Predicting quality measure completion among 14 million low-income patients enrolled in Medicaid

Sanjay Basu

Abstract: Low-income populations have disproportionately low completion of recommended healthcare services, from missed vaccinations to cancer screenings. While machine learning models help identify high-risk patients for targeted treatment, they have rarely been evaluated for quality measure gap completion—or among low-income populations underrepresented in typical datasets. Analyzing 14.2 million Medicaid recipients—including those excluded from electronic health records and without prior utilization—we developed models to predict gaps in nine nationally adopted quality measures, including preventive care and chronic disease management. Using clinical data to prioritize outreach, the clinical-only model improved accuracy by 32.5 percentage points over non-predictive methods such as alphabetical calling or birthday reminders (AUROC: 0.88, F1-score: 0.69). Incorporating social determinants of health data further improved performance by 2.0 percentage points in accuracy (to 84.5%) and increased F1-score by 5.0 percentage points (to 0.74), with no change in AUROC. Compared to the clinical-only model, the SDoH model also reduced pre-existing Black-White disparities in prediction accuracy. Model performance was especially sensitive to SDoH factors like healthcare workforce and facility availability.