Incorporating Prescription Drugs Into Affordable Care Act Risk Adjustment.

BACKGROUND: The 2010 Patient Protection and Affordable Care Act reformed the individual and small group health insurance markets and established a risk adjustment program to create a level playing field for competition. A new set of predictive models for measuring enrollee risk across plans was developed for the Patient Protection and Affordable Care Act-reformed markets, referred to as the Department of Health and Human Services Hierarchical Condition Category (HHS-HCC) models. Beginning in 2018, selected prescription drug classes were added to the models as risk markers. OBJECTIVE: We describe the motivations, concerns, methodology, and results of adding prescription drug utilization to the HHS-HCC models. METHODS: Separate HHS-HCC models are estimated by enrollee age and plan actuarial value. We defined and added 10 prescription drug classes, called RXCs, to the HHS-HCC adult models. RESULTS: Using selected RXCs alongside demographic and diagnostic indicators yielded modest overall improvement in HHS-HCC models' predictive power. Also, adding RXCs captures the higher costs of enrollees taking certain expensive pharmaceuticals and allows imputation of diagnoses for enrollees utilizing a drug but lacking the associated diagnosis. CONCLUSIONS: Including selected drugs in risk adjustment improved the models' predictive power. In addition, inclusion of selected drugs may discourage insurers from using formulary and drug benefit design to avoid enrollment of patients taking high-cost drugs, such as for HIV, multiple sclerosis, and rheumatoid arthritis, and improve access for enrollees taking these drugs. Adding RXCs also may improve plan risk measurement for plans with less complete diagnosis reporting.

A Comparison of Health Risk and Costs Across Private Insurance Markets.

BACKGROUND: The Patient Protection and Affordable Care Act (PPACA) established new parameters for the individual and small group health insurance markets starting in 2014. We study these 2 reformed markets by comparing health risk and costs to the more mature large employer market. STUDY DATA: For 2017, claims data for all enrollees in PPACA-compliant individual and small group market plans as well as claims data from a sample of large employer market enrollees. VARIABLES AND METHODOLOGY: Risk scores and total (unadjusted and risk-adjusted) per-member-per-month (PMPM) allowed charges. Differences across markets in enrollment duration, age, and geographic distribution are addressed. The analysis is descriptive. RESULTS: Compared with large employer market enrollees, health risk was 3% lower among PPACA small group market enrollees and 20% higher among PPACA individual market enrollees. After adjusting for differences in health risk, enrollees in the PPACA individual market had 27% lower PMPM allowed charges than enrollees in the large employer market and enrollees in the PPACA small group market had 12% lower PMPM allowed charges than enrollees in the large employer market. CONCLUSIONS: On average, the PPACA individual market enrolls sicker individuals than the 2 group markets. But this does not translate to higher health costs; in fact, enrollees in the PPACA individual market accumulate lower allowed charges than enrollees in the large employer market. Lower-income enrollees particularly accumulate lower allowed charges. Narrower networks and increased enrollee cost-sharing among individual market plans, though they may reduce the value of coverage, likely significantly reduce allowed charges.

Medicare Outpatient Physical Therapy Expenditures Vary by Diagnosis and Functional Mobility.

