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AI-assisted health-insurance workflows can go wrong when they apply outdated or overly restrictive rules, miss evidence, mishandle data, or hide patterns that should trigger review. Preventing harm means making each decision traceable: the rule version, source records, system processing, human actions, and reason given to the member or provider should all be reviewable.
These risks matter even when AI is not the cause. Audits have documented errors in Medicare Advantage prior authorization and payment review, but those findings are not AI error rates. AI is reported in insurer work such as prior authorization, claims adjudication, fraud detection, and risk adjustment; that does not establish that every insurer uses it in every workflow. The NAIC’s 2025 online survey included responses from 93 insurance companies and was conducted from November 2024 through January 2025.
Seven ways health-insurance workflows can fail
1. Prior-authorization criteria drift or exceed coverage rules
A reviewer—human or automated—can apply a criterion that is outdated, configured incorrectly, or stricter than the governing coverage policy. In a 2022 report, HHS Office of Inspector General (OIG) found that 13% of sampled Medicare Advantage (MA) prior-authorization denials met Medicare coverage rules. The sample covered decisions from June 1–7, 2019; OIG described examples in which plans used clinical criteria that were not in Medicare’s coverage rules. This was a sample of denials, not an AI performance test or a finding about every MA plan. Read the OIG report.
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2. Relevant documentation is missing, overlooked, or misclassified
A workflow may not retrieve a relevant record, may classify it incorrectly, or may label the evidence insufficient even when the file supports medical necessity. OIG’s prior-authorization review described sampled cases in which plans considered documentation insufficient but OIG reviewers found existing records sufficient. In other settings, the file may genuinely be absent, incomplete, or unreadable; those are different problems and should not be collapsed into a single “insufficient evidence” outcome.
Prevention: Check document completeness before review, preserve links from extracted facts to source pages, and flag unreadable or missing files as explicit exceptions. Send conflicting evidence or uncertain extraction results to a human reviewer rather than silently treating them as absent.
3. Manual review mistakes persist inside automated workflows
Automation around a decision does not remove the possibility of a handling error. In the same OIG report, 18% of sampled MA payment denials met Medicare coverage and MA organization (MAO) billing rules. OIG reported that most such payment denials resulted from manual review mistakes, such as overlooking a document, or system-processing errors. These sampled findings concern decisions from June 1–7, 2019—not a current, system-wide AI error rate.
Prevention: Reconcile the records received with the records considered before finalizing a decision. Use a reviewer checklist for critical evidence and examine reversal reasons to find recurring points where documents or facts were missed.
4. A policy or configuration update is stale, incomplete, or misapplied
A correct policy can still produce a wrong outcome if the system uses the wrong version, an update is incomplete, or implementation introduces a defect. OIG identified system-processing errors, including systems that were not programmed or updated correctly, among the issues behind sampled MA payment denials. That finding identifies a failure mode; it does not establish that a particular corrective control has been tested or proven effective.
Prevention: Keep version-controlled rules and release records. Before deployment, test changes against representative cases, document approval, and monitor outcomes after release so that a faulty update can be identified and corrected.
5. Coding or risk-adjustment inputs are unsupported or inaccurate
Automated extraction or coding can produce incomplete or incorrect diagnosis data. The underlying record may also fail to support a diagnosis because it is missing, illegible, or otherwise insufficient. CMS explains that MA Part C payments use diagnosis data submitted by MA organizations to determine risk scores, and that inaccurate or incomplete diagnosis data may result in improper payments. CMS does not attribute all such problems to AI.
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6. Eligibility verification is omitted or recorded against the wrong case
Eligibility workflows can fail when required verification is not obtained, retained, or correctly associated with the person or application. CMS identifies the absence of a record of required eligibility verification as one circumstance behind improper payments in Medicaid, the Children’s Health Insurance Program (CHIP), and the Federally Facilitated Exchange. This is a program-administration risk; CMS does not identify it as specifically AI-caused.
