexisting ratesetting approach.**

The DRG rate assigned to a hospital is constructed

from data on the hospital’s own costs as well as those

of all other similar hospitals in the State.*** A hospitalspecific preliminary cost base (PCB) is first established

by taking the hospital’s actual expenditures in a base

year (2 years before the rate year). This PCB includes

direct patient care costs, indirect (overhead) costs, allowances for the replacement of capital facilities, bad

debt and charity care, and working capital. Only the

● A detailed description of the New Jersey DRG payment system is provided in a separate working paper, ‘Using Diagnosis Related Groups (DRGs)

in Hospital Payment: The New Jersey Experience, ” by Joanne E. Finley (22).

● *Even these cases would not be reimbursed on a cost-reimbursement basis.

They would be paid the DRG rate plus a perdiem rate for each day beyond

the “trim point.”

● **Hospitals are classified in three categories: major teaching, minor teaching, and nonteaching.
71

direct care component of the PCB is assumed to vary

with the DRG. The direct care costs are allocated

among DRGs using formulas that presumably reflect

actual resource use by patients in various DRGs. For

example, nursing costs are allocated among the hospital’s DRGs according to the percentage of total patient days in each DRG, while ancillary department

costs are allocated among DRGs on the basis of the

percentage of total department charges in each DRG.

Each hospital’s DRG-specific average direct patient

care cost computed as above is the basis for calculation of a statewide average cost per DRG, which becomes a standard DRG rate. The hospital’s rate becomes a blend of the hospital’s own average direct care

cost per DRG and the peer group average (or standard)

DRG cost. The portion of each cost average that is

used (i. e., the hospital’s own or the standard) varies

across DRGs depending on the amount of statewide

variance in the costs of treating patients within a given

DRG. If there is substantial variation in the costs of

treating a DRG, greater reliance is placed on the hospital’s own cost experience. The percentage of the

statewide standard cost used in setting rates ranges

from a low of zero percent to a high of 100 percent,

with most DRGs falling into the 40- to 75-percent range

(62).

After DRG-specific direct care costs are estimated,

hospital-based physician costs and overhead costs are

added, and the total is inflated to the rate year. Other

allowable costs (e.g., allowances for capital facilities

and equipment and charity care) are calculated and

allocated among DRGs on a percentage basis. Hospitals are then paid these final DRG-specific rates

throughout the rate year.

Under this system, hospitals may keep any surplus

achieved by reducing per-case costs, but beginning in

the 1982 rate year, a part of any surplus resulting from

increasing admissions is taken back in the final reconciliation. Similarly, increases in costs per case must be

absorbed by the hospitals, but revenue losses due to

decreases in admissions are moderated by a formula

at reconciliation.

In theory, this method of DRG price construction

contains built-in annual adjustments to DRG rates

through changes in the base-year costs to reflect changing levels of resource use. The hospital’s rate for a particular DRG could change as a result of either changes

in its own costs of providing services or statewide peer

group changes in the costs of treating the DRG. The

rate facing a particular hospital can change even if its

own and the statewide peer-group average costs do not

change. For example, if the variance among patients

in the cost of treating cases in a particular DRG were

to decrease due to greater standardization of treatment

across the State, the rate in subsequent years would

be based more heavily on the statewide average cost

and less on the hospital’s own costs than in the previous year. In practice, staff and budget limitations have

precluded timely updating of the base year. The 1983

DRG rates are still based on 1979 costs, with only inflation factors changing in recent years (65). The Commission expects to update the base year to 1982 for

the 1984 rate year (96).

Changes in specific DRG rates are also possible

through an appeals process in which any interested

party, be it a hospital, a payer, a patient, or the Commission itself, may request a review of a rate in one

or more DRG category if it believes it is offering services using new, more costly technology. As of February 1983, however, only a few DRG appeals had been

filed, and none had been completed (96).

Per-Case Payment With a DRG

Case-Mix Index: TEFRA

In August 1982, Congress passed landmark legislation, the Tax Equity and Fiscal Responsibility Act of

1982 (TEFRA) (Public Law 97-248), which moved the

entire Medicare system toward DRG payment beginning in October 1982. TEFRA made major revisions

in traditional cost-based reimbursement with the imposition of a hospital-specific maximum limit on the

amount of reimbursable inpatient operating costs.

The new Medicare approach, which went into effect in October 1982, has two key elements:

● For 3 years starting in October 1982, a hospital’s

inpatient operating costs per case will have a “target” rate of growth determined by the general rate

of wage and price inflation in the hospital’s region. If its operating costs per case are below this

target rate it may keep 50 percent of the savings,

up to 5 percent of the target rate. If the hospital’s

costs exceed the target rate, it will receive only

25 percent of its excess costs in the first 2 years,

and none in the third.

