MEDICARE 

ADVANTAGE

CMS Should Improve 

the Accuracy of Risk 

Score Adjustments for 

Diagnostic Coding 

Practices

Report to Congressional Requesters

January 2012

GAO-12-51

United States Government Accountability Office

GAO
United States Government Accountability Office

Highlights of GAO-12-51, a report to 

congressional requesters

January 2012

MEDICARE ADVANTAGE

CMS Should Improve the Accuracy of Risk Score 

Adjustments for Diagnostic Coding Practices

Why GAO Did This Study

The Centers for Medicare & Medicaid 

Services (CMS) pays plans in 

Medicare Advantage (MA)—the private 

plan alternative to Medicare fee-forservice (FFS)—a predetermined 

amount per beneficiary adjusted for 

health status. To make this adjustment, 

CMS calculates a risk score, a relative 

measure of expected health care 

costs, for each beneficiary. Risk scores 

should be the same among all 

beneficiaries with the same health 

conditions and demographic 

characteristics. Policymakers raised 

concerns that differences in diagnostic 

coding between MA plans and 

Medicare FFS could lead to 

inappropriately high MA risk scores 

and payments to MA plans. CMS 

began adjusting for coding differences 

in 2010. GAO (1) estimated the impact 

of any coding differences on MA risk 

scores and payments to plans in 2010 

and (2) evaluated CMS’s methodology 

for estimating the impact of these 

differences in 2010, 2011, and 2012. 

To do this, GAO compared risk score 

growth for MA beneficiaries with an 

estimate of what risk score growth 

would have been for those 

beneficiaries if they were in Medicare 

FFS, and evaluated CMS’s 

methodology by assessing the data, 

study populations, study design, and 

beneficiary characteristics analyzed.

What GAO Recommends

GAO recommends that CMS should 

improve the accuracy of its MA risk 

score adjustments by taking steps 

such as incorporating adjustments for 

additional beneficiary characteristics,

using the most current data available, 

accounting for all relevant years of 

coding differences, and incorporating 

the effect of coding difference trends. 

What GAO Found

GAO found that diagnostic coding differences exist between MA plans and 

Medicare FFS. Using data on beneficiary characteristics and regression analysis, 

GAO estimated that before CMS’s adjustment, 2010 MA beneficiary risk scores 

were at least 4.8 percent, and perhaps as much as 7.1 percent, higher than they 

likely would have been if the same beneficiaries had been continuously enrolled 

in FFS. The higher risk scores were equivalent to $3.9 billion to $5.8 billion in 

payments to MA plans. Both GAO and CMS found that the impact of coding 

differences increased over time. This trend suggests that the cumulative impact 

of coding differences in 2011 and 2012 could be larger than in 2010.

In contrast to GAO, CMS estimated that 3.4 percent of 2010 MA beneficiary risk 

scores were attributable to coding differences between MA plans and Medicare 

FFS. CMS’s adjustment for this difference avoided $2.7 billion in excess 

payments to MA plans. CMS’s 2010 estimate differs from GAO’s in that CMS’s

methodology did not include more current data, did not incorporate the trend of 

the impact of coding differences over time, and did not account for beneficiary 

characteristics other than age and mortality, such as sex, health status, Medicaid 

enrollment status, beneficiary residential location, and whether the original 

reason for Medicare entitlement was disability. 

Percentage of 2010 MA Risk Scores Attributable to Coding Differences and Effect on 

Payments to MA Plans

CMS did not update its coding adjustment estimate in 2011 and 2012 to include 

more current data, to account for additional years of coding differences, or to 

incorporate the trend of the impact of coding differences. By continuing to

implement the same 3.4 percent adjustment for coding differences in 2011 and 

2012, CMS likely underestimated the impact of coding differences in 2011 and 

2012, resulting in excess payments to MA plans.

GAO’s findings underscore the importance of both CMS continuing to adjust risk 

scores to account for coding differences and ensuring that those adjustments are 

as complete and accurate as possible.

In its comments, CMS stated that it found our findings informative. CMS did not 

comment on our recommendation. 

View GAO-12-51. For more information, 

contact James C. Cosgrove at (202) 512-7114 

or cosgrove@gao.gov.
Page i GAO-12-51 Medicare Advantage Diagnostic Coding

Letter 1 

Background 7

Diagnostic Coding Differences Accounted for Estimated MA Risk 

Score Growth of at Least $3.9 Billion in 2010, with Likely Larger 

Impacts in 2011 and 2012 9

CMS’s Adjustment for Coding Differences Likely Resulted in 

Excess Payments to MA Plans 12

Conclusions 14

Recommendations for Executive Action 14

Agency Comments and Our Evaluation 14

Appendix I Scope and Methodology 17

Appendix II Comments from the Centers for Medicare & Medicaid Services 25

Appendix III GAO Contact and Staff Acknowledgments 27

Tables

Table 1: Annual Risk Score Growth Due to Coding Differences for 

GAO Study Population 20

Table 2: Impact of Adjustments for Coding Differences on Total 

Payments to MA Plans in 2010 23

Figures

Figure 1: Percentage of 2010 MA Risk Scores Attributable to 

Coding Differences and Effect on Payments to MA Plans 10

Figure 2: Annual Impact of Coding Differences on 2010 MA Risk 

Scores for GAO’s Study Population, 2005 to 2010 21

Contents
Page ii GAO-12-51 Medicare Advantage Diagnostic Coding

Abbreviations

CMS Centers for Medicare & Medicaid Services

CY calendar year

ESRD end-stage renal disease

FFS fee-for-service 

HCC hierarchical condition category

HCERA Health Care and Education Reconciliation Act of 2010

HMO health maintenance organization

MA Medicare Advantage

PFFS private fee-for-service 

PPO preferred-provider organization

PSO provider-sponsored organization

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Page 1 GAO-12-51 Medicare Advantage Diagnostic Coding

United States Government Accountability Office

Washington, DC 20548

January 12, 2012

Congressional Requesters

In 2010, the federal government spent about $114 billion on the Medicare 

Advantage (MA) program, a private plan alternative to the original 

Medicare fee-for-service (FFS) program that covers about a quarter of all 

Medicare beneficiaries.1 The Centers for Medicare & Medicaid Services 

(CMS), the agency that administers Medicare, pays MA plans a monthly 

amount to provide health care services for each beneficiary enrolled in 

these plans. CMS adjusts the payment to account for a beneficiary’s 

health status, a process known as risk adjustment.2

To risk adjust payments, CMS calculates a risk score for every Medicare 

beneficiary, including those in MA plans and the FFS program. A 

beneficiary’s risk score is the ratio of expected health care expenditures 

for that beneficiary under Medicare FFS relative to the average health 

care expenditures for all Medicare FFS beneficiaries.

