C (3:36)

100% agreed. And as we do that, one of the critical things that is kind of the next layer beyond governance is who you involve. And it's, it's critical that we involve frontline staff early, the people who are closest to the work being done. It validates the problem, it ensures the relevance and buy in. And as smart as the executive team may be, it is critical that we get the people closest to the work involved early in the process to help to shape that work.

B (4:06)

For those who are listening only and not seeing me nod my head as vigorously as a head can be nodded right now, I just want to narrate that for you because I remember like 10 years ago, we had developed this terrific machine learning model to predict readmissions in the health system. I was working at the time. We spent nine months in development, like the numbers on the model were terrific. And then we showed it to the people who are going to use it and they said, what's this? We don't need this. And by the way, I don't want to use that. It took us a whole another nine months to walk through the people side. So that is a lesson that you learn exactly once in your career. And it sounds like it's being put to use by your group at hca.

C (4:45)

Agreed. And as we bring those folks forward, one of the things that we try to make sure we're focusing on is augmentation, not necessarily automation. How do we enhance human decision making, not replace it, especially in the clinical space? We think that's a very important decision point.

B (5:01)

Yeah, there are, there's a great article that was in Axios a few months ago that the two editors of Axios wrote together about the impact of AI on their own professional infrastructure. They recommended all leaders ask themselves the question, what are the things that I and my team need to do to be incredibly successful? And then ask the follow up Question. How can AI either automate some portion of that so I can focus my human time on a different part? That's going to make a big difference. Do you have some specific examples, Randy, about how you're seeing that put to use and how perhaps those of ideas have flown through the governance process and then into action?

C (5:44)

Yeah, there's a few areas that we've addressed I mentioned in the last episode. Working towards being able to reduce variance in the way that we staff our nursing units. And it sounds kind of banal, but it's remarkable when you think about the variance in how we staff those based off the individual who's staffing it and the time it takes them to do that. It is incredible. And by, by offloading that burden from them, it allows them to actually lead rather than spend hours of their day managing a schedule.

B (6:21)

The staffing example is, is fascinating because I interviewed Dr. Schlosser two years ago and Mike Schlosser, for those who didn't listen to episode one, is one of the HCA leaders who helped drive this technological transformation across the enterprise. And I remember him describing that nursing model in some detail because the number and DE of variables about weekend option versus not weekend option, Tuesdays and not Fridays. And for a human to try to manage your nursing unit can have hundreds of nurses who you're trying to mix and match in a way that meets personal lives as best as possible with patient and clinical needs. This is exactly the kind of problem that AI is built to solve because AI finds patterns and helps develop the solution. Right.

C (7:09)

And on the clinical side too, of that. Chris, you know, this isn't just an exercise in how do you staff a nursing unit with the right number of humans for the number of patients that are there. There's the ability as you, as you head further down that road to say what kind of patients are on that unit today on this shift, at this moment, disease states, resource intensity, and then try to match that up with a dynamic of nurses who meet those needs. You don't want to staff an entire unit with all new grads, nor do you necessarily want an entire unit of all 20 year experienced nurses. How do we create the right dynamic of skill sets and experiences to meet the needs of the patients? There's a clinical value in the work that's done beyond just the offloading of that administrative burden from folks on the offloading administrative burden. I know a lot of folks have been using AI as a vehicle to assist with documentation where physicians, nurses, PAs, whoever can just talk and patients can just Talk. And that information is then aggregated and then put into the medical record in a way that it is consumable and it understands nuance, it understands context. And it plugs things in, regardless of the order in which you ask the questions, it plugs it into your documentation. And the idea of creating a greater level of completeness of our electronic health record has an incredible value to us. I mean, one on the, on the administrative burden side, it can remove literally hours worth of work of our physicians who need to be entering the information, editing the information, signing off on the information, all of that stuff. That's just an administrative burden. It literally can offload hours of it. Also, when you think about having a more accurate, complete medical record, the ability to transfer knowledge from shift to shift, and the ability to make clinical decisions based off the complexities of the individual, it's just better for patients. And I really see a value in that space. And that's another one that we're exploring and pursuing is we've got an entire cohort of physicians, both ER physicians and hospitalists. We're exploring the cardiac space as well. In Texas, that was our pilot area in the Dallas area that have been utilizing this and helping us to learn how do we allow this to give time back to the doctors, more time at the bedside, less time at the computer, at the same time, enhance the information that is able to be transferred, shift to shift, and the information that's utilized by each shift for clinical decision making. It's a really exciting space.

B (9:52)

Ambient AI, the technology that you're referencing, and there are a variety of companies who are in that space, is one reason that when I talk to trainees, so folks in medical school or residency or nursing school, I say, folks, you have picked the best possible time to go into medicine or nursing or healthcare in general. Because you and I trained on paper and we were walking around units with giant charts and writing orders in triplicate. And then our entire world got electronified. And in doing so, we pulled people away from FaceTime with patients. And I see the value in all of that structured data. My background is a CQO as well, on a much smaller scale than you. So I love the fact that we now, thanks to the electronification of healthcare, have all of the structured data and the metrics. But my gosh, we transferred such face to face to face to screen time that this ambient technology is giving it back. It really is a way not only to, as you indicated, improve documentation, which has never been anyone's favorite part of their job, but also improve the experience of our workforce. It is such a crucial thing to focus on while we are in the midst of a workforce crisis that we will never be able to recruit our way out of.

C (11:06)

Completely agree with you, Chris.

B (11:08)

I think we've got time for one last question and I'm just wondering, obviously you all have had a number of successful implementations. You've probably had some that tried and were cut off. What other, you know, single biggest lesson would you have steering the AI ship at, you know, the, the 190 plus hospital entity that is HCA Healthcare are folks listening in who are perhaps trying to do the same thing within their own communities?

C (11:37)

I would tell you one of the most important things that folks can do. One. Well, there's a couple of things you asked for. One I'm going to give you more than one. Well, one is, you know, make sure that, you know, if you're the clinical leader trying to use AI to advance care in your space, bring together a team that isn't just clinical, you have to have all different lines of sight to really solve these problems. Second, you want to define a single high impact problem that has a low risk if you don't get it right. You want to make sure that if you fail, you're going to fail safely in this. And then the last thing, don't start with vendor selection. You know, that becomes a solution. Looking for a problem, you need to first identify the problem you're trying to solve and then identify the solution that best allows you to solve for that.

B (12:27)

Wise words and ones that I think could be applied just as easily in a critical access hospital in Oregon as they can to a multi state system like HCA. Dr. Fagan, it has been a privilege and again, my guess is we'll be asking you back again sometime in the future. Thanks for spending time with us today Chris.

C (12:44)

An absolute privilege anytime.

B (12:46)

Thank you. Take care everyone.

A (12:48)

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