Medicare Advantage: CMS Should Improve the Accuracy of Risk Score Adjustments for Diagnostic Coding Practices
Released: 2012-01-26T00:00:00

Letter

Background

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.

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

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.

CMS’s Adjustment for Coding Differences Likely Resulted in Excess Payments to MA Plans

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.

Conclusions

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.

Recommendations for Executive Action

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.

Agency Comments and Our Evaluation

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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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.

Estimating the Impact on MA Risk Scores

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.

Estimating the Impact on Payments to MA Plans in 2010

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.

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

Appendix III: GAO Contact and Staff Acknowledgments

GAO Contact

Staff Acknowledgments

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.