What is AI in prior authorization?
The AI Glossary
In our classes, we often pause to explain the ideas behind AI and the language used to describe it. These posts grew out of those conversations. They’re for anyone who would like to understand AI better, with no technical background assumed.
AI in prior authorization uses language processing, prediction or document-handling tools to support requests for advance coverage approval from a health insurer. Prior authorization is the process through which a payer checks whether specified services meet its coverage requirements before they are delivered. The details vary by plan, service and jurisdiction.
Several tasks can be separated. A tool may identify which policy applies, locate supporting information in the record, prepare a request or track its status. A payer may use software to help assess the request. Preparing evidence and deciding coverage are different activities, with different responsibilities and risks.
The most useful starting point is often completeness. A system can check whether the required report is present and highlight the passage supporting a criterion. It should preserve the source, date and policy version. Generating persuasive wording is not a substitute for evidence that the criterion is actually met.
In the United States, CMS-0057-F requires specified payer groups to improve aspects of prior authorization and electronic information exchange. Some operational provisions began in 2026; major API requirements generally begin in 2027, with details varying by payer type. An API is a defined way for software systems to exchange requests and information. The rule’s prior-authorization provisions exclude prescription drugs, which have been addressed only in separate proposed rulemaking.
These interoperability requirements do not make every automated coverage decision appropriate. Nor do they create one universal national rule that every denial must receive the same specialty review. Organizations need to examine the applicable coverage, review and appeal requirements for their setting.
For a clinic, a bounded assistant might assemble the relevant documentation and show an authorized reviewer where each claim came from. It should flag missing or conflicting evidence rather than invent it. Evaluation should follow the patient’s journey: was the request accurate, was a decision reached, and did care proceed appropriately? Faster preparation is useful, but not if it simply produces more incomplete requests or makes an incorrect denial harder to challenge.
Sources: CMS — Interoperability and Prior Authorization Final Rule CMS-0057-F; CMS — Interoperability and Prior Authorization: General FAQs
To learn more, take a look at the AI in Health Care Certificate of Specialization from the Advanced Learning Academy at Harvard T.H. Chan School of Public Health. You can take an individual course or work toward the full certificate.
AI is a fast-moving field. This explanation reflects our class discussions and related material at the time of writing. Terminology, capabilities and tools may change.
For suggested additions or corrections, please email me at tpanch@hsph.harvard.edu.
Dr. Trishan Panch
Program Director, AI in Health Care
Harvard T.H. Chan School of Public Health — Advanced Learning Academy