Care coordinators were manually reviewing patient records to identify gaps in care, flag rising-risk members, and route follow-ups. The process was slow, inconsistent, and couldn't scale across a growing member population. The team needed a system that could combine structured claims data with unstructured clinical notes to surface actionable insights without requiring a human in every loop.
Built an agentic orchestration layer that combined deterministic rules (claims triggers, lab value thresholds, care gap logic) with LLM-augmented signal extraction from clinical documentation. Structured data drove the core decision graph, while the language model identified contextual nuance from notes that rules alone would miss. Designed graduated levels of autonomy: fully automated for high-confidence, well-defined actions and human-in-the-loop for ambiguous or high-stakes recommendations.
Reduced average care gap identification time by over 70%. Coordinators shifted from manual chart review to exception-based workflows, focusing their time on complex cases where human judgment mattered most.
Have a similar challenge? Let's talk about what we can build for you.
Book a 20-Min Intro Call