To test this theory, we explored whether the results differed by the local market's competitiveness as measured by the weighted average of each county's HerfindahlHirschman Index (HHI) for the concentration of MA insurers. Coding intensity in Medicare Advantage, by state, 2015. Results in Table1 show a reduction in expected OOP of $0.03 or less for each potential additional dollar from coding. Federal government websites often end in .gov or .mil. Second, we defined coding intensity by dividing each contract's average HCC risk score by the corresponding average for prescription drugbased risk scores and then dividing this ratio by the HCCtoRxMG ratio for the population enrolled in TM in each beneficiary's county. 10 18 ; or (b) more generous costsharing may hurt a plan's risk selection profile, so insurers avoid passingback larger rebates to lower OOP spending. , 5 When governments contract with private insurers to provide health benefits, insurer payments are often adjusted to compensate for differences in the health risk of enrollees. The site is secure. Additionally, we found that MA plan costs were higher by between $0.28 and $0.40 for each potential dollar of revenue increase from coding intensity. , When a plan's bid is below the local benchmark, beneficiaries are not required to pay any additional premium to enroll in the MA plan. 8 Plans are required to use rebated amounts to provide additional benefits to enrollees either in the form of additional coverage (eg, dental or vision care), lower costsharing (eg, lower deductibles or copays), or lower premiums (eg, lower Part D prescription drug premiums). official website and that any information you provide is encrypted CMS provides estimates for beneficiaries in various states of selfreported health (excellent, very good, good, fair, and poor). 22 government site. We also found that between $0.21 and $0.45 went toward reducing MLRs in response to each potential $1 from coding intensity. 1, There is no other setting in the US health system where risk adjustment has been studied as closely as the Medicare Advantage (MA) program. 10.1111/1475-6773.13591 14. 1 21. Estimates derived from ordinary least squares regressions of the outcome variable on either the CMS Hierarchical Condition Code (HCC) risk score variable holding prescription drugbased scores constant or the ratio of HCC to RxMG scores in the MA population divided by the ratio of those same scores in the TM population in the counties where beneficiaries in the MA contract resided. All regressions include yearspecific fixed effects, the local benchmark, whether the contract mostly had plans of the same plan type (eg, health maintenance organization), whether the contract mostly had special needs plans, indicators for nine Census regions, and the percentage of beneficiaries enrolled in the lowincome subsidy program for Part D benefits. And because both bids and coding are likely influenced by the plan's provider network and identifiable characteristics of the enrolled population, we included control variables for: whether the contract had mostly (75 percent or more) HMO, LPPO, RPPO, or PFFS plans, whether the contract had mostly (75 percent or more) SNP plans, whether the contract had most of its enrollment (50 percent or more) in one of nine Census regions, and quartile identifiers for the percentage of each contract's Medicare population that is enrolled in the Part D LowIncome Subsidy program. We extracted National Drug Codes from the 100 percent Part D claims data and linked each claims codes to at least one RxDefined Morbidity Group (RxMG) using version 11.1 of the Johns Hopkins Adjusted Clinical Group System (ACG). Jacobs PD, Kronick R. The effects of coding intensity in Medicare Advantage on plan benefits and finances. We develop a method for calculating coding intensity that relies on prescription drug utilization data to estimate the health risk of beneficiaries, which is not subject to the inconsistent reporting of medical coding in the MA and TM sectors. 3 But we are unaware of any studies that show how coding intensity has changed the provision of MA plan benefits. Coding intensity in 2015 varied widely across the United States as measured by our HCCRxMG standardized difference approach. We assessed coding intensity effects on riskstandardized bids and rebates to remove any mechanical relationship between those measures and planreported risk scores. We found that, for each $1 increase in potential revenue resulting from coding intensity, MA plan bid submissions declined by $0.10 to $0.19, and another $0.21 to $0.45 went toward reducing plans medical loss ratios, an indication of higher profitability. One interpretation is that prescription drug risk scores do not precisely measure risk, and that variation in the CMSHCC score, controlling for the prescription drug score, at least partially reflects variation in the morbidity of enrollees. California, Because MA plans may influence how providers