← Advancing Health
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- [00:01]
A
Welcome to advancing health. You can't go any higher than your performance and safety. As we HEAR in the first of this two part podcast, HCA's Patient Safety Journey has taken it to some unusual places to study other recipes for success, including industrial manufacturers and even a military base, as well as a close look at what AI has to offer.
- [00:28]
B
Welcome folks to another episode of our podcast. I'm Dr. Chris Durienzo, the Chief Physician Executive for the American Hospital association and time for a quick moment of transparency. When we started recording today's podcast, we thought it was only going to be one episode. But it turned out that getting to spend time with Dr. Randy Fagan, who is the Chief Quality Officer for HCA Healthcare, meant that we had a lot more to talk about than we could pack into our typical 12 to 15 minute episode. So you are about to listen to episode one of a two part series and I would really encourage you to listen in now. But also stay tuned because part two is pretty exciting as well. Joining us for a conversation around patient safety and AI. Randy, thanks so much for joining us and welcome to the podcast.
- [01:13]
C
It's absolutely a pleasure.
- [01:15]
B
Well, I think before we jump into the main meat of the story, it's important for listeners to get a sense of exactly the degree of scope and scale that you are working with when you talk about being the CQO for hca. Could we just start there and tell folks a little bit about you, your work and how you're approaching it across the country?
- [01:35]
C
Sure. So HCA Healthcare, which some folks may or may not be familiar with, it's about 2,000 points of care. Just under 200 of them are hospitals. The remainder are ASCs, freestanding EDs, urgent cares, physician clinics, imaging. And about half the US states have an HCA presence in them. So as we think about the work that we do, it touches a fairly broad swath of both geographies as well as from rural to urban. So different levels of communities that we serve in terms of resource intensity, kind of across the spectrum. And in the Chief Quality Officer role, there's when they formed this role. It's a brand new role that we didn't have until they moved me into it in January of this year. We pulled a number of areas together under a single construct to provide greater efficiency and hopefully a force multiplying effect in terms of the impact we can have broadly across the organization. So they were well but independently managed areas of patient safety, medication compliance, regulatory readiness, regulatory response research, clinical advisory services or service lines, clinical excellence research, and kind of Background work, medication management, and all these things are brought together into a construct that now is our quality group, division, whatever you want to call it. And it's really focused in four core areas. One is in patient safety, the other is in regulatory compliance. The third is in our clinical service lines and the outcomes we're looking to drive through that. And then the fourth is what I call claims insights and research. So how do we look at everything from medical liability to scanning the horizon from the political environment, policy, federal or state, legal changes, competitive changes, business changes, economic changes, how do we scan that horizon and use those signals as a way to inform our clinical strategies? So those are the four big pillars inside of the body of work that I oversee.
- [03:44]
B
Well, let's go a level deeper there. Specifically, I want to make sure we get to how you all are using AI enabled solutions on your journey to drive improvements in safety. But we are both physicians and patient safety is kind of built into the core of what we do. And in conversations you and I have had, you think a little bit differently about patient safety and how it relates more broadly to the quality umbrella than other folks who I have spoken with. So let's go there for a moment and then we'll sort of blow out and go big picture.
- [04:17]
C
So from a safety perspective, I mean, if I kind of go back to our pillars I mentioned of patient safety, regulatory compliance, clinical service line performance, and then kind of call claims kind of medical liability as an entity of itself. I mean, safety is foundational. Like you cannot achieve regulatory compliance without a foundation in safety absence safety, your clinical outcomes cannot be what they need to be. And with safety events, you're going to have higher, you know, medical liability claims against you. Safety becomes a ceiling that everything else kind of bumps itself up against. And you can't go any higher than your performance and safety. It's a limiting reagent or a governor on the engine, so to speak, for all the others. And it's interesting, you know, on our journey, you know, we as an enterprise, I mean, we perform on par with, in many cases, better than national averages when you look at our industry, healthcare and safety. But if you think broadly about safety, there are industries that are substantively better than healthcare when it comes to safety. High speed rail, chemical manufacturing, general mechanical manufacturing, oil and gas, military aviation, they're 10, 20, 100, 1000 fold better if you look at the literature in terms of their safety performance. And we could say we're different. I'm not sure how different we are. At the very least, what I can say is there are scalable and industry agnostic lessons that we can and should learn from these high performers that can be applied into health care, applied into our hospitals in a way that doesn't need to change our operating models. It just needs to be embedded in the way that we work.
- [05:56]
B
Well, let's talk about how you, how you develop those insights because I agree, as you've indicated, that the health care field was slower to the party on the safety movement than industrial engineering and other areas obvious. Obviously we work with people. Healthcare is and will always be a uniquely human experience. But I love that before we talk technology, you have done a pretty deep, intensive effort to understand the people and process components of these fields that have been using this more structured approach to safety for decades longer than healthcare. How have you approached that and what have you learned?
- [06:35]
C
In addition to about a dozen books and about as many reams of paper of printed articles later, we actually aggregated all those learning learnings and then said which industries? Based off of what we've learned, based off of the theory and practical application, who should we learn from? So we contacted a number of different industries that we went on field trips. We got together a core team that involved everybody from our safety to HR to our ethics compliance to nursing. I mean a broad swath of our business. We all got together and we went on field trips. We spent time with GE Manufacturing in Waukesha, Wisconsin at their CT manufacturing plant. We spent time on the manufacturing floor with them, visit DuPont Chemical in Wilmington, Delaware and went to their manufacturing site and spent time on their floors. We went and visited the 160th Airborne Division which is a special forces unit that they do helicopter based delivery of special forces humans into and out of the most dangerous places in the world at night. Like that's what they do. We've talked to the Defense Health Agency, we've talked to Virginia Mason on the healthcare side. We've tried to spend time physically with and most often on site with people who have created substantive levels of safety and performance and reliability to see what we can learn and how can we take those things that they do and bring that practical application back to healthcare.
