A Machine Learning Model That Triages Claims Before a Human Ever Sees Them
A digital health startup needed to triage a growing volume of insurance claims faster than its clinical ops team could scale. We built a production ML model that pre-sorts claims by urgency and likely outcome.
September 18, 2025
Sprightline Health's clinical operations team was manually reviewing every incoming claim to determine urgency and routing — a process that scaled linearly with headcount and increasingly couldn't keep up with claim volume.
We built a machine learning triage model trained on Sprightline's historical claims data, validated against a held-out test set with clinical ops sign-off on every risk threshold before deployment. The model runs the moment a claim arrives, automatically routing low-risk, high-confidence cases and flagging the rest for human review with a recommended priority.
Today, 71% of incoming claims are triaged automatically, with a 3.2x improvement in average time-to-first-action. The clinical ops team now spends its time on the claims that genuinely require judgment, not on repetitive sorting.
Nevinex built our claims triage model from a rough idea to a production system our clinical ops team relies on every day. Genuinely senior engineers, not junior contractors learning on our dime.
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