prescribe medicine compared with standalone Part D drug plans, in AppendixS4, we validated the robustness of the prescription drugbased estimates by excluding pharmaceuticals for which physicians have discretion in prescribing behavior. Figure Figure11 shows considerable differences visible in the statespecific HCCtoRxMG ratios. , To achieve more thorough coding, plans might attempt to pay providers higher rates to find those diagnoses, which may explain why the association between coding and costs was nearly entirely explained by increases in claims costs. We obtained data on each plan's enrollee premiums and expected OOP costsharing for enrollees. For additional model details, see AppendixS1; for variable means, see AppendixS2. Because plans may retain revenues not spent to reduce bids or to finance costs associated with more intense coding, we also explored effects on contractlevel finances including the MLR and the difference between revenues and costs. Estimates derived from ordinary least squares regressions of the outcome variable on either the CMS Hierarchical Condition Code (HCC) risk score variable holding prescription drugbased scores constant or the ratio of HCC to RxMG scores in the MA population divided by the ratio of those same scores in the TM population in the counties where beneficiaries in the MA contract resided. We found only a small impact on beneficiary's projected outofpocket costs in a plan, which serves as a measure of the generosity of plan benefits, and a $0.11 to $0.16 reduction in premiums. 4 However, increased revenues from coding appear to have larger effects on MLRs, and, likely plan profits, than on the benefits offered to beneficiaries. Growth in coding intensity in Medicare Advantage, by state, 20082015. We also examined effects on contract revenues, costs, and claims costs. 5. First, we included the CMSHCC risk score as well as the prescription drugbased score for each contract as independent variables, under the assumption that higher HCC scores holding prescription drug scores constant indicated greater coding intensity. We measure coding intensity as the difference between a plan's HCC risk score and a measure of underlying health risk as proxied by prescription drug use. Note: Coding intensity defined as the ratio of CMSHCC risk scores relative to prescription drugbased risk scores in the Medicare Advantage population relative to the same ratio of scores in the Traditional Medicare population in each state in 2015. In recent years, differences between Medicare Advantage (MA) and Traditional Medicare (TM) in patterns of coding caused risk scores in MA to be approximately 7% to 10% higher than they would have been if the same beneficiary were receiving services in TM. Additionally, risk adjustment helps to dampen insurer incentives to design benefits or costsharing to dissuade highrisk enrollees or attract lowrisk ones. Additionally, we did not have access to contract or planspecific bidding and rebate data, and instead relied on countylevel averages at the plan type level. 5 CMS produces estimates of the expected OOP spending that beneficiaries would face in each plan using details about plan deductibles, outofpocket maximums, and other costsharing information. Coding intensity was measured by comparing the CMS risk score for each MA contract with a contract level risk score developed using prescription drug data. We hypothesized that more intense codinglarger differences between HCC and prescription drug scoreswould be associated with differences in plan benefits and finances as detailed below. This Standardized Difference definition of coding intensity is explained in detail in the text [Color figure can be viewed at wileyonlinelibrary.com]. Below we refer to this model as the HCCRxMG standardized difference approach. Corresponding to this reduction in bids, rebates increased between $0.06 and $0.15 for each $1 increase in potential revenues. Risk adjustment models that rely exclusively on pharmaceutical utilization are comparable in performance to those using medical diagnoses. As mentioned above, we expect a negative relationship between coding intensity and bids. We measured coding intensity in two different ways. All regressions include yearspecific fixed effects, the local benchmark, whether the contract mostly had plans of the same plan type (eg, health maintenance organization), whether the contract mostly had special needs plans, indicators for nine Census regions, and the percentage of beneficiaries enrolled in the lowincome subsidy program for Part D benefits. The new PMC design is here! While coding increases plan revenues, insurers also incur costs to obtain codes, including investments in information technology to track beneficiaries and supplying inhome nursing assessments and chart reviews. Yearspecific fixed effects were included in all models. Changes in Medicare Advantage plan characteristics associated with a $1 increase in potential revenues from coding