I mean, you name it data. It's definitely being applied to data. , we spent some time with Microsoft. , early on in the week and looked at their Microsoft fabric. Solution heard from a health system that was implementing it, a couple of health systems that were implementing it. , we heard from nuance. , about the, the, , , co-pilot. , solution Zack's copilot solution that, , is going to allow them, , essentially drive the cost. Of taking the nuanced solution throughout your, the dragon experience. Across your enterprise and drive it down. By as much as a third or more. And so you're going to be able to implement that across more seats, because for the same amount of money that you were paying before, you're going to be able to get more licenses. Now, what we were hearing is again, that solution is ready for outpatient. , yeah, outpatient, but for inpatient, they're still a little bit more coding to do. And obviously there's just more complexities, more specialties, more complexity. Associated with that. So, That's going to take a little bit more time. At the end of the day, AI is coming into everything and the. The question becomes, where, where are we at with our readiness? Are we ready to implement? Do we have a strategy? Have we looked in it? , again, I come back to, and I've been talking about this a lot over the last couple of weeks. So forgive me if you've heard this before. There's a couple ways you can go. The easy button is. Let your vendors implement it. And pull it in through existing contracts. That is one of the ways to do it. There's a, , the, the hard button is essentially go out, hire the team, start building out your own custom LLMs and those kinds of things. I don't think we're going to be going in that direction. But there's also probably a middle section where essentially you, you decide, you know, what. , there's gotta be solutions that we absolutely get through our existing vendors. And then there's going to be some things that we want to create some custom solutions around. And there are partners around that. And I talked about what a, an AI enabled health system would look like last week. You can go and listen to that show and it's exciting. I mean, it's exciting to realize that we're going to be able to take the structured and unstructured data. We're going to be able to make that make meaning from that data and then query that data from all aspects of the health system. From general reporting to operations. To billing and coding to the patient. In an authenticated and unauthenticated manner. , it's just an exciting, , exciting time to be in healthcare. This is a moment in healthcare. That we were going to look to and say, this is the year that AI really started to take off. And I think five years from now, we're not even going to be able to recognize what we were doing today. We are now realizing the promise of digitizing healthcare. Yes. It's been a tough long. 15 years, 10, 15 years, whatever it's been since meaningful use. But we now are starting to see the fruits of that digitization. And it's because the technology has caught up and we now have tools to interact with it. In a, again, the way we interact with everything else in a natural language way. All right. That's all for today. That's probably my rundown for this. I might talk about, , I'm talking about this conference a little bit more through the remainder of the week. But, , that's all for today. So don't forget. Sure. Share this podcast with a friend or colleague, keep the conversation going. We want to thank our channel sponsors who are investing in our mission. They are short test our site parlance certified health. Notable and 📍 service now check them out at this week Health. Dot com slash today Thanks for listening That's all for now