Watch how a self-evaluating extraction agent eliminates 70% of manual claims processing while maintaining ≥95% accuracy — because basic LLMs silently fail on complex edge cases, introducing garbage data into billing systems with zero audit trail.
A complete walkthrough of MedExtract's 4-stage agentic pipeline — from document upload to autonomous accept/reject decisioning.
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Request Extraction Pipeline AuditFrom trigger to resolution — fully automated, zero human intervention.
The system receives a medical document (PDF, image, or scan), detects its type — discharge summary, invoice, lab report, insurance claim — and selects the correct extraction schema.
Gemini 2.0 Flash extracts structured clinical data using schema-enforced JSON output — patient demographics, diagnosis codes, billing amounts, dates, provider info — with zero hallucination guardrails.
A deterministic validation layer performs field-level completeness checks, cross-field business rule verification (e.g., admission date before discharge), and severity-based issue scoring.
The system generates a Self-Evaluation Report with confidence scores, then autonomously routes each document to ACCEPTED, NEEDS_REVIEW, or REJECTED — no human in the loop for clean extractions.
Document Classification → Schema-Specific Extraction → Business Rule Validation → Confidence-Based Decisioning, orchestrated via n8n.
Every extraction generates a confidence report with field-level scoring, issue severity analysis, and autonomous accept/reject decisioning.
Handles discharge summaries, patient invoices, insurance claims, lab reports, and referral letters with type-specific extraction schemas.
Real-time pipeline statistics, document history, extraction accuracy metrics, and full Self-Evaluation Report visualization.
Strict structured output from Gemini 2.0 Flash eliminates hallucinations — every field is validated against clinical data standards.
All documents stored with presigned URLs for time-limited, secure access. Full audit trail for every extraction attempt.
4-stage agentic workflow orchestration with webhook triggers, HTTP routing, and conditional branching for document pipeline management.
Schema-enforced LLM extraction with structured JSON output, document classification, and clinical context understanding.
Production-grade API backend with TypeORM, handling document metadata, extraction results, and pipeline statistics.
Secure document storage with presigned URLs for time-limited access, supporting PDFs, images, and scanned clinical documents.
Tell me about your current document processing workflow and I'll design a self-evaluating extraction system that eliminates manual data entry and maintains audit-grade accuracy.