For example, 

beneficiaries in poorer health are generally expected to use more health 

care services relative to beneficiaries in better health. Therefore, CMS’s 

risk adjustment tends to increase payments to those plans serving 

beneficiaries in poorer health to compensate for the expected higher 

health care spending by those plans. Risk adjustment helps ensure that a 

plan’s financial incentive to enroll and care for beneficiaries is similar for 

all beneficiaries regardless of their health status or the resources they are 

likely to consume.

3

 

1

Medicare FFS consists of Medicare Parts A and B. Medicare Part A covers hospital and 

other inpatient stays. Medicare Part B is optional insurance and covers hospital outpatient, 

physician, and other services. Medicare beneficiaries have the option of obtaining 

coverage for Medicare Part A and B services from private health plans that participate in 

the MA program—also known as Medicare Part C. Medicare beneficiaries may purchase 

optional coverage for outpatient prescription drugs under Medicare Part D.

Information on a 

beneficiary’s age, sex, Medicaid enrollment status, original reason for 

2

The payment to an MA plan is based on a plan’s bid—the projected revenue required by 

the plan to provide Medicare coverage—and a benchmark—the maximum amount 

Medicare will pay the plan to provide Medicare coverage in each county within the plan’s 

service area.

3

For example, a beneficiary with a risk score of 1.05 would have expected expenditures 

that were 5 percent greater than the average Medicare FFS beneficiary, who is assigned a 

risk score of 1.00.

 
 

Page 2 GAO-12-51 Medicare Advantage Diagnostic Coding

Medicare entitlement (i.e., age or disability), and major medical conditions 

all factor into the calculation of the risk score.4

Risk scores for beneficiaries with the same health conditions, age, and 

other characteristics should be identical, regardless of whether the 

beneficiaries are in an MA plan or Medicare FFS. This will be true if MA 

plans and FFS providers code medical diagnoses with the same level of 

reliability and completeness. However, MA plans and FFS providers may 

code medical diagnoses differently. Since 2004, when CMS transitioned 

from using only a beneficiary’s principal inpatient diagnosis to using a 

larger set of major medical conditions to risk adjust MA payments, MA 

plans have had a financial incentive to ensure that all relevant diagnoses 

are coded, as this can increase beneficiaries’ risk scores and ultimately 

the payment plans receive. In contrast, CMS pays many Medicare FFS 

providers for services provided rather than beneficiaries’ diagnoses.

To gather information on 

medical diagnoses for beneficiaries in Medicare FFS, CMS analyzes the 

claims that FFS providers submit for payment. For beneficiaries enrolled 

in MA plans, instead of submitting claims, CMS requires plans to submit 

certain diagnosis codes for each beneficiary.

5

Policymakers have expressed concern that risk scores for MA 

beneficiaries have grown at a faster rate than those for Medicare FFS 

beneficiaries and that systematic coding differences have contributed to 

FFS 

providers that are paid based on services provided have less of a 

financial incentive to code all relevant diagnoses. If patterns of diagnostic 

coding differ systematically between MA plans and Medicare FFS, it is 

possible for beneficiaries in MA plans to be assigned higher risk scores, 

and appear to be sicker, than identical beneficiaries in Medicare FFS. 

Because payment adjustments are estimated using FFS data, higher MA 

risk scores due to diagnostic coding that is more comprehensive than 

FFS would result in MA plan payments that are too high.

 

4

See Pope et al., “Risk Adjustment of Medicare Capitation Payments Using the CMS-HCC 

Model,” Health Care Financing Review, vol. 25, no. 4, 2004, pp. 119-141.

5

One important exception is hospital acute inpatient services, for which Medicare payment 

is based on Medicare severity diagnosis related groups rather than services.
 

Page 3 GAO-12-51 Medicare Advantage Diagnostic Coding

such growth.6 Under the Deficit Reduction Act of 2005, CMS was required 

to adjust risk scores for MA beneficiaries in 2008, 2009, and 2010 to take 

into account differences in treatment and diagnostic coding between MA 

plans and Medicare FFS providers to the extent that the impact of such 

differences on risk scores could be identified.7 CMS did not adjust MA risk 

scores in 2008 or 2009. However, for 2010, CMS estimated that 

3.41 percent of MA beneficiary risk scores were attributable to differences 

in diagnostic coding over the previous 3 years and reduced MA 

beneficiaries’ 2010 risk scores by 3.41 percent. This adjustment, intended 

to ensure that individuals with identical health conditions and other 

characteristics have the same risk score regardless of whether they were 

in an MA plan or FFS, resulted in an estimated $2.7 billion in savings to 

Medicare.8

The Health Care and Education Reconciliation Act of 2010 (HCERA) 

required CMS to continue adjusting risk scores for coding differences until 

CMS implements risk adjustment using MA diagnostic, cost, and use 

data.9 CMS reduced 2011 MA beneficiary risk scores by 3.41 percent, the 

same amount that the agency estimated and used for 2010, and will use 

for 2012.10

 

6

CMS estimated that from 2004 through 2006, the risk scores of beneficiaries in MA plans 

rose more than twice as fast as risk scores of beneficiaries in Medicare FFS, increasing 

an average of 4.5 percent compared to 2 percent per year, respectively. See CMS, 

“Announcement of Calendar Year (CY) 2008 Medicare Advantage Capitation Rates and 

Payment Policies,” p. 16 (Apr. 2, 2007).

In addition, HCERA required CMS to reduce MA risk scores by 

at least 1.3 percent more than the 2010 adjustment (a total of 

4.71 percent) in 2014 and that the annual minimum percentage reduction 

7

Pub. L. No. 109-171, §5301(b), 120 Stat. 4, 51.

8

The Medicare savings estimate is based on our analysis of Medicare data. To estimate 

the savings to Medicare we calculated the difference between total projected payments to 

MA plans with and without an adjustment for coding differences applied.

9

CMS will begin collecting the additional data necessary for risk adjustment based on 

diagnostic, cost, and use data from MA plans in 2012. Pub. L. No. 111-152, §1102(e), 

124 Stat. 1029, 1046 (codified at 42 U.S.C. §1395w-23(a)(1)(C)(ii)). 

10CMS had proposed that it would reduce 2011 MA risk scores by 3.41 percent before 

HCERA was enacted. See CMS, “Advance Notice of Methodological Changes for 

Calendar Year (CY) 2011 for Medicare Advantage (MA) Capitation Rates, Part C and 

Part D Payment Policies and 2011 Call Letter” (Feb. 19, 2010).
 

