An Artificial Intelligence Oracle for Proactive Population Health Quality Improvement

For patients with complex health needs, the periods between clinical encounters are times of significant vulnerability, during which unobserved risks can escalate into acute events. The Waymark community-based care management program was designed to reduce this vulnerability for the patients it serves who are enrolled in Medicaid programs, but the organization faced the challenge of processing thousands of unstructured daily encounter notes from its field-based teams. To meet this challenge in a scalable way, the authors developed and implemented an artificial intelligence (AI) oracle, a system that continuously analyzes these notes. The oracle has three functions: (1) to identify urgent safety red flags; (2) to surface opportunities for preventive care; and (3) to generate monthly, strengths-based feedback to foster a learning health system. Following a phased implementation that began with a silent validation period, the system now delivers real-time, actionable alerts directly into the care teams' messaging system. Iterative refinement of the AI prompts, driven by clinical leadership review, improved the actionable true-positive rate of safety alerts from 68% of 147 to over 95% of 203 over 12 weeks. The system's value lies in its ability to synthesize disparate data streams - such as community health worker observations; pharmacy data; rising risk scores; and hospital admission, discharge, and transfer feeds - to reveal complex risks that could be missed by manual, siloed review. The AI oracle has become an integrated, "over-the-shoulder" digital coach, transforming a reactive care model into a proactive, learning health system and offering a scalable solution to improve outpatient safety for vulnerable populations.