R1 is the leader in healthcare revenue management, helping providers achieve new levels of performance through smart orchestration. With more than twenty years of experience, R1 partners with one thousand providers, including ninety five of the top one hundred US health systems and handles over two hundred seventy million payer transactions annually. If you want to learn more about how you can transform your revenue cycle operations, visit us at w w w r one r c m dot com. Com. Hello, and welcome to the Becker's Healthcare podcast. My name is Will Riley from r one. I'm joined today by Dan Lillianquist. Dan is the chief strategy officer for Intermountain Health. Welcome, Dan. Good to be here with you again, Will. Great to see you too, Dan. Great to see you too again. Okay. Tell us a little bit about you. Tell us about Intermountain. So I serve as the chief strategy officer for Intermountain Health. Intermountain Health is a large integrated delivery system headquartered in Salt Lake City, Utah, but operating in six states. We've got about sixty eight thousand caregivers that work with us, over thirty hospitals, four hundred or so clinics, and and a large health plan. Yes. Great. Great summary. Thank you. Let's start by talking a bit about technology, if we can, as our first theme. Historically, health care has moved fairly, cautiously, let's say, with new technology. Right? It's not necessarily seen as a technology innovator. How is that changing, or is it changing with the advent of AI technologies? Are you seeing health care move faster than it has done in previous eras of technology? Well, Menlo Ventures just published a report a couple of weeks ago about the adoption of AI in different industries, and health care is one of the fastest adopters of AI. And we're actually it couldn't be more timely. The whole industry is in flux and, you know, this massive demographic shift that we're seeing as a as a society at the same time of clinician burnout is at all time highs. And so AI actually gives us some pretty interesting new tools to help us simplify, really, the work that needs to occur for our patients. And so I think you're seeing a fast adoption of AI tools. In fact, Intermountain, we have about three hundred different AI programs underway, and then almost every one of our technology vendors are leaning in as well. And so we're seeing some pretty fast adoption, partly because we're building right off of the of the platforms that we're currently using. And it's it's kind of exciting for us. And we're this is one of those areas where there's actually a tailwind for the industry instead of a headwind. It feels that way, doesn't it? It feels different from previous evolutions of technology somehow. So you say there's a tailwind rather than a headwind. Can you unpack that a bit more? What do mean? Well, health care is enormously complex, and what these tools do is allow us to actually simplify that work and use an AI agent to to streamline and to pull in information that would take, you know, hours of somebody's time to do. I'll just give you an example. Appeals letter is is a great example. We we're taking about thirty minutes off each appeal letter we have for a payer because of our ability to use AI to scrape through and get the information we need for the appeal letter. And, again, that's just one of dozens and dozens of different uses that are just making our jobs simpler and easier to do. So the the realization of the benefit is pretty rapid Yeah. In some of these examples that you that you that you have. Well, yeah, I think the industry, we've moved all of our data into the cloud. And because we're in a cloud environment, it's a secure environment. We're on Microsoft Azure. We're able to actually use these tools, a lot of the OpenAI code, etcetera, to go directly into our data and build new applications using Vibe coding and other kind of pretty fascinating techniques and and, you know, capabilities that AI can bring To do things that that would take a programmer, you know, weeks of time. It takes the ROI to that type of initiative down to near zero. Yeah. And so, yeah, we've moved really quickly on those things, so it's it's fun to see. In in health care innovation, you often hear about two archetypes. Incumbents, the large established health systems, established payers, or or legacy vendors, right, who control all the data and the infrastructure and and have all the scale. And then you hear about insurgents, newer entrants, right, people who are trying to disrupt established models or ways of working. How how do you see those two sides playing out in the AI evolution that you're working through? Think incumbents will have an advantage for a while, partly because the data already exists inside. The data to train AI is already inside our firewalls, inside our environments. And so that I think we have an advantage for a time if we lean in. Short of that, the real risk is to the industry is a complete disintermediation between the system patient relationship with a new startup that does something better, more effective, gains the trust of the patient. And so, you know, we don't have the luxury of sitting back and just waiting for things to happen. Think we're going to have to lead it. But I do think building from the advantages of incumbency, you know, we know kind of what we need to do to make things better. Yeah. And so I I think that the health systems that lean in will be just fine. Those who fall behind have a risk of being disintermediated by by new players in this space. And and by the way, I think that's always been with almost any industry. We're just, these these new tools and these AI capabilities make that a lot a lot more present, that risk that's always interested. But you went first to data as the advantage? Yeah. Absolutely. Yeah. Yeah. And that's because you can train models more easily. You can like, tell us more about that data advantage. Yeah. Look, AI, to to make it safe safe for use and to really make it effective, you've gotta actually understand the underlying data it's using to create the output you're looking for. And if you train AI on bespoke datasets, you get bespoke AI instances that that, you know, may not lead you into the right future. And so really understanding your data and having that data organized, the barrier to entry for a lot of these AI applications are really, really small. But the one barrier you have to overcome is data, and the the incumbent systems have the data. Yeah. Yeah. Where are you collaborating effectively with, insurgents, for want of a better word? Like, you you bring these advantages as an incumbent, but there's obviously room for innovation too. So Well, for sure. I mean, look. Really, what makes an insurgent? I I I I do think there's been a lot of attempts over the years to to break into health care and do things in a simpler and better way for patients. And that can work, you know, one or two inches deep into somebody's health care journey. But the moment it becomes complex That's when those models are falling apart. You've seen a variety of new