What Physicians Should Know About AI Reviewing Their Prior Authorizations
Payers and practices are both deploying algorithms in prior authorization and claims review. Understanding what those systems see, and what they miss, changes how you document and appeal.
A prior authorization request goes out in the morning. A denial comes back before lunch. No human at the payer has likely read the note in any depth, and the reason given is a short code that does not match anything in the chart. For many physicians, this is their first real encounter with automated review, and it rarely inspires confidence.
Algorithms now sit on both sides of the prior authorization and claims process. Payers use them to sort requests, flag outliers, and apply coverage criteria at scale. Practices increasingly use them to assemble requests, predict denials, and draft appeals. Physicians do not need to understand the engineering, but they do need to understand how these systems read what they write.
What the machine is looking for
Automated review generally works by matching the request against structured criteria. It looks for specific diagnoses, prior treatments tried, test results, and time frames. Clinical reasoning buried in narrative text may be missed or weighed less heavily than a clearly stated fact.
- State the criteria explicitly. If a policy requires a failed trial of a prior therapy, name the therapy and when it was stopped.
- Use the specific diagnosis that justifies the request, not a general one.
- Keep supporting results and dates easy to find in the note.
- Read the payer's published coverage policy for high volume requests and build templates around it.
Know your right to a human
A denial generated or triaged by software can still be appealed, and physicians can generally request a peer to peer discussion with a clinician at the payer. Regulators, including CMS for the plans it oversees, have signaled that coverage decisions must rest on the individual patient's circumstances and not on an algorithm alone. The details vary by plan type and state, so practices should know the rules that apply to their major payers and track which denials are overturned on appeal.
An algorithm can only approve what your documentation makes legible to it.
Use AI on your side carefully
Tools that draft prior authorization requests or appeals can save real staff time, especially for repetitive requests. But the physician still signs what goes out. Any generated letter should be checked for accuracy against the chart, because an appeal that overstates a history or invents a prior treatment creates a far bigger problem than a denial.
Track the data. Which requests are denied most, by which payers, and how often are those denials reversed? Patterns of automatic denials that are routinely overturned are worth raising with payer representatives, medical societies, and state regulators. The technology is not going away. The practices that do well will be the ones that learn to write for it, check it, and challenge it when it gets the patient wrong.
Dr. Reyes practices emergency medicine and writes about clinical decision making under pressure and the technology entering the ED.
This article is for professional education and does not replace clinical judgment. Treatment decisions should be based on the individual patient and current guidelines.
