Extract structured data from claims documents using an OCR/IDP pipeline

Verified steps

1. Ingest the source documents (police reports, medical bills, repair estimates, recorded statements) from your document management system or email intake queue; normalize to PDF or TIFF format for consistent OCR processing.
2. Submit the document to an Intelligent Document Processing (IDP) service (e.g., AWS Textract, Google Document AI, Azure Form Recognizer, or an insurance-specific IDP vendor such as Indico or Hyperscience); select the appropriate model for the document type.
3. Receive the extracted key-value pairs and table data from the IDP API response; map extracted fields to your claims data schema (e.g., date of loss, at-fault party, vehicle damage description, medical diagnosis codes, billed amounts).
4. Apply confidence-score thresholds to extracted fields: route low-confidence extractions to a human review queue; auto-accept high-confidence fields above your defined threshold (determine the threshold based on document type and downstream use).
5. Write accepted extracted data back to the claim record via the claims management system API, linking each extracted value to the source document page and bounding box for auditability.
6. Retrain or fine-tune the IDP model periodically using confirmed corrections from the human review queue to improve accuracy on your specific document types.