Case study / Project archive
VaidyaScribe
A clinical-note concept exploring bilingual transcription and structured notes.
Historical team prototype; individual contribution is not independently established. AI-assisted portfolio development.
The idea
The problem
Explore converting a bilingual consultation transcript into structured note fields while keeping transcription, entity extraction, and record mapping distinct.
The implementation
What I built
The source joins an audio-session pipeline to structured-note mapping. The README identifies a team project; personal implementation boundaries have not been independently established.
- Flutter
- Dart
- FastAPI
- Python
- Whisper
- PostgreSQL
Features
- Accept consultation audio chunks and send transcription/status updates through a session workflow.
- Explore Hindi/English transcription, speaker separation, and clinical-entity extraction.
- Map extracted entities into patient, encounter, condition, observation, and medication-oriented FHIR structures.
Architecture
- Flutter client configuration accompanies a FastAPI backend with session, transcription, and notes routes.
- Session orchestration calls separate ASR, NLP, and FHIR-mapping modules, then saves structured notes through database helpers.
Validation and measured results
- Source reviewed on 15 October 2026. The application and its external integrations were not executed in this review.
- ASR, session orchestration, and FHIR mapping were read. No clinical audio, custom model training, FHIR conformance test, or ABDM integration was exercised.
Limits and unfinished work
- Historical latency and medical-speech training claims are not adopted. Generated notes require clinical review; no clinical accuracy is established.
- Older source can contain superseded descriptions. Numerical impact, adoption, awards, and deployment claims are not carried into this entry.
Evidence and sources
- Reviewed source and implementation
Public README and selected implementation files; source inspection only, without a reproduced runtime or benchmark. Inspected files: backend/agents/asr_agent.py, backend/agents/orchestrator.py, backend/agents/fhir_mapper.py, frontend/pubspec.yaml.