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Where AI fits in medical billing and insurance
Medical billing and insurance involve several steps between a visit and payment. AI can be applied at different points in that chain, but the system’s role matters: it may organize information, flag an item for review, or support a decision rather than make the final coverage determination.
| Workflow | What AI may do | What the evidence establishes |
|---|---|---|
| Documentation and coding | Help prepare or organize billing codes, chart information, or visit notes. | The American Medical Association’s 2026 physician survey reports expectations for these uses; it does not establish national adoption or improved billing results. |
| Claims processing and review | Support automated processing of complex claims or review for errors, inconsistencies, and compliance with policy terms. | HHS lists these as potential uses in its 2025 AI Strategic Plan, not as proven industry-wide outcomes. |
| Prior authorization | Help manage requests for approval before a service, including through electronic workflows. | CMS’s WISeR model tests technology-supported review for selected Original Medicare services. CMS says licensed clinicians make final decisions that requests fail coverage requirements under that model. |
| Payment-integrity review | Find billing patterns that may warrant investigation or enforcement. | CMS says analytics including AI and machine-learning models were used to flag unusual Medicare laboratory billing patterns. Its reported enforcement total is not an estimate of savings caused by AI alone. |
How insurers may use AI to review claims
AI-assisted claim review can be designed to check submitted information against policy terms, identify inconsistencies, or prioritize claims for additional attention. HHS describes automated complex-claim processing and automated review for errors and policy compliance as potential applications. That description does not show how widely those tools are deployed, how accurately a particular system performs, or whether a specific insurer uses AI on a particular claim.
“AI review” also does not, by itself, tell a patient who made the decision. A system might assist staff or route a claim for review; a particular program may have its own rules about who can make a coverage determination. The available sources do not establish one process that applies across all private insurers or Medicare claims.
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Does AI decide whether insurance will cover treatment?
There is no single answer for every payer or service. CMS says its Wasteful and Inappropriate Service Reduction (WISeR) Model uses enhanced technology, including AI and machine learning, alongside human clinical review. In CMS’s description of the model, licensed clinicians—not machines—make final decisions that requests for selected services do not meet Medicare coverage requirements. That assurance is specific to WISeR; it should not be generalized to every insurer’s AI process.
What WISeR covers
CMS says WISeR runs for six performance years, from January 1, 2026, through December 31, 2031, in New Jersey, Ohio, Oklahoma, Texas, Arizona, and Washington. It tests technology-supported review for selected services in Original Medicare, with examples including skin and tissue substitutes, electrical nerve stimulator implants, and knee arthroscopy for knee osteoarthritis. The model is not evidence that AI is used to review all Medicare claims or all insurer coverage decisions.
What happens when a request is not approved
For WISeR, CMS states that a licensed clinician makes the final decision when a request for a selected service does not meet Medicare coverage requirements. CMS Administrator Dr. Mehmet Oz described the model as combining the speed of technology with experienced clinicians while testing a streamlined prior-authorization process. The stated purpose and review arrangement describe the model’s design; they do not establish results for every service or payer.
What physician survey figures say—and do not say
The American Medical Association’s 2026 physician AI sentiment report surveyed 1,342 physicians. Among respondents who considered each use case relevant, its end-of-2026 figures show:
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- 61% were already using or expected to use AI for documentation of billing codes, medical charts, or visit notes by the end of 2026.
- 43% were already using or expected to use AI for automation of insurance prior authorization by the end of 2026.
These are survey responses about existing use or expectations, among a filtered group of physicians. They are not measured national adoption rates, and they do not show that AI reduced costs, errors, or time spent on billing.
How AI is being used to flag unusual billing
AI and other advanced analytics can help identify claims with patterns that merit closer scrutiny. In an August 28, 2026 announcement, CMS said analytics including AI and machine-learning models were used to mine Medicare fee-for-service claims for unusual laboratory billing patterns.
CMS reported that enforcement actions had stopped more than $1.6 billion in potentially improper laboratory payments since the start of the administration. The agency said that total included provider revocations, payment suspensions, recoupments, and law-enforcement referrals. It is an agency-reported enforcement figure, not an isolated estimate of how much AI itself saved or caused to be recovered.
What is changing in electronic prior authorization
CMS’s electronic prior-authorization initiative describes a stakeholder pledge aimed at changing the process around authorization—not just adding automation. Its stated directions include:
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- Standardize electronic prior authorization using FHIR-based APIs.
- Reduce the services subject to prior authorization.
- Honor existing authorizations when a person changes insurance.
- Improve transparency and communication about decisions and appeals.
- Expand real-time approvals for most requests by 2027. This is a stated goal, not a completed result.
- Ensure medical professionals review all clinical denials.
These commitments identify useful measures for judging whether an automated workflow makes authorization easier in practice: fewer requests, timely decisions, understandable explanations, continuity across coverage changes, and meaningful review of clinical denials.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Could AI lower costs—or add administrative work?
AI could reduce some manual work, but that does not automatically mean lower costs for patients or the health system. HHS warns that providers investing in AI for revenue-cycle work and payers investing in payment-integrity tools could create additional administrative costs. A workflow might save time for one organization while requiring more documentation, review, or reconciliation from another.
The available sources do not establish net industry-wide savings from AI. Nor do they provide independently validated comparisons of vendor accuracy or performance. Claims about savings, faster payment, fewer denials, or better coding should therefore be evaluated against evidence for the specific system and workflow, rather than assumed from the use of AI.
How to evaluate an AI-driven billing or insurance process
For a patient, provider, or organization assessing a particular process, the label “AI-powered” is less informative than the details of how it works. Ask:
Quick Recap
- Which step uses AI? Is it documentation and coding, claim intake or adjudication, prior authorization, or fraud and payment-integrity review?
- What role does the system play? Does it organize information, prioritize cases, recommend an action, or make an operational determination? Which decisions require review by a clinician?
- Which rules does it apply? What payer policy, coverage criteria, or coding rules are used, and how are changes kept current?
- Can it connect to existing systems? Does the workflow use standards such as FHIR-based APIs and work with the relevant electronic health record and payer systems?
- Can people understand and challenge the result? What explanation, correction, communication, and appeal routes are available?
- What evidence supports the claimed benefit? Separate a proposed use, a survey expectation, a model design, and a measured outcome; look for independent evaluation where performance claims matter.
- Where does the work go? Check whether less manual work for one party creates extra paperwork, review, or follow-up for patients or another organization.
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