Page 4 GAO-12-51 Medicare Advantage Diagnostic Coding

gradually increase to not less than 5.70 percent in 2019 and subsequent 

years.11

The accuracy of the adjustments to risk scores can have important 

consequences for both Medicare spending and MA plans. If CMS does 

not accurately estimate the effect on MA beneficiary risk scores of coding 

differences between MA plans and Medicare FFS, then payments to MA 

plans will not accurately reflect the health status of MA beneficiaries. For 

example, if the adjustment to account for differences in coding is too 

small, then MA payments would be set too high and plans would be 

overpaid due to differences in coding patterns. In contrast, if the 

adjustment is larger than the actual impact of coding differences on risk 

scores, then payments to MA plans would be set too low and MA plans 

would be underpaid for the beneficiaries they served.

You asked us to analyze differences in diagnostic coding practices 

between MA and Medicare FFS and review CMS’s methodology for 

quantifying differences in coding practices and associated payment 

adjustments. This report (1) determines the extent to which differences, if 

any, in diagnostic coding between MA plans and Medicare FFS affected 

risk scores and payments to MA plans in 2010; and (2) evaluates CMS’s 

methodology for estimating the percentage of MA beneficiary risk scores 

in 2010, 2011, and 2012 that was attributable to differences in diagnostic 

coding between MA plans and Medicare FFS.

To determine the extent to which differences in diagnostic coding 

between MA plans and Medicare FFS affected 2010 risk scores and 

payments to MA plans, we compared actual risk score growth for 

beneficiaries in MA plans with the estimated risk score growth MA 

beneficiaries would have had if they were enrolled in Medicare FFS, and 

then estimated the impact on payments to MA plans. To do this we 

calculated changes in disease scores—the portion of the risk score that is 

based on a beneficiary’s coded diagnoses—for MA beneficiaries and 

used regression analysis to estimate what changes in disease scores 

would have been if those beneficiaries were enrolled in Medicare FFS. In 

our regression analysis, we accounted for beneficiary characteristics that 

could affect disease score growth, including characteristics that may 

affect the frequency with which beneficiaries interact with health care 

 

1142 U.S.C. §1395w-23(a)(1)(C)(ii)(III). 
 

Page 5 GAO-12-51 Medicare Advantage Diagnostic Coding

providers and therefore the completeness with which providers code 

diagnoses. We attributed differences between actual and estimated 

disease score growth to differences in coding practices between MA 

plans and Medicare FFS.12

We estimated the extent to which differences in diagnostic coding 

between MA plans and Medicare FFS affected 2010 risk scores by 

estimating the cumulative impact of coding differences over the 3 year 

period from 2007 to 2010. Our use of 2007 risk scores, based on prior 

year diagnoses, as the first risk scores to contribute to our cumulative 

coding estimate assumes that MA plans and Medicare FFS had similar 

coding patterns at that time.13

Because 2008 data were the most recent available at the time of our 

analysis, we projected the estimated impact of coding differences to 

2010. We analyzed a retrospective cohort by using risk score data to 

identify MA beneficiary risk scores in 2008 and following them back to 

2005.14,15

 

12We accounted for the following beneficiary characteristics: age, sex, diagnoses as a 

proxy for health status, mortality, Medicaid enrollment status, beneficiary residential 

location, and whether the original reason for Medicare entitlement was disability.

To estimate the impact of coding differences on risk scores for 

2005 to 2008, we estimated the risk score growth due to coding 

differences for those beneficiaries over three 2-year periods (2005 to 

2006, 2006 to 2007, and 2007 to 2008). We then projected risk score 

growth due to coding differences for 2008 through 2010 and calculated 

13CMS estimated the cumulative impact of coding differences on risk scores over the 

same period.

14Risk scores are based on data collected for services provided during the prior calendar 

year. By analyzing 2005 to 2008 risk scores, we addressed diagnoses coded during 2004 

to 2007. 

15We analyzed beneficiaries enrolled in health maintenance organization (HMO), 

preferred-provider organization (PPO), and private fee-for-service (PFFS) plans, as well 

as plans offered by provider-sponsored organizations (PSO). Coverage for beneficiaries in

HMOs is generally restricted to services from providers within a network, while 

beneficiaries in PPOs are covered for services from both in-network and out-of-network 

providers but must pay higher cost-sharing amounts if they use out-of-network services. 

Prior to 2011, PFFS plans generally did not have provider networks, and beneficiaries 

were able to see any provider that accepted the plan’s payment terms. However, 

beginning in 2011, the Medicare Improvement for Patients and Providers Act of 2008 

requires most PFFS plans to have provider networks in certain areas. Pub. L. No. 110-

275, § 162, 122 Stat. 2494, 2569 (codified at 42 U.S.C. § 1395w-22(d)(5)-(6)). PSOs offer 

MA plans with provider networks that are operated by a provider or providers.
 

Page 6 GAO-12-51 Medicare Advantage Diagnostic Coding

the weighted sum of the estimated impact for 2007 to 2008 and the 

projections of the estimated impact for 2008 to 2010, which were based 

on trends from 2005 to 2008. We made two different projections for 2008 

to 2010 using different assumptions of trends: the lower projection 

assumed that the impact of coding differences on risk scores for 2008 to 

2010 was the same as it was for 2007 to 2008, while the higher projection 

assumed that the trend of impact on our study population from 2005 

through 2008 continued through 2010. Finally, we estimated the impact of 

coding differences on MA risk scores when we restricted our sample of 

MA beneficiaries to those who were enrolled in MA plans with provider 

networks since these plans may be better able to influence provider 

coding patterns.16

We also performed an additional analysis to determine how sensitive our 

results were to our assumption that coding patterns for MA and FFS were 

similar in 2007. CMS believes that MA coding patterns may have been 

less comprehensive than FFS when the CMS-Hierarchical Condition 

Categories (CMS-HCC) model was first implemented, and that coding 

pattern differences caused MA risk scores to grow faster than FFS; 

therefore, there may have been a period of “catch-up” before MA coding 

patterns became more comprehensive than FFS coding patterns. While 

the length of the “catch-up” period is not known, we evaluated the impact 

of assuming the actual “catch-up” period was shorter, and that MA and 

FFS coding patterns were similar in 2005.17

To evaluate CMS’s methodology for estimating the percentage of MA 

beneficiary risk scores in 2010, 2011, and 2012 that was attributable to 

differences in diagnostic coding between MA plans and Medicare FFS,18

 

16Plans with provider networks include HMOs, PPOs, and plans offered by PSOs.

we reviewed documentation on CMS’s methodology and interviewed 

CMS officials. We assessed the data, study population, and study design 

that CMS used in its calculation and examined the extent to which CMS 

accounted for relevant beneficiary characteristics that could affect the 

estimate.

17Specifically, we evaluated the impact of analyzing two additional years of coding 

differences by estimating the impact of coding differences from 2005 to 2010.

18CMS calls this percentage the Coding Pattern Difference Adjustment factor.
 

