Healthcare claims validation with a full audit trail
Claims are read, checked against reference data and reviewed by an agent before they go to the payer. Every extraction, validation and decision is recorded per claim, so each outcome can be explained later.
The challenge
Healthcare provider platforms depend on claims being right the first time. A mistyped provider identifier, a diagnosis coded at the wrong level of detail or an entry that should never have been accepted sends a claim into rework, and each cycle costs staff time and delays payment.
Manual checking against registries and code books is slow and inconsistent, and it leaves little evidence behind. When a regulator or auditor asks why a claim was submitted, teams often cannot show what was checked, by whom, or on what basis.
What it does
Provider identity checks
Each provider identifier on a claim is validated and looked up in the national provider registry, so wrong or invented identities are caught before submission instead of after rejection.
Diagnosis code mapping
Diagnoses written in clinical wording are matched to ICD-10 codes from reference data, with checks that each code exists, is billable and is specific enough to use.
Document intake and extraction
Claim documents arrive through a secure file pipeline and are read into structured fields, so the checks work on data rather than on images of pages.
Agent cross-check
An AI agent compares provider and diagnosis results with the rest of the claim and flags anomalies for a person to review before the claim moves on.
Exclusion screening
Providers can be screened against the federal exclusion list as part of the check, so claims tied to an excluded provider are held for review.
Per-claim audit trail
Every extraction, validation and decision is logged against the claim it belongs to, giving compliance teams one record to inspect for each submission.
How it works
Claims arrive
Claim files are delivered over a secure transfer pipeline into the organisation's storage, where they wait for processing under access rules set by your team.
Fields are extracted
Document reading turns each claim into structured fields such as provider identifier, diagnoses and procedures, and the extracted values are stored with the source document.
Reference checks run
The provider registry and ICD-10 reference servers are queried through the Model Context Protocol, and the agent compares what they return with the claim.
Review and record
Anomalies are flagged for a person to confirm before submission. Each step and each decision is written to the claim's audit trail.
Under the hood
- Reference data servers
- NPI MCP Server
- ICD-10 MCP Server
- Model Context Protocol
- Intake and extraction
- SFTP
- OCR and document parsing
- Structured JSON
- Healthcare formats
- X12 claims
- HL7 v2
- FHIR R4
- ZapperMsgParser
- Records and access
- Relational database
- Role-based access control
- Retrieval pipeline
Inside the harness
Audit
Extraction, each validation result and each decision are logged per claim, so a regulator or internal reviewer can follow how a claim reached submission.
Security
Claim documents and extracted fields stay in the organisation's own environment, with role-based access so only authorised staff see patient-related content.
Guardrails
The agent works from registry and code-set results rather than model memory, and anomalies are held for review instead of being corrected silently.
Policy
Rules set by your team decide which checks are required before submission and which claims need human sign-off, in a design aligned with HIPAA expectations.
Business outcome
Fewer errors reach the payer. Identity and coding problems are flagged before submission, so less time goes on resubmission and correction after a claim has already been rejected.
Less manual registry work. Staff no longer look up providers and codes by hand for each claim. They review only the cases the checks and the agent have flagged.
Evidence for every decision. Each claim carries its own record of what was extracted, checked and decided, which supports audits, regulator questions and later disputes.
Privacy built into the design. Access control and logging are part of the pipeline from the start, aligned with HIPAA expectations, rather than added after the process is already running.
Where it is used
- Checking provider identifiers on incoming professional claims before they are sent to the payer.
- Reviewing diagnosis coding for specificity and billable status before submission.
- Screening claims for providers who appear on a federal exclusion list.
- Preparing a per-claim evidence pack when an auditor asks how a submission was validated.
Frequently asked questions
Which reference sources does the check use?
Provider identities are checked through the NPI MCP Server and diagnosis codes through the ICD-10 MCP Server. Both are public projects, and the AI agent answers from their reference data rather than from model memory.
Does the agent approve claims on its own?
No. The agent flags anomalies and records what it found. A person confirms flagged claims before submission, and the decision is added to the same audit trail.
Is this a HIPAA certified solution?
No. The design is aligned with HIPAA expectations through access control, logging and data staying in your environment. Certification and compliance status depend on your own deployment and review.
Can it work with claim formats other than documents?
Parsers for X12, HL7 v2, FHIR and C-CDA messages exist as an open-source project and can supply structured claim data. Which inputs are used depends on what your systems send.
See it on your own documents and data
Tell us what you want to automate and we will show you how it runs inside the harness.