BACKGROUND: Clinical characteristics driving variations in Medicare outpatient physical therapy expenditures are inadequately understood. OBJECTIVE: The objectives of this study were to examine variations in annual outpatient physical therapy expenditures of Medicare fee-for-service beneficiaries by primary diagnosis and baseline functional mobility, and to assess whether case mix groups based on primary diagnosis and functional mobility scores would be useful for expenditure differentiation. DESIGN: This was an observational, longitudinal study. METHODS: Volunteer providers in community settings participated in data collection with Continuity Assessment Record and Evaluation-Community (CARE-C) assessments for Medicare fee-for-service beneficiaries. Annual outpatient physical therapy expenditures were calculated using allowed charges on Medicare claims; primary diagnosis and baseline functional mobility were obtained from CARE-C assessments. Whether annual expenditures varied significantly across primary diagnosis groups and within diagnosis groups by functional mobility was examined. RESULTS: Data for 4210 patients (mean [SD] age = 72.9 [9.9] years; 64.6% women) from 127 providers were included. Mean expenditures differed significantly across 12 primary diagnosis groups created from CARE-C clinician-reported diagnoses (F = 12.73; df = 11). Twenty-five pairwise differences in 66 pairwise diagnosis group comparisons were statistically significant. Within 8 diagnosis groups, expenditures were significantly higher for low-mobility subgroups than for high-mobility subgroups; borderline significance was achieved for 1 diagnosis group. LIMITATIONS: The small convenience sample limited the statistical power and the generalizability of the results. CONCLUSIONS: Significant variations in physical therapy expenditures based on primary diagnosis and baseline functional mobility support the use of these variables in predicting outpatient physical therapy expenditures. Although Medicare's annual therapy spending cap was repealed effective January 2018, the data from this study provide an initial foundation to inform any future policy efforts, such as targeted medical review, risk-adjusted therapy payments, or case mix groups as potential payment alternatives. Additional research with larger samples is needed to further develop and test case mix groups and improve generalizability to the national population. Refined case mix groups could also help providers prognosticate physical therapy expenditures based on patient profiles.

Episode-Based Payment for the Medicare Outpatient Therapy Benefit.

OBJECTIVE: To conduct an analysis of Medicare outpatient therapy episodes of care and associated payment implications. DESIGN: Retrospective observational design using Medicare claims data. To descriptively analyze the composition of outpatient therapy episodes, both variable- and fixed-length episodes are explored. The variable-length episode definition organizes services into episodes based on the time pattern of therapy service utilization, using 60-day clean periods. Fixed-length episodes are also examined, beginning with the first therapy utilization in calendar year 2010 and extending 30, 60, and 90 days. SETTING: The study is focused on community-dwelling users of outpatient therapy. PARTICIPANTS: The sample includes all Medicare patients who used outpatient therapy beginning at any point in 2010. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Mean episode payments and episode lengths in calendar days. RESULTS: Variable-length outpatient therapy episodes have a mean payment of $881. On average, outpatient therapy episodes last 43 calendar days. Mean therapy durations for the 30-, 60-, and 90-day fixed-length episodes are 20, 31, and 38 calendar days, respectively. The 30-, 60-, and 90-day fixed-length initial episodes account for 40%, 55%, and 63%, respectively, of total Medicare payments. Simulations of episode-based payment illustrate the difficulty of avoiding a large number of substantial underpayments, because of the right-skewed distribution of total actual payments. CONCLUSIONS: A strength of episode payment is that it reduces cost and potentially wasteful variation within episodes. Given the substantial variation in therapy episode expenditures, absent improvements in available data and in predictive information, a pure lump sum episode payment would result in substantial revenue changes for many episodes. Additional data are needed to better explain the wide variation in episode expenditures.

Refinements of the Medicare Outpatient Therapy Annual Expenditure Limit Policy.

