Prescription-grade longitudinal burn wound monitoring and clinical triage.
- Overview
- The Clinical Gap
- Core Technology Stack
- Regulatory & Compliance
- How It Works
- Clinical Evidence
- Reimbursement Model
BurnMonitor AI is an FDA 510(k) cleared platform designed for patients discharged home following a burn injury. By combining longitudinal photo tracking, multimodal AI analysis, and a verified clinician-in-the-loop layer, the app provides monitoring quality non-inferior to in-person visits at a fraction of the cost.
- Target: Patients in the critical 5–14 day post-discharge window.
- Goal: Prevent sepsis/infection readmissions and eliminate low-value follow-up appointments.
- Model: Prescription-based (DTx), reimbursed via CPT codes.
Burn patients face a dangerous monitoring gap between ED discharge and their first follow-up (typically 5–14 days).
- The Problem: Sepsis and wound infection develop silently. Meanwhile, clinics are overwhelmed by patients whose wounds are healing normally.
- The Solution: A remote triage system that catches deteriorating patients early while safely deprioritizing those who do not require intervention.
| Component | Technology | Purpose |
|---|---|---|
| Frontend | React / React Native | High-fidelity, accessible UI for patients and clinicians. |
| Database | InterSystems IRIS | Vectorized storage for wound sequences and clinical RAG. |
| AI Engine | Computer Vision | Longitudinal photo analysis (surface color, size, eschar). |
| Backend | Node.js / Python | Secure, HIPAA-compliant API orchestration. |
| Infrastructure | AWS/Azure Medical | High-availability, encrypted cloud hosting. |
- FDA Status: Class II Medical Device via 510(k) pathway.
- EU Market: CE Marking under EU MDR Class IIa (Parallel Track).
- Audit Trail: Full logging of AI outputs and Nurse Reviewer overrides for regulatory accountability.
- Equity: AI training includes diverse skin tones across the full Fitzpatrick Scale to prevent diagnostic bias.
At discharge, the clinician takes a baseline photo with a scale reference. This serves as the "Ground Truth" for all subsequent AI comparisons.
The patient performs a daily check-in:
- Guided Photo Upload: Ensures consistent angle and lighting.
- Symptom Mapping: Pain scores (tracking nerve destruction vs. healing) and checklist for fever or drainage.
- AI Risk Scoring: Generates a unified score based on visual cues and symptom trajectories.
The app provides three clear, actionable states:
- 🟢 Continue Home Care: Healing is on trajectory.
- 🟡 See GP (48h): Concerning signals detected; nurse review triggered (4h SLA).
- 🔴 Go to ED Now: Red flags for infection/sepsis; nurse review triggered (30m SLA).
Note: Every AI-flagged case is reviewed by a verified burns nurse before the patient receives a high-level escalation.
Our model doesn't just look at a photo; it looks at sequences.
- Foundation Dataset: Retrospective longitudinal sequences from SGH Burns Centre.
- Adversarial Weighting: Specifically trained on "hidden depth" cases where 1st-degree burns progressed to 2nd-degree.
- Vector Search: Powered by InterSystems IRIS, allowing the RAG-grounded chatbot to compare a patient's current trajectory against thousands of similar historical outcomes.
The "Actuarial Case" for BurnMonitor AI:
- For Hospitals: Replaces 2+ low-value appointments per patient. Reduces 30-day readmissions (a core KPI).
- For Insurers: Sepsis admissions cost $30k–$50k. Remote monitoring costs $30–$50/mo.
- For Clinicians: Billable under CPT 99457 & 99458 (Remote Physiological Monitoring).
Recovery is more than clinical. The app includes a Peer Recovery Community:
- Closed, verified feed for burn survivors.
- Connected by similarity (burn type/location).
- AI-triggered "Milestone Sharing" to celebrate healing progress.
Developed for the future of decentralized wound care. Visit Website | View Clinical Whitepaper