Healthcare claims extraction hits 98% accuracy on unstructured records

Overview

Healthcare claims arrive as a mess ” scanned PDFs, photographed forms, handwritten notes ” and every one has to become structured, billable data.

An information-extraction model that reads those documents and pulls patient demographics, diagnosis codes, and procedures only earns trust at a quality bar high enough to act on. In claims, a wrong ICD-10 or CPT code is a denied claim or a compliance problem.

The model behind this use case had to extract structured data from 50,000+ unstructured medical records across PDFs, scanned images, and handwritten notes ” at 98% accuracy, under HIPAA.

That bar isn't reached with generic OCR. It takes training and validation data authored by people who know medical terminology and know what a correct extraction looks like.

Firstsource produces that data and that discipline ” entity-recognition labels tuned to medical language, expert validation, and structured error analysis. This is Intelligence that Operates behind a claims-extraction model.

Challenges

Reaching claims-grade extraction meant clearing three problems at once:

• Unstructured, multi-format source documents: Claims don't arrive clean. PDFs, scanned images, and handwritten notes each read differently, and the model has to handle all of them without dropping fields or inventing them.
• Medical terminology is unforgiving: Diagnosis and procedure codes ” ICD-10, CPT ” carry no margin for approximation. The wrong code isn't a typo; it's a denied claim or a compliance exposure, so the training data behind the model has to be coded correctly in the first place.
• A 98% bar under HIPAA: The accuracy target is high enough that low-confidence fields can't be quietly passed through, and every record carries protected health information that has to be handled compliantly end to end.

How We Made It Happen

Firstsource produces the entity-recognition training data and validation discipline a medical information-extraction model needs to reach claims-grade accuracy.

• Entity recognition tuned to medical language: Extraction labels for patient demographics, diagnosis codes, and procedures, built by annotators who know medical terminology ” not generic text labeling.
• Expert validation by certified medical coders: Extractions are validated by domain experts so the model learns what a correct, billable reading looks like, with low-confidence fields flagged for review rather than passed through silently.
• Structured error analysis: Extraction failures are systematically analyzed, so the model's weak spots are known and addressed rather than hidden behind an aggregate accuracy number.

Conclusion

Document understanding only earns a place in a claims workflow when the extraction is right often enough to act on and honest about when it isn't. Firstsource produces the training data and expert validation that gets a medical information-extraction model there ” Intelligence that Operates.