Medicare Advantage: CMS Should Improve the Accuracy of Risk Score Adjustments for Diagnostic Coding Practices
Published: 2012-01-12T00:00:00

Fast Facts:

Highlights:
section_title: Why GAO Did This Study
paragraphs: The Centers for Medicare & Medicaid Services (CMS) pays plans in Medicare Advantage (MA)the private plan alternative to Medicare fee-for-service (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.
section_title: What GAO Found
paragraphs: 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.
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.
section_title: What GAO Recommends
paragraphs: 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.

section_title: Letter
paragraphs: 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. 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. 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. Information on a beneficiary’s age, sex, Medicaid enrollment status, original reason for  Medicare entitlement (i.e., age or disability), and major medical conditions  all factor into the calculation of the risk score. 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. 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.
One important exception is hospital acute inpatient services, for which Medicare payment  is based on Medicare severity diagnosis related groups rather than services. such growth. 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. 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.
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. 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. 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  gradually increase to not less than 5.70 percent in 2019 and subsequent  years.
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  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.
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.
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.
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. 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.
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.
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,  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.
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.
subsections: section_title: Background
paragraphs: 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. In 2004, CMS began adjusting payments to MA plans based  on the CMS-HCC model.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   HCCs, which represent major medical  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   For example, CMS used MA beneficiary  scores for the following year.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.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, and
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.
CMS implemented this same adjustment of 3.41 percent in 2011 and has  announced it will implement this same adjustment in 2012.
section_title: 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
paragraphs: 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.
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.
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.
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.
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   While we did not have more recent data,  increased from 2004 to 2008. the trend of coding differences through 2008 suggests that the impact of  coding differences in 2011 and 2012 could be larger than in 2010.
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.
section_title: CMS’s Adjustment for Coding Differences Likely Resulted in Excess Payments to MA Plans
paragraphs: 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.
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  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.
section_title: Conclusions
paragraphs: 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.
section_title: Recommendations for Executive Action
paragraphs: 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.
section_title: Agency Comments and Our Evaluation
paragraphs: 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  cumulative impact estimates, and the trend line used to generate our high  estimate.
CMS did not comment on our recommendation for executive action.
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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.
section_title: Appendix I: Scope and Methodology
paragraphs: 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.
subsections: section_title: Estimating the Impact on MA Risk Scores
paragraphs: 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.Our MA study population included three types of beneficiaries, each of  which we analyzed separately for each period:
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.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   To estimate the change in  influenced change in disease score. 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. 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.
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.
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).
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. The  result is the cumulative impact of coding differences from 2007 to 2010 on  MA risk scores in 2010.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 separately estimated the cumulative impact  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.
section_title: Estimating the Impact on Payments to MA Plans in 2010
paragraphs: 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. 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).
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.
section_title: Appendix II: Comments from the Centers for Medicare & Medicaid Services
section_title: Appendix III: GAO Contact and Staff Acknowledgments
subsections: section_title: GAO Contact
section_title: Staff Acknowledgments
paragraphs: 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.