Presentation (CTI)

OBJECTIVES: To compare quality-of-care outcomes provided by two types of Medicare in the U.S.--privately managed Medicare Advantage (MA) versus traditional Medicare Fee-for-Service (FFS)--while accounting for pre-enrollment demographic, clinical, and social risk differences that may influence both plan selection and subsequent outcomes.
METHODS: Individuals transitioning to Medicare from commercial insurance or Medicaid between 2018 and 2022 were identified. Pre-enrollment demographic and clinical characteristics were derived from claims prior to Medicare enrollment and linked to Inovalon’s Social Drivers of Health (SDOH) data warehouse to capture near-neighborhood characteristics, including income, education, language proficiency, transportation access, and housing-related factors. Propensity score matching was used to create comparable MA and FFS cohorts based on pre-enrollment characteristics, minimizing bias from differential program selection and potential post-enrollment coding differences. Quality outcomes were evaluated during the two years following Medicare enrollment.
RESULTS: The study included 11,922 MA and 136,974 FFS enrollees from 2018-2022. Prior to matching, MA enrollees exhibited substantially greater clinical and social risk, including higher rates of comorbid conditions, low income (44.1% vs. 26.9%), racial/ethnic minority status, living alone, limited English proficiency, and lower rates of home and vehicle ownership. Thirty-day all-cause hospital readmission was evaluated as a key quality measure. Before matching, readmission rates were 10.8% among MA beneficiaries and 18.7% among FFS beneficiaries. After matching, rates were 9.1% (better) for MA beneficiaries and 20.6% (worse) for FFS beneficiaries, indicating a larger difference in performance after adjustment for clinical, demographic, and social risk.
CONCLUSIONS: Results of this real-world data analysis demonstrate how comparisons of healthcare quality are sensitive to underlying differences in population characteristics. Analyses that account for clinical and social risk factors may provide a more accurate assessment of quality outcomes and plan performance. These findings highlight the importance of robust risk adjustment when evaluating healthcare quality and informing policy decisions.