Page 7 GAO-12-51 Medicare Advantage Diagnostic Coding

To quantify the impact of both our and CMS’s estimates of coding 

differences on payments to MA plans, we estimated the risk score growth 

attributable to coding differences, as described above, and using data MA 

plans submitted to CMS that were used to determine payments to MA 

plans, calculated total risk-adjusted payments for each MA plan before 

and after applying a coding adjustment. We then calculated the difference 

between the two payment levels.

The CMS data we analyzed on Medicare beneficiaries are collected from 

Medicare providers and MA plans. We assessed the reliability of the CMS 

data we used by interviewing officials responsible for using these data to 

determine MA payments, reviewing relevant documentation, and 

examining the data for obvious errors. We determined that the data were 

sufficiently reliable for the purposes of our study. (See app. I for more 

details on our scope and methodology.)

We conducted this performance audit from October 2009 through 

December 2011 in accordance with generally accepted government 

auditing standards. Those standards require that we plan and perform the 

audit to obtain sufficient, appropriate evidence to provide a reasonable 

basis for our findings and conclusions based on our audit objectives. We 

believe that the evidence obtained provides a reasonable basis for our 

findings and conclusions based on our audit objectives.

CMS’s method of adjusting payments to MA plans to reflect beneficiary 

health status has changed over time. Prior to 2000, CMS adjusted MA 

payments based only on beneficiary demographic data. From 2000 to 

2003, CMS adjusted MA payments using a model that was based on a 

beneficiary’s demographic characteristics and principal inpatient 

diagnosis.19 In 2004, CMS began adjusting payments to MA plans based 

on the CMS-HCC model.20

 

19This model was called the Principal Inpatient Diagnostic Cost Group model.

HCCs, which represent major medical 

conditions, are groups of medical diagnoses where related groups of 

diagnoses are ranked based on disease severity and cost. The CMSHCC model adjusts MA payments more accurately than previous models 

20CMS published the details of the CMS-HCC risk adjustment model on March 28, 2003, 

and May 12, 2003. CMS-HCC model adjustments to MA payments were phased in from 

2004 to 2010. Payments to MA plans in 2011 are adjusted solely by the CMS-HCC model. 

Background 
 

Page 8 GAO-12-51 Medicare Advantage Diagnostic Coding

because it includes more comprehensive information on beneficiaries’ 

health status.

The CMS-HCC risk adjustment model uses enrollment and claims data 

from Medicare FFS. The model uses beneficiary characteristic and 

diagnostic data from a base year to calculate each beneficiary’s risk 

scores for the following year.21

CMS estimated that 3.41 percent of 2010 MA beneficiary risk scores was 

attributable to differences in diagnostic coding between MA and Medicare 

FFS since 2007. To calculate this percentage, CMS estimated the annual 

difference in disease score growth between MA and Medicare FFS 

beneficiaries for three different groups of beneficiaries who were either 

enrolled in the same MA plan or in Medicare FFS from 2004 to 2005, 

2005 to 2006, and 2006 to 2007. CMS accounted for differences in age 

and mortality when estimating the difference in disease score growth 

between MA and Medicare FFS beneficiaries for each period. Then, CMS 

calculated the average of the three estimates.

For example, CMS used MA beneficiary 

demographic and diagnostic data for 2007 to determine the risk scores 

used to adjust payments to MA plans in 2008.

22

• CMS multiplied the average annual difference in risk score growth by 

its estimate of the average length of time that 2010 MA beneficiaries 

had been continuously enrolled in MA plans over the previous 

3 years,

To apply this average 

estimate to 2010 MA beneficiaries,

23

 

21The CMS-HCC model uses one calendar year of data to estimate each beneficiary’s 

expected Medicare expenditures for the following year. Expected Medicare expenditures 

for each beneficiary are divided by the average Medicare expenditures for all Medicare 

FFS beneficiaries to generate a risk score.

and

22The average was weighted by the number of beneficiaries enrolled in the same MA plan 

during each time period.

23CMS used MA enrollment data for MA beneficiaries in 2009 and the previous 3 years to 

estimate the average length of time that 2010 MA beneficiaries had been continuously in 

their MA plan during the previous 3 years.
 

Page 9 GAO-12-51 Medicare Advantage Diagnostic Coding

• CMS multiplied this result by 81.8 percent, its estimate of the 

percentage of 2010 MA beneficiaries who were enrolled in an MA plan 

in 2009 and therefore were exposed to MA coding practices.24

CMS implemented this same adjustment of 3.41 percent in 2011 and has 

announced it will implement this same adjustment in 2012.

We found that diagnostic coding differences exist between MA plans and 

Medicare FFS and that these differences had a substantial effect on 

payment to MA plans. We estimated that risk score growth due to coding 

differences over the previous 3 years was equivalent to $3.9 billion to 

$5.8 billion in payments to MA plans in 2010 before CMS’s adjustment for 

coding differences. Before CMS reduced 2010 MA beneficiary risk 

scores, we found that these scores were at least 4.8 percent, and 

perhaps as much as 7.1 percent, higher than the risk scores likely would 

have been as a result of diagnostic coding differences, that is, if the same 

beneficiaries had been continuously enrolled in FFS (see fig. 1). Our 

estimates suggest that, after accounting for CMS’s 3.4 percent reduction 

to MA risk scores in 2010, MA risk scores were too high by at least 

1.4 percent, and perhaps as much as 3.7 percent, equivalent to 

$1.2 billion and $3.1 billion in payments to MA plans.

 

24CMS’s estimate of the percentage of 2010 MA beneficiaries whose risk scores reflected 

MA diagnostic coding was based on the percentage of 2009 MA beneficiaries who were 

also in MA plans in 2008.

Diagnostic Coding 

Differences 

Accounted for 

Estimated MA Risk 

Score Growth of at 

Least $3.9 Billion in 

2010, with Likely 

Larger Impacts in 

2011 and 2012
 

Page 10 GAO-12-51 Medicare Advantage Diagnostic Coding

Figure 1: Percentage of 2010 MA Risk Scores Attributable to Coding Differences 

and Effect on Payments to MA Plans

Notes: To estimate the percentage of 2010 MA risk scores attributable to coding differences between 

MA and Medicare FFS over the previous 3 years, we analyzed a retrospective cohort of beneficiaries 

from 2005 to 2008. We used two different assumptions of the effect of coding differences on risk 

scores from 2008 to 2010. GAO’s low estimate assumes that the percentage of risk score growth 

attributable to coding differences from 2008 to 2010 was the same as it was from 2007 to 2008. 

GAO’s high estimate assumes that the percentage of risk score growth attributable to coding 

differences from 2008 to 2010 continues the trend for our study population from 2005 to 2008.