intensity, by level of parent company concentration in county, 20082015. Premium reductions were associated with, in roughly equal proportion, lower supplemental MA premiums from plans bidding above the benchmark and plans that used rebates to lower Part D premiums. For details on the methodology developing these scores, see main text. CMS adjusts plan payments by their risk score, which is referred to as the Hierarchical Condition Code (HCC) score. Getting what we pay for: how do riskbased payments to Medicare advantage plans compare with alternative measures of beneficiary health risk? 20 Importantly, while we attempted to control for some observable determinants of plan outcomes that could be associated with intensity of coding, including type of network arrangement, our estimates may be biased by other determinants we were not able to identify. This is also consistent with earlier evidence suggesting that limited competition in the MA market enhances profitability. As a conservative approach, we used a cutoff of 3000 to identify markets where MA insurers may have significant market power. We then regressed nondrug spending for TM beneficiaries in 2015 on demographic and therapeutic class identifiers for 2014 and used the coefficients to predict relative risk. The views expressed in this article are those of the authors, and no official endorsement by the Department of Health and Human Services or AHRQ is intended or should be inferred. 18 and transmitted securely. We conducted regressions of plan outcomes, estimating the relationship between outcomes and coding intensity. A comparative analysis of claimsbased tools for health risk assessment, Accuracy of claimsbased risk scoring models. We addressed this issue by analyzing data at the county level and redefining health risk measures using the entire MA and TM populations at the county level. 13 Although using the MLR as an outcome will not distinguish between changes in administrative costs and profits, other research has established a connection between lower MLRs and higher margins both for insurers with larger market power and for individual market insurers subject to the minimum MLR requirements. Medicare claims and drug utilization data for Traditional Medicare (TM) beneficiaries were used to calibrate an independent measure of health risk. The .gov means its official. For details on the methodology developing these scores, see main text. USA. 23 Additionally, we found coding intensity does help finance more generous offerings in the MA market and thus reducing revenues would, to some extent, likely lead to higher premiums, fewer benefits, and thus possibly slower enrollment growth. We denominated outcome variables and coding intensity measures in both levels and logs. Steps to reduce favorable risk selection in Medicare advantage largely succeeded, boding well for health insurance exchanges, Measuring coding intensity in the Medicare advantage program, Upcoding Evidence from Medicare on Squishy Risk Adjustment, Report to the congress: Medicare payment policy, Projected coding intensity in Medicare advantage could increase Medicare spending by $200 billion over ten years, Insurers profit from Medicare Advantage's incentive to add coding that boosts reimbursement, Competitive bidding in Medicare Advantage: effect of benchmark changes on plan bids. Center for Financing, Access, and Cost Trends, We present results using crosssectional models calculated at the contract level and at the county level (described below). However, virtually nothing is known about what MA plans do with this potential revenue increase. Estimates derived from ordinary least squares regressions of the outcome variable on either the CMS Hierarchical Condition Code (HCC) risk score variable holding prescription drugbased scores constant or the ratio of HCC to RxMG scores in the MA population divided by the ratio of those same scores in the TM population in the counties where beneficiaries in the MA contract resided. To generate a single summary statistic for OOP costsharing, we used the CMS estimate for beneficiaries in good health status. But, given that coding likely has some marginal costs, in a perfectly competitive market, we should expect some, although not all, of the increased revenues from coding to show up as reductions in bids, with insurers using the larger rebates to provide additional benefits to attract and retain beneficiaries. , Thus, coding intensity in excess of the coding intensity adjustment potentially increased MA revenue by 1%4%. Bid reductions mostly translated into lower beneficiary premiums rather than OOP costs, a finding that is consistent with previous work on the passthrough of increased payments to MA plans. These levels of coding intensity were roughly 15 to 25 percent higher than in the three states with the smallest ratios: Minnesota (0.963), Hawaii (0.978), and New York (0.997), where the HCCtoRxMG ratios among MA enrollees were lower than or roughly equivalent