BACKGROUND: A Medicare beneficiary's annual outpatient therapy expenditures that exceed congressionally established caps are subject to extra documentation and review requirements. In 2011, these caps were $1,870 for physical therapy and speech-language pathology combined and $1,870 for occupational therapy separately. OBJECTIVE: This article considers the distributional effects of replacing current cap policy with equal caps by therapy discipline (physical therapy, occupational therapy, and speech-language pathology) or a single combined cap, and risk adjusting the physical therapy cap using beneficiary characteristics and functional status. METHODS: Alternative therapy cap policies are simulated with 100% Medicare claims for 2011 therapy users (N=4.9 million). A risk-adjusted cap for annual physical therapy expenditures is calculated from a quantile regression estimated on a sample of physical therapy users with diagnoses and clinician assessments of functional ability merged to their claims (n=4,210). RESULTS: Equal discipline-specific caps of $1,710 each for physical therapy, occupational therapy, and speech-language pathology result in the same aggregate Medicare expenditures above the caps as 2011 cap policy. A single combined-disciplines cap of $2,485 also results in the same aggregate expenditures above the cap. Risk adjustment varies the physical therapy cap by as much as 5 to 1 across beneficiaries and equalizes the probability of exceeding the physical therapy cap across diagnosis and functional status groups. LIMITATIONS: One limitation of the study was the assumption of no behavioral response on the part of beneficiaries or providers to a change in cap policy. Additionally, analysis of risk adjusting the therapy caps was limited by sample size. CONCLUSIONS: Equal discipline-specific caps for physical therapy, occupational therapy, and speech-language pathology are more equitable to high users of both physical therapy and speech-language pathology than current cap policy. Separating the physical therapy and speech-language pathology caps is a change that policy makers could consider. Risk adjustment of the therapy caps is a first step in incorporating beneficiary need for services into Medicare outpatient therapy payment policy.

Affordable Care Act risk adjustment: overview, context, and challenges.

Beginning in 2014, individuals and small businesses will be able to purchase private health insurance through competitive marketplaces. The Affordable Care Act (ACA) provides for a program of risk adjustment in the individual and small group markets in 2014 as Marketplaces are implemented and new market reforms take effect. The purpose of risk adjustment is to lessen or eliminate the influence of risk selection on the premiums that plans charge and the incentive for plans to avoid sicker enrollees. This article--the first of three in the Medicare & Medicaid Research Review--describes the key program goal and issues in the Department of Health and Human Services (HHS) developed risk adjustment methodology, and identifies key choices in how the methodology responds to these issues. The goal of the HHS risk adjustment methodology is to compensate health insurance plans for differences in enrollee health mix so that plan premiums reflect differences in scope of coverage and other plan factors, but not differences in health status. The methodology includes a risk adjustment model and a risk transfer formula that together address this program goal as well as three issues specific to ACA risk adjustment: 1) new population; 2) cost and rating factors; and 3) balanced transfers within state/market. The risk adjustment model, described in the second article, estimates differences in health risks taking into account the new population and scope of coverage (actuarial value level). The transfer formula, described in the third article, calculates balanced transfers that are intended to account for health risk differences while preserving permissible premium differences.

The HHS-HCC risk adjustment model for individual and small group markets under the Affordable Care Act.

Beginning in 2014, individuals and small businesses are able to purchase private health insurance through competitive Marketplaces. The Affordable Care Act (ACA) provides for a program of risk adjustment in the individual and small group markets in 2014 as Marketplaces are implemented and new market reforms take effect. The purpose of risk adjustment is to lessen or eliminate the influence of risk selection on the premiums that plans charge. The risk adjustment methodology includes the risk adjustment model and the risk transfer formula. This article is the second of three in this issue of the Review that describe the Department of Health and Human Services (HHS) risk adjustment methodology and focuses on the risk adjustment model. In our first companion article, we discuss the key issues and choices in developing the methodology. In this article, we present the risk adjustment model, which is named the HHS-Hierarchical Condition Categories (HHS-HCC) risk adjustment model. We first summarize the HHS-HCC diagnostic classification, which is the key element of the risk adjustment model. Then the data and methods, results, and evaluation of the risk adjustment model are presented. Fifteen separate models are developed. For each age group (adult, child, and infant), a model is developed for each cost sharing level (platinum, gold, silver, and bronze metal levels, as well as catastrophic plans). Evaluation of the risk adjustment models shows good predictive accuracy, both for individuals and for groups. Lastly, this article provides examples of how the model output is used to calculate risk scores, which are an input into the risk transfer formula. Our third companion paper describes the risk transfer formula.

Risk transfer formula for individual and small group markets under the Affordable Care Act.

