United States Government Accountability Office
GAO Report to Congressional Requesters
January 2012
MEDICARE
ADVANTAGE
CMS Should Improve
the Accuracy of Risk
Score Adjustments for
Diagnostic Coding
Practices
GAO-12-51
January 2012
MEDICARE ADVANTAGE
CMS Should Improve the Accuracy of Risk Score
Adjustments for Diagnostic Coding Practices
Highlights of GAO-12-51, a report to
congressional requesters
Why GAO Did This Study What GAO Found
The Centers for Medicare & Medicaid GAO found that diagnostic coding differences exist between MA plans and
Services (CMS) pays plans in Medicare FFS. Using data on beneficiary characteristics and regression analysis,
Medicare Advantage (MA)—the private GAO estimated that before CMS’s adjustment, 2010 MA beneficiary risk scores
plan alternative to Medicare fee-for- were at least 4.8 percent, and perhaps as much as 7.1 percent, higher than they
service (FFS)—a predetermined likely would have been if the same beneficiaries had been continuously enrolled
amount per beneficiary adjusted for in FFS. The higher risk scores were equivalent to $3.9 billion to $5.8 billion in
health status. To make this adjustment, payments to MA plans. Both GAO and CMS found that the impact of coding
CMS calculates a risk score, a relative differences increased over time. This trend suggests that the cumulative impact
measure of expected health care
of coding differences in 2011 and 2012 could be larger than in 2010.
costs, for each beneficiary. Risk scores
should be the same among all In contrast to GAO, CMS estimated that 3.4 percent of 2010 MA beneficiary risk
beneficiaries with the same health scores were attributable to coding differences between MA plans and Medicare
conditions and demographic FFS. CMS’s adjustment for this difference avoided $2.7 billion in excess
characteristics. Policymakers raised payments to MA plans. CMS’s 2010 estimate differs from GAO’s in that CMS’s
concerns that differences in diagnostic methodology did not include more current data, did not incorporate the trend of
coding between MA plans and the impact of coding differences over time, and did not account for beneficiary
Medicare FFS could lead to
characteristics other than age and mortality, such as sex, health status, Medicaid
inappropriately high MA risk scores
enrollment status, beneficiary residential location, and whether the original
and payments to MA plans. CMS
began adjusting for coding differences reason for Medicare entitlement was disability.
in 2010. GAO (1) estimated the impact Percentage of 2010 MA Risk Scores Attributable to Coding Differences and Effect on
of any coding differences on MA risk Payments to MA Plans
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 CMS did not update its coding adjustment estimate in 2011 and 2012 to include
score adjustments by taking steps more current data, to account for additional years of coding differences, or to
such as incorporating adjustments for incorporate the trend of the impact of coding differences. By continuing to
additional beneficiary characteristics, implement the same 3.4 percent adjustment for coding differences in 2011 and
using the most current data available, 2012, CMS likely underestimated the impact of coding differences in 2011 and
accounting for all relevant years of 2012, resulting in excess payments to MA plans.
coding differences, and incorporating
GAO’s findings underscore the importance of both CMS continuing to adjust risk
the effect of coding difference trends.
scores to account for coding differences and ensuring that those adjustments are
View GAO-12-51. For more information, as complete and accurate as possible.
contact James C. Cosgrove at (202) 512-7114
or cosgrove@gao.gov. In its comments, CMS stated that it found our findings informative. CMS did not
comment on our recommendation.
United States Government Accountability Office
Contents
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
Page i 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 ii 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 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.
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. 3 Information on a
beneficiary’s age, sex, Medicaid enrollment status, original reason for
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.
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 1 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 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.
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. 5 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.
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
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 2 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 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
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).
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)).
10
CMS 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 3 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
11
42 U.S.C. §1395w-23(a)(1)(C)(ii)(III).
Page 4 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 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
12
We 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.
13
CMS estimated the cumulative impact of coding differences on risk scores over the
same period.
14
Risk 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.
15
We 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 5 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
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.
16
Plans with provider networks include HMOs, PPOs, and plans offered by PSOs.
17
Specifically, we evaluated the impact of analyzing two additional years of coding
differences by estimating the impact of coding differences from 2005 to 2010.
18
CMS calls this percentage the Coding Pattern Difference Adjustment factor.
Page 6 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
Background 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 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 CMS-
HCC model adjusts MA payments more accurately than previous models
19
This model was called the Principal Inpatient Diagnostic Cost Group model.
20
CMS 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.
Page 7 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 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.
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. 22 To apply this average
estimate to 2010 MA beneficiaries,
• 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, 23 and
21
The 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.
22
The average was weighted by the number of beneficiaries enrolled in the same MA plan
during each time period.