to those ratios among TM beneficiaries. We separately computed coding's effect on the supplemental MA premium for plans bidding above the benchmarkexcluding any effect on Part D premiumsand found that, across all models, approximately half the effect of coding intensity lowered supplemental MA premiums and the remaining half lowered Part D premiums (not shown). These estimates are roughly twice the magnitude of the effects on bids, suggesting revenues from coding intensity are passed back at higher rates to MA insurers than to beneficiaries. we included the CMSspecified benchmark as a control variable. Changes in Medicare Advantage plan financial characteristics associated with a $1 increase in potential revenues from coding intensity, 2014. The Department of Justice considers markets with an HHI above 2500 to be highly concentrated. We excluded beneficiaries: (a) with endstage renal disease; (b) in CMS demonstration projects; (c) using hospice care; and (d) for whom Medicare was a secondary payer. MA plan characteristics and administrative records from the Centers for Medicare and Medicaid Services (CMS) for the sample of beneficiaries enrolled in both MA and Part D between 2008 and 2015. We found that each potential additional dollar earned from coding intensity was associated with a reduction in premiums of between $0.11 and $0.16. Our paper is the first we are aware of to establish a link between coding intensity and the level of benefits and premiums that beneficiaries face. Maryland, Plan rebates increased between $0.06 and $0.15 for each $1 increase in potential revenues when estimated at the contract level and between $0.10 and $0.17 across the six countylevel models. , The validity of our empirical approach assumes the independence of our coding measure with other potential determinants of plan outcomes. Accessibility Using these prescription drug scores, our estimates are the first to show that insurers with higher levels of coding intensity both lower beneficiary premiums and increase their expected profitability through lower medical loss ratios. 9. Note: Coding intensity defined as the ratio of CMSHCC risk scores relative to prescription drugbased risk scores in the Medicare Advantage population relative to the same ratio of scores in the Traditional Medicare population in each state. For each additional $1 in coding intensity, beneficiaries could expect lower premiums in contracts participating in competitive counties (between $0.14 and $0.20) than those participating in less competitive counties (between $0.02 and $0.04). The adoption of policies to more completely adjust for coding intensity would likely affect both beneficiaries and plan profits. For each potential dollar received due to coding intensity, between $0.21 and $0.45 went toward reducing MLRs. The measure was constructed by decomposing how prospective Medicare claims for hospital and ambulatory services are related to the therapeutic classes associated with prescription drug utilization. Note though that our results for premiums and OOP spending, which were derived from plan level data, are consistent with outcomes measured at the county level. about navigating our updated article layout. Table1 summarizes four methods for calculating effects on plan characteristics: including level and log versions of both the model with HCC and RxMG scores included separately and the HCCRxMG standardized difference model. Any net revenues from coding may enable insurers to offer additional benefits or lower premiums in the hopes of attracting enrollees. Using this measure of coding intensity, the national average in 2015 was 1.077. Changes in Medicare Advantage plan characteristics associated with a $1 increase in potential revenues from coding intensity, 20082015. will also be available for a limited time. Many of the states with high levels of coding intensity in 2015 also experienced high rates of growth between 2008 and 2015 (Figure (Figure2).2). However, evidence suggests MA insurers do not behave in ways that models of perfect competition would predict. As documented elsewhere, we found substantial variation in MA contracts coding intensity. 8600 Rockville Pike PMC legacy view We included any supplemental enrollee premium when the MA plan bid exceeded the local benchmark(s). When plans bid above the benchmark, they must charge enrollees the difference between the bid and the benchmark as an additional enrollee premium to cover their projected costs. Our preferred results were from analyses of outcomes at the contract level, because, as noted earlier, coding intensity results from strategies pursued by insurers. How do Medicare advantage beneficiary payments vary with tenure? By showing how net revenues from coding intensity are allocated in the form of bids, rebates, benefits, and plan finances including the MLR and underlying costs, our findings add to the literature on competition by linking market power in MA to the extent of passthrough to benefits. Centers for Medicare and Medicaid Services While higher drug scores could be a result of greater generosity of the Part D prescription drug plan, in AppendixS6, we show that the score is not related to whether the beneficiary enrolled in supplemental coverage to cover costsharing. , An official website of the United States government. 