Diabetes Surveillance: Community-Level Artificial Intelligence/Machine Learning for Risk and Equity Action

Building on the success of the Pediatric Asthma Surveillance System (PASS), this session introduces a scalable, AI/ML-driven diabetes surveillance system designed to identify and address community-level disparities in diabetes care. The system integrates acute care utilization, past emergency department and outpatient visits, medication prescription and refill patterns, comorbidities, and Non-Medical Drivers of Health (NMDoH) to predict diabetes-related risk at the census tract level. Early findings reveal distinct geographic and demographic patterns of risk, uncovering actionable insights to guide targeted, equity-focused interventions. Attendees will gain a blueprint for deploying predictive surveillance tools to transform chronic disease management and advance health equity in vulnerable populations.