Today in health, it stream of consciousness. I'm just going to talk about, I've been at conferences for the last two weeks and I have a 2 29 event next week. So I'm just with a lot of people in the industry. I want to give you a couple of thoughts of things that we are discussing. My name is bill Russell. I'm a former CIO for a 16 hospital system and creator of this week health. Instead of channels and events dedicated to transform healthcare. One connection at a time. We want to thank our show sponsors who are investing in developing the next generation of health leaders. Short test artist site parlay it's certified health, notable and service. Now check them out at this week.

We ask you to join us, hit our website. And in the top right hand column, you're going to see a logo for a lemonade stand, click on that to give today. We believe in the generosity of our community and we thank you in advance. Just one last thing, share this podcast with a friend or colleague, you said as a foundation for daily or weekly discussions on topics that are relevant to you. And the industry. They can subscribe wherever you listen to podcasts. All right. I'm on the west coast. I've been on the west coast and back and forth between the east and west coast for the last couple of weeks. Went to the notable conference last week, noteworthy. Talking a lot about AI, actually, you can't go anywhere. These days and not be talking about AI. , regardless I was at notable last week, I would started the, , Sunday it at the health conference Sunday, Monday, Tuesday, and then I bet at healthcare to healthcare, which is a CDW event. Out here in Scottsdale, Arizona. And, , tomorrow morning, I'm going to get an opportunity to, or this morning, if you're listening to this, I'm going to be. At the lab at honor health. And taking a look at some of the really cool things that they're doing there. And interviewing some of the people that have set up their command center. And their training center. So, , looking forward to having that discussion and learning some more. , and again, I get back and turn right around and go out to Charleston, South Carolina, where I'll be spending a couple of days with a children's hospital CEOs and sub chief data officers. , doing our normal 2 29 event. , format. So looking forward to those discussions, but I've been around a lot of CEO's lately, a lot of discussions around AI. A lot of discussions around, , the application of technology. And that's what I want to talk a little bit about today. And it is. I find that organizations who are implementing AI focus on one of two things. One is. The implementation of AI and the other is solving problems. The organizations that are focusing on the implementation of AI, that is discussions around AI and what should we do about AI and all those other kinds of things they are, , going to circle for a while. They're going to try to figure out the right governance and the right policies and the right approach. And should we take a technology platform or what should we do? That's one group. And to be honest with you, I think it's the larger health systems that are moving a little slowly. The other group is solving problems. And they see AI as a tool, not as a technology that has to be figured out before they implemented. , quite frankly, most of us couldn't tell you how a car works. We get in one every day and we drive to work. We couldn't tell you how our iPhone works. But we pick it up every day and we utilize it calculator. Same thing. , computer, probably the same thing for most of us. Can't explain. How it processes and produces video and all those other things, but we use it every day as a tool. To solve problems. And , it was interesting. I had a conversation today with a Vera health. And we were talking about their implementation of computer vision. And AI. And to be honest with you, it started with, we had a problem. And the problem was we are a rural healthcare system. We have a just huge amount of geography to cover and not enough nurses to cover that geography. Therefore we had to solve that problem. They looked at the tool sets that were out there. And the tool set they chose was artists' site, which is a computer vision tool. And that has an AI backend and does all sorts of interesting things. Cause it's a platform, right? So not only does it do computer vision and identify falls, and by the way, it took their falls from 17. A month down to one. And that's a significant gain. But in those same rooms, same camera, same technology, same platform. They went to their next use case. And their next use case is doing some documentation. , while the nurses are in those rooms and they saw 60 hours being saved in their first week of the pilot. And those are significant numbers. And so again, I find organizations fall into two categories right now, talking about AI. And actually solving problems, not talking about AI, just seeing it as a tool in the tool belt. To solve problems. And if I were a CIO today, I would go back to the problem, set the problem bucket. And look at it and start to consider where AI could be applied to that problem. Set. And I would ask this question. What's the problem we're trying to solve. And how is it measured? Because what you're going to want to do is you're not only going to want to approach this from a problem centric standpoint, you're going to want to approach it from a highly measured standpoint. Measure the current state measure the heck out of the current state. If it's ours. If it's, , resources, if it's, , whatever. If it's falls, whatever it ha it could be a quality measure, whatever it happens to be measure the heck out of it. And its current state. Because if you're going to spend any money in today's day and age, you're going to have to show return. On the other side, this is the other reason why approaching a problem instead of introducing a technologies is the best way to go. What's the problem we're trying to solve. How has it measured? What's its current