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For operations and compliance teams, the central risk is broader than a faulty model: an incorrect rule, missing record, manual mistake, stale configuration, or weak oversight can all produce a harmful outcome in a workflow that includes automation. HHS Office of Inspector General findings discussed below concern sampled Medicare Advantage decisions, not a measured AI error rate. The practical response is to make the rule, evidence, decision, and route to correction traceable.
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1. Prior-authorization criteria drift or overreach
How it fails
A reviewer or decision-support system may apply a criterion that is stricter than the applicable coverage rules, or use a rule that is out of date. This can turn a technically consistent decision into an incorrect denial if the system is enforcing the wrong standard.
In a 2022 report based on sampled Medicare Advantage (MA) prior-authorization decisions from June 1–7, 2019, HHS OIG found that 13% of sampled denials met Medicare coverage rules. OIG described examples in which plans used clinical criteria not contained in Medicare coverage rules. This was a sample-specific finding; it does not show that AI made those decisions or establish an error rate for all MA plans. HHS OIG’s prior-authorization report
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How to prevent it
- Maintain a versioned library of coverage criteria, with an owner responsible for each rule and its effective date.
- Require documented review and approval when a rule changes; retain the version used for each decision.
- Audit denials against the governing coverage policy, including a sample of cases after policy or system updates.
2. Relevant documentation is missing, overlooked, or misclassified
How it fails
Records may not arrive, may be unreadable, or may be attached to the wrong request. Even when the documents are present, automated extraction may fail to find relevant evidence, or a reviewer may miss it and conclude that the record does not support medical necessity. In its MA prior-authorization review, OIG found cases in which reviewers considered documentation insufficient even though OIG reviewers found existing records sufficient.
Documentation gaps also affect program payment estimates. CMS reported that 79.11% of FY2024 Medicaid improper payments resulted from insufficient documentation; its Medicaid improper-payment estimate was 5.09%, or $31.10 billion, based on reviews conducted in 2022–2024. CMS explains that an improper payment can include a case where available records are insufficient to determine whether payment was proper; the estimate is not a fraud rate. CMS FY2024 Improper Payments Fact Sheet
How to prevent it
- Check that expected documents are present and associated with the right member, service, and decision before review begins.
- Link extracted facts to their source pages or records so a reviewer can verify them.
- Route unreadable files, missing evidence, and conflicts between extracted data and the record to a human queue rather than treating them as proof that evidence does not exist.
3. Manual claims-review errors survive inside automated workflows
How it fails
Automating intake, routing, or payment calculations does not eliminate mistakes at the review step. A person can overlook a document or enter the wrong information; a system can also process a decision incorrectly. In the same 2022 OIG report, 18% of sampled MA payment denials met Medicare coverage and MA organization billing rules. OIG attributed most of those payment denials to manual review mistakes, such as overlooking a document, or to system-processing errors. The sample covered decisions from June 1–7, 2019, and is not a current, system-wide, or AI-specific error rate. HHS OIG’s prior-authorization and payment-denial report
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How to prevent it
- Reconcile the final claim decision against the source record and applicable billing rules.
- Use reviewer checklists for evidence that is critical to payment or denial, with clear steps for documenting exceptions.
- Review reversals and corrected claims by reason to identify recurring missed evidence or handling errors.
4. A policy or configuration update is stale, incomplete, or applied incorrectly
How it fails
A policy can be updated in one system but not another, a rule can be entered incorrectly, or a software change can alter how cases are routed or calculated. 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 any particular control has been proven to eliminate it.
How to prevent it
- Keep configuration changes under version control and record the intended policy, effective date, approver, and release date.
- Run regression tests on representative cases—including boundary cases—before releasing a change.
- Monitor decisions after deployment for unexpected shifts in denials, payments, or exception volume, and retain a rollback path.
5. Coding and risk-adjustment inputs are inaccurate or unsupported
How it fails
Extraction or coding automation can produce an incomplete or incorrect diagnosis record. A diagnosis may also lack legible, adequate source documentation. These errors matter in MA because CMS uses diagnosis data submitted by MA organizations to determine risk scores; inaccurate or incomplete data may result in improper payments. CMS estimated a 5.61% Medicare Part C improper-payment rate, or $19.07 billion, for FY2024. This program estimate is not an AI error rate or a finding that all improper payments arose from coding automation. CMS FY2024 Improper Payments Fact Sheet
How to prevent it
- Require each coded diagnosis to be traceable to supporting source documentation.
