Encounter-level evidence
See what the model recommended, the confidence and review threshold, whether a human reviewed it, any override and its reason, the final codes, and the final disposition. One bundle per encounter, searchable.
Encounter evidence for AI medical coding
Show how each AI-coded encounter was handled.
AuditLayer turns AI-assisted coding activity into searchable encounter evidence, exception monitoring, and review-ready exports for revenue integrity, AI governance, customer diligence, and internal audit.
Why this matters
Coding teams need more than logs.
As AI takes on more of the coding work, reviewers still need a clear record for each encounter: what the model recommended, whether a human reviewed it, what changed and why, and what finally shipped.
AuditLayer builds that record, one bundle per encounter.
What it produces
One bundle per encounter, searchable.
Exception monitoring
Automatic findings for encounters where required review did not happen, where an override has no recorded rationale, or where the disposition and codes disagree. Each exception carries a status and traces back to its source record.
Review-ready exports
Exports that feed your existing audit, BI, GRC, and review workflows. Hand a packet to revenue integrity, internal audit, or a customer diligence reviewer without rework.
How it works
From coding activity to encounter evidence.
Step 1
Send deidentified records
Provide 100 to 1,000 deidentified AI-assisted coding records in a flat file. No production access, no protected health information.
Step 2
We build encounter evidence
AuditLayer normalizes the records into one evidence bundle per encounter and runs exception monitoring across the set.
Step 3
You review and export
Search any encounter, review exception findings, read the auditor narrative sample, and export review-ready packets into the workflows you already run.
The auditor narrative is a written summary for reviewers. It is a narrative, not an auditor conclusion.
10 business days
The 10-Day AI Coding Evidence Sprint.
Turn 100 to 1,000 deidentified coding records into encounter-level evidence bundles, exception findings, and review-ready exports in 10 business days. Kickoff on day one, readout on day ten.
What you provide
Deidentified coding records in a flat file and one point of contact.
What you get back
Encounter-level evidence bundles, an exception findings report, a searchable encounter index, review-ready exports, and an auditor narrative sample.
A fixed sprint at $2,500 to $5,000, credited toward an annual contract if it converts. This is a proposed structure for early engagements, not a fixed price.
What it exports
Built to feed the workflows you already use.
AuditLayer produces outputs that drop into existing audit, BI, GRC, and review tools. It does not replace them.
- Encounter evidence bundles in CSV and a formatted summary.
- Exception findings grouped by type and status.
- A searchable encounter index filterable by payer, site of care, service line, workflow, model, and disposition.
- An auditor narrative sample in review-ready language.
- Review-ready packets for revenue integrity, internal audit, and customer diligence.
Who uses it
Who uses AuditLayer.
AI coding vendors
Need to show customers and prospects how each AI-coded encounter was handled after go-live.
RCM technology vendors
Embed AI coding and field diligence questions from health system buyers.
Coding QA and revenue integrity leaders
Need searchable proof of what the model recommended, what a human reviewed, and what shipped.
Product, implementation, and customer success leaders
Get asked for post-deployment evidence during customer diligence and internal audit.
Reference implementation
Built to be inspected.
The reference implementation includes an ingestion endpoint, a JSON coding evidence schema, deidentified demo records, a searchable encounter index, an exception findings view, and review-ready exports.
Coding evidence schema
github.com/DeuceAllMighty/auditlayer-specGovernance resource
A checklist for AI coding evidence.
The RUAIH checklist is a supporting governance mapping. Use it to pressure-test whether an AI coding workflow can produce the evidence a reviewer would ask for: what happened, what data was used, where human review occurred, which exceptions were flagged, and what can be exported.
A practical evidence checklist for teams preparing AI coding workflow review. Not legal advice. Not a certification.
- Searchable encounter evidence
- Exception monitoring
- Human review checkpoints
- Review-ready exports