The Affordable Care Act provides for a program of risk adjustment in the individual and small group health insurance markets in 2014 as Marketplaces are implemented and new market reforms take effect. The purpose of risk adjustment is to lessen or eliminate the influence of risk selection on the premiums that plans charge. The risk adjustment methodology includes the risk adjustment model and the risk transfer formula. This article is the third of three in this issue of the Medicare & Medicaid Research Review that describe the ACA risk adjustment methodology and focuses on the risk transfer formula. In our first companion article, we discussed the key issues and choices in developing the methodology. In our second companion paper, we described the risk adjustment model that is used to calculate risk scores. In this article we present the risk transfer formula. We first describe how the plan risk score is combined with factors for the plan allowable premium rating, actuarial value, induced demand, geographic cost, and the statewide average premium in a formula that calculates transfers among plans. We then show how each plan factor is determined, as well as how the factors relate to each other in the risk transfer formula. The goal of risk transfers is to offset the effects of risk selection on plan costs while preserving premium differences due to factors such as actuarial value differences. Illustrative numerical simulations show the risk transfer formula operating as anticipated in hypothetical scenarios.

Competitive bidding for Medicare Part B clinical laboratory services.

The traditional Medicare fee-for-service program may be able to purchase clinical laboratory test services at a lower cost through competitive bidding. Demonstrations of competitive bidding for clinical laboratory tests have been twice mandated or authorized by Congress but never implemented. This article provides a summary and review of the final design of the laboratory competitive bidding demonstration mandated by the Medicare Modernization Act of 2003. The design was analogous to a sealed bid (first price), clearing price auction. Design elements presented include covered laboratory tests and beneficiaries, laboratory bidding and payment status under the demonstration, composite bids, determining bidding winners and the demonstration fee schedule, and quality under the demonstration. Expanded use of competitive bidding in Medicare, including specifically for clinical laboratory tests, has been recommended in some proposals for Medicare reform. The presented design may be a useful point of departure if Medicare clinical laboratory competitive bidding is revived in the future.

Improvements in Medicare Part D risk adjustment: beneficiary access and payment accuracy.

INTRODUCTION: The continued success of the Medicare Part D program is contingent on appropriate Medicare payment adjustments for the projected drug costs of Part D plan enrollees. This article describes a major revision of these "risk adjustments," intended to more accurately match payments to costs, especially for high-cost, disadvantaged populations. METHODS: For the first time actual Part D data are used to calibrate risk adjustment. The sample is Medicare beneficiaries with fee-for-service enrollment in 2007 and Part D standalone prescription drug plan enrollment in 2008 (N = 14,224,301). Part D plan liability expenditures are predicted using demographic and diagnostic factors in a weighted least squares regression. Models for Medicare subpopulations are analyzed. The predictive accuracy of risk adjustment models is evaluated using R and predictive ratio statistics. RESULTS: Based on differences in both mean expenditures and incremental expenditures by diagnosis, separate Part D risk adjustment models are calibrated for 5 Medicare subpopulations: aged not low income; aged low income; nonaged not low income; nonaged low income; and institutionalized. The variation in plan liability drug expenditures (R) explained by these models ranges from 13% to 29%. The 5 separate models accurately predict mean plan liability expenditures ranging from $967 to $1762 across subpopulations and account for differences in incremental disease coefficients by subpopulation. CONCLUSIONS: The refined Part D risk adjustment model represents a significant improvement in the accuracy and fairness of payment to Part D plans. The new model provides greater incentives for drug plans to compete for low-income and institutionalized enrollees.

Minimum savings requirements in shared savings provider payment.

Payer (insurer) sharing of savings is a way of motivating providers of medical services to reduce cost growth. A Medicare shared savings program is established for accountable care organizations in the 2010 Patient Protection and Affordable Care Act. However, savings created by providers cannot be distinguished from the normal (random) variation in medical claims costs, setting up a classic principal-agent problem. To lessen the likelihood of paying undeserved bonuses, payers may pay bonuses only if observed savings exceed minimum levels. We study the trade-off between two types of errors in setting minimum savings requirements: paying bonuses when providers do not create savings and not paying bonuses when providers create savings.

