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AI wound-care documentation app (HIPAA)

Mobile + AI voice transcription replaces 2-hour nurse documentation cycles with 15-minute workflows.

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CASE FILE · CS-01 SHIPPED
“PHI flow was modeled in week one — by the security audit, there was nothing to retrofit.”
Faster nurse documentation12×
ClientU.S. wound-care platform
SectorHealthcare
Service linesBuild · Agents
Window6 weeks fixed
READ THE FILE ↓

Challenge

Nurses spent 2+ hours per patient on manual documentation across paper forms, EMR data entry, and follow-up emails. Field connectivity gaps in home visits made standard cloud apps unreliable. The team needed a HIPAA-compliant mobile workflow with offline-first sync, voice-to-text accurate enough for clinical notes, and seamless integration with the existing ECW EMR.

Solution

A cross-platform React Native app with offline-first architecture (intelligent sync when reconnected), AI voice transcription via Deepgram, and automated patient registration through email parsing. Backend on AWS serverless (Lambda + Cognito + DynamoDB + S3) with AES-256 encryption at rest and TLS 1.3 in transit. ECW EMR integration via secure FHIR endpoints. PHI flow modeled from kick-off so audit was a non-event.

Engagement

  • Sector: Healthcare
  • Service lines: Build · Agents
  • Client: U.S. wound-care + home-healthcare provider (anonymized)
Clinician using a mobile app at a patient bedside
CASE FILE · CS-01 · AI WOUND-CARE DOCUMENTATION APP (HIPAA)
Mobile + AI voice transcription replaces 2-hour nurse documentation cycles with 15-minute workflows.
ENGAGEMENT TIMELINE · 6 WEEKS FIXED

Every engagement runs through the same five gates of the FORGE method. Here’s how this case ran.

W0 · FRAME
PHI flow modelled, FHIR endpoint review, ECW EMR integration constraints, BAA paperwork in motion.
W1 · OUTLINE
React Native + offline-first architecture, AWS Cognito IAM, Deepgram transcription pipeline, encrypted-at-rest sync model.
W2–4 · REBUILD
Mobile app + offline queue + reconnect-sync + AI transcription + ECW push. Field-tested on weak-signal home visits.
W5 · GOVERN
HIPAA security audit, encryption verification, audit-log review, runbook handover, on-call rota agreed.
W6 · ENGAGE
Production rollout to 100+ providers, first-month support, data-drift monitoring on transcription accuracy.
RESULTS · KEY METRICS
12×
Faster documentation (2 hr → 15 min per patient)
7×
Faster patient registration (20 min → 3 min via AI email parsing)
95%+
Voice transcription accuracy on clinical vocabulary
$850/mo
Total infrastructure cost supporting 100+ providers
100%
HIPAA-compliant architecture (BAA + audit log + encryption)
STACK · CS-01SHIPPED
SectorHealthcare
MarketUnited States
ServicesBuild · Agents
ClientU.S. wound-care + home-healthcare provider (anonymized)
React Native Capacitor TypeScript AWS Lambda API Gateway AWS Cognito DynamoDB S3 Deepgram AI ECW EMR / FHIR AES-256 TLS 1.3
Client voice
PHI flow was modeled in week one — by the security audit, there was nothing to retrofit.
CTO · U.S. wound-care platform
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