- [08:10]
B
One of the core areas of conversation for this particular podcast is technology. We know, looking across the healthcare ecosystem that AI enabled solutions are not just showing promise, they're now proving outcomes. I know you joined us on stage at AHA's leadership summit some time ago to talk about some of those. But before we get to specific use cases, how are you now bringing this technology pillar and specifically AI enabled technology into that sort of multi part approach to driving improvement in patient safety.
- [08:43]
C
It's interesting, I think, Chris, that AI almost becomes not just an enabler, but I think it is going to accelerate our ability and maybe even create an ability we wouldn't otherwise have to embed some of these practices in our own organization. For example, one of the things that these high reliability safety organizations do extremely well is they move upstream on their measurements. They don't just look at the thing that happened, the mortality, the fall, the pressure ulcer, the complication, they move upstream from that to the near misses, the behaviors, the environment. How do you measure the compliance with environmental safety measures, with behavioral safety processes with near misses? And moving upstream from measurement is challenging, honestly, because as a hospital system we're wired to measure all those downstream events. We're literally not wired to measure a lot of those upstream events. And I believe that AI is going to give us a foundation to be able to measure some of the things that otherwise we wouldn't be able to measure. There is a lot of if then pattern recognition in behaviors, in signals that we are collecting that when aggregated by AI, serve as that upstream measure that we otherwise wouldn't have access to by any other means.
- [10:10]
B
I love that point. I remember being a NICU fellow and the NICU that I trained in had something like 65, 70 beds. And I remember thinking to myself, I'm at like the sickest kids bedside right now, but I wish that there was some kind of technology that wasn't relying on individual humans to be heroic, to recognize which kids is the next bedside that I'm going to be at and is there anything we can do about it beforehand? And you're right, I think that is the promise of both this predictive technology. But also we see that the generative technology being adopted in a number of use cases. Are there use cases that you all are working with today across the HCA landscape that you think are worth highlighting for our listeners?
- [10:55]
C
Yeah, so we started where most folks start, which is, I'll call it the safe zone. Like we start with those areas that are low risk for our enterprise, which means not clinical, not patient care. Those are the higher risks. So we started in areas of workflow, inefficiencies, staff scheduling, those sort of things that create cognitive and administrative burden that can be offloaded in a way that makes it more efficient and more consistent. So that's where we started, where we're moving to. And we actually have inside of HCA Healthcare, a digital transformation innovation arm of the company, Dr. Michael Schlosser, one of my colleagues, leads that body of work, and a number of us, myself included, serve as business unit owners over sectors of it. So one of the business units that I sit over as a sponsor is clinical AI. How do we use signals from our environments to inform decision making? And I think that last point of informing decision making is a really important one. We do not see AI as a replacement for decision making. This is not going to tell a doctor what they should or shouldn't do. This is not going to force algorithmic approaches to environments that require a level of expertise and a person who's been trained in the care of that patient, that specialty, to make that final decision. But I do believe, and we've seen already, that AI is a vehicle for us to, I'll say, democratize understanding of certain concepts that may have varying levels of understanding based on the experience of the individual. And by democratizing understanding, if you reduce the variance in the knowledge base, you reduce the variance in decision making. And if you reduce the variance in decision making, you reduce the variance in outcomes. One of the areas that we've started in this space clinically is in partnership with GE Healthcare and building out a vehicle for measuring fetal heart rate tracings. And in an oversimplified, simplified way, there's fetal heart rate tracings. And you know this better than I do, fetal heart rate tracings that are, it's totally fine, all is good. There's tracings there that are, this is really bad, intervene quickly. And then there's a bunch of stuff in the middle. And it literally is like a bell curve where the lion's share of it is kind of like, it's not good, it's not bad. We need to keep an eye on this. So we've been actually training an AI algorithm, and it's being submitted for FDA approval, to be able to recognize those ones that are in the middle and swing them to the left or to the right and say, are these more likely to be something we don't need to worry about, or are these more likely something we do need to worry about? And by doing that, empower physicians with the knowledge to make more informed decisions that are less dependent on their individual experience and more dependent on their knowledge and expertise. And I think that's a remarkable and important way that we can impact. Well, I mean, you know, certainly as a neonatologist, one of our most vulnerable populations.
- [14:00]
B
Most definitely, Randy. And when I think about what you're describing, what it solves for is there would never be enough human workforce to be able to watch every single one of those strips over thousands of hours, over thousands of patients, to be able to identify those patterns. And that is is where AI is making a difference today, is helping with pattern recognition and consuming and monitoring reams of data right now, which is staying as data and not being transformed into information. And it comes as no surprise to me that you all are leading the way in trying to help drive the bus to a place where this AI enabled technology is making a difference not just for patients, but for our workforce. Dr. Fagan, it has been a real privilege. Thank you again for joining us on the podcast. We will clearly want to ask you back as you have brought that technology through all the processes it needs to go to, because I'm confident the outcomes are going to be pretty significant in a few years.
- [15:00]
C
Well, thank you, Chris. And we are excited about the journey and committed to sharing our learnings with others. One of the things that we feel very strongly about is those things that we learn to do not just once, but at scale are things that need to be shared broadly with the enterprise, kind of writ large to ensure that everybody benefits from the, from this body of work. Because at the end of the day, we're all caring for our communities and we need to do this together.
- [15:25]
B
You know, Randy, I think that's where we're going to have to leave it for the end of part one. We got to have some suspense for folks to build into the next part of our conversation. So thanks folks for listening today and stay tuned because the next episode is going to drop very soon.
- [15:39]
A
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