Disparity Considerations in an Alternative Eligibility Criterion for Medicare Medication Therapy Management Programs...

Current eligibility criteria for Medicare Medication Therapy Management (MTM) programs may disadvantage racial and ethnic minorities. As health plans increasingly apply predictive modeling to guide MTM eligibility, these tools risk perpetuating existing disparities. This study examined whether hospitalization and emergency room (ER) costs could serve as an alternative eligibility criterion to improve minority representation in MTM enrollment and whether machine learning models reflect racial and ethnic differences.

A 10% random sample of fee-for-service Medicare beneficiaries in 2019 was analyzed. The outcome was inclusion in the top quartile of hospitalization and ER costs. Racial and ethnic differences were assessed using a multivariable logistic regression. Five machine learning algorithms (regularized logistic regression, support vector machines, neural networks, random forest, and gradient boosting trees) were developed, with their predictions combined in a consensus model. A multivariable fractional logistic regression was used to test whether predicted probabilities reproduced observed racial and ethnic differences.

The analytic sample included 1 848 654 beneficiaries. Black beneficiaries were significantly more likely than non-Hispanic White (White) beneficiaries to be in the top cost quartile (odds ratio [OR] = 1.09, 95% confidence interval [CI] = 1.07-1.12), whereas Asian (OR = 0.66, 95% CI = 0.63-0.69) and Other racial groups (OR = 0.87, 95% CI = 0.85-0.89) were less likely. No significant Hispanic-White difference was observed. Machine learning models largely reproduced these patterns.

Hospitalization and ER costs represent a potential alternative MTM eligibility criterion that may enhance inclusion of Black beneficiaries but underrepresent other minority groups. This study highlights the importance of equity-focused design in predictive tools.