19 sharing sensitive information, make sure youre on a federal Growth in coding intensity expressed as average annual percentage point change in this Standardized Difference definition. , HHI was defined as the sum of the squares of each insurer's market share in a county. Herbert Wertheim School of Public Health, University of California San Diego, 3 No Other Disclosures. 2 The https:// ensures that you are connecting to the As expected, coding intensity's effect on bids was substantially larger in counties with higher levels of MA competition than in less competitive counties. To address this, in AppendixS5, we analyze whether countylevel outcomes were related to countyspecific measures of coding intensity, removing risk selection between MA plans as a potential explanation of our results. Analyses based on the MLR data were only available for 2014. How much do they retain as extra profit? clinical coding, cost sharing, insurance premiums, managed competition, Medicare advantage, The effects of coding intensity in Medicare Advantage on plan benefits and finances, Creaming, skimping and dumping: provider competition on the intensive and extensive margins. Alternatively, insurers can retain the surplus as profits as long as they are not constrained by competitive forces or medical loss ratio (MLR) requirements. Each year, plans submit an estimate of the monthly revenues they require to cover their costs of providing Medicare benefits to a beneficiary in average health in the county, referred to as the plan's bid. The Centers for Medicare and Medicaid Services (CMS) compares the plan's bid to the benchmark for the county, where the benchmark is an estimate of the amount that feeforservice Medicare spends for a beneficiary in average health. There was a distinct regional pattern to coding intensity, with more coding intensity in the South, Southwest, and West Coast, and less coding in the Middle Atlantic, New England, and Upper Midwest states. Estimated financial effects of the Patient Protection and Affordable Care Act, as Amended, Options for reducing the deficit: 2019 to 2028: Modify payments to Medicare advantage plans for health risk, http://medpac.gov/docs/defaultsource/reports/mar19_medpac_entirereport_sec.pdf, https://www.modernhealthcare.com/article/20180901/NEWS/180839977/insurersprofitfrommedicareadvantagesincentivetoaddcodingthatboostsreimbursement, https://www.soa.org/Files/Research/Projects/riskassessmentc.pdf, https://www.soa.org/globalassets/assets/files/research/research2016accuracyclaimsbasedriskscoringmodels.pdf, https://www.cms.gov/Medicare/MedicareAdvantage/PlanPayment/PlanPaymentData.html, https://www.justice.gov/atr/herfindahlhirschmanindex, http://assets.milliman.com/ektron/zerodollarmapdpremiumplan.pdf, https://www.gao.gov/assets/660/659836.pdf, https://www.cms.gov/ResearchStatisticsDataandSystems/Research/ActuarialStudies/downloads/PPACA_20100422.pdf, https://www.cbo.gov/budgetoptions/2018/54736. However, the prescription drugbased score may not perfectly identify actual health risk and therefore our estimates of the effect of HCC scores conditional on RxMG scores may be subject to measurement error. *** P<.01; ** P<.05; * P<.10; RxMG=Prescription Drug Morbidity Group risk score; HHI=HerfindahlHirschman Index. We found MA contracts used a portion of the revenues from increased coding intensity to reduce bids by between $0.10 and $0.19 for every extra dollar of potential revenue due to coding intensity. AppendixS3 provides additional details of our sample selection. Economics provides some intuition for the relationship we should expect between coding intensity and plan behavior. How much of it do they return to beneficiaries in extra benefits? Agency for Healthcare Research and Quality, Previous research has clearly documented that MA plans code health conditions for their beneficiaries more intensely than those conditions are coded in TM. Joint Acknowledgment/Disclosure Statement: The authors would like to thank Pete Welch from the Office of The Assistant Secretary for Planning and Evaluation at the Department of Health and Human Services for his guidance and assistance throughout this project and Thomas M. Selden, Patricia S. Keenan, and Joel W. Cohen of the Agency for Healthcare Research and Quality (AHRQ) as well as Pete Welch for their comments on earlier drafts of the manuscript. Because we assigned morbidity using prescription drug claims, our measure of relative risk does not depend on planreported medical diagnoses. ***P<.01; **P<.05; *P<.10; RxMG=Prescription Drug Morbidity Group risk score. *** P<.01; ** P<.05; * P<.10; RxMG=Prescription Drug Morbidity Group risk score. Further, our results suggest contracts in competitive counties disproportionately used net revenues from coding intensity to reduce beneficiary costs compared with contracts in more consolidated counties. 