← Becker’s Healthcare Podcast
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- [00:00]
A
Hi everyone, this is Lucas Vaz with Becker's Healthcare. Thanks so much for tuning in to the Becker's Healthcare podcast series and welcome to today's episode, from detection to prevention, AI's role in payment Integrity. And I'm very excited to welcome Steve Sutherland, he's the Senior Vice President, Information Systems at Cirrus for our conversation today. Steve has close to 30 years of experience in IT and joined Cirrus in 1996. He's been passionate and very involved in proactively building efficiencies and leveraging technology to keep healthcare organizations organizations ahead of the curve. Steve, I did not call you old. Welcome to the podcast.
- [00:38]
B
Thank you. Thank you. Lucas, thank you very much for the introduction. Yeah, 30 years. Time flies when you're having fun, as they say, right?
- [00:45]
A
Exactly. I'm so excited to have you just because of all of the experience that you bring to our conversation today. And I want to start off with putting things into perspective for our audience as we're looking at our topic today. How do you define payment integrity today and how has that definition evolved as AI has become more embedded in payer operations?
- [01:11]
B
Sure, that's a great question. In today's environment I would say payment integrity really spans the entire claim life cycle. So end to end set of processes from claim submittal all the way to final payment for in a prepayment setting as well as the traditional post payment reviews along the way. There are many tools, analytics technologies out there used to ensure the healthcare payments are getting paid or getting adjudicated paid, accurately compliant and timely. AI is obviously at the forefront of all of that. Just about every organization's strategy and roadmap are intertwined wrapped around that, if you will, these days. So I feel we're really just starting to scratch the surface with the impacts that AI can and will have down the road. We're seeing, we're already seeing some major impacts with process improvements, automation obviously. However, I do think that some organizations might be taking a slightly more conservative approach or slower pace, let's say to roll some of these things out just due to governance, regulatory data use challenges. Just making sure those boxes are ch.
- [02:22]
A
Checked and you've seen so many trends evolve over your time in healthcare it and there's certainly a lot of change that's happening right now, especially 2025 and then now obviously in 2026 from your work with these organizations across the country, how has the integration of AI and machine learning changed the strategic approach then to payment integrity for leaders? And is there anything that surprises you about that process, yeah, sure.
- [02:52]
B
I think the biggest change over the last year or so has been the shift from a traditional post payment or a pay and chase model to prioritization of prepayment solutions. So as I mentioned previously, audits that are happening during the payment cycle as opposed to after AI has really helped fuel this due to the acceleration of development tools and the speed at which some of these solutions, sophisticated solutions, if you will, can be deployed. I would say I'm most surprised by the rapid adoption rate that we've already experienced. Everybody is using these tools. They're everywhere. You're hearing about them constantly. Also on the flip side of that really is the significant amount of resources and effort required for oversight and governance. This is an area that sometimes can lag behind the development cycle, but it really cannot be minimized or ignored. You know, the question today is not is no longer are you using AI, it's how are you using AI and what are the guardrails and framework that you have established around it?
- [03:56]
A
Yeah, and you mentioned the fast adoption rates and everybody sort of jumping to or on that trend, so to speak. But that doesn't necessarily mean there is an impact right away for a lot of organizations. Where are you seeing the most meaningful impact for AI today? And is there an example that sticks out to you where AI really surfaced patterns that traditional rule based systems that we've touched on likely would have missed?
- [04:23]
B
Definitely the most meaningful impact I'm seeing today is the ability to convert and process very large, complicated, unstructured documents and even data sets into useful, structured, easily machine readable data which can be processed much more efficiently and accurately. So I guess an example I would say is so traditional rules based systems usually only detect specific patterns, right. Programmatic deterministic algorithms that humans have developed as where on the flip side, where machine learning and large language models are able to analyze ambiguous notes, narrative patterns across these very large data sets that those traditional rules would have missed. A great example of this is medical records. So these documents are typically very large, many, many pages, sometimes hundreds if not thousands of pages in these documents. Very cumbersome to handle, challenging to process and for, you know, humans to review. So using a model to summarize that information, extract key data points, can really assist clinical auditors by increasing their efficiency, accuracy, consistency to go through this information.
- [05:40]
A
Yeah, and we're seeing already seeing that impact on organizations. I think I wanted to come back to the governance piece that you've mentioned and sort of a couple of related topics in regards to governance, because I think this is a very important topic. Again, you've talked about it. Automation can improve speed and consistency. It's very crucial. But there is concern around fairness, false positives, provider trust. Again, we're sort of getting into that governance territory here. How can organizations balance efficiency with that clinical accuracy and credibility at scale? Really?
- [06:12]
B
Yeah, I think finding this balance is really one of the most urgent challenges that we face today in the payment integrity space. So just as with the development tools and the technology, the appropriate resources and prioritization must be dedicated specifically to these areas. These areas as in, you know, the governance security, those things. So building a strong governance program, transparent controls, always keeping humans in the loop and continuous modeling, not only model monitoring, but, you know, solution monitoring to make sure, you know, of model drift and the things that are of concern are at the forefront. I would say those are some of the keys, the main keys, I would say, to ensure trust and credibility in this space.
- [07:01]
A
Now, turning some of these topics that you've mentioned into strategic elements, right. What should payer, IT and operations leaders be doing right now to prepare for that next phase of AI driven payment integrity?
- [07:17]
B
Sure, I can share a couple of at least points that I think are important here. So IT leaders should be assessing and improving their teams. Teams from a staffing standpoint, experience technology, governance and data infrastructure. As we just talked about a little bit, AI success really hinges on high quality, accessible, interoperable data. So data is key here. So having a clear, concise, consolidated data foundation again is the most important thing when it comes to AI. Preparing to support the shift to prepay solutions by proactively considering some of the core systems, the foundational applications and data and workflows that would support that, considering integrated ecosystems and prioritizing API integrations with partners over, you know, more traditional, fragmented, point to point solutions and finally building equity and fairness testing into model governance. Always maintaining a human in the loop, creating clear and transparent documentation along with defensible policies and procedures to back everything up. The future of AI driven payment integrity isn't really just about more automation. It's about trusted, transparent, human aligned intelligence that's embedded across the entire claim lifecycle.
- [08:41]
A
Three decades of experience in 15 minutes. Steve, thanks so much for, for being here. Thanks so much for your time. It's so great to have you.
- [08:51]
B
It's truly an exciting time to be working in this industry and at the intersection of payment integrity and technology. We're watching AI reshape not just healthcare, but nearly every industry and the pace of innovation is just accelerating daily. So the work we're doing now will define the next generation of accuracy, fairness, efficiency in the healthcare space. And I'm just grateful to be a part of the transformation and working with the Saris team and our partners. I also appreciate the opportunity to collaborate with Beckers and share these conversations. So I appreciate you having me.
- [09:23]
A
Absolutely. It's great to have you. Thanks for your time. And we also want to thank our podcast sponsor, Cirrus. You can tune into more podcasts from Becker's Healthcare by visiting our podcast page at beckershospitalreview.
- [09:34]
B
Com.