Prediction of non emergent acute care utilization and cost among patients receiving Medicaid

Title: Prediction of non emergent acute care utilization and cost among patients receiving Medicaid
Presenter: Sadiq Patel, Data Science Team Lead, Waymark; Adjunct Professor, University of Pennsylvania
Abstract: Patients receiving Medicaid often experience social risk factors for poor health and limited access to primary care, leading to high utilization of emergency departments and hospitals (acute care) for non-emergent conditions. As programs proactively outreach Medicaid patients to offer primary care, they rely on risk models historically limited by poor-quality data. Following initiatives to improve data quality and collect data on social risk, we tested alternative widely-debated strategies to improve Medicaid risk models. Among a sample of 10 million patients receiving Medicaid from 26 states and Washington DC, the best-performing model tripled the probability of prospectively identifying at-risk patients versus a standard model, without increasing “false positives” that reduce efficiency of outreach, and with a ~ tenfold improved coefficient of determination when predicting costs. Our best-performing model also reversed the lower sensitivity of risk prediction for Black versus White patients, a bias present in the standard cost-based model. Our results demonstrate a modeling approach to substantially improve risk prediction performance and equity for patients receiving Medicaid.
Bio: Sadiq Patel, PhD, MS, MSW is an experienced data scientist and health technologist with 10+ years of experience in health AI, health data, and health economics. At Waymark (a16z, NEA, Lux Capital, and CVS Ventures funded health tech startup), his team oversees data strategy and governance and the design, build, and deployment of ML/AI/LLM-informed data science tools to improve effectiveness, efficiency, and automation of care delivery. He is also an Adjunct Professor at the University of Pennsylvania, where he teaches graduate-level machine learning courses. Prior to Waymark, Sadiq was a research fellow at Harvard Medical School and Microsoft AI for Good, senior data scientist and team lead at Accenture for commercial and government clients, and educator through Teach for America. He holds a PhD and MS in social policy and biostatistics from the University of Chicago, MSW from the University of Michigan, and a BS in biochemistry and mathematics from the University of Illinois, where he studied as a Howard Hughes Medical Scholar. Sadiq’s research has been published in healthcare and medical journals, including JAMA, British Medical Journal, Nature, Health Affairs, Health Affairs Blog, and American Journal of Public Health. His work has been featured in major media, including the Wall Street Journal, presented to the Congressional Budget Office, and cited in congressional hearings and supreme court briefs.