entrants come into the market with great fanfare only to be, you know, gone five years later when the capital runs out. Right. I I I do think what we're looking for and we lean in with is is partners who really understand what we're trying to do and and really bring added value to our overall strategy. And so could that be a a threat to us competitively? Not particularly. Not you know, we work with partners in ways where we clearly understand their business model, they understand ours. And so that's been able to accelerate what we're doing. I mean, Epic, Microsoft, Salesforce, Workday are the big platforms we're using. And but but by and large, we're not as concerned about, you know, a private equity backed roll up of a, you know, certain, you know, set of capabilities because, you know, at the end of the day, I think we see those kind of plays as short term plays. They eventually exit. And, and we're just, we just keep plowing ahead with our strategy. Got it. How about, governance, Dan? Has AI brought new challenges for you in terms of governance or roles and, responsibilities in the c suite? You know, our, board and our c suite, we've been very thoughtful about AI governance. In fact, we have we have dozens of different projects coming through each month through a governance process depending on the level of risk that escalates up all the way up into our board conversation, board level approvals for certain uses of AI. And but not every use is really that controversial. I mean, there's so there's what we've gotten really, I I think we're good at, we're gonna get better at, is streamlining and understanding, okay, what's the use case we're trying to build and and scoping the risk based on the use case? And that has allowed us to actually move much faster than that we would otherwise move. Again, we have over three hundred AI projects in flight, and we're taking in between ten and fifteen each month in new projects that we're running through this governance process. So there are a lot of no regrets moves that AI is helping us do, But there's some that are more complex that we're we're anxious to develop that require a different level of visibility and governance. From a an ELT perspective, from a C suite perspective, we're spending a lot of time looking through how AI might help us do our work at a much lower cost base for our community. And and, you know, the entire industry is under pressure there. We're an industry that's largely built on labor. And that labor that labor market is getting tighter and tighter. And over the next five years, you know, many of the people we've built this model around are retiring, and there's not enough people coming back through to replace them. So we're anxious to see you know, to push the boundaries what AI can do so we don't have to, you know, shrink back from meeting the needs of our communities. Instead, we're looking to expand what we can do. You mentioned some labor saving moves in revenue cycle right at the start. Tell us more about those and maybe some other areas in your three hundred projects that are interesting you from that from the perspective of that paradigm shift from labor first to tech first. Well, there are several areas, and we're spending, as a country, seven hundred and forty billion dollars a year in health care just on back office work, administrative work. And there are four main buckets that we think AI will help us address. One is revenue cycle. And, again, it's a repetitive task, rules based. AI will do a great job sitting into that space. And where I know we're with r one, we're leaning into all of that new capability because the cost to collect should go down materially. And so we're we're leaning in there. Again, you push some of these tools. The cost can get to almost near zero to actually do the work that required hundreds and hundreds of people to do. But we also think that there's opportunities in analytics and call centers and, you know, supply chain. Again, think of, you know, repetitive tasks that are rules based. Those are the opportunities where AI can can help us move forward. And, frankly, some of those are some of the areas that are hardest for us to recruit into and, are expensive jobs to fill and to train, and and you have a lot of turnover in those areas. And so these are, you know, real, I guess, opportunities to systematize what we do with new tools and do it at a much lower cost base. Yeah. Okay. Okay. What do you think some of the implications are of the technology on providers and patients? We've talked a bit about operators and administrators, but, like, what's going to be the impact from a patient perspective, do you think, and from a provider perspective? Well, I think it's gonna change the practice of medicine significantly. And I think the biggest thing you'll see is, again, over the next five years, a quarter of our providers in in the United States are gonna enter retirement by twenty forty or twenty thirty five. Forty percent of those providers are gone, right at a time when the demand for health care services is skyrocketing. So we need to change the practice of medicine. The idea that a doctor is the only person who could do medication titration for your blood pressure medication or that you need to see a doctor every year to renew your prescription, those models are breaking. People will not have access like they have. And so I think we are excited at Intermountain to lean into those types of interventions. Should that be the practice of medicine? Or can AI, with oversight from a doctor, help make medication titration decisions after an initial diagnosis? You know, twenty percent of people who go on blood pressure medication, only twenty percent of them actually hit their target blood pressure range because the titration of that is actually more precise than you can get out, you know, once a year visit with the doctor. So we're really excited about at Intermountain about leaning in to find new ways to have AI help us be much more situationally aware of what's happening with our patients, help them get to the right stable medication doses, and not require them to come back and see a doctor every time they have, you know, a tweak in their medication. So we see AI as a way to extend what we're doing in very low cost ways to better meet the needs of our community to be proactive for them, to simplify their experience, and help us partner with them better across the course course of their lives. And and that's, you know, our mission is to help people live the healthiest lives possible so it fits right with what we're trying to do. Fantastic, Dan. Thank you. This has been fascinating. Is there anything else on your mind that you wanna share? No. I just appreciate just appreciate, Becker. Appreciate you doing this, and it's good to see you again. And we aspire to be a learn it all, share it all organization, so we're definitely gonna So we're definitely gonna share what we're learning and, of course, wanna learn from, you know, as many people who want to share as well. Thank you so much. Thanks, Dan. Thank you. Take care.
