# Agent context: Claims Denied by Insurers

Source: https://aiplaybooks.app/industries/healthcare/revenue-cycle/claim-denials
AI suitability: conditional

## Objective

Reduce the initial denial rate on a provider's claims by stopping preventable errors before submission and routing every denial to the team that owns its root cause, without slowing claim release or changing clinical coding to suit an insurer.

## Constraints

- All claims, remittance, registration, and clinical data is protected health information. Use it only for payment and operations, keep it inside the provider's controlled environment, and pass it to any outside service only under a business associate agreement and only the fields that service needs.
- Build eligibility, prior authorisation, and claim edit checks before any model. Do not train a model to predict errors that a deterministic check can stop.
- Never change a diagnosis or procedure code, add clinical documentation, submit a claim, or send an appeal automatically. A biller, coder, or clinician decides each one.
- Medical necessity is a clinical judgement. A model may flag an encounter for review but must not be presented as deciding whether care was necessary.
- Store and train on each claim as it was submitted, not as later corrected, and do not use fields filled in after submission as features.
- Treat an insurer's change to its authorisation list or coverage policy as a break in the history. Do not train across it without checking performance on claims after the change.
- Do not treat claim adjustment reason codes as root causes. Record the root cause assigned by the person who worked the denial.
- Size any ranked review list to the number of claims billers or reviewers can actually work each day.

## Inputs

- Registration records with insurer, plan, member ID, and the eligibility response and its date.
- Each insurer's current list of services requiring prior authorisation, coverage policies, and filing and appeal deadlines.
- Authorisation records with number, approved service, units, and date range.
- Claims as submitted, with procedure and diagnosis codes, modifiers, place of service, providers, charges, and authorisation number.
- Remittance per claim line, with claim adjustment reason codes, remark codes, amounts, and the outcome of any resubmission or appeal.
- For large health systems only, admission and discharge notes linked to inpatient encounters.

## Outputs

- Holds on claims that fail an eligibility, authorisation, or claim edit check, each with the rule that fired and what to fix.
- Denial work queues sorted by insurer, value, and appeal deadline, with a recorded root cause and owning team per denial.
- A monthly root-cause report per insurer for the patient access, coding, and documentation leads.
- Where justified, a ranked list of claims most likely to be denied among those that passed every check, sized to biller capacity.

## Success criteria

- Initial denial rate falls for eligibility, authorisation, and missing claim data denials, per insurer, against the starting point.
- The value written off because a denial was never worked or missed its appeal deadline falls.
- Days from service to claim submission do not rise because of holds.
- Recurring root causes fall after they are fed back to the owning team.
- Any risk score finds correctable errors on a meaningful share of the claims it flags, at real biller capacity, measured on claims after the most recent insurer rule change.

## Known pitfalls

- Buying a prediction tool before fixing eligibility and authorisation checks, so the model learns to predict the front-end errors a deterministic check would have stopped.
- Training a model on reason codes as if they were causes, when insurers return vague codes and a large share fall into an other category.
- Measuring success as denials overturned on appeal, which rewards working denials rather than preventing them.
- Letting a model or an automated rule change diagnosis or procedure codes to avoid a denial, which turns a billing tool into a coding compliance risk.
- Training on remittance history from a period before a major insurer rule change, such as a new authorisation list, so the model scores current claims against rules that no longer apply.
- Letting the denials team rework the same denial every month without a route back to the front-end or coding team that caused it.

## When not to use AI here

- Eligibility verification, prior authorisation tracking, and claim edits are incomplete or manual, so the deterministic checks will remove more denials than any model.
- The organisation is a small practice with too few denials per insurer for a model to learn from, where a clearinghouse scrubber and a weekly denial review cover most of the value.
- The question is whether care was medically necessary, which is a clinical judgement for a clinician, not a prediction.
- Remittance data does not record the reason codes or the outcome of resubmissions and appeals, so there is no reliable label to train on.
- A recent insurer rule change has made the historical denial pattern a poor guide to the current one.

