The Challenge
A regional health network operating 14 facilities generated over 12,000 clinical documents daily — discharge summaries, lab reports, referral letters, and progress notes. A team of 23 medical coders manually reviewed, classified, and extracted structured data from these documents for billing, compliance, and patient record management.
The process was slow (average 4.2 hours per batch), error-prone (8% error rate on coding), and couldn't scale with growing patient volume. The network needed automation that met HIPAA compliance, handled the variability of clinical language, and maintained accuracy that human reviewers would trust.
Our Approach
Week 1-2: Clinical Data Analysis
We worked with the medical coding team to understand their workflow end-to-end. We analyzed 50,000 historical documents to categorize document types, identify extraction patterns, and map the specific data fields needed for each downstream system. We identified 12 document categories and 87 distinct data fields that needed extraction.
Week 3-5: RAG Architecture
We designed a retrieval-augmented generation system specifically tuned for clinical language:
- Document ingestion pipeline — OCR for scanned documents, PDF parsing for digital records, with automatic quality detection and routing
- Medical-domain embeddings — fine-tuned embedding model on clinical vocabulary for accurate semantic retrieval
- Structured extraction prompts — carefully engineered prompt templates for each document category, validated against 2,000 gold-standard examples
- Confidence scoring — every extraction includes a confidence score; anything below 92% routes to human review
Week 6-8: Integration & Validation
We integrated the system with the network's existing EHR (Epic), billing platform, and compliance reporting tools. Validation was rigorous: we ran 5,000 documents through both the AI system and human reviewers, comparing results field-by-field. The AI matched or exceeded human accuracy on 99.2% of extractions.
Week 9-10: Deployment & Training
Phased rollout across the 14 facilities, starting with the three highest-volume locations. We trained the medical coding team to work with the new system — reviewing AI extractions instead of doing manual extraction. The role shifted from data entry to quality assurance.
Tech Stack
Results
- 78% reduction in processing time — from 4.2 hours to 55 minutes per batch
- 99.2% extraction accuracy — exceeding the 92% human baseline on key fields
- 12,000 documents processed daily with consistent quality across all 14 facilities
- Error rate dropped from 8% to 0.9% on medical coding
- Medical coding team redeployed from data entry to complex case review and quality assurance
We evaluated three enterprise AI firms before choosing Arkyon. They delivered faster, at a fraction of the cost, with better results. The ROI was immediate.
P.K. — VP Engineering, Regional Health Network
What Made This Work
- Domain immersion — we spent the first two weeks embedded with the coding team, not in our own office
- Confidence-based routing — the system knows what it doesn't know, routing uncertain cases to humans instead of guessing
- HIPAA-first architecture — data never leaves the client's Azure tenant; all processing happens within their compliance boundary
- Measurable validation — 5,000-document head-to-head comparison gave stakeholders confidence before go-live