baseline. Then you can bring the technology in, do a pilot, maybe it's 20 beds, maybe it's 60 beds. It depends on the size of your organization. What's a meaningful sample size in order to get a good metric out of it. And if it's 60 beds, then you're able to say, this is what the baseline was before. This is what it's now amongst these 60 beds. And then when you want to go to 600 beds, That the assumption from the financial people who are going to give you the money. Is essentially, if it did that for 60 beds, it's going to do 10 times that for 600 beds. Now we know that not all beds are created equally and you're going to have to adjust those numbers. But at the end of the day, somewhere around that order of magnitude is what's going to happen. So again, what's your problem bucket? What's your set of problems. What's the possibility that AI or these new sets of tools, don't even say the word AI. What's the possibility that this new set of tools. It can be applied to the problem that you're looking at and what has changed since the last time you looked at this problem that potentially moves you down the road. All right. So what's the problem and what's the problem set. And then how has it measured? What's the current baseline? What do you think is possible with AI and build the whole use case around the problem set? So that's one thing that I've been reviewing and, , thinking about pretty significantly. It was interesting to sit through. , some of these presentations. Over the last two weeks. I really am finding that the smaller health systems and it could be out of necessity. Are moving a lot faster than larger health systems. And I granted it is hard to scale solutions across a large health system, but that should not deter the pilots. And it should not deter the progress, moving in a direction, actually getting more real world data around. The problems and the solution sets and seeing if it's worth expanding. Now with that being said, Heard of some larger health systems that are moving forward with some of these things.

Which is extremely encouraging from my vantage point. As I look at this. . I am in the camp of on a scale of one to 10. How much will AI impact healthcare? I think it's a 10. Now, granted. We all know the hype cycle. We know that generative AI is at the peak of the hype cycle. Its next move is to take a dive. And it will be the difficulty in training the models. It'll be the cost of moving our data into those models. It will be, , the lack of resources around those models. It will be the cost of, , not only moving into those models, but operating those models on an ongoing basis, it could be around. , , climate change and those kinds of things. Where you get a significant pushback. , could be regulation like the EU regulation that's coming for. AI could be, , AI regulation that comes out of the United States. Any number of things could slow this down. , significantly. And we know. The Gartner hype cycle has been fairly well tested by now. So there will be a. A period of disillusionment now. I believe AI falls into a different category, a different bucket. It falls into the internet bucket. When you think about a technology that has had a long lasting impact on our world, the internet. Is that kind of technology. I think AI falls into that bucket doesn't mean it's not going to go through a trough. It doesn't mean it's not going to step off a cliff in the next. I don't know, six to 12 months, it likely will. And people will become disillusioned with it. But at the end of the day, it will have a rapid rise out of that. And continue. To make inroads into solving serious problems. And we do have a set of serious problems in healthcare that AI can be applied to. I'm really encouraged because it is pretty broad the direction that AI is going. And yes, I understand the need for safeguards. I understand the need for. , good policy, understand the need for transparency into the models and for equity to be built into the models. I understand all those things and I appreciate all those things. But I also appreciate the need to make progress and to solve the problems that are facing healthcare because these problems like rural health care. Organizations going out of business. These are serious issues, more serious. Than a, an equity problem with an AI model. , or a transparency model with an AI. , model. It's more important to keep that rural healthcare system open. So maybe. As the academic medical centers and the institutions start to do that kind of research and publish that research that will help us to fine tune the model going forward. But I'd like to see those institutions that have the most acute problems. Move forward and move quickly to adopt these technologies. We're seeing some really fascinating numbers in terms of savings. In terms of, , efficiency. In terms of access, the ability to serve more patients across the board. These are really encouraging numbers and really encouraging progress. So anyway, been talking to a lot of people. What's your problem? What's your problem set and measure the heck out of it. How has it measured? I get those metrics. Get the baseline set. And then do a pilot, figure out how much we can move the ball forward. And if the pilot works. Scale it as far as you possibly can. All right. Well, we'll see what happens next week. Have a lot of, lot of conversations too. Go on there. And, , I don't know. I may end up taking a couple of days off on the today's show. I will let you know on tuesday Cause i will do a show on tuesday i'll let you know what the rest of the week looks like All right. that's all for today don't forget to share this podcast with a friend or colleague Keep the conversation moving forward We want to thank our channel sponsors who are investing in our mission to develop the next generation of health leaders Short test artist site parlay it's certified health. Notable and service 📍 now take them out at this week health Dot com slash today Thanks for listening That's all for now