← Becker’s Healthcare Podcast
Becker’s Healthcare Podcast
Dan Liljenquist, JD, Chief Strategy Officer, Intermountain Health
00:14:22
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- [00:02]
A
R1 is the leader in healthcare revenue management, helping providers achieve new levels of performance through smart orchestration. With more than 20 years of experience, R1 partners with 1,000 providers, including 95 of the top 100 US health systems, and handles over 270 million payer transactions annually. If you want to learn more about how you can transform your revenue cycle operations, Visit us at www. R1RCM.
- [00:31]
B
Hello and welcome to the Beckers Healthcare podcast. My name is Will Riley from R1. I'm joined today by Dan Lillienquist. Dan is the Chief Strategy Officer for Intermountain Health. Welcome, Dan.
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C
Good to be here with you again, Will.
- [00:47]
B
Great to see you too, Dan. Great to see you too again. Okay, tell us a little bit about you. Tell us about Intermountain.
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C
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 68,000 caregivers that work with us, over 30 hospitals, 400 or so clinics, and a large health plan.
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B
Yes, great, great summary. Thank you. Let's start by talking a bit about technology, if we can, as our first theme. Historically, healthcare has moved fairly cautiously, let's say, with new technology.
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C
Right.
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B
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 healthcare move faster than it has done in previous eras of technology?
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C
Well, Menlo Ventures just published a report a couple of weeks ago about the adoption of AI in different industries. And healthcare 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 this massive demographic shift that we're seeing as a society at the same time of clinician burnout is at all time highs. AI actually gives us some pretty interesting new tools to help us simplify really the work that needs to occur for our patients. I think you're seeing a fast adoption of AI tools. In fact, Intermountain, we have about 300 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 facet 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.
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B
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 you mean?
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C
Well, healthcare is enormously complex and what these tools do is allow us to actually simplify that work, Use an AI agent to streamline and pull in information that would take hours of somebody's time to do. I'll just give you an example. Appeals letter is a great example. We're taking about 30 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.
- [03:32]
B
So the, the realization of the benefit is pretty rapid in some of these examples that you, that you, that you have.
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C
Well, yeah, I think the industry, we've moved all of our data into the cloud and because we're in a cloud environment that's a secure environment, we're on Microsoft Azure, we're able to actually use these tools, a lot of the OpenAI code, et cetera, to go directly into our data and build new applications using VIVE coding and other kind of pretty fascinating techniques and capabilities that AI can bring to do things that would take a programmer weeks of time. It takes the ROI to that type of initiative down to near zero. And so, yeah, we've moved really quickly on those things. So it's fun to see.
- [04:20]
B
In healthcare innovation, you often hear about two archetypes, incumbents, the large established health systems, established payers or legacy vendors who control all the data and the infrastructure and have all the scale. And then you hear about insurgents, newer entrants, people who are trying to disrupt established models or ways of working. How do you see those two sides playing out in the AI evolution that you're working through?
- [04:54]
C
I 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, we don't have the luxury of sitting back and just waiting for things to happen. I think we're going to have to lead it. But I do Think building from the advantage and advantages of incumbency, you know, we know kind of what we need to do to make things better.
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B
Yeah.
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C
And so I think that the health systems that lean in will be just fine. Those who fall behind have a risk of being disintermediated by new players in the 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, a lot more present that risk that's always existed.
- [06:02]
B
And. But you went first to data as the advantage.
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C
Yeah, absolutely.
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B
Yeah, yeah. And, and that's because you can train models more easily. You can like tell us more about, about that data advantage.
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C
Yeah, look, AI to, to make it safe, safe for the use and to really make it effective, you've gotta actually understand the underlying data it's using to create the outputs you're looking for. And if you train AI on bespoke data sets, you get bespoke AI instances that 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 incumbent systems have the data.
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B
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.
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C
Well, for sure. I mean, look, really what makes an insurgent? I do think there's been a lot of attempts over the years to break into healthcare 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 healthcare journey. But the moment it becomes complex, that's when those models have fallen 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.
- [07:29]
B
Right.
- [07:30]
C
I do think what we're looking for and we lean in with, is partners who really understand what we're trying to do and really bring added value to our overall strategy. And so could that be a threat to us competitively? Not particularly not. You know, we work with partners in ways where we clearly understand, understand their business model and 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. But by and large we're not as concerned about a private equity backed roll up of a certain set of capabilities. Because at the end of the day, I think we see those kind of plays as short term plays. They eventually exit and we just keep plowing ahead with our strategy.
- [08:24]
B
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?
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C
You know, our board and our C suite, we've been very thoughtful about AI governance. In fact, 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 conversations, board level approvals for certain uses of AI and but not every use is really that controversial. So there's what we've gotten really I think we're good at, we're going to get better at is streamlining and understanding. Okay, what's the use case? We're trying to build and scoping the risk based on the use case. And that has allowed us to actually move much faster than we would otherwise move again. We have over 300 AI projects in flight and we're taking in between 10 and 15 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 anxious to develop that require a different level of visibility and governance from an ELT perspective. From a C Suites 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 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.
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B
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 300 projects that are interesting you from that, from the perspective of that paradigm shift from labor first to tech first.
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C
Well, there are several areas and we're spending as a country $740 billion a year in healthcare 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 I know with R1 we're leaning into all of that new capability because the cost to collect should go down materially. And so 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 your repetitive tasks that are rules based. Those are the opportunities where I 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 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.
- [11:52]
B
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?
- [12:09]
C
Well, I think it's going to 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 the United States are going to enter retirement by 2040, or 2035, 40% of those providers are gone. Right at a time when the demand for healthcare 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, 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, 20% of people who go on blood pressure medication, only 20% 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 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 to 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.
- [13:57]
B
Fantastic Dan, thank you. This has been fascinating. Is there anything else on your mind that you want to share?
- [14:02]
C
I just appreciate, just appreciate Beckers appreciate 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 going to share what we're learning and of course want to learn from as many people who want to share as well.
- [14:17]
B
Thank you so much. Thanks Dan.
- [14:18]
C
Thank you.
- [14:19]
B
Take care.