Everyone's pitching AI as the solution to prior authorization. And they're right — the technology is about to solve it. Ambient scribes capturing every detail. Clinical decision support guiding every order. Automated systems submitting perfectly optimized requests. Approval rates heading toward the high 90s.

But here's what nobody's talking about: what happens to healthcare costs when a system designed around 15-20% of requests getting denied suddenly starts approving almost everything?

In this episode, I break down the three distinct layers of AI in prior authorization — and why most people are lumping them together when they have very different implications. I dig into a UCSF study showing physicians using AI scribes saw a 5.8% RVU increase with no rise in claim denials. I explain why over 80% of appealed denials get overturned, but only 12% are even appealed — revealing that prior auth was never really about clinical evaluation.

And I make the case that once AI solves the coding problem, the question shifts from "did you code this correctly?" to "should you have ordered this at all?"

The end game isn't faster paperwork. It's AI evaluating medical judgment. The bar is going up, not down.

Full source list and research citations available on Substack.