Our two estimates were based on different assumptions of the impact of 

coding differences over time. We found that the annual impact of coding 

differences for our study population increased from 2005 to 2008. Based 

on this trend, we projected risk score growth for the period 2008 to 2010 

and obtained the higher estimate, 7.1 percent, of the cumulative impact of 

differences in diagnostic coding between MA and FFS. However, coding 

differences may reach an upper bound when MA plans code diagnoses 

as comprehensively as possible, so we produced the lower estimate of 

4.8 percent by assuming that the impact of coding differences on risk 

scores remained constant and was the same from 2008 to 2010 as it was 

from 2007 to 2008.25

 

25See app. I for more detail on our methodology.
 

Page 11 GAO-12-51 Medicare Advantage Diagnostic Coding

Plans with networks may have greater potential to influence the 

diagnostic coding of their providers, relative to plans without networks. 

Specifically, when we restricted our analysis to MA beneficiaries in plans 

with provider networks (HMOs, PPOs, and plans offered by PSOs), our 

estimates of the cumulative effect of differences in diagnostic coding 

between MA and FFS increased to an average of 5.5 or 7.8 percent of 

MA beneficiary risk scores in 2010, depending on the projection 

assumption for 2008 to 2010.26

Altering the year by which MA coding patterns had “caught up” to FFS 

coding patterns, from our original assumption of 2007 to 2005, had little 

effect on our results. Specifically, we estimated the cumulative impact of 

coding differences from 2005 to 2010 and found that our estimates for all 

MA plans increased slightly to 5.3 or 7.6 percent, depending on the 

projection assumption from 2008 to 2010.27

Our analysis estimating the cumulative impact of coding differences on 

2010 MA risk scores suggests that this cumulative impact is increasing. 

Specifically, we found that from 2005 to 2008, the impact of coding 

differences on MA risk scores increased over time (see app. 1, table 1). 

Furthermore, CMS also found that the impact of coding differences 

increased from 2004 to 2008.28 While we did not have more recent data, 

the trend of coding differences through 2008 suggests that the impact of 

coding differences in 2011 and 2012 could be larger than in 2010.

 

26Prior to 2011, PFFS plans were not required to have a network; however, beginning in 

2011, PFFS plans in certain areas were required to have a provider network. In 2011, 

72 percent PFFS enrollees were in counties where PFFS plans were required to have a 

network.

27We found the cumulative impact of coding differences from 2005 to 2010 for plans with 

provider networks (HMOs, PPOs, and PSOs) to be 6.1 or 8.4 percent of MA beneficiary 

risk scores in 2010, depending on the projection assumption from 2008 to 2010.

28CMS analysis provided to us showed annual risk score growth due to coding differences 

to be 0.015 from 2004 to 2005, 0.015 from 2005 to 2006, 0.026 from 2006 to 2007, and 

0.038 from 2007 to 2008.
 

Page 12 GAO-12-51 Medicare Advantage Diagnostic Coding

CMS’s estimate of the impact of coding differences on 2010 MA risk 

scores was smaller than our estimate due to the collective impact of three 

methodological differences described below. For its 2011 and 2012 

adjustments, the agency continued to use the same estimate of the 

impact of coding differences it used in 2010, which likely resulted in 

excess payments to MA plans.

Three major differences between our and CMS’s methodology account 

for the differences in our 2010 estimates. First, CMS did not include data 

from 2008. CMS initially announced the adjustment for coding differences 

in its advance notice for 2010 payment before 2008 data were available. 

While 2008 data became available prior to the final announcement of the 

coding adjustment, CMS decided not to incorporate 2008 data into its 

final adjustment. In its announcement for 2010 payment, CMS explains 

that it took a conservative approach for the first year that it implemented 

the MA coding adjustment. Incorporating 2008 data would have increased 

the size of CMS’s final adjustment. Second, CMS did not take into 

account the increasing impact of coding differences over time. However, 

without 2008 data, the increasing trend of the annual impact of coding 

differences is less apparent, and supports the agency’s decision to use 

the average annual impact from 2004 to 2007 as a proxy for the annual 

impact from 2007 to 2010. Third, CMS only accounted for differences in 

age and mortality between the MA and FFS study populations. We found 

that accounting for additional beneficiary characteristics explained more 

variation in disease score growth, and consequently improved the 

accuracy of our risk score growth estimate.29,30

CMS did not update its estimate in 2011 and 2012 with more current data, 

even though data were available. CMS did not include 2008 data in its 

2010 estimate due to its desire to take a conservative approach for the 

first year it implemented a coding adjustment, and the agency did not 

 

29Specifically, our model explained less than 1 percent of the variation in disease score 

growth when we accounted only for differences in age and mortality (the only two factors 

that CMS included); however, our model explained about 20 percent of the variation when 

we also accounted for additional characteristics, including: sex, diagnoses as a proxy for 

health status, Medicaid enrollment status, beneficiary residential location, and whether the 

original reason for Medicare entitlement was disability.

30We also assessed the impact of including only MA beneficiaries who remained in the 

same plan for each time period, as CMS did in its analysis, as opposed to including all MA 

beneficiaries and found that this methodological difference had little impact on our 

estimates.

CMS’s Adjustment for 

Coding Differences 

Likely Resulted in 

Excess Payments to 

MA Plans
 

Page 13 GAO-12-51 Medicare Advantage Diagnostic Coding

update its estimate for 2011 or 2012 due to concerns about the many MA 

payment changes taking place. While maintaining the same level of 

adjustment for 2011 and 2012 maintains stability and predictability in MA 

payment rates, it also allows the accuracy of the adjustment to diminish in 

each year. Including more recent data would have improved the accuracy 

of CMS’s 2011 and 2012 estimates because more recent data are likely 

to be more representative of the year in which an adjustment was made.

By not updating its estimate with more current data, CMS also did not 

account for the additional years of cumulative coding differences in its 

estimate: 4 years for 2011 (2007 to 2011) and 5 years for 2012 (2007 to 

2012). While CMS stated in its announcement for 2011 payment that it 

would consider accounting for additional years of coding differences, 

CMS officials told us they were concerned about incorporating additional 

years using a linear methodology because it would ignore the possibility 

that MA plans may reach a limit at which they could no longer code 

diagnoses more comprehensively. We think it is unlikely that this limit has 

been reached. Given the financial incentives that MA plans have to 

ensure that all relevant diagnoses are coded, the fact that CMS’s 

3.41 percent estimate is below our low estimate of 4.8 percent, and 

considering the increasing use of electronic health records to capture and 

maintain diagnostic information, the upper limit is likely to be greater than

the 3 years CMS accounted for in its 2011 and 2012 estimates.

