Inside the Healing Trajectory agent: how AI predicts wound outcomes per visit
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A deep dive into how the Healing Trajectory agent turns each wound visit into a prediction — stalled, healing, or at risk — and what clinicians see on the dashboard.
Wound care hinges on one question at every visit: is this wound healing on trajectory, or not? The Healing Trajectory agent exists to answer that question with data, not gut feel.
What the agent actually does
At every visit, the agent ingests wound measurements, tissue composition, exudate levels, and prior visit history. It outputs a trajectory classification and a predicted time-to-heal window.
The inputs it depends on
Accurate predictions require consistent measurement. That's why AI-powered wound imaging captures length, width, depth, and tissue type the same way every time — regardless of which clinician holds the camera.
What clinicians see
The healing dashboard shows each wound's trajectory line against its predicted curve. Wounds pulling below the curve get flagged for intervention review — debride, refer, change dressing protocol, or reassess perfusion.
Why per-visit prediction matters
Retrospective analytics tell you a wound stalled last month. Per-visit prediction tells you it's stalling now, when there's still time to change the plan. That's the difference between analytics and decision support.
How it changes the clinic workflow
Instead of scanning weeks of notes to sense a pattern, clinicians open the chart and see the trajectory verdict immediately. Documentation stays in wound healing software; the decision surfaces at the top.
What it doesn't do
The agent doesn't diagnose comorbidities or replace clinical judgment on referral. It flags trajectory deviations. The clinician decides the next move.