Physician Notes & Medical Forms Document Extraction
AI/ML Consultant — AutomateThis
Replaced a department's manual workload
- OCR
- Browser automation
- LLM extraction
- Confidence-threshold routing
- Human-in-the-loop review
Problem
CPCG, a pharmacy and clinic services provider, had a department manually transcribing structured data out of physician notes and medical intake forms — handwritten and typed, inconsistent formats, no two clinics filling them out the same way. It was slow, it didn't scale with volume, and it was the kind of task nobody wanted to own long-term.
Approach
Built a document extraction pipeline that combines OCR and LLM-based field extraction, but the core design decision was around trust, not accuracy on paper: every extracted field carries a confidence score, and anything below threshold routes to a human reviewer instead of silently shipping a wrong answer. That routing logic — not the extraction model itself — is what made the system safe to put in front of a real clinical workflow.
Outcome
The system now handles the department's extraction workload end-to-end, with low-confidence cases surfaced for review rather than guessed at. What used to be a full-time manual transcription function is now a review queue that only sees the cases that actually need a human.