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Three sets of use cases for Generative AI. Clinical Efficiency, Revenue Cycle, and Operational. This is moving faster than anyone imagined.

Transcript

Today in health, it we're going to take a look at use cases for generative AI. Have this list here from a conference I was at. Revenue cycle operational clinical efficiency, bunch of use cases. And, , I thought I'd share with you. It's actually pretty good. So, , that's where we're going to go. My name is bill Russell. I'm a former CIO for a 16 hospital system in crater. This week health set of channels and events dedicated to leveraging the power of community. To propel healthcare forward. We want to thank our show sponsors who are investigating, developing the next generation of health leaders. A short test artist, site parlance, certified health, notable and service. Now check them out at this week. health.com/today.

All right. Hey, we're excited. Our work with Alex's lemonade. Stand to support childhood cancer and cures for childhood cancer has been great this year. You guys have responded. And, , as I said, we set a goal for 50,000 work up over 50,000 and we raised another 5,000. This past week with the support of our sponsors and a 2 29 event. And so you're going to see that number go up to over $55,000. We're so excited, but we're not done. We're going to keep plowing through. We'd love for you to participate. If you would like to do that. Go ahead to our website this week out.com top right hand column. You're going to see a logo for the lemonade. Stand, click on that to give today. We believe in the generosity of our community and we thank you in advance. All right, one last thing. If you really want to help us out, share this podcast with a friend or colleague, use it 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, let's get to it. Artificial intelligence and healthcare, and to give credit where credit is due. , it is, , from the epic conference from UGM and I stopped by the artificial intelligence booth and they handed out this piece of paper. Which had some of the use cases that they're looking at. It's a pretty good. And actually I I've been out on the web and I've shared some of these over the. Over the last month or so it's interesting. This topic has become so hot that people are getting tired of hearing about it. However, Every time we have a 2 29 event, which is our round tables. We had a Cisco round table last week. We have CIO round tables and others. CMIO round tables. I will ask this question scale of one to 10. How much of an impact will generative AI have on healthcare? One being none at all. I don't know why we're talking about it. I wish people would stop talking about it. 10 being, this is a game changer. And I would say on average, the responses nine or 10, and the reason for that is because in every group there's like somebody who says three. And that's, what's holding the average down, but for the most part, people are 10. If there's a scale higher than 10, it would be, it would be above 10. And I think the reason for that is we're using it where, , you know, I've now put it on my phone and I use it for various things and I will ask you questions, things I used to go to Google for. I will now start there. If it's a question that's more of a brainstorming kind of thing. And, , it's pretty effective.

So anyway, , so we believe this is going to be transformative. This is, these are some of the ways that epic is telling their clients to consider artificial intelligence and healthcare. And they're not the only ones Meditech's doing the same thing. I'm sure Oracle is. I've not stayed up on what Oracle is doing. , but, , on the Meditech side, I know they've partnered on with Google on the epic side. They've partnered with Microsoft and, , and others, by the way, it's not going to be a single kind of thing. This is for generative AI. You're typically looking at Microsoft and Google for partnerships. But there are AI models scattered throughout the EHR. So just something to consider. All right. Use cases for generative AI. , clinical efficiency. So these are in the category of clinical efficiency generate advanced note text. To transform the discrete data captured in smart forms into clinical narrative. Now some of these things are going to be specific to epic in their language. And so. But it's that concept of taking all this discrete data? And creating a narrative from it. So it's good at creating a narrative. It's also good at summarizing. So that's one thing, provide a headstart in queuing up drafts of inbox messaging. We've talked about that. , several times. In fact, we covered that in a recent podcast. And that's going that podcast. If you didn't attend that, that will be released on our conference channel. I think a week from this Friday. So not this Friday, but the following week, you're going to hear that. That, , that AI conversation we had with print land from UNC, Christopher Long Hurst from, , UCS D. And Michael Pfeffer from Stanford and that's worth listening to, , it goes on CRI patient summaries to reduce the amount of time it takes to get up to speed on a patient. So summarizing it's really good that. And by the way, the hallucinations are not as prevalent when you're asking it to summarize specific data. It tends to pull from that data. In fact, we do, , our interviews and when we put the information through generative AI, we say, pull out the top five quotes from our guests. From this. And what we're finding is it's not hallucinating when it's pulling directly from text. It hallucinates in other areas, like when you ask it to be generative and that kind of stuff. But when it's pulling specifically, it tends to get a pretty high mark and you can also tweak the settings to even be more specific anyway, go on from there. , simplify handoffs by providing specialty specific write-ups of shifts to facilitate communication during nursing and physician handoffs. Again, you get this whole idea of a summarizing. , information that happens over a period of time. Generate personalized patient instructions. I think that's a great use case capture, exam information, discreetly, and generate notes using voice. That's essentially a shout out to nuance, summarize and translate my chart. Questionnaires, summarizing questionnaires, just in general, , regardless of the EHR you're using or how you're collecting them, you could collect these things on your website or other places. And you can summarize that information using generative AI. It's really good at that. Help find information faster using natural language to explore. The chart with awareness of specialty or role specific needs. And that's, , what we're seeing is navigation is one of the key areas where you're seeing generative AI. You're seeing out on the Meditech side. And they're doing it with Google. And, , and they've also really integrated Google search within the EHR as well. I think it's really powerful if you haven't seen it, it's worth it, regardless of what EHR you're using, it's worth stopping by their booth. If they happen to be there to show you, , how they've integrated that search into the entire EHR.

