Client work is confidential. This describes the kind of problem, the approach, and what a strong outcome looks like — it is illustrative of the work rather than an account of a specific named client.
The Challenge
Healthcare networks often hit critical bottlenecks in patient triage: overwhelmed departments, long wait times, and clinical staff burning out from manual assessment workflows. The goal is to route patients to the right level of care faster — without compromising diagnostic accuracy or removing clinicians from the decision.
The Approach
- 1
Run an AI-readiness audit across sites, assessing data infrastructure, EHR integration capabilities, and staff digital literacy.
- 2
Design a multi-modal triage model that combines symptom analysis, patient history, and real-time vitals into risk-scored routing recommendations.
- 3
Pilot on a small footprint with a human-in-the-loop approach — AI recommendations reviewed by triage nurses before action.
- 4
Build a training program for clinical staff focused on AI-assisted decision making and override protocols.
- 5
Stand up monitoring dashboards tracking accuracy, throughput, and outcomes to drive iterative improvement.
What a Strong Outcome Looks Like
- Shorter time-to-routing without removing clinical judgment from the loop.
- Lower manual-assessment burden on triage staff.
- A measurable, monitored baseline so every change is evaluated against real outcomes — not assumptions.
Representative Tooling
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