In addition to not including more recent data, CMS did not incorporate the 

impact of the upward trend in coding differences on risk scores into its 

estimates for 2011 and 2012. Based on the trend of increasing impact of 

coding differences through 2008, shown in both CMS’s and our analysis, 

we believe that the impact of coding differences on 2011 and 2012 MA 

risk scores is likely to be larger than it was on 2010 MA risk scores. In 

addition, less than 1.4 percent of MA enrollees in 2011 were enrolled in a 

plan without a network, suggesting that our slightly larger results based 

on only MA plans with a network are more accurate estimates of the 

impact of coding differences in 2011 and 2012. By continuing to 

implement the same 3.41 percent adjustment for coding differences in 

2011 and 2012, we believe CMS likely substantially underestimated the 

impact of coding differences in 2011 and 2012, resulting in excess 

payments to MA plans.
 

Page 14 GAO-12-51 Medicare Advantage Diagnostic Coding

Risk adjustment is important to ensure that payments to MA plans 

adequately account for differences in beneficiaries’ health status and to 

maintain plans’ financial incentive to enroll and care for beneficiaries 

regardless of their health status or the resources they are likely to 

consume. For CMS’s risk adjustment model to adjust payments to MA 

plans appropriately, diagnostic coding patterns must be similar among 

both MA plans and Medicare FFS. We confirmed CMS’s finding that 

differences in diagnostic coding caused risk scores for MA beneficiaries 

to be higher than those for comparable Medicare FFS beneficiaries in 

2010. This finding underscores the importance of continuing to adjust MA 

risk scores to account for coding differences and ensuring that these 

adjustments are as accurate as possible. If an adjustment for coding 

differences is too low, CMS would pay MA plans more than it would pay 

providers in Medicare FFS to provide health care for the same 

beneficiaries. We found that CMS’s 3.41 percent adjustment for coding 

differences in 2010 was too low, resulting in $1.2 billion to $3.1 billion in 

payments to MA plans for coding differences. By not updating its 

methodology in 2011 or in 2012, CMS likely underestimated the impact of 

coding differences on MA risk scores to a greater extent in these years, 

resulting in excess payments to MA plans. If CMS does not update its 

methodology, excess payments due to differences in coding practices are 

likely to increase.

To help ensure appropriate payments to MA plans, the Administrator of 

CMS should take steps to improve the accuracy of the adjustment made 

for differences in diagnostic coding practices between MA and Medicare 

FFS. Such steps could include, for example, accounting for additional 

beneficiary characteristics, including the most current data available, 

identifying and accounting for all years of coding differences that could 

affect the payment year for which an adjustment is made, and 

incorporating the trend of the impact of coding differences on risk scores.

CMS provided written comments on a draft of this report, which are 

reprinted in appendix II.

In its comments, CMS stated that it found our methodological approach 

and findings informative and suggested that we provide some additional 

information about how the coding differences between MA and FFS were 

calculated. In response, we added additional details to appendix I about 

the regression models used, the calculations used to generate our 

Conclusions 

Recommendations for 

Executive Action

Agency Comments 

and Our Evaluation
 

Page 15 GAO-12-51 Medicare Advantage Diagnostic Coding

cumulative impact estimates, and the trend line used to generate our high 

estimate.

CMS did not comment on our recommendation for executive action.

As agreed with your offices, unless you publicly announce the contents of 

this report earlier, we plan no further distribution until 30 days from the 

report date. At that time, we will send copies to the Secretary of HHS, 

interested congressional committees, and others. In addition, the report is 

available at no charge on the GAO website at http://www.gao.gov. 

If you or your staff has any questions about this report, please contact me 

at (202) 512-7114 or cosgrovej@gao.gov. Contact points for our Offices 

of Congressional Relations and Public Affairs may be found on the last 

page of this report. GAO staff who made major contributions to this report 

are listed in appendix III.

James C. Cosgrove

Director, Health Care
Page 16 GAO-12-51 Medicare Advantage Diagnostic Coding

List of Requesters

The Honorable Henry A. Waxman

Ranking Member

Committee on Energy and Commerce

House of Representatives

The Honorable Frank Pallone, Jr.

Ranking Member

Subcommittee on Health

Committee on Energy and Commerce

House of Representatives

The Honorable Pete Stark

Ranking Member

Subcommittee on Health

Committee on Ways and Means

House of Representatives

The Honorable John D. Dingell

The Honorable Charles B. Rangel

House of Representatives
Appendix I: Scope and Methodology

Page 17 GAO-12-51 Medicare Advantage Diagnostic Coding

This appendix explains the scope and methodology that we used to 

address our objective that determines the extent to which differences, if 

any, in diagnostic coding between Medicare Advantage (MA) plans and 

Medicare fee-for-service (FFS) affect risk scores and payments to MA 

plans in 2010.

To determine the extent to which differences, if any, in diagnostic coding 

between MA plans and Medicare FFS affected MA risk scores in 2010, 

we used Centers for Medicare & Medicaid Services (CMS) enrollment 

and risk score data from 2004 to 2008, the most current data available at 

the time of our analysis, and projected the estimated impact to 2010. For 

three periods (2005 to 2006, 2006 to 2007, and 2007 to 2008), we 

compared actual risk score growth for beneficiaries in our MA study 

population with the estimated risk score growth the beneficiaries would 

have had if they were enrolled in Medicare FFS. Risk scores for a given 

calendar year are based on beneficiaries’ diagnoses in the previous year, 

so we identified our study population based on enrollment data for 2004

through 2007 and analyzed risk scores for that population for 2005 

through 2008.

Our MA study population consisted of a retrospective cohort of MA 

beneficiaries. We included MA beneficiaries who were enrolled in health 

maintenance organization (HMO), preferred provider organization (PPO), 

and private fee-for-service (PFFS) plans as well as plans offered by 

provider-sponsored organizations (PSO). Specifically, we identified the 

cohort of MA beneficiaries who were enrolled in MA for all of 2007 and 

followed them back for the length of their continuous enrollment to 2004. 

In addition, for beneficiaries who were enrolled in Medicare FFS and 

switched to MA in 2005, 2006, or 2007, we included data for 1 year of 

Medicare FFS enrollment immediately preceding their MA enrollment.1

 

1

We included 1 year of FFS data for beneficiaries who were enrolled in FFS in 2004 and 

MA in 2005 to 2007; in FFS in 2005 and MA in 2006 to 2007; and FFS in 2006 and MA in 

2007. By including 1 year of baseline FFS data in our study period for MA beneficiaries 

who had been enrolled in FFS prior to joining an MA plan, we were able to analyze the 

impact of coding differences for MA beneficiaries during their first year in an MA plan.

Our MA study population included three types of beneficiaries, each of 

which we analyzed separately for each period:

Appendix I: Scope and Methodology

Estimating the Impact 

on MA Risk Scores
Appendix I: Scope and Methodology

Page 18 GAO-12-51 Medicare Advantage Diagnostic Coding

• MA joiners—beneficiaries enrolled in Medicare FFS for the entire first 

year of each period and then enrolled in MA for all of the following 

year,

• MA plan stayers—beneficiaries enrolled in the same MA plan for the 

first and second year of the period, and

• MA plan switchers—beneficiaries enrolled in one MA plan for the first 

year of the period and a second MA plan in the following year.

