UC49: Wound Care Progress Monitor

The Problem

Chronic wound healing (diabetic ulcers, pressure ulcers, surgical wounds) is tracked subjectively through nurse assessments and photos. Progress or deterioration is detected through subjective comparison or documented measurements, which is inconsistent. Computer vision analysis of wound photos can objectively quantify healing (size, depth, tissue type) and alert clinicians to stalled or regressing wounds before clinical deterioration occurs.

What the Agent Does

Nurse photographs wound using smartphone or tablet (patient bedside or home)
Agent analyzes wound photo using computer vision: measures wound dimensions (length × width), depth assessment from shadows, tissue composition (healthy tissue %, slough %, eschar %, granulation %)
Compares current photo to historical photos: calculates healing trajectory (% improvement per week)
Flags deterioration: "Wound size increased 0.5cm² compared to 3 days ago; recommend dressing change and provider evaluation"
Alerts clinician if healing stalls for >2 weeks or if signs of infection (increased erythema, drainage, odor)
Generates wound care trend report for clinician review; suggests protocol adjustments (dressing type, frequency, offloading)
Tracks outcomes: documents healing trajectory toward closure goal

Data Requirements

Primary Data Sources: Wound photos (smartphone/tablet), wound assessment records (size, depth, tissue type, drainage, odor), treatment protocols (dressing type, frequency), patient comorbidities (diabetes, immunosuppression)
Classification: PHI/PII (patient identifiers, wound photos)
Quality Metrics: Wound dimension measurement accuracy >95% (validated against manual measurements); tissue classification accuracy >90%; healing trajectory prediction accuracy >80%
Complexity: Very High (medical image analysis, computer vision, tissue classification)
Systems: Epic EHR, Net Health Wound Expert platform

Score Breakdown

Criterion	Weight	Score (1-5)	Weighted
Time Recaptured	15%	3	0.45
Error Reduction	10%	3	0.30
Cost Avoidance	10%	3	0.30
Strategic Leverage	5%	3	0.15
Data Availability	15%	4	0.60
Process Clarity	15%	4	0.60
Ease of Implementation	10%	3	0.30
Fallback Available	10%	3	0.30
Audience (Int/Ext)	10%	4	0.40
Composite	100%		3.55

Why It Scores Well

Objective Assessment: Reduces subjective variation in wound assessment; enables comparison across providers
Early Intervention: Alerts clinician to deterioration before clinical crisis
Treatment Optimization: Guides dressing and therapy selection based on objective healing progress
Home Health Scalability: Enables remote wound monitoring for home health patients
Regulatory Fit: Wound care is standard clinical practice; no novel autonomy concerns

Regulatory Alignment

FDA: Wound assessment tool may require 510(k) review depending on autonomy level and claim specificity. Advisory-only tools typically exempt; autonomous wound debridement recommendations may require premarket approval.
Medicare Wound Care Coverage: Covered services include assessment and dressing changes; agent optimizes coverage documentation
HIPAA: Wound photos are PHI; standard access controls and secure storage required
State Nursing Practice: Nurses perform wound assessment; agent is decision support

Sprint Factory Fit

3 to 4 sprints (computer vision model development, wound data integration, photo capture workflow, clinician interface, FDA pathway review)

Sprints Required: 3 to 4 sprints (computer vision model development, wound data integration, photo capture workflow, clinician interface, FDA pathway review)
Timeline: 8 to 10 weeks from requirements to pilot
Governance: Wound care specialist review of computer vision accuracy in first 50 wounds; validate against manual measurements
Success Metrics: Wound dimension measurement accuracy >95%; stalled healing detection sensitivity >85%; clinician satisfaction >4.0/5

Comparable Implementations

Net Health Wound Expert: wound care management
WoundPlot: wound healing analytics
FDA: Medical Device Classification for Wound Assessment Tools

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