Healthcare Referral Automation

The Challenge

A busy sleep medicine practice was drowning in paper. Every day, 50 to 100 physician referrals arrived via fax — each from a different EMR system (Epic, Cerner, or custom forms), each with a different layout, and each requiring a staff member to manually read and enter patient data into their system.

The manual process consumed hours of staff time daily, introduced transcription errors, and created bottlenecks that delayed patient care.

The Approach

We built an AI-powered document processing pipeline purpose-built for the messy reality of healthcare referrals.

The system handles the full lifecycle:

• Intelligent document classification — automatically identifies referral type, source EMR system, and form layout
• Structured data extraction — pulls patient demographics, referring physician details, diagnosis codes (ICD-10), insurance information, and clinical notes from unstructured faxed documents
• Confidence scoring — every extracted field includes a confidence score, flagging low-confidence extractions for human review rather than silently inserting errors
• EMR integration — extracted data flows directly into the practice’s systems, eliminating the re-keying step entirely

The AI model was fine-tuned on the practice’s actual referral volume, learning the specific patterns and edge cases of their referral sources.

The Result

The proof of concept validated the approach: 98%+ extraction accuracy across all referral types and source EMR systems, tested against the practice’s real referral volume.

Estimated annual savings at full deployment: $89,000 in eliminated manual data entry — with the system designed to scale as referral volume grows without adding headcount.

The POC proved that AI can reliably handle the messy, unstructured reality of healthcare documents. Full production deployment is pending HIPAA compliance and liability insurance requirements — the technical problem is solved, the regulatory path is next.