● In no case can the hospital’s reimbursement exceed a per-case limit on operating costs established

by DHHS. The hospital’s new limit is 120 percent*

of the mean cost per case for hospitals of the same

type. (Each hospital is categorized according to

its bedsize and location. ) The limit is adjusted up

or down by a DRG-based index of case mix for

each hospital.

Neither of these provisions puts any limit on capital

costs (depreciation and interest), direct teaching ex-

● This limit will be reduced over the 3 years to 110 percent.
72

penses, or outpatient services. These remain “passthrough” items.

The DRG case-mix index for a particular hospital

has been computed as the sum over all DRGs of the

number of cases in the DRG times its national relative

cost weight. The relative cost weights were constructed

from a 1979 20-percent sample of Medicare inpatient

claims (the “MedPar” file) which contains data on

hospitals’ charges and clinical information. The weight

assigned to a particular DRG is the ratio of the average

charge (adjusted and standardized) per case in that

DRG to the average charge per case across all DRGs

(18). Although the amounts that hospitals charge patients do not necessarily correspond to the cost of

treating patients (69), a study of the relationship between hospitals’ overall 1979 DRG index value and

their total 1979 inpatient operating costs revealed a

simple correlation coefficient of 0.60 between the two

(69). Further analysis has shown that a given percentage difference in the case-mix index is met with roughly

the same percentage difference in operating costs

among hospitals (69).

TEFRA did not represent a wholesale abandonment

of cost-based reimbursement. For those hospitals

whose costs are below both the per-case limit and the

target rate, reimbursement will be on the basis of cost,

with a small incentive payment added. Over time,

however, as the limit becomes tighter, a greater proportion of hospitals will find themselves with the percase limits as real price constraints.

It was not clearly specified in the law how often

HCFA was to update the hospital’s case-mix index.

There appeared to be no plans to update the index

throughout the life of TEFRA. Annual changes in the

case-mix index value to reflect changing case loads are

considered unnecessary on the assumption that in the

short run, a hospital’s case mix is relatively stable and

not easily manipulated (see ref. 2). This decision underscores the temporary nature of TEFRA provisions,

which will be phased out after 3 years as the new Medicare law is implemented.

DRG-Specific Rates Per Case:

The Medicare Law

In April 1983, the President signed into law a sweeping revision of Medicare’s inpatient hospital payment

system (Public Law 98-21). Begining in October 1983,

the new payment method will evolve over a 3-year

transition into a national set of DRG-specific prices

adjusted only for the hospital’s area wage rate and its

urban or rural location. DRG prices will apply to virtually all short-term acute-care general hospitals in the

United States.

The new system will gradually supercede TEFRA,

which moved the entire Medicare system from retrospective cost-based reimbursement toward DRG payment. The provisions of TEFRA (summarized above)

did not represent complete abandonment of cost-based

reimbursement, but after the 3-year transition period,

the new Medicare system will virtually replace retrospective cost-based reimbursement with a prospective

payment system based on DRG prices.

During the 3-year phase-in period, only part of the

hospital’s payment will be on the basis of a DRG price;

the remainder (a percentage decreasing each year) will

be made on the basis of its own reasonable costs (with

maximum limits as designated by TEFRA). Capital

costs will continue to be paid for totally on a retrospective cost basis* until the end of the transition period,

at which time the law contemplates, but does not specify the method for, the incorporation of payment for

capital into the DRG pricing system.

The pricing system will apply to all inpatient admissions except for a small number of cases (set as a

percentage of the total by statute) with unusually long

lengths of stay. The rate of payment for these cases

will be increased by the estimated incremental marginal costs of care during the extended stay.

The initial national set of DRG prices will be based

on the 1980 average inpatient operating cost per case

for each DRG in a 20-percent sample of Medicare inpatient claims. The law requires that DRG prices be

updated regularly in two ways. First, an overall annual rate of increase is applied to every DRG to keep

pace with the general level of inflation and rate of technological change in the economy. Second, the relative

prices (i.e., the ratio of one price to another) must be

assessed and recalibrated at least once every 4 years,

with the first recalibration scheduled for October 1985.

The recalibration process must reflect changes in treatment patterns, technology, and other factors that alter

the relative use of hospital resources among DRGs.

The Prospective Payment Assessment Commission established by the law will be responsible both for making recommendations regarding recalibration and for

evaluating any such adjustments made by the Secretary of DHHS.

