Agentic Medical Document Extraction
Transform your documents into actionable insights with advanced AI analysis. Extract key information, identify patterns, and generate comprehensive reports from any document type.
Introduction
Document Extraction is an AI-driven healthcare document extraction system designed to convert faxed medical files into structured digital data. It automates OCR, field extraction, validation, and EHR integration to reduce manual processing effort.
Problem
Healthcare organizations receive large volumes of unstructured fax documents (PDF/images). Manual extraction is slow, inconsistent, and error-prone, causing delays in clinical and administrative workflows.
Objectives
Automate extraction of key patient and insurance fields from fax documents.
Improve accuracy and consistency of captured data.
Integrate extracted output with Epic FHIR.
Support secure multi-tenant operations with auditability.
Enable bulk processing and export-ready outputs.
Requirements
Upload support for PDF, PNG, JPG/JPEG.
OCR with high-quality text recognition.
AI-based key-value extraction from varied form layouts.
Validation layer with template mapping and null-field tracking.
Epic FHIR DocumentReference integration.
Role-based authentication and tenant isolation.
Storage for source/processed data and processing history.
Export to JSON/Excel.
Tech Stack
Backend: Python 3.12+, FastAPI
Frontend: React 18 + Vite
OCR: Azure Document Intelligence (+ LayoutLMv3 support)
LLM Extraction: Azure OpenAI GPT-4o (LangChain orchestration)
Database: PostgreSQL (JSONB)
Queue/Async: Celery + Redis
Storage: Azure Blob Storage
Auth: Azure AD OAuth2/OpenID + local auth
Integration: Epic FHIR (R4)
Architecture
User authenticates via Azure AD/local auth.
Document uploaded from frontend.
File stored securely in Azure Blob Storage.
OCR extracts document text/layout.
LLM performs structured key-value extraction.
Validation/template mapping/null checks run.
Processed output stored in PostgreSQL.
Epic FHIR receives DocumentReference.
Results shown in UI and available for export.
Implementation
Built modular services for OCR, extraction, mapping, and Epic integration.
Added deduplication using file hash.
Stored extracted payloads and metadata in processed_files.
Implemented ground_truth and null_field_tracking for QA monitoring.
Enabled asynchronous and bulk processing with Celery workers.
Added frontend flows for upload, review, correction, and export.
Challenges
Handling highly variable fax quality and inconsistent form layouts.
Balancing extraction flexibility with structured output reliability.
Managing missing critical fields in incomplete documents.
Ensuring tenant-level data isolation and secure access.
Maintaining stable Epic matching (patient/encounter context).
Testing
API and database connectivity checks.
OCR/extraction validation across sample fax formats.
Deduplication and bulk-processing verification.
Null-field and template-mapping behavior validation.
End-to-end tests from upload to Epic write/export.
Role/session handling and multi-tenant flow checks.
Results
Significant reduction in manual data-entry workload.
Faster fax-to-EHR turnaround.
Better extraction consistency via validation and template mapping.
Improved quality visibility through null-field analytics.
Scalable batch processing with auditable processing records.
Future Scope
Add field-level confidence dashboards and automated quality scoring.
Expand template intelligence for more specialty form types.
Introduce human-in-the-loop review routing for low-confidence cases.
Strengthen referential integrity between processing-related tables.
Add broader EHR integrations beyond Epic.
Conclusion
The Document Extraction project successfully delivers an end-to-end AI document extraction pipeline for healthcare operations. By combining OCR, LLM-based extraction, validation, and Epic FHIR integration, it improves speed, accuracy, and scalability of fax document processing while maintaining secure multi-tenant architecture.