MyScribe
AI-powered medical transcription for clinicians
A clinician-focused AI assistant that automatically transcribes patient conversations and generates SOAP-format clinical notes. Reduces documentation time by 70%, letting doctors focus on patients instead of paperwork.
What it is
An AI-powered medical scribe that listens to patient-clinician conversations in real-time, automatically generates structured SOAP clinical notes, and integrates into existing healthcare workflows.
Who it serves
Independent clinicians, small clinics, and telehealth providers who cannot afford human medical scribes but need accurate, HIPAA-compliant documentation.
Business value
Reduces documentation time by 70%, allowing clinicians to see more patients and reduce burnout. Costs a fraction of human scribe services.
Primary users
Physicians, nurse practitioners, physician assistants, and telehealth providers.
Category
Healthcare Platform
Role
Full-Stack Developer
Timeline
6 months
Status
Production
Team
Solo
Platform
Web
Industry
Healthcare Technology
Frontend
Vue.js
Clinicians spend 30-50% of their work hours on documentation. Existing solutions are either expensive transcription services with 24-hour lag or general speech-to-text tools that fail on medical terminology. Small clinics cannot afford $30,000/year for human scribes.
Real-Time Medical Transcription
Fine-tuned Whisper model achieves 94% accuracy on medical conversations. Captures terminology, medication names, and clinical context that general STT models miss.
AI-Generated SOAP Notes
Automatically structures conversation transcripts into Subjective, Objective, Assessment, and Plan format. Includes ICD-10 code suggestions based on the clinical assessment.
HIPAA-Compliant Architecture
End-to-end encryption for all audio data. AI processing runs on dedicated instances within a VPC with no outbound internet access. BAA-compliant infrastructure.
Editable Notes with Audit Trail
Clinicians can review and edit AI-generated notes before saving. All changes are tracked with version history for compliance and quality assurance.
Frontend
Backend
Database
Infrastructure
Tools & Services
Real-time audio is captured in the browser via WebSocket and streamed to a Laravel backend that orchestrates the AI pipeline. A fine-tuned Whisper model handles speech-to-text, while a quantized Llama 2 13B model generates SOAP notes. The Vue.js frontend displays live transcription and provides the note editing interface.
General speech-to-text models fail on medical terminology, causing clinically significant errors.
Fine-tuned Whisper on 5,000 hours of medical conversations. Built a custom medical vocabulary dictionary for term override. Achieved 94% accuracy vs 82% baseline.
LLM-based note generation took 45-60 seconds per patient visit, too slow for clinical workflow.
Applied 4-bit quantization reducing model size from 26GB to 7GB. Implemented streaming generation where clinicians see notes written section by section.
HIPAA compliance for cloud AI processing required data never leaving secure infrastructure.
Deployed dedicated GPU instances within a VPC with no outbound internet. Pre-loaded model weights. All processing stays within the secure environment.
Interested in building something similar?
I am always open to discussing new projects, technical challenges, and engineering opportunities.