The national market for Medicare clinical laboratory testing: implications for payment reform.

Current Medicare payment policy for outpatient laboratory services is outdated. Future reforms, such as competitive bidding, should consider the characteristics of the laboratory market. To inform payment policy, we analyzed the structure of the national market for Medicare Part B clinical laboratory testing, using a 5-percent sample of 2006 Medicare claims data. The independent laboratory market is dominated by two firms--Quest Diagnostics and Laboratory Corporation of America. The hospital outreach market is not as concentrated as the independent laboratory market. Two subgroups of Medicare beneficiaries, those with end-stage renal disease and those residing in nursing homes, are each served in separate laboratory markets. Despite the concentrated independent laboratory market structure, national competitive bidding for non-patient laboratory tests could result in cost savings for Medicare.

Medicare risk adjustment for the frail elderly.

CMS has had a continuing interest in exploring ways to incorporate frailty adjustment into the CMS Hierarchical Condition Categories (CMS-HCC) risk adjustment methodology for Medicare Advantage and other Medicare private organizations. In this article we present research results for Medicare risk adjustment of the frail elderly since the adoption of frailty adjustment for Program of All-Inclusive Care for the Elderly (PACE) organizations in 2004. In particular, we present results on the revised frailty adjuster that is being phased in for PACE organizations between 2008 and 2012.

Profiling efficiency and quality of physician organizations in Medicare.

This article presents a methodology for profiling the cost efficiency and quality of care of physician organizations (POs). The method is implemented for the Boston metropolitan area using 2002 Medicare claims. After adjustments for case mix and other factors, 4 of 30 organizations are identified with different than average efficiency Twenty-one of 30 organizations are identified with a different composite quality of care than average. Without changes in PO behavior, the gains from redirecting patients from lower to higher efficiency and quality providers are likely to be limited.

Medicare physician group practice demonstration design: quality and efficiency pay-for-performance.

The Medicare Physician Group Practice (PGP) demonstration is Medicare's first physician pay-for-performance (P4P) initiative. The demonstration, which is legislatively mandated, establishes incentives for quality improvement (QI) and cost efficiency at the level of the PGP Ten large physician groups are participating in the demonstration, which started on April 1, 2005, and will run for 3 years. In this article the authors provide an overview of the PGP demonstration's key design elements, including the selection process for PGP participants; beneficiary assignment; comparison population; measurement of demonstration savings; performance payments; and quality measurement and reporting. A summary of early case study findings is also provided.

Medicare preferred provider organization demonstration: plan offerings and beneficiary enrollment.

As preferred provider organizations (PPOs) become the dominant model of managed health care in the private sector, policymakers have increasingly viewed PPOs as an attractive option for Medicare. In part to understand how PPOs might operate under the Medicare Program, CMS launched the Medicare PPO demonstration in January 2003. In this article, we examine how PPOs have operated so far under the demonstration, including PPO availability and market entry; premiums, benefits, and beneficiary cost sharing; and enrollment, market share, enrollee characteristics, and disenrollment to date.

Impact of nonresponse on Medicare Current Beneficiary Survey estimates.

The Medicare Current Beneficiary Survey (MCBS) has been used by policymakers and research analysts to provide information on a wide array of topics about the Medicare Program. Nonresponse bias is potentially one of the most important threats to the validity of the estimates from the MCBS. In this article we present results of our methodological study that analyzes the impact of nonresponse on MCBS estimates, including initial round unit nonresponse, panel attrition, and item nonresponse. Our findings indicate that for most of the measures studied, the bias caused by differences between nonrespondents and respondents in the MCBS was substantially reduced or eliminated by the nonresponse procedures currently employed.

Evaluating the performance of medicare fee-for-service providers using the health outcomes survey: a comparison of two methods.

