MACHINE LEARNING EVALUATION OF RACIAL AND ETHNIC INCLUSION UNDER ALTERNATIVE COST-BASED APPROACHES TO MEDICARE MEDICATION THERAPY MANAGEMENT ELIGIBILITY
Author(s)
Chi Chun Steve Tsang, PhD1, Yan Cui, PhD2, William Cushman, MD2, Junling Wang, PhD1;
1University of Tennessee Health Science Center College of Pharmacy, Memphis, TN, USA, 2University of Tennessee Health Science Center College of Medicine, Memphis, TN, USA
1University of Tennessee Health Science Center College of Pharmacy, Memphis, TN, USA, 2University of Tennessee Health Science Center College of Medicine, Memphis, TN, USA
OBJECTIVES: The Centers for Medicare and Medicaid Services have launched the Enhanced Medication Therapy Management (MTM) demonstration, granting Part D plans greater flexibility to test alternative eligibility strategies, with many plans adopting predictive modeling and cost-based criteria. Their equity implications remain unclear. This study aims to assess how alternative cost-based eligibility schemes, implemented using machine learning models, influence predicted racial and ethnic inclusion in potential MTM eligibility.
METHODS: A retrospective, cross-sectional analysis was conducted using a 10% national sample of Medicare fee-for-service beneficiaries from 2019. Two binary outcomes were defined using the top quartile of total healthcare costs and combined hospitalization and emergency room (ER) costs as eligibility thresholds. Five machine learning algorithms were developed for each cost-based scheme. Their predicted probabilities were combined using a soft-voting ensemble to generate consensus estimates of potential eligibility. A multinomial logistic regression model was then used to assess associations between race/ethnicity and predicted inclusion across the two cost-based schemes, adjusting for predisposing, enabling, and need factors based on the Gelberg-Andersen behavioral model.
RESULTS: The analytic sample included 1,848,654 Medicare beneficiaries, with 75% designated as the training population and the remaining 25% as the test population. Compared with White beneficiaries, Black (relative risk ratio [RRR] = 4.31, 95% confidence interval [CI] = 3.92-4.75) and Hispanic (RRR = 2.28, 95% CI = 2.04-2.56) beneficiaries had a higher likelihood of inclusion under the hospitalization and ER-based scheme only, relative to inclusion under the total healthcare cost-based scheme only, whereas Asian (RRR = 0.24, 95% CI = 0.19-0.29) and Other (RRR = 0.43, 95% CI = 0.36-0.52) racial groups had lower likelihoods.
CONCLUSIONS: Alternative cost-based definitions of MTM eligibility identify different beneficiary populations across racial and ethnic groups. Incorporating hospitalization and ER costs may improve representation of Black and Hispanic beneficiaries in potential MTM eligibility.
METHODS: A retrospective, cross-sectional analysis was conducted using a 10% national sample of Medicare fee-for-service beneficiaries from 2019. Two binary outcomes were defined using the top quartile of total healthcare costs and combined hospitalization and emergency room (ER) costs as eligibility thresholds. Five machine learning algorithms were developed for each cost-based scheme. Their predicted probabilities were combined using a soft-voting ensemble to generate consensus estimates of potential eligibility. A multinomial logistic regression model was then used to assess associations between race/ethnicity and predicted inclusion across the two cost-based schemes, adjusting for predisposing, enabling, and need factors based on the Gelberg-Andersen behavioral model.
RESULTS: The analytic sample included 1,848,654 Medicare beneficiaries, with 75% designated as the training population and the remaining 25% as the test population. Compared with White beneficiaries, Black (relative risk ratio [RRR] = 4.31, 95% confidence interval [CI] = 3.92-4.75) and Hispanic (RRR = 2.28, 95% CI = 2.04-2.56) beneficiaries had a higher likelihood of inclusion under the hospitalization and ER-based scheme only, relative to inclusion under the total healthcare cost-based scheme only, whereas Asian (RRR = 0.24, 95% CI = 0.19-0.29) and Other (RRR = 0.43, 95% CI = 0.36-0.52) racial groups had lower likelihoods.
CONCLUSIONS: Alternative cost-based definitions of MTM eligibility identify different beneficiary populations across racial and ethnic groups. Incorporating hospitalization and ER costs may improve representation of Black and Hispanic beneficiaries in potential MTM eligibility.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
HPR126
Topic
Health Policy & Regulatory
Topic Subcategory
Health Disparities & Equity, Insurance Systems & National Health Care, Reimbursement & Access Policy
Disease
No Additional Disease & Conditions/Specialized Treatment Areas