Case study — Healthcare · AI
An AI-assisted diagnosis platform for a 14-hospital regional network — rebuilding the triage workflow so nurses could trust machine intelligence under pressure.
01 — Challenge
Fourteen emergency departments, one bottleneck: triage decisions made on paper, under pressure, with incomplete history.
Average diagnosis-to-treatment time had grown to 4.2 hours. Patient history lived in three disconnected systems, and triage severity scoring varied widely between nurses and shifts.
Previous attempts at "AI triage" had failed for a human reason: clinicians didn't trust a score they couldn't interrogate. Any new system had to explain itself.
Industry context
Healthcare networks are under pressure to adopt AI without compromising the clinical judgment that keeps patients safe. Regulatory scrutiny, data residency requirements, and clinician trust all shape what a deployable system looks like — a model that scores well in a lab is not the same as one nurses will actually use at 3am.
This project set the pattern we now bring to every healthcare engagement: explainable outputs instead of black-box scores, on-premise or region-locked infrastructure where compliance demands it, and rollout paced around clinical workflow rather than a software release calendar.
02 — Approach
03 — Solution
Explainable by design
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04 — Outcome
“They think like owners, not vendors. The system explains itself, so our nurses actually use it.”