- Validate code sets and effective dates, and preserve the input and provenance used to generate coded output.
- Send unsupported, conflicting, or low-confidence diagnoses for review instead of allowing them to pass through without a documented basis.
6. Eligibility verification is skipped or recorded against the wrong case
How it fails
An eligibility process may fail to obtain, retain, or correctly associate a required verification—for example, an income element relevant to the program. A system that treats a blank field as a valid result, or stores evidence without linking it to the right person and determination, can leave an eligibility decision unsupported.
CMS identifies the absence of a record of required eligibility verification as one circumstance behind improper payments in Medicaid, CHIP, and the Federally Facilitated Exchange. The agency does not attribute this program-administration risk specifically to AI. CMS FY2024 Improper Payments Fact Sheet
How to prevent it
- Check required information against the designated source of truth and validate required fields before a determination can be finalized.
- Send missing, conflicting, or unverified information to an exception queue instead of silently treating it as complete.
- Retain a dated evidence trail showing what was checked, what was returned, and how the eligibility decision was reached.
7. Oversight misses adverse patterns, vendor problems, or unequal effects
How it fails
A decision process can appear to work case by case while producing unusually high denial rates for a service, contractor, or population. Appeal outcomes may reveal that initial reviews are breaking down, but they represent only cases that were appealed and cannot be generalized to every denial.
In a 2026 report, OIG found that 12% of skilled nursing facility (SNF) admission requests reviewed across 19 MA organizations were denied in June 2024. It also found that 95% of the SNF admission denials appealed in its review were overturned in favor of the enrollee. OIG called for request-level data and assessment of initial-review breakdowns and variation. The overturn figure applies to appealed denials, not all denials. HHS OIG’s SNF prior-authorization report
HHS has described a CMS oversight use case that looks for outliers in claims, payments, and complaints and examines possible noncompliance or negative beneficiary outcomes associated with plans’ AI and potential bias. HHS/ONC’s description of CMS plan oversight
How to prevent it
- Monitor outcomes by service, contractor, relevant population, and decision stage—not only as a single organization-wide rate.
- Analyze appeals, reversals, complaints, and reasons for denial together to find patterns that an overall accuracy measure can hide.
- Assign vendors and internal owners responsibility for investigating anomalies, documenting remediation, and escalating unresolved risks.
What CMS-0057-F changes for covered prior-authorization workflows
CMS-0057-F sets a phased regulatory baseline for specified Medicare Advantage 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. It is not a blanket rule for every commercial insurer or every drug authorization. Many operational provisions generally begin January 1, 2026, while API development and enhancement requirements generally begin January 1, 2027; exact applicability and dates vary by payer category. CMS-0057-F fact sheet
- For impacted payers, the rule generally requires prior-authorization decisions within 72 hours for expedited requests and seven calendar days for standard requests. Federally Facilitated Exchange QHP issuers are excluded from these timeframe requirements.
- Beginning in 2026, impacted payers must provide a specific reason when denying a non-drug prior-authorization request.
- The Prior Authorization API must identify covered items or services and documentation requirements, support requests and responses, and communicate an approval, a denial with a specific reason, or a request for more information.
These requirements can make decision reasons and information requirements more visible, but they do not replace internal controls over rule accuracy, evidence handling, configuration, or outcome monitoring. Organizations should check current CMS implementation guidance and the specific dates that apply to their payer category.
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Make every decision traceable and correctable
For each consequential workflow, retain enough information to reconstruct what happened: the applicable policy and version, the evidence considered, the system or reviewer actions, the reason communicated, and any later appeal or correction. Set monitoring around the points where failures become visible—missing-document exceptions, unusual denial patterns, reversals, configuration releases, and differences across services or contractors.
These safeguards are practical responses to documented failure modes and oversight expectations, not guarantees established by an effectiveness trial. Their value is that they help an organization detect an incorrect outcome, identify where it entered the workflow, and give the affected person or provider a meaningful path to challenge it.
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