12 18 This approach adjusts for local variation in prescription drug utilization, care delivery patterns, and local differences in selection between MA and TM by expressing HCC and prescription drug score differences relative to those differences among TM beneficiaries in a local area. For details on the methodology developing these scores, see main text. By including both the HCC and RxMG scores, this approach allowed for a flexible functional form. Similarly, we hypothesize that the degree to which insurers passthrough increased net revenues from coding intensity to bids will be proportional to the level of competition. For bids and rebates, we relied on publicly available CMS data summarized at the county and plan type level. FOIA Most importantly, our estimates were not sensitive to defining risk scores among all Medicare enrollees (column 5), reducing the likelihood that our results are a spurious result of biased selection in Medicare. More generous benefits resulting from higher rates of coding intensity may be one explanation for the continued MA enrollment growth in the face of benchmark reductions. 4 In theory, the current MA risk adjustment system appropriately pays insurers for their expected risk of enrollee spending because payments are adjusted for the demographic characteristics and medical conditions that plans report (age, gender, Medicaid status, institutionalized status, and a series of medical condition codes). Bethesda, MD 20894, Web Policies AppendixS5 also confirmed coding intensity's larger effect on premiums than on OOP costs. To develop prescription drug scores, we assigned therapeutic classes to beneficiaries based on their prescription drug utilization. For the regression analyses of MA plan outcomes, we limited the sample to beneficiaries enrolled in MA as of July in the second year of each cohort. Through a combination of strategies including inhome health assessments by nurses and retrospective chart reviews, MA plans have been successful in increasing the reported risk scores of their enrollees compared with those in TM. This measure assumes that the utilization of prescription drugs is not subject to the same inflationary pressures that incentivize greater reporting of diagnosis codes for Parts A and B. Adjusting for enrollee risk compensates insurers for their underlying costs thereby encouraging participation in public insurance programs. For Part D enrollees in TM throughout all of 2014, we regressed Medicarepaid Part A and Part B spending in 2015 (from the 100 percent Standard Analytic Files for Parts A and B) on these RxMGs. Regressions include the local benchmark, whether the contract mostly had plans of the same plan type (eg health maintenance organization), whether the contract mostly had special needs plans, indicators for nine Census regions, and the percentage of beneficiaries enrolled in the lowincome subsidy program for Part D benefits. Careers, GUID:0476190B-C593-40BB-BF7C-ECD1149B35DD, GUID:2221F602-8252-4149-AC3A-DD4B38421288. Unfortunately, reliable data on the nature of the cost function for coding Medicare Advantage enrollees are sparse, so the profitability of coding is not clear. The three states with the largest HCCtoRxMG ratios were: Alaska (1.223), Nevada (1.211), and Georgia (1.151). In 2018, the coding intensity adjustment applied by CMS was 5.91%. Alternatively, because plans incur costs to find additional diagnoses, costs are higher in plans that code more intensely. Health Serv Res.2021;56:178187. , Evidence from Medicare advantage, Hang on tight! Why maintaining a zerodollar MAPD premium plan is worth the effort, Medicare advantage: 2011 profits similar to projections for most plans, but higher for plans with specific eligibility requirements, Who benefits when the government pays more? With greater coding intensity, plans bidding below their local benchmarks could obtain higher rebates, which would translate into lower enrollee premiums or lower enrollee costsharing. We assigned bid and rebate averages to beneficiaries based on the county they resided in during their last month of MA enrollment in each calendar year as well as the type of plan in which they were enrolled (health maintenance organization (HMO), private feeforservice plan (PFFS), local or regional preferred provider organization (LPPO/RPPO), or special needs plan (SNP)). Our final sample varies depending on the year from 7.2 million beneficiaries enrolled in MA in 2008 to 13.8 million in 2015. 7. 17 Specifically, when insurers increase risk scores by coding more intensely than in TM, their revenues increase without corresponding changes in the risk profile of their enrollees, although plan costs may simultaneously rise to implement the more intense coding practices.