Prevention: Check information against the appropriate source of truth, validate required fields, create an exception queue for missing or conflicting values, and retain an evidence trail showing what was verified and when.
7. Oversight misses adverse patterns, vendor problems, or unequal effects
A workflow can appear to operate normally while denials vary sharply by service, contractor, or population—or while appeals reveal a problem in initial review. In a 2026 report examining MA skilled nursing facility (SNF) admission requests, OIG found that 12% of requests reviewed across 19 MAOs were denied in June 2024. Of the SNF admission denials that were appealed, 95% were overturned in favor of the enrollee. That overturn figure applies only to appealed denials, not all denials. OIG called for request-level data and assessment of initial-review breakdowns and variation. Read the OIG report.
Prevention: Monitor decisions by service, contractor, and relevant population; analyze appeals, reversals, and their stated reasons; hold vendors and contractors accountable for review quality; and define escalation paths for concerning patterns. HHS describes a CMS use case for identifying outliers in claims, payments, and complaints and examining possible noncompliance or negative beneficiary outcomes associated with plans’ AI and potential bias. See the HHS/ONC description of the oversight use case.
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What improper-payment estimates can—and cannot—tell you
CMS’s FY2024 estimates put the Medicare Part C improper-payment rate at 5.61%, or $19.07 billion. For Medicaid, CMS estimated a 5.09% rate, or $31.10 billion, based on reviews conducted in 2022–2024; 79.11% of FY2024 Medicaid improper payments resulted from insufficient documentation. These are program-level estimates, not measurements of AI performance. CMS also cautions that an improper payment is not necessarily fraud: some cases are classified this way because the available documentation is insufficient to determine whether payment was proper. See CMS’s FY2024 fact sheet.
These figures should not be compared as though they shared the same population, period, or denominator as OIG’s sampled MA denial findings. Use each statistic only for the program and measurement CMS or OIG describes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What CMS’s prior-authorization rule changes for covered workflows
CMS-0057-F establishes a phased baseline for specified payers, not a universal rule for every commercial insurer or every drug authorization. Covered entities include specified MA organizations, state Medicaid and CHIP fee-for-service programs, Medicaid managed-care plans, CHIP managed-care entities, and Qualified Health Plan issuers on the Federally Facilitated Exchanges (FFEs). The exact applicability and dates depend on payer category, so organizations should check current CMS guidance for their plans.
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For impacted payers, many operational provisions generally begin January 1, 2026; API development and enhancement requirements generally begin January 1, 2027. The rule sets prior-authorization decision timeframes of 72 hours for expedited requests and seven calendar days for standard requests, but excludes FFE QHP issuers from that timeframe requirement. Beginning in 2026, impacted payers must provide a specific reason when denying a non-drug prior-authorization request.
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The Prior Authorization API must identify covered items and services and relevant documentation requirements, support requests and responses, and communicate whether a request is approved, denied with a specific reason, or needs more information. These requirements can make decisions more explainable, but they do not by themselves establish that the underlying criteria or evidence handling are correct. Read CMS’s CMS-0057-F fact sheet. CMS also maintains information on prior-authorization and pre-claim review initiatives.
How to make failures visible and correctable
Use a decision record that lets an auditor reconstruct what happened, rather than keeping only the final approve-or-deny outcome. For each relevant decision, retain the policy and rule version, evidence considered, system processing or extracted values, reviewer actions, final rationale, and any later correction or appeal result. This is a practical control approach based on the failure modes and oversight needs described above, not a guarantee of error-free decisions.
Then review whether the controls are working by checking operational signals such as missing or unreadable files routed to exception handling, changes in manual corrections or reversals after a release, and appeal outcomes broken down by service, contractor, and relevant population. Investigate unexplained differences and assign an owner and escalation path for corrective action. A single overall denial or reversal rate can conceal where a process is breaking down.
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