23
CMS 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 8 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
Diagnostic Coding Medicare FFS and that these differences had a substantial effect on
Differences 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
Accounted for $5.8 billion in payments to MA plans in 2010 before CMS’s adjustment for
Estimated MA Risk coding differences. Before CMS reduced 2010 MA beneficiary risk
Score Growth of at 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
Least $3.9 Billion in have been as a result of diagnostic coding differences, that is, if the same
2010, with Likely beneficiaries had been continuously enrolled in FFS (see fig. 1). Our
estimates suggest that, after accounting for CMS’s 3.4 percent reduction
Larger Impacts in to MA risk scores in 2010, MA risk scores were too high by at least
2011 and 2012 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.
24
CMS’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.
Page 9 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
25
See app. I for more detail on our methodology.
Page 10 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.
26
Prior 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.
27
We 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.
28
CMS 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 11 GAO-12-51 Medicare Advantage Diagnostic Coding
CMS’s estimate of the impact of coding differences on 2010 MA risk
CMS’s Adjustment for scores was smaller than our estimate due to the collective impact of three
Coding Differences methodological differences described below. For its 2011 and 2012
adjustments, the agency continued to use the same estimate of the
Likely Resulted in impact of coding differences it used in 2010, which likely resulted in
Excess Payments to excess payments to MA plans.
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
29
Specifically, 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.
30
We 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.
Page 12 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 13 GAO-12-51 Medicare Advantage Diagnostic Coding
Risk adjustment is important to ensure that payments to MA plans
Conclusions 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
Recommendations for CMS should take steps to improve the accuracy of the adjustment made
Executive Action 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
Agency Comments reprinted in appendix II.
and Our Evaluation
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
Page 14 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 15 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
Page 16 GAO-12-51 Medicare Advantage Diagnostic Coding
Appendix I: Scope and Methodology
Appendix I: Scope and Methodology
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
Estimating the Impact between MA plans and Medicare FFS affected MA risk scores in 2010,
on MA Risk Scores 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
Our MA study population included three types of beneficiaries, each of
which we analyzed separately for each period:
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.
Page 17 GAO-12-51 Medicare Advantage Diagnostic Coding
Appendix I: Scope and Methodology
• 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 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
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.
Page 18 GAO-12-51 Medicare Advantage Diagnostic Coding
Appendix I: Scope and Methodology
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.
Page 19 GAO-12-51 Medicare Advantage Diagnostic Coding
Appendix I: Scope and Methodology
Table 1: Annual Risk Score Growth Due to Coding Differences for GAO Study
Population
All MA
Period MA joiners MA plan stayers MA plan switchers 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).
Page 20 GAO-12-51 Medicare Advantage Diagnostic Coding
Appendix I: Scope and Methodology
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.
Page 21 GAO-12-51 Medicare Advantage Diagnostic Coding
Appendix I: Scope and Methodology
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 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.
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.
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.
Page 22 GAO-12-51 Medicare Advantage Diagnostic Coding
Appendix I: Scope and Methodology
To quantify the impact of both our and CMS’s estimates of coding
Estimating the Impact differences on payments to MA plans in 2010, we used data on MA plan
on Payments to MA bids—plans’ proposed reimbursement rates for the average beneficiary—
which are used to determine payments to MA plans. We used these data
Plans in 2010 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
a
4.8 percent (GAO) 3.4 3.9 billion
b
7.1 percent (GAO) 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.
10
We assumed that MA plans did not adjust their bids in 2010 as a result of the
adjustment for coding differences.
11
We 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.
Page 23 GAO-12-51 Medicare Advantage Diagnostic Coding
Appendix I: Scope and Methodology
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.
Page 24 GAO-12-51 Medicare Advantage Diagnostic Coding
Appendix II: Comments from the Centers for
Appendix II: Comments from the Centers for
Medicare & Medicaid Services
Medicare & Medicaid Services
Page 25 GAO-12-51 Medicare Advantage Diagnostic Coding
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
Appendix III: GAO Contact and Staff
Acknowledgments
Acknowledgments
James C. Cosgrove, (202) 512-7114 or cosgrovej@gao.gov
GAO Contact
In addition to the contact named above, Christine Brudevold, Assistant
Staff Director; Alison Binkowski; William Black; Andrew Johnson; Richard
Acknowledgments Lipinski; Elizabeth Morrison; and Merrile Sing made key contributions to
this report.
(290782)
Page 27 GAO-12-51 Medicare Advantage Diagnostic Coding
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Medicare Advantage: CMS Should Improve the Accuracy of Risk Score Adjustments for Diagnostic Coding Practices
Published by the Government Accountability Office on 2012-01-12.
Below is a raw (and likely hideous) rendition of the original report. (PDF)