AI-Powered Wound Care EMR

WoundScribe is an AI-native wound care platform where a chain of specialised agents works the encounter alongside the clinician. It reads the chart, reasons through it, and drafts the documentation, orders, referrals, codes, and patient education — ready for human review. ### How the agent chain works Four agents run in sequence, each handing context to the next: 1. Summarizer — structures the raw visit (symptoms, measurements, wound findings) into clean clinical data. 2. Assessor — interprets the findings against the patient's longitudinal profile and comorbidities. 3. Planner — drafts notes, orders, and referrals grounded in what's actually in the chart. 4. Validator — checks accuracy and completeness before anything reaches the clinician for sign-off. Three design choices keep it trustworthy: the right model per task, prompts that live in configuration, and a clinician reviewing every draft. Nothing enters the legal record until it's signed. ### What it looks like in an encounter For a patient like Margaret, WoundScribe pulls the longitudinal profile — active wounds, visit history, comorbidities — then drafts the SOAP, nurse, and procedure notes. From the wound image, it estimates tissue composition, calibrates dimensions, and surfaces the healing trend: 60% granulation, peri-wound erythema, all flagged for confirmation. One surface hosts four AI order-writers — medications, labs, imaging, and referrals. Drug selection is matched to the patient, checking allergies, renal function, and coverage, with reasoning shown. Referrals (Podiatry, Infectious Disease, Vascular Surgery) are tracked through closure. At wrap-up, the platform proposes E/M and CPT codes tied directly to the note, and translates the visit into a plain-language take-home summary. One thread runs through every step: AI drafts, the clinician approves, every note is locked with a full audit trail. Less time documenting, more time with the patient.