Risk Adjustment in the Medicare Advantage Population Using Encounter Data

OBJECTIVE: To estimate the Centers for Medicare and Medicaid Services (CMS) Hierarchical Condition Category (HCC) risk model using Medicare Advantage (MA) encounter data, as an initial step toward recalibrating risk-adjusted MA payments. DATA SOURCES AND STUDY SETTING: A 20% sample of Traditional Medicare (TM) claims and MA encounter data for 2016-2022. Standardized fee schedules provide a measure of resource use in TM claims and MA encounters. STUDY DESIGN: Ordinary least squares regression replication of the CMS HCC version 28 model for relative resource use among community-dwelling, non-dual aged and disabled individuals. Robustness testing includes replication with version 22 model structure, sensitivity to MA chart review records, MA contracts with complete encounter data, and patterns of care during the COVID pandemic. PRINCIPAL FINDINGS: Using TM data from 2016 to 2022 results in modest changes in estimated coefficients and 1.7% lower average HCC scores, relative to 2018-2019 TM data used for CMS's HCC model v28. Using MA data result in 8.9% lower average scores than TM-based scores. The differences between MA- and TM-based HCC scores vary across the distribution of scores. When re-estimating HCC v28 coefficients, increasing trends in TM and MA diagnosis prevalence are associated with smaller (diluted) coefficients in TM and MA-based HCC risk models. Trends in medical technology can increase (e.g., high-cost targeted cancer therapies) or decrease (e.g., lower-cost biosimilars) HCC model coefficients, with evidence of larger technology-related decreases in an MA-based model. The decrease in MA-based scores does not create new disincentives to enroll beneficiaries who are racial/ethnic minorities or rural residents. CONCLUSIONS: We make an important contribution to the policy debate about MA risk adjustment. Any changes in risk-adjusted MA payment need to be reviewed in the full context of MA payment policy and MA plan enrollment incentives.