The mechanics of risk adjustment and incentives for coding intensity in Medicare.

Objective: To study diagnosis coding intensity across Medicare programs, and to examine the impacts of changes in the risk model adopted by the Centers for Medicare and Medicaid Services (CMS) for 2024.
Data Sources and Study Setting: Claims and encounter data from the CMS data warehouse for Traditional Medicare (TM) beneficiaries and Medicare Advantage (MA) enrollees.
Study Design: We created cohorts of MA enrollees, TM beneficiaries attributed to Accountable Care Organizations (ACOs), and TM non-ACO beneficiaries. Using the 2019 Hierarchical Condition Category (HCC) software from CMS, we computed HCC prevalence and scores from base records, then computed incremental prevalence and scores from health risk assessments (HRA) and chart review (CR) records.
Data Collection/extraction Methods: We used CMS's 2019 random 20% sample of individuals and their 2018 diagnosis history, retaining those with 12 months of Parts A/B/D coverage in 2018.
Principal Findings: Measured health risks for MA and TM ACO individuals were comparable in base records for propensity-score matched cohorts, while TM non-ACO beneficiaries had lower risk. Incremental health risk due to diagnoses in HRA records increased across coverage cohorts in line with incentives to maximize risk scores: +0.9% for TM non-ACO, +1.2% for TM ACO, and + 3.6% for MA. Including HRA and CR records, the MA risk scores increased by 9.8% in the matched cohort. We identify the HCC groups with the greatest sensitivity to these sources of coding intensity among MA enrollees, comparing those groups to the new model's areas of targeted change.
Conclusions: Consistent with previous literature, we find increased health risk in MA associated with HRA and CR records. We also demonstrate the meaningful impacts of HRAs on health risk measurement for TM coverage cohorts. CMS's model changes have the potential to reduce coding intensity, but they do not target the full scope of hierarchies sensitive to coding intensity.
(© 2024 Health Research and Educational Trust.)