The latest articles on DEV Community by VivaLyn Labs (@vivalyn_labs_c967abc7e3ed). https://dev.to/vivalyn_labs_c967abc7e3ed https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3940543%2Fcc3bcf1b-4afd-48c1-a6fe-29d5449c42df.png https://dev.to/vivalyn_labs_c967abc7e3ed en VivaLyn Labs Tue, 19 May 2026 14:56:35 +0000 https://dev.to/vivalyn_labs_c967abc7e3ed/building-an-ai-medical-scribe-that-actually-works-in-indian-clinics-1ie7 https://dev.to/vivalyn_labs_c967abc7e3ed/building-an-ai-medical-scribe-that-actually-works-in-indian-clinics-1ie7 <h2> The Problem </h2> <p>Doctors in India spend 2–3 hours daily on clinical documentation. Most EMR systems are glorified data entry forms that add to the burden rather than reduce it.</p> <p>We built MedScribe — an AI-powered ambient clinical documentation tool that listens to doctor-patient conversations and generates structured SOAP notes, ICD-10 codes, and prescriptions in real time.</p> <h2> The Tech Stack </h2> <ul> <li> <strong>Frontend:</strong> React JS</li> <li> <strong>Database:</strong> PostgreSQL 18 with a headless CMS layer</li> <li> <strong>AI Pipeline:</strong> Multilingual ASR → Medical NER → LLM structured output</li> <li> <strong>Deployment:</strong> Azure App Service (web) + on-premise GPU nodes (inference)</li> </ul> <h2> Key Technical Challenges </h2> <h3> 1. Multilingual Voice Capture </h3> <p>Indian doctors code-switch between English, Hindi, and regional languages mid-sentence. Off-the-shelf ASR models fail here. We fine-tuned Whisper on 500+ hours of Indian clinical audio with domain-specific vocabulary.</p> <h3> 2. Structured Output from Messy Conversations </h3> <p>A 10-minute consultation produces unstructured dialogue. The LLM pipeline extracts:</p> <ul> <li>Chief complaint &amp; history of present illness</li> <li>Differential diagnosis</li> <li>SOAP note sections</li> <li>ICD-10 codes</li> <li>Prescriptions with dosage</li> </ul> <p>We use constrained decoding + JSON schema validation to ensure output is always machine-parseable.</p> <h3> 3. Privacy-First Architecture </h3> <p>Healthcare data can't leave the clinic network. Our inference runs on-premise with zero data retention — no patient audio or text hits external servers. The EMR syncs only anonymized metadata for analytics.</p> <h3> 4. ABDM Compliance </h3> <p>India's Ayushman Bharat Digital Mission requires specific health record formats (FHIR bundles), ABHA ID linking, and consent management. We built a middleware layer that translates our internal models to ABDM-compliant payloads.</p> <h2> Results </h2> <ul> <li>Documentation time reduced from 15 min → 2 min per patient</li> <li>95%+ accuracy on ICD-10 coding for common specialties</li> <li>Works offline — no internet dependency during consultations</li> </ul> <h2> What I Learned </h2> <ol> <li> <strong>Healthcare AI is a regulatory problem first, ML problem second.</strong> Get compliance right before optimizing accuracy.</li> <li> <strong>Doctors won't change workflows.</strong> Build around how they already work, not how you think they should.</li> <li> <strong>On-premise isn't dead.</strong> When data sensitivity is non-negotiable, cloud-only is a dealbreaker.</li> </ol> <p>We're building this at <a href="https://www.vivalynlabs.com" rel="noopener noreferrer">VivaLyn Labs</a>. If you're working on healthcare AI or building for the Indian market, I'd love to connect.</p> <p>What's the hardest compliance/privacy challenge you've faced while building AI products?</p> emr healthcare ai ehr