Professional Claim (CMS-1500) Extractor

Extract the claim number, patient, provider NPI, payer, diagnoses, procedures, and charged, allowed and paid amounts from a CMS-1500 claim PDF.

The full guide: Read a CMS-1500 by box number: Box 21 and Box 24

How should we start?

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Need to scale? Create an API key, then run this from your own code or an agent.

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Start with a document

Upload a file or pick a sample, and see the fields come back.

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Questions about Professional Claim (CMS-1500) Extractor.

What does the professional claim extractor pull from a CMS-1500?

The claim number and date, claim status, patient name, date of birth, member ID and gender, the billing provider name, NPI and tax ID, the payer name and ID, the service period, and the place of service, plus any prior authorization and referring provider NPI.

Are the service lines itemized?

Yes. The service lines table returns one row per line with the procedure code, modifier, service date, quantity, charge amount, allowed amount, paid amount, patient responsibility, diagnosis pointer, and any denial code, so a multi-line claim comes back ready to reconcile.

How are diagnoses and procedures captured?

ICD-10-CM diagnosis codes and CPT, HCPCS, or ICD-10-PCS procedure codes are read at the claim level, and the diagnoses table returns each diagnosis number, code, description, and a primary indicator.

Which money fields come out?

Total charge amount, allowed amount, paid amount, and patient responsibility at the claim level, alongside the adjudication outcome, so a submitted claim or its remittance both map to the same figures.

What happens to a claim containing PHI after processing?

It runs through the Talonic API for extraction only, is not retained for training, and is not shared. Redact identifiers where possible; for BAA-covered work, ask about the Talonic platform. PDF only, up to 10MB and 100 pages.

Doing this to one file, or to ten thousand?

The tool reads a single document. The platform reads the whole estate once and keeps it queryable — the same engine, with a memory.

See document data extraction if you run this for clinical and billing teams, or the extraction API if you are building it in.