Certain kinds of hospitals, such as long-term, psychiatric, and children’s hospitals, will be exempted

from DRG payment. Teaching hospitals are included,

but for the present the direct costs of teaching (e.g.,

residents’ and interns’ stipends) will be retrospectively reimbursed on the basis of cost, and a further adjustment will be made for the indirect costs associated

with teaching. In addition, the law requires the Med-

‘For-profit hospitals will be paid a return on equity as part of the capital

cost reimbursement.
73

icare program to participate in any State-legislated

alternative prospective payment program that covers

at least 75 percent of the State’s population, makes provisions for competitive health plans, assures the

Federal Government that access to hospital care for

Medicare and Medicaid beneficiaries will not decline,

and assures the Federal Government that hospital costs

in the aggregate will be no higher under the State program. If the State system leads to hospital costs that

are higher than would be expected under DRG payment, Medicare is empowered to recoup such overpayments from hospitals in subsequent years. Thus,

States will probably move cautiously to adopt alternative all-payer prospective payment systems.

The law also puts into place a mechanism for quality

assurance and utilization review by requiring hospitals

to contract with regional peer review organizations at

a fixed price per review as a condition of payment.

The payments for such reviews will come out of the

Medicare Hospital Insurance Trust Fund and are

guaranteed by statute.

DRG Case-Mix Adjustment: Maryland’s

Guaranteed Inpatient Revenue System

The State of Maryland has been regulating hospital

rates since 1974, when hospitals’ charges were frozen

pending the implementation of a new ratesetting approach. From its inception until 1977, the Health Services Cost Review Commission was empowered to set

rates for all payers except Medicare and Medicaid. In

1977, a waiver from Medicare and Medicaid regulations was granted by HCFA; since then, the system

has included all payers.

The ratesetting program has evolved over time with

different methods applied to different hospitals. The

Guaranteed Inpatient Revenue (GIR) method, which

uses DRGs, was first employed in 1976 in 14 hospitals

(53). Today, approximately 30 of Maryland’s 51 hospitals are paid by this method, including all hospitals

with 400 beds or more.

Maryland’s GIR method is essentially a revenue control system, where the allowed amount of revenue per

case in each DRG is based on the hospital’s actual

revenue per case in the DRG in a selected base year.

Hospitals do not bill the payer directly by the case;

they bill on the basis of approved charges for each

service provided. At the end of the year, the actual

revenue per case in each DRG is compared to the allowed revenue for that DRG. If actual revenue received

per case exceeds the previously set allowed revenue

per case in a DRG, implying that the intensity of services and/or length of stay have increased from the base

year, the hospital’s revenue per case for the following

year is reduced by the amount of the difference. If actual revenues per DRG case fall short of approved revenues for the DRG, then the hospital receives the difference in an increase in the following year’s approved

revenues (8). Hospitals may keep all savings from reductions in per-case costs, but a part of the revenue

obtained from increases in admissions is recaptured by

the Commission. The hospital is also partly protected

from losses due to decreases in admissions.

The GIR system was modified in 1980 to bring interhospital comparisons into the computation of approved revenues per case. Currently, all hospitals in

the State are classified by geographic area. For each

DRG for each payer category (i.e., Medicare, Medicaid, Blue Cross, and others), an average charge for

the group is established. Each hospital’s actual charge

per DRG in each payer category is then compared to

this standard in the group. If, on the average, the

hospital’s charge is higher than the group standard by

more than twice the allowable inflation rate, the

hospital’s approved revenue is adjusted downward.

For example, if the allowable rate of inflation is 6 percent, and the hospital’s DRG-specific charges are 15

percent above the group standard, then the hospital

is allowed an inflation rate of only 3 percent (15 percent - [2 x 6 percent]) in its computation of allowed

revenue for that DRG.

A second modification is also under consideration.

For all GIR hospitals, the Commission is moving

toward DRG-based reimbursement at the level of the

group standard, as opposed to the hospitals’ own baseyear revenue levels. If, for example, the hospital’s DRG

revenues are 10 percent higher than the standard across

all DRGs, but are well above the standard, say by 40

percent or so, for one or two DRGs, the Commission

will move the hospital’s rate toward the 10 percent

figure on all DRGs. This may have the effect of providing disincentives to the hospital to increase the

volume of cases in the more profitable DRGs.

Per-Case Payment With DRGs:

Georgia’s Medicaid Program

Georgia used DRGs as part of its hospital grouping

system in a 1981-82 Medicaid reimbursement experimerit. ’ As in any grouping scheme, the underlying assumption was that similar hospitals with similar case

mixes and service characteristics should consume similar amounts of resources per admission (80). The

grouping was accomplished by using two data sets,

one containing the number of patients in each of the

● Since Jan. 1, 1983, Georgia’s Medicaid program has been operating on

a cost-based reimbursement system using 1980 costs plus an inflation factor.
74

original 383 DRGs and the other containing data on

20 service characteristics (e.g., bed size, surgical

facilities, diagnostic radiology, etc.). The case-mix and

service characteristics data sets yielded over 400 bits

of descriptive information which were then used to

group similar hospitals via a cluster analysis. In 1981,

12 groups were formed ranging in size from 7 to 20

hospitals. When the process was repeated for 1982,

there were 11 groups, one with 5 hospitals and the

others with 10 to 20 (101). ’

After grouping the hospitals, the Medicaid program

compared the operating costs, excluding certain costs

such as malpractice premiums, depreciation on capital,

and education. A group limit on costs for the next year

was set based on 130 percent of the mean. It had been

estimated that 10 to 12 percent of the hospitals would

be outside their group limits.