BACKGROUND: Health status measures are now being used for evaluating the performance of health care organizations. Trends in SF-36 component scores have previously been examined for Medicare-managed care plans but not for providers serving Medicare fee-for-service (FFS) beneficiaries. We compared 2 methods for evaluating the performance of Medicare FFS providers, the Research Triangle Institute (RTI) and Health Assessment Laboratory (HAL) methods. METHODS: Data were collected from 6547 Medicare FFS beneficiaries in 10 cohorts. SF-36 Physical Health (PCS) and Mental Health (MCS) component scores were computed at baseline and after a 2-year follow-up. The RTI approach predicts follow-up scores based on a standard care regression model. The HAL approach determines the percentage of beneficiaries whose status is the "same or better" at follow-up. Both approaches then compare observed to expected scores for each cohort. RESULTS: The HAL method did not detect any statistically significant differences for the PCS; the RTI method detected a small PCS difference for one cohort. The HAL method identified 4 cohorts that had significantly higher MCS scores; the RTI approach identified one cohort with significantly lower scores. CONCLUSIONS: The 2 approaches provided consistent assessments of provider performance for the PCS but not for the MCS. The differences in the MCS results may have been affected by differing treatment of deaths during follow-up. The HAL approach disregards deaths for the MCS, whereas the RTI method imputes values for death. Implications of using self-reported health status for monitoring provider performance are discussed.

Predicting pharmacy costs and other medical costs using diagnoses and drug claims.

BACKGROUND: Predicting health care costs for individuals and populations is essential for managing care. However, the comparative power of diagnostic and drug data for predicting future costs has not been closely examined. OBJECTIVE: We sought to compare the predictive performance of claims-based models using diagnoses, drugs claims, and combined data to predict health care costs. SUBJECTS: More than 1 million commercially insured, nonelderly individuals in a national (MEDSTAT MarketScan) research database comprised our sample. MEASURES: We used 1997 and 1998 drug and diagnostic profiles to predict costs in 1998 and 1999, respectively. To assess model performance, we compared R2 values and predictive ratios (predicted costs/actual costs) for important subgroups. RESULTS: Models using both drug and diagnostic data best predicted subsequent-year total health care costs (highest R2 = 0.168 versus 0.116 and 0.146 for models based on drug or diagnostic data alone, respectively), with highly accurate predictive ratios (0.95-1.05) for subgroups of patients with major medical conditions. Models predicting pharmacy costs had substantially higher R2 values than models predicting other medical costs (highest R2 0.493 versus 0.124). Drug-based models predicted future pharmacy costs better than diagnosis-based models (highest R2 = 0.482 versus 0.243), whereas diagnosis-based models predicted total costs (highest R2 = 0.146 versus 0.116) and nonpharmacy costs (highest R2 = 0.116 versus 0.071) more effectively than drug-based models. Newer models had markedly higher R values than older ones, largely because of richer data rather than model refinements. CONCLUSIONS: Combined drug and diagnostic data predicts total health care costs better than either type of data alone. Pharmacy spending is particularly predictable from drug data, whereas diagnoses are more useful than drugs for predicting other medical costs and total costs. Using even slightly more recent data can substantially boost model performance measures; thus, model comparisons should be conducted on the same dataset.

Estimation of non-response bias in the Medicare FFS HOS.

We examined non-response bias in physical component summary scores (PCS) and mental component summary scores (MCS) in the Medicare fee-for-service (FFS) Health Outcomes Survey (HOS) using two alternative methods, response propensity weighting and imputation for non-respondents. The two approaches gave nearly identical estimates of non-response bias. PCS scores were 0.74 points lower and MCS scores 0.51 points lower after adjustment for non-response through imputation and 0.63 and 0.46 lower after adjustment for propensity weighting. These levels are small for component scores suggesting that survey non-response to the FFS HOS does not adversely affect estimates of average health status for this population.