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VoiceChart.io

Dictate your patient notes, flawlessly.

June 2026 – present
ReactTypeScriptNode.jsWebSocketsOpenAI WhisperGPT-4PostgreSQLRedisAWS ECSTerraform
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Physicians spend nearly two hours on documentation for every hour of direct patient care. VoiceChart.io was built to close that gap — a real-time, voice-powered clinical documentation platform that listens during patient encounters, structures the output into compliant SOAP and APSO notes, and pushes finished drafts directly into the EHR before the doctor leaves the room. The result is a tool clinicians actually want to use, not one they endure.

Overview

VoiceChart.io is a B2B SaaS platform serving independent physician practices and mid-sized hospital groups. It captures ambient clinical conversation via browser-based audio streaming, transcribes and semantically structures the encounter in near-real-time, and delivers a formatted, review-ready clinical note in under 90 seconds. The platform integrates with Epic and Athenahealth via FHIR R4 APIs, keeping existing EHR workflows intact while eliminating the documentation burden that drives physician burnout.

The Problem

The client's pilot survey of 40 primary care physicians revealed a brutal reality: doctors were spending an average of 3.1 hours per day on after-hours charting — colloquially called "pajama time." Existing voice-to-text tools produced raw transcripts that still required heavy manual editing. Structured templates were rigid and slow. The documentation gap was directly correlated with physician attrition, patient throughput limits, and compliance risk from incomplete notes submitted days after the encounter.

The engineering challenge was equally sharp: streaming audio, low-latency transcription, and clinical NLP had to work together reliably across variable network conditions in real clinical environments.

The Solution

I designed and built a streaming pipeline that captures microphone input in the browser, chunks audio every 250ms via WebSockets, and feeds it to a self-hosted Whisper instance for continuous transcription. A fine-tuned GPT-4 prompt layer then classifies utterances into SOAP note sections in real time, surfacing a live structured draft on screen as the encounter unfolds. Physicians review, make minor edits, and sign — the entire post-encounter workflow collapses to under two minutes.

The architecture was built for fault tolerance: Redis-backed session state ensures a dropped connection never loses a partial note, and all audio is discarded after processing to satisfy HIPAA requirements.

My Role

I served as sole architect and lead engineer across the full stack. Responsibilities included:

  • System architecture — designed the real-time audio pipeline, NLP orchestration layer, and FHIR integration strategy from scratch
  • Frontend — built the React/TypeScript encounter UI with live note preview, inline editing, and one-click EHR push
  • Backend — engineered the Node.js WebSocket server, Whisper inference wrapper, and GPT-4 prompt chain with structured output validation
  • Infrastructure — provisioned HIPAA-eligible AWS ECS clusters with Terraform, including audit logging and encryption at rest and in transit
  • Clinical validation — collaborated directly with three attending physicians to iterate on note structure and terminology accuracy

Key Features

VoiceChart.io ships a focused, high-impact feature set built around the actual clinical workflow:

  • Ambient capture mode — no push-to-talk; the system listens continuously and filters non-clinical speech automatically
  • Live SOAP structuring — sections populate in real time as the encounter progresses, not after it ends
  • Smart review UI — flagged low-confidence segments are highlighted for physician attention, reducing blind sign-offs
  • FHIR R4 push — finished notes write directly to Epic or Athenahealth with a single confirmation click
  • Specialty templates — configurable note schemas for primary care, urgent care, and behavioral health
  • Audit trail — every edit, timestamp, and user action is immutably logged for compliance

Results & Impact

After a 90-day rollout across two pilot practices covering 18 physicians and approximately 4,200 encounters per month, the numbers were unambiguous. After-hours charting dropped 58%, with the average note completion time falling from 8.4 minutes to 1.9 minutes. Physician satisfaction scores on internal surveys rose from 5.8 to 8.7 out of 10. Note completeness scores — measured against the practice's existing quality rubric — improved by 22%, largely because the ambient model captured clinical details physicians previously omitted when typing under time pressure.

The platform processed over 12,000 encounters in the pilot period with 99.94% uptime and zero HIPAA incidents. One practice reported a measurable increase in daily patient throughput after physicians reclaimed time previously lost to documentation.

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