Approximately 160 hospitals participated in this

project. In the first year, 19 were outside the limit, and

in the second year, 22 were higher than the limit. The

penalty for being outside the group limit depended on

how the hospital’s base-year costs had compared to

the mean. Hospitals would lose the difference between

the allowable inflation rate and the percentage above

the base-year mean, with the maximum penalty being the allowable inflation rate. For example, if a

hospital were 8 percent over the group limit and if the

overall allowable inflation rate were 10 percent, that

hospital would have an allowable inflation rate of 2

percent (101).

Per-=Case Payment Without DRGs:

California Medicaid

From 1980 to the present, California’s Medicaid program (Medi-Cal) has operated under a per-case hospital payment system without an explicit adjustment

for case mix. ** For each patient, hospitals have been

reimbursed the lowest of: 1) customary charges,

2) Medicare reasonable costs, or 3) a maximum cost

per discharge (CPD) calculated in a fixed base year

(generally fiscal year 1980). The CPD comprised the

hospital’s own base period costs adjusted by an inflation index, growth in service intensity, and passthrough costs (including items such as depreciation,

● If the system had continued after Dec. 31, 1982, it was anticipated that

the grouping would be an annual exercise until the groups stabilized. The

Georgia Medicaid program staff had discussed changes in the procedure that

might have led to stabilization (e.g., using the original 83 Major Diagnostic

Categories with some partitioning based on whether or not surgery was performed) (101).

● *This method is gradually being phased out in favor of a new “Selected

Provider Contracting Program, ” under which hospitals will be awarded contracts on the basis of competitive bidding to provide inpatient care for MediCal recipients. Except for emergency admissions, Medi-Cal will pay for care

delivered to its enrollees only at contract hospitals.

interest, utility costs, and malpractice premiums).

Changes in the number of discharges from the base

year were reflected in adjustments to the CPD limit.

Beginning in October 1981, the program began to reduce allowable fixed costs in hospitals with very low

occupancy (below 55 percent), thus reducing the cost

per discharge limit in those hospitals as a penalty.

Hospitals have the right to appeal their CPD limit

to the Department of Health. For example, if a hospital

were to find that its Medicaid patient load shifted from

more routine cases to a high-cost load of, say, cardiac

surgery, the hospital would have recourse to the appeals process. Otherwise, such case-mix changes from

the base year to any rate year would not be reflected

in the CPD limit.

Per-Case Payment Without DRGs:

The AI-1A April 1982 Proposal

AHA proposed a Medicare payment system for inpatient care based on a prospective fixed rate per

discharge. Although beneficiary liabilities for deductibles and copayment would remain, hospitals

would be permitted to charge up to $1,000 per discharge in addition to the rate received from Medicare

if they do not agree to accept the Medicare fixed price

as payment in full. Each hospital’s rate would be based

on its own allowable costs in a base year with adjustments for capital expenditures, compliance expenditures, return on equity, high Medicare and Medicaid

volume, and self-insurance against professional liability suits. These costs, with adjustments in a given year,

would be divided by the number of Medicare discharges to obtain the initial rate. An inflation factor

set by an independent panel of economists would be

a forecast intended to reflect input price inflation,

medical technology advances, and regional differences.

The Medicare fixed rate would be computed by multiplying the base rate by the inflation factor. Hospitals

would be paid this fixed rate per Medicare discharge.

In its proposal, AHA asserted that hospitals’ case

mixes would not change in the short term, for which

this program was intended, because of long-standing

admitting patterns, medical staff relationships, and

hospital policies and procedures. The proposal called

for an appeals process, however, which would have

allowed hospitals to appeal their rates because of increases in the complexity of their case mixes.

Per-Case Reimbursement Without DRGs:

Idaho’s Medicaid Program

Since 1979, Idaho’s Medicaid program has had a percase maximum limit on payment for Medicaid hospital
75

stays. The limit in any year is calculated on the basis rate. At present, one-third of Idaho’s 47 hospitals are

of the hospital’s previous year’s audited costs per case subject to the per-case limit (81). However, since Medwith an adjustment for inflation. Hospitals are reim- icaid represents only about 5 percent of hospital exbursed the lower of billed charges, allowable costs, or penditures in Idaho, the program is not likely to have

the per-case limit (92). The per-case limit is implicitly had much impact on hospitals’ fiscal positions or readjusted for changes in case mix over time by the use havior.

of the previous year’s costs in calculating each year’s
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