But what are some of the types of things that AI is helping you spot, maybe a little more specific for listeners to understand the types of maybe fraud or abuse or other things that you're able to spot?

If I think about maybe going all the way from what I consider the left-hand side of claims errors all the way to the far right of true fraud scheme, I think AI is kind of used in each part of that from what I've seen.

From things like on the far left-hand side, you know, I mentioned ambient listening systems and coding systems using AI where...

These technologies are allowing providers to now capture member encounters, allow the clinicians to spend more face-to-face time with the patients, spend more time in an exam room versus charting their notes and their medical records.

So creating a lot of efficiency, I think from that perspective, all the way through from a payment integrity perspective, some of the ways that we've leveraged it over the past couple of years are things like natural language processing, which allows us to upload a medical record that maybe is a thousand pages in length and really highlight and call out some of the key areas that we need to focus on as part of our review.

So instead of having a clinician or a coder, you know, pulling up a claim and a medical record and kind of holding those things together and trying to go back and forth and figure out, hey, does the record match what's being put on a claim and where do I go in a medical record to find that information? Technology is allowing us to do that.

So it's allowing us to look at more claims.

Do you have any advice about guardrails and how to implement them in your organization?