AI chart review
AI medical record review reads a chart against configured coding, documentation and payer rules, then flags possible issues for a human reviewer. In Arkangel AI, every finding names the rule it checked, quotes the chart text it relies on, shows a confidence level and stays “Not verified” until a reviewer accepts or dismisses it.
Three findings on a synthetic outpatient and emergency record. The rules and wording are illustrative; in a pilot, the rules come from your coding guidelines, payer policies and internal criteria.
Illustrative example on a synthetic chart. Not patient data.
History of DVT, completed anticoagulation in 2023. No current symptoms.(Progress note, assessment, page 2)
No total-time statement found in the note or its addenda.(Office visit note, all sections searched)
Low back pain for 2 weeks, no trauma, no neurologic deficits.(Emergency department note, history and exam)
The reviewer accepts, dismisses or asks for more records on every finding. Arkangel AI does not deny claims, change codes or contact providers on its own.
Start from deterministic rules: coding guidelines, payer policies and your internal audit criteria, written down so everyone knows what is being checked.
The AI reads notes, reports and PDFs, extracts the facts each rule needs and keeps a link to the exact text and page each fact came from.
Rules run against the extracted facts. Each result carries the rule, the cited evidence and a confidence level. When the evidence is insufficient, the result stays “Not verified” instead of guessing.
A coder, clinical reviewer or auditor validates each finding against the record. The final decision is always human.
Pre-bill or retrospective review of diagnosis and procedure codes against the documentation, to surface possible unsupported or missed codes.
Missing elements that a billed service or a quality measure depends on, such as time statements, signatures or required findings.
Medical-necessity criteria from configured payer policies, checked against what the chart actually documents.
In an anonymized case with a Colombian health insurer, the average audit cycle went from 94 to 29 days across 18 batches (42,300 line items), with the human auditor making the final decision.
In the same case, findings with an explicit reason and cited evidence rose from 54% to 96%, and manually reviewed lines fell from 100% to 31% after prioritization.
PANDORA, presented at the 2025 IEEE Conference on Artificial Intelligence (CAI), studied automatic extraction of unstructured clinical data and a clinical risk score from records.
70+ security controls, penetration testing and continuous monitoring via Vanta. See the Trust Center for details.
Send protected health information only under an Enterprise agreement. Inputs on Free, Pro and Team plans are not covered by HIPAA.
Data sources such as Epic Clarity, Google Drive, SharePoint, Azure Blob, AWS S3, Google Cloud and SAP.
| Part | What it includes |
|---|---|
| Your input | One batch of charts under an Enterprise agreement, plus the rules and payer policies you want checked. |
| Our work | Configure the rules, run the review and deliver every finding with its rule, cited evidence, confidence and status. |
| Your work | Reviewers validate a sample of findings and share their decisions, so rules can be tuned. |
| Output | A finding list ready for reviewer decisions and a summary of results by rule. |
| Not included | Automatic denials, claim submission or appeals, and any guarantee of recoveries or audit outcomes. |
It is software that reads clinical records against configured rules, such as coding guidelines and payer policies, and flags possible issues for a person to check. A good tool cites the chart text behind each finding and the rule it applied, so coders, clinical reviewers and auditors can verify it quickly instead of rereading every page.
Payment integrity makes sure claims are paid correctly: the services were documented, coded accurately, medically necessary under the payer's policy and billed at the agreed rate. AI chart review supports it by checking records against those rules and surfacing possible errors, while reviewers make the final decision on each claim or line item.
Each finding shows the rule that was checked and the exact chart text and page it relies on, and a reviewer makes the decision. When the evidence is incomplete, the finding stays “Not verified” instead of becoming a denial. Your team can explain every decision and revisit it if new records arrive during an appeal.
Deterministic rules run first, and every result shows a confidence level, so reviewers can start with the strongest findings and dismiss weak ones quickly. In an anonymized Colombian payer case, manually reviewed lines fell from 100% to 31% after prioritization. Results depend on your rules and data, so a pilot measures them on your own batch.
Arkangel AI is ISO 27001:2022 certified, with HIPAA compliance and a BAA on Enterprise plans, 70+ security controls, penetration testing and continuous monitoring via Vanta. Protected health information belongs only on an Enterprise agreement; inputs on Free, Pro and Team plans are not covered by HIPAA. Security documentation is in our Trust Center.
Tell us about your audit or coding workflow. We will walk through sample findings and scope a pilot on one batch.
Last reviewed: . Sample findings are illustrative and use a synthetic record. Coverage, timing and results depend on the configured rules, the data and the review process; payment and audit outcomes are not guaranteed.