Our control population consisted of a retrospective cohort of FFS 

beneficiaries who were enrolled in FFS for all of 2007 and 2006. We 

followed these beneficiaries back to 2004 and included data for all years 

of continuous FFS enrollment. For both the study and control populations, 

we excluded data for years during which a beneficiary (1) was diagnosed 

with end-stage renal disease (ESRD) during the study year; (2) resided in 

a long-term care facility for more than 90 consecutive days; (3) died prior 

to July 1, 2008; (4) resided outside the 50 United States; Washington, 

D.C.; and Puerto Rico; or (5) moved to a new state or changed 

urban/rural status.

We calculated the actual change in disease score—the portion of the risk 

score that is based on a beneficiary’s coded diagnoses—for the MA study 

population for the following three time periods (in payment years): 2005 to 

2006, 2006 to 2007, and 2007 to 2008.2

 

2

We calculated disease scores using the 2007 version of the CMS-Hierarchical Condition 

Category (CMS-HCC) risk adjustment community model (used for payment in 2007 and 

2008), and summing the appropriate coefficients for each of the HCC variables. We 

normalized disease scores for each year to 2005 by using the FFS normalization factor 

that CMS used to normalize risk scores in 2008. Normalization keeps the average 

Medicare FFS risk score constant at 1.0 over time and is necessary to compare disease 

scores across years.

To estimate the change in 

disease scores that would have occurred if those MA beneficiaries were

enrolled continuously in FFS, we used our control population to estimate 

a regression model that described how beneficiary characteristics 
Appendix I: Scope and Methodology

Page 19 GAO-12-51 Medicare Advantage Diagnostic Coding

influenced change in disease score.3 In the regression model we used 

change in disease score (year 2 - year 1) as our dependent variable and 

included age, sex, hierarchical condition categories (HCC), HCC 

interaction variables, Medicaid status, and original reason for Medicare 

entitlement was disability as independent variables as they are specified 

in the CMS-HCC model. We also included one urban and one rural 

variable for each of the 50 United States; Washington, D.C.; and Puerto 

Rico as independent variables to identify beneficiary residential 

location.4,5 Then we used these regression models and data on 

beneficiary characteristics for our MA study population to estimate the 

change in disease scores that would have occurred if those MA 

beneficiaries had been continuously enrolled in FFS.6

We identified the difference between the actual and estimated change in 

disease scores as attributable to coding differences between MA and FFS 

because the regression model accounted for other relevant factors 

affecting disease score growth (see table 1). To convert these estimates 

of disease score growth due to coding differences into estimates of the 

impact of coding differences on 2010 MA risk scores, we divided the 

disease score growth estimates by the average MA risk score in 2010. 

Because 2010 risk scores were not available at the time we conducted 

our analysis, we calculated the average MA community risk score for the 

most recent data available (risk score years 2005 through 2008) and 

projected the trend to 2010 to estimate the average 2010 MA risk score.

 

3

The regression model explained 22.05 percent of the variation (adjusted R-squared) in 

disease scores when it was run on 2005-2006 data. It explained 22.79 percent of the 

variation when run on 2006-2007 data, and 18.67 percent when run on 2007-2008 data. In 

all three models, nearly all of the independent variables in the regression were statistically 

significant at the 5 percent level. We also performed an additional analysis to determine 

how sensitive our results were to the variables we accounted for. Specifically, we 

evaluated the impact on our results of only accounting for age and mortality. 

4

Beneficiary residential location is a proxy for other factors that vary with geography and 

that may affect the frequency with which beneficiaries interact with health care providers 

and therefore the completeness with which providers code diagnoses, such as physician 

practice patterns.

5

Except for rural variables for Washington, D.C.; New Jersey; and Rhode Island because 

these locations are entirely urban.

6

Our analysis also accounted for mortality by requiring all beneficiaries in our study 

populations to be alive through July 1, 2008.
Appendix I: Scope and Methodology

Page 20 GAO-12-51 Medicare Advantage Diagnostic Coding

Table 1: Annual Risk Score Growth Due to Coding Differences for GAO Study 

Population

Period MA joiners MA plan stayers MA plan switchers

All MA 

beneficiaries

2005-2006 0.0079 -0.0086 -0.0080 -0.0082

2006-2007 -0.0027 0.0211 0.0288 0.0200

2007-2008 0.0122 0.0253 0.0330 0.0249

Source: GAO.

Notes: We analyzed a retrospective cohort of beneficiaries from 2005 to 2008 to estimate the impact 

of coding differences between MA and Medicare FFS on MA risk scores. MA joiners are beneficiaries 

enrolled in Medicare FFS for the entire first year of each period and then enrolled in MA for all of the 

following year, MA plan stayers are beneficiaries enrolled in the same MA plan for the first and 

second year of a given period, and MA plan switchers are beneficiaries enrolled in one MA plan for 

the first year of a time period and a second MA plan in the following year.

We projected these estimates of the annual impact of coding difference 

on 2010 risk scores through 2010 using two different assumptions. One 

projection assumed that the annual impact of coding differences on risk 

scores was the same from 2008 to 2010 as it was from 2007 to 2008. The 

other projection assumed that the trend of increasing coding difference 

impact over 2005 to 2008 continued through 2010 (see fig. 2).7

 

7

For the latter projection, we fit a log-linear trend line to 2005-2006, 2006-2007, and 2007-

2008 impact estimates and used the resulting expression to extrapolate impact estimates 

to 2008-2009 and 2009-2010. We used the following coordinates (annual impact, period) 

from table 1 for all MA beneficiaries to estimate the model: (-0.0082, 1), (0.0200, 2), and 

(0.0249, 3).
Appendix I: Scope and Methodology

Page 21 GAO-12-51 Medicare Advantage Diagnostic Coding

Figure 2: Annual Impact of Coding Differences on 2010 MA Risk Scores for GAO’s Study Population, 2005 to 2010

Notes: We analyzed a cohort of beneficiaries from 2005 to 2008 to estimate the impact of coding 

differences between MA and Medicare FFS on MA risk scores. We used two different assumptions of 

the effect of coding differences on risk scores from 2008 to 2010. GAO’s low estimate assumes that 

the percentage of risk score growth attributable to coding differences from 2008 to 2010 was the 

same as it was from 2007 to 2008. GAO’s high estimate assumes that the percentage of risk score 

growth attributable to coding differences from 2008 to 2010 continues the trend from 2005 to 2008. 

