What is Prior Authorization?

Why It Matters

Prior authorization consumes a documented amount of clinical time: AMA surveys put it at roughly 40 requests and 12 to 13 hours per physician per week, with most physicians reporting it drives burnout. It is administrative work with a clinical signature attached, which makes it a good first target for AI and a bad first target for unattended automation.

It is also the rare workflow with a deadline. Under CMS-0057-F, impacted payers must answer an urgent request within 72 hours and a standard request within seven calendar days, with electronic prior-authorization APIs to follow.

How It Works

The work is assembly and argument: gather the chart evidence, map it to the payer’s own clinical criteria, draft the medical-necessity statement, submit, track the decision, and appeal a denial with specific criteria cited.

An agent can do the assembly. It can read the chart, pull the policy, match criteria to findings, and draft the packet with every clinical claim tied to a source. What it should not do is certify the claim. The submission asserts medical necessity on behalf of a clinician, and that assertion needs the clinician’s signature.

Where It Breaks

The first failure is unattended submission. An agent that files its own packet moves the liability to whoever deployed it, and the clinician who signs later cannot verify a claim they did not review.

The second is a stale criteria library. Payer policies change, and an agent that keeps drafting against last quarter’s criteria produces denials that look like clinical failures rather than configuration failures.

The third is measuring throughput instead of outcomes. A system that submits more packets faster without tracking approval rates and appeal success has optimized the easy half of the process.

How Flytebit Handles It

The agent assembles and drafts; a credentialed person verifies and signs, and the clock starts at the signature rather than at generation. Every claim in the packet carries its source, so the reviewer checks evidence instead of re-reading the chart, and uncertain cases move through the escalation router with the same package attached. Approval rates, appeal outcomes, and edit rates are tracked as outcomes rather than inferred from volume. The industry application is on our Healthcare & Life Sciences page, and the operating model is our LLMOps work.