Presentation (CTI)

OBJECTIVES: Evidence suggests current eligibility criteria for Medicare Medication Therapy Management (MTM) programs, which focuses on the number of chronic conditions, the number of covered medications, and an annual threshold for drug spending, may disadvantage racial/ethnic minority groups. As health plans increasingly apply predictive modeling on healthcare costs 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/ethnic differences.
METHODS: A cross-sectional analysis of a 10% random sample of fee-for-service Medicare beneficiaries in 2019 was conducted. The outcome was inclusion in the top quartile of hospitalization and ER expenditures. Racial/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 tested whether predicted probabilities reproduced observed racial/ethnic difference.
RESULTS: 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), while Asian (OR=0.66, 95% CI=0.63-0.69) and Other 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.
CONCLUSIONS: Hospitalization and ER costs represent a potential alternative MTM eligibility criterion that may enhance inclusion of Black beneficiaries but may lead to underrepresentation of other minority groups. Machine learning models reinforced observed differences, highlighting the importance of equity-focused design in predictive tools. Incorporating hospitalization and ER costs into MTM eligibility could extend outreach to patients with substantial acute care utilization, yet relying on this measure alone may risk overlooking other beneficiaries.