To calculate the cumulative impact of coding differences on MA risk scores for 2007 through 2010, 

we summed the annual impact estimates for that period and adjusted each impact estimate to 

account for beneficiaries who disenrolled from the MA program before 2010.
Appendix I: Scope and Methodology

Page 22 GAO-12-51 Medicare Advantage Diagnostic Coding

To calculate the cumulative impact of coding differences on MA risk 

scores for 2007 through 2010, we summed the annual impact estimates 

for that period and adjusted each impact estimate to account for 

beneficiaries who disenrolled from the MA program before 2010.8 The 

result is the cumulative impact of coding differences from 2007 to 2010 on 

MA risk scores in 2010.9

We also performed an additional analysis to determine how sensitive our 

results were to our assumption that coding patterns for MA and FFS were 

similar in 2007. CMS believes that MA coding patterns may have been 

less comprehensive than FFS when the CMS-HCC model was 

implemented, and that coding pattern differences caused MA risk scores 

to grow faster than FFS; therefore, there may have been a period of 

“catch-up” before MA coding patterns became more comprehensive than 

FFS coding patterns. While the length of the “catch-up” period is not 

known, we evaluated the impact of assuming the actual “catch-up” period 

was shorter, and that MA and FFS coding patterns were similar in 2005. 

Specifically, we evaluated the impact of analyzing two additional years of 

coding differences by estimating the impact of coding differences from 

2005 to 2010.

We separately estimated the cumulative impact 

of coding differences from 2007 to 2010 on MA risk scores in 2010 for 

beneficiaries in MA plans with provider networks (HMOs, PPOs, and 

PSOs) because such plans may have a greater ability to affect provider 

coding patterns.

 

8

For 2006 and 2007, we used the actual disenrollment rates from our retrospective cohort 

of MA beneficiaries, while for 2008, 2009, and 2010 we used an annual disenrollment rate 

of 18.3 percent. To calculate our low and high estimates, we summed the annual impact 

estimates for 2007 to 2008, 2008 to 2009, and 2009 to 2010, each weighted by the 

percent of the 2010 MA cohort enrolled in that time period (see fig. 2):

GAO’s Low Estimate: 4.8 % = (54.5 % x 2.4 %) + (66.8 % x 2.4 %) + (81.7 % x 2.4 %)

GAO’s High Estimate: 7.1 % = (54.5 % x 2.4 %) + (66.8 % x 3.5 %) + (81.7 % x 4.2 %)

Weighted annual estimates may not sum to cumulative estimates due to rounding.

9

Our use of 2007 risk scores, based on prior year diagnoses, as the first risk scores to 

contribute to our cumulative coding estimate assumes that MA plans and Medicare FFS 

had similar coding patterns at this time. CMS estimated the cumulative impact of coding 

differences on risk scores over the same period.
Appendix I: Scope and Methodology

Page 23 GAO-12-51 Medicare Advantage Diagnostic Coding

To quantify the impact of both our and CMS’s estimates of coding 

differences on payments to MA plans in 2010, we used data on MA plan 

bids—plans’ proposed reimbursement rates for the average beneficiary—

which are used to determine payments to MA plans. We used these data 

to calculate total risk-adjusted payments for each MA plan before and 

after applying a coding adjustment, and then used the differences 

between these payment levels to estimate the percentage reduction in 

total projected payments to MA plans in 2010 resulting from adjustments 

for coding differences.10 Then we applied the percentage reduction in 

payments associated with each adjustment to the estimated total 

payments to MA plans in 2010 of $112.8 billion and accounted for 

reduced Medicare Part B premium payments received by CMS, which 

offset the reduction in MA payments (see table 2).11

Table 2: Impact of Adjustments for Coding Differences on Total Payments to MA 

Plans in 2010

Reduction in MA payments in 2010

Adjustment applied to reduce MA 

risk scores in 2010 (source) Percentage Dollars

3.4 percent (CMS) 2.4 2.7 billion

4.8 percent (GAO)a 3.4 3.9 billion

7.1 percent (GAO)b 5.2 5.8 billion

Source: GAO analysis of Medicare data.

Notes: We analyzed a retrospective cohort of beneficiaries from 2005 to 2008 to estimate the impact 

of coding differences on MA risk scores and used two different assumptions of the effect of coding 

differences on risk scores from 2008 to 2010. The percentage reduction in 2010 MA payments is less 

than the adjustment applied to 2010 MA risk scores because the impact of the adjustment to risk 

scores is reduced by additional payments some MA plans are eligible to receive.

a

GAO low estimate assumes the annual impacts from 2008 to 2010 are the same as the impact from 

2007 to 2008.

b

GAO high estimate assumes the annual impacts from 2008 to 2010 continue the trend of increasing 

annual impacts from 2005 to 2008.

 

10We assumed that MA plans did not adjust their bids in 2010 as a result of the 

adjustment for coding differences.

11We estimated $112.8 billion to be the total payments to MA plans without adjustments 

CMS made in 2010 for budget neutrality and for coding differences. Each estimate in table 

2 does not incorporate the impact of CMS’s 2010 adjustment. All estimates of the dollar 

impact of the adjustment for coding differences account for an 11.73 percent offset due to 

reduced Medicare Part B premiums received by Medicare, and do not include Medicare 

savings for a small number of beneficiaries with ESRD whose risk scores were adjusted 

for coding differences.

Estimating the Impact 

on Payments to MA 

Plans in 2010
Appendix I: Scope and Methodology 

Page 24 GAO-12-51 Medicare Advantage Diagnostic Coding

The CMS data we analyzed on Medicare beneficiaries are collected from 

Medicare providers and MA plans. We assessed the reliability of the CMS 

data we used by interviewing officials responsible for using these data to 

determine MA payments, reviewing relevant documentation, and 

examining the data for obvious errors. We determined that the data were 

sufficiently reliable for the purposes of our study.
Appendix II: Comments from the Centers for 

Medicare & Medicaid Services

Page 25 GAO-12-51 Medicare Advantage Diagnostic Coding

Appendix II: Comments from the Centers for 

Medicare & Medicaid Services
Appendix II: Comments from the Centers for 

Medicare & Medicaid Services

Page 26 GAO-12-51 Medicare Advantage Diagnostic Coding
Appendix III: GAO Contact and Staff 

Acknowledgments

Page 27 GAO-12-51 Medicare Advantage Diagnostic Coding

James C. Cosgrove, (202) 512-7114 or cosgrovej@gao.gov

In addition to the contact named above, Christine Brudevold, Assistant 

Director; Alison Binkowski; William Black; Andrew Johnson; Richard 

Lipinski; Elizabeth Morrison; and Merrile Sing made key contributions to 

this report.

Appendix III: GAO Contact and Staff 

Acknowledgments 

GAO Contact

Staff 

Acknowledgments 

(290782)
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