# Rectifying Disparities Associated with Risk Score-Based MTM Eligibility in Enhanced MTM > **NIH NIH R01** · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · 2024 · $306,976 ## Abstract The Centers for Medicare & Medicaid Services (CMS) has been testing a 5-year demonstration program, Enhanced Medication Therapy Management (MTM), since 2017 to ramp up the traditional Medicare MTM program, whose eligibility criteria have underperformed in equity and effectiveness as has been reported partly by our research team. To foster marketplace innovation, CMS purposefully granted the prescription drug (Part D) plans complete liberty to devise new MTM eligibility criteria in Enhanced MTM. Although previous literature reported that risk scores often generated through machine learning can perpetuate historical racial/ethnic disparities in health outcomes, all participating plans in Enhanced MTM have used risk scores in MTM eligibility determination. A critical barrier to tackling potential disparities arising from risk score-based MTM eligibility is that all stakeholders have been silent about the disparity implications of such initiatives. Our proposed project will explore strategies to identify and resolve disparities associated with risk score-based MTM eligibility in Enhanced MTM. Our long-term goal is to improve health outcomes among the diverse older adult population by reducing disparities in medication utilization. Our objective is to mainly analyze 100% Medicare Parts A/B/D data (2018-2021; two years before and two years during COVID-19 to account for the impact of COVID- 19). Our team has extensive experience in claims data, disparities, MTM, and machine learning. The study outcomes will be MTM eligibility and patient health outcomes. Aim 1. To determine racial/ethnic disparities associated with risk score-based MTM eligibility. We will first employ regular machine learning algorithms and use the same model outputs/outcomes as in Enhanced MTM. We will then modify the machine learning algorithms by experimenting with alternative model outputs, applying deep transfer learning, and preprocessing data to address potential disparities. Disparities in MTM eligibility will be compared between regular/modified machine learning and the traditional Medicare MTM program. Aim 2. To determine implications of racial/ethnic disparities in risk score-based MTM eligibility on patient health outcomes. Higher disparities in health outcomes among the MTM-ineligible than MTM-eligible individuals would suggest that the MTM eligibility criteria assessed may lead to worse disparities. Aim 3. To determine the comparative effectiveness of the MTM eligibility criteria based on regular and modified machine learning algorithms across racial/ethnic groups. The effectiveness of the MTM eligibility will be measured by the proportion of individuals deemed MTM-eligible among patients with medication utilization issues. Successes of Aims 2 & 3 are independent of Aim 1 because the success of Aim 1 is ensured due to well-documented potential racial/ethnic disparities associated with risk scores. Impact: Our project can prevent risk score-based MTM eligibility from caus... ## Key facts - **NIH application ID:** 10907009 - **Project number:** 5R01AG040146-09 - **Recipient organization:** UNIVERSITY OF TENNESSEE HEALTH SCI CTR - **Principal Investigator:** Junling None Wang - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $306,976 - **Award type:** 5 - **Project period:** 2011-09-01 → 2028-04-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10907009 ## Citation > US National Institutes of Health, RePORTER application 10907009, Rectifying Disparities Associated with Risk Score-Based MTM Eligibility in Enhanced MTM (5R01AG040146-09). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10907009. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*