Presentation (CTI)

OBJECTIVES: Although Medicare Medication Therapy Management (MTM) programs have demonstrated clinical and economic benefits, racial/ethnic minority groups face challenges meeting eligibility criteria for enrollment. In 2017, the Centers for Medicare and Medicaid Services launched the 5-year Enhanced MTM demonstration, granting Part D plans flexibility in identifying eligible beneficiaries. However, because participating plans adopted predictive modeling to determine eligibility, concerns persist that this approach may perpetuate existing racial/ethnic disparities. This study aims to assess whether health cost-based MTM eligibility differs across race/ethnicity and whether machine learning models reproduce observed disparities in predicted eligibility.
METHODS: This study analyzed 2019 Medicare administrative data linked to the Area Health Resources Files for a 10% random sample of fee-for-service beneficiaries. Outcomes were binary indicators of top-quartile medication and healthcare costs, each measured from the Medicare and healthcare system perspectives. Multivariable logistic regression was employed to assess racial/ethnic disparities in top-quartile costs, using six algorithms - regularized logistic regression, random forest, gradient boosted trees, support vector machine, multilayer perceptron, and a consensus model. Predicted probabilities were computed to assess disparities in model outputs using multivariable fractional logistic regression.
RESULTS: Among 1 848 654 Medicare beneficiaries, Black and Hispanic individuals had significantly lower adjusted odds of top-quartile costs across all cost outcomes compared to their non-Hispanic White counterparts. For instance, the odds of being in the top quartile for total medication costs were 28% lower for Black beneficiaries (odds ratio [OR]=0.72, 95% confidence interval [CI]=0.70-0.75) and 21% lower for Hispanic beneficiaries (OR=0.79, 95% CI=0.74 - 0.84). Machine learning models reproduced these disparities in predicted probabilities, mirroring patterns in the empirical data.
CONCLUSIONS: Implementing cost-based MTM eligibility through predictive algorithms may perpetuate racial/ethnic disparities in MTM program access. Future research should explore strategies to mitigate such a potential when using such modeling to determine MTM eligibility.