Smarter Prior Authorization: Using AI to Improve Care Access and Cut Costs

In this interview, Brian Covino, MD, and Siva Namasivayam of Cohere Health discuss how AI is revolutionizing the prior authorization process by dramatically improving speed, reducing administrative burden, and enhancing care accuracy—while keeping clinicians in the loop and addressing provider concerns.

Please introduce yourself by stating your name, title, and any relevant experience you’d like to share.

Brian Covino, MD: I’m Brian Covino. I’m an orthopedic surgeon. I practiced orthopedic surgery for 28 years. I’ve been with Cohere Health as chief medical officer for 5 years.

Siva Namasivayam: I am Siva Namasivayam CEO and co-founder of Cohere Health. I’ve been with the company since the beginning.

How are health plans currently leveraging AI in the prior authorization process?

Dr Covino: Health plans are actually slow to utilize AI in the prior authorization process. That could be for multiple reasons. They turn to other companies, like us, to help them with that initiative and use it in a responsible way.

Namasivayam: AI can be used for approving prior authorization requests. One of the issues today is that there is a lot of paperwork involved and a lot of back and forth that happens any time a prior authorization request is submitted. The decisions do take some time. At Cohere, we ask the provider to submit the medical record, other information about the patient, and the service that’s required. This is unstructured data. This is a lot of written notes and things like that. That’s where AI comes in. AI is able to interpret the unstructured data and get the information out of it.

If the request can be approved, based on the AI algorithms, it can be immediately approved within 10 or 15 seconds. However, at Cohere, if the requests require the judgment of a clinician, it goes to our nurses and clinicians in the same specialty. Clinicians are the only ones who can make a determination about whether something needs to be denied or changed. The clinician is always in the loop, and they are the ones that make those decisions. The other approvals can be done very fast using AI.

Dr Covino: Just to level set—so you understand where the industry has been and where the industry still is, in a large place—other than the process that Siva just talked about, a lot of times it’s a purely manual review. That means a nurse or physician has to actually read through all the documents. That is a slow process, which results in delays. It’s a significant administrative expense for the health plan or the delegate that the health plan uses. There’s probably about a 10% error rate, if you look at the literature on that.

The second method that some plans and vendors use is to ask a series of questions. They ask the doctor’s staff to answer a series of questions. Answering questions is what takes the staff the longest in completing the prior authorization request. Probably about 30% of the time, the answers to the questions don’t match what’s in the medical records. So, there can be a high degree of inaccuracy.

By using clinician-trained machine learning models, it’s much faster to approve appropriate care, like Siva said, in real time. The cost of doing it is much less if you take the people out of it, other than the clinicians who are training the models. The error rate is much less, probably in the low single digits.

In what ways have you seen AI improve or complicate the efficiency and accuracy of prior authorization decisions?

Dr Covino: If you look at efficiency, I was just talking about speeding up the time to appropriate care, where 80 to 85% of appropriate care can be approved in real time. What we’ve seen is that when we survey the providers and their staff who use our AI systems, they tell us that up to 80% of the time, they can schedule their patients faster for that appropriate care. Again, reducing the delays.

I talked about increased accuracy, that’s been proven as well. For the end user who is submitting the requests, the time to submit the request is much less. We’ve done timestamp studies to see, and we’ve seen AI cut down their administrative expense by about 40% to 45% in terms of the submission they request. Across the board, accuracy, efficiency, and decreased administrative expense and time for the requester have all improved.

Read the full interview