## Candidate approaches

- Eligibility, authorisation, and claim edit checks before submission (primary): Deterministic checks at registration, scheduling, and claim release that confirm active coverage, match each service to the prior authorisation its insurer requires, and stop claims with missing or invalid data before they reach the insurer.
- Denial work queues routed by root cause (supporting): Rules that route each denied claim to the staff who know that insurer and that kind of denial, record the step that caused it, and send recurring causes back to the patient access, coding, or documentation team that owns them.
- Denial risk scoring before submission (supporting): A model trained on the organisation's own submitted claims and the insurer's response to each that ranks claims which passed every edit by their likelihood of denial, so billers review the riskiest ones before they are sent.
- Denial prediction from admission notes (discovery): A language model trained on a health system's own clinical notes that reads each inpatient admission's history and physical note and flags encounters whose claims are likely to be denied, so utilisation review and clinical documentation staff can check them during the stay.

## Human oversight

Patient access staff confirm eligibility and authorisation before the service, and billers decide every correction and resubmission. Clinicians and clinical documentation staff decide every response about medical necessity or clinical documentation. A risk score or a language model only ranks claims for review or drafts material for a person to check, and it never changes a code, submits a claim, or sends an appeal on its own.

## Sources

- [Strategic improvements in your RCM to reduce your practice's claim denials](https://www.mgma.com/mgma-stat/strategic-improvements-in-your-rcm-to-reduce-your-practices-claim-denials), Medical Group Management Association, practitioner
- [6 keys to addressing denials in your medical practice's revenue cycle](https://www.mgma.com/mgma-stats/6-keys-to-addressing-denials-in-your-medical-practice-s-revenue-cycle), Medical Group Management Association, practitioner
- [The Complexity Of Billing And Paying For Physician Care](https://doi.org/10.1377/hlthaff.2017.1325), Health Affairs, 2018, research
- [A Denial a Day Keeps the Doctor Away](https://doi.org/10.1093/qje/qjad035), The Quarterly Journal of Economics, 2023, research
- [Health system-scale language models are all-purpose prediction engines](https://doi.org/10.1038/s41586-023-06160-y), Nature, 2023, research
- [Enhancing Financial Performance: An Application of Lean Six Sigma to Reduce Insurance Claim Denials](https://doi.org/10.1097/qmh.0000000000000175), Quality Management in Health Care, 2018, research
- [Claims Denials and Appeals in ACA Marketplace Plans in 2024](https://www.kff.org/patient-consumer-protections/claims-denials-and-appeals-in-aca-marketplace-plans-in-2024/), KFF, 2026, research
- [Some Medicare Advantage Organization Denials of Prior Authorization Requests Raise Concerns About Beneficiary Access to Medically Necessary Care](https://oig.hhs.gov/reports/all/2022/some-medicare-advantage-organization-denials-of-prior-authorization-requests-raise-concerns-about-beneficiary-access-to-medically-necessary-care/), HHS Office of Inspector General, 2022, research
- [The Change Healthcare 2022 Revenue Cycle Denials Index](https://blr.postclickmarketing.com/Global/FileLib/HLM/2022-revenue-cycle-denials-index.pdf), Change Healthcare, a revenue cycle vendor, 2022, case-study
- [A Thorn in Your Side: How to Buck the Denials Trend](https://veradigm.com/img/resource-change-healthcare-2020-denials-index.pdf), Change Healthcare, a revenue cycle vendor, 2020, case-study
- [CMS Interoperability and Prior Authorization Final Rule CMS-0057-F](https://www.cms.gov/newsroom/fact-sheets/cms-interoperability-and-prior-authorization-final-rule-cms-0057-f), Centers for Medicare & Medicaid Services, regulation
- [Wasteful and Inappropriate Service Reduction (WISeR) Model](https://www.cms.gov/priorities/innovation/innovation-models/wiser), Centers for Medicare & Medicaid Services, regulation
- [45 CFR 164.501, Definitions](https://www.ecfr.gov/current/title-45/subtitle-A/subchapter-C/part-164/subpart-E/section-164.501), Electronic Code of Federal Regulations, regulation
- [45 CFR 164.506, Uses and disclosures to carry out treatment, payment, or health care operations](https://www.ecfr.gov/current/title-45/subtitle-A/subchapter-C/part-164/subpart-E/section-164.506), Electronic Code of Federal Regulations, regulation