It's really interesting. And then what you're going to see is a natural language front end on top of that, which is really going to be powerful, I think, for the clinician. So there, there, there you have it. Those are the clinician efficiency, , revenue cycle simplify. Prior auth to identify whether prescribed medical services, meet the payer's requirements for reimbursement. Generative AI is going to consuming tons of information. And by the way, don't think you're confined to just open AI chat, GPT and Google Bard or whatever they're releasing now. , there are models being spun up all the time where you can actually filter all your information in there and train the model on your information, and then it can spit things back. So for instance, the payer requirements and whatnot, you could actually train a model on that and then send information over, , about prior auth.

Now, with that being said, there are plenty of company. If you're in a bi-modal set of a build boat, there's plenty of companies. That are running sprinting down this past. So revenue cycle let's go on, provide a summary to explain a patient's balance and help them understand what they L again, it's great. It's summarizing speed up coding and charging by summarizing clinician documentation for coders to make it easier to find the necessary support for charges. Fantastic. Draft appeal letters to save insurance followups. Save time. Our staff time, draft appeal letters to save insurance, follow up staff time and accelerate the appeals process. Again, graded, summarizing finding information, pulling all that together. Automate patient communication by answering patients nonclinical questions. And providing directions to sites of care and I've heard of a health system, that's essentially feeding all of their logistics. If for lack of a better term, , you know, Where to park, how to find a doctor, all that stuff. They're feeding that into a generative AI model. And the hope is that they will eliminate a significant number of the questions that come into their help desk or support desk. And even if they don't and those calls keep coming in, what you could have is the ability, the ability to. , train. An automated attendant that is going to give that information four times when there isn't somebody on the phone. And then finally operational summarize reporting dashboards to give users better understanding of report logic. That's fantastic. Gather patient preferences, query the auto scheduler for availability and book appointments with a chat bot. , read follow-up notes that doctors write and auto create appointment requests, and then finally use natural language processing. To ask reporting, , tools in their case, slicer, dicer a question and get an answer, no training required. And again, , I've talked about that with some, , data scientists and whatnot that is front ending, the data. And , with, with natural language and being able to query it with natural language and then replying. With the appropriate information and it looks like, , epic is heading down that path as well. And I think that's going to be a very common way of interacting with large data sets and being able to query it then after the fact, in terms of Def in terms of your. , your data governance and your data definitions and those kinds of things. You're going to be able to ask a questions and it's going to be able to respond and say, Hey, here, here's the definitions we're using. Here's where this information was sourced and those kinds of things. So you're going to be able to put all sorts of metadata around it. That is going to be queryable by voice. Pretty amazing. Anyway, I want to thank epic. For putting this together and I'm sure we're going to see loads and loads of these use cases. , I thought I would start here, share these with you and we will continue to collect them and make them available on our website as well. So you can find them all right. That's all for today. If you. Want to help. One of the things you can do is share this podcast with a friend or colleague. We really appreciate it. We want to thank our channel sponsors who are investing in our mission to develop the next generation of health leaders. They are short test artist site. Parlance certify health. Notable 📍 and service. Now, check them out at this week. health.com/today. Thanks for listening. That's all for now.

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