TL;DR
Care teams were catching high-risk members too late, working from claims data months out of date. I led an interpretable ML platform that flagged risk 3–6 months earlier, enabling proactive care and ~$15M in first-year cost avoidance.
The Problem
At Highmark, nurses relied on claims data that was already months old, so interventions often arrived after costs had piled up. Leadership wanted a way to stratify risk proactively — without heavy new spend and without disrupting the tools care teams already used.
The Approach
I built the plan with engineering and clinical leadership around existing data and existing caregiver tools. We chose a Random Forest model for a strong balance of accuracy and interpretability — important because clinicians needed to trust and explain the scores. I worked closely with data science to make features clinically meaningful (pharmacy fills, chronic conditions, social determinants), not just statistically convenient.
The Solution
The solution was an interpretable Random Forest risk-scoring service that surfaces a 0–100 risk score plus the top contributing factors directly inside the tools nurses and care managers already used, so teams could act on rising risk months before claims would show it. Clinicians were wary of a black box, so I went frugal and concrete: a simple mockup showing exactly how risk scores would appear in their workflow, walked through real patient examples on a whiteboard so they could see when and why an alert would fire. That built the trust needed for adoption. We rolled out first for high-cost chronic conditions like diabetes and heart failure, then expanded.
The Impact
Care teams flagged high-risk members 3–6 months earlier than before, enabling proactive care that avoided roughly $15M in costs in the first year — and it set the foundation for the organization to scale predictive analytics more broadly.