We've incurred the pain of digitizing the medical record, what was it all for. Today we discuss whats possible today.
Today in health, it, what an AI enabled health system will look like. My name is bill Russell. I'm a former CIO for 16 hospital system. And creator this week health, a 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 investing in developing the next generation of health leaders. Short test artist, site parlance, certified health, notable and service. Now check them out at this week. Health. Dot com slash today. As you know, we've partnered with Alex's lemonade. Stand to raise. Money and awareness for, , cures for childhood cancer all year long, we have a goal to raise $50,000. We've raised $55,000 for the year, but we want to plow through that number. I hit our website and in the top right-hand column. You'll see our logo for the lemonade stand. You can click on that to be a part of it. And give today, we believe in the generosity of our community and we thank you in advance. One last thing, share this podcast with a friend or colleague. Get the conversation started, 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. This week I am at. The notable conference. It's , called noteworthy it's notable conference out here in California. And, , I did a couple of interviews with health systems that are using notable and I did. A walkthrough of some of the things that they're doing in their demo lounge. , I don't usually talk specifically about a product set, but I think it's a great example of where. , to give people a vision for where this AI can take a health system. So notable is an AI platform. , I think class puts it in the patient experience. , section, you can put it in a lot of different sections, to be honest with you. I think it's hard. To classify because it is an AI platform essentially. What you have is a system that. Takes NL. P OCR. , , machine learning, gen AI, large language model, large language models, multiple large language models. And it sits it on top of your health system and it adjusts everything. It ingests, , your, , your structured data. It ingests your unstructured data. And that's the magic of it. It didn't ingest all that information and not just within the EHR, although that is a primary source of the information. It also will ingest data from a, by the way, it's using a RPA as well. It'll also will ingest data from your payers and collect information on. , , on prior authorizations and what's required and what documentation and all that, all those kinds of things. So if you can imagine. Chat GBT, if you will, I'm just going to simplify this for. , purposes of this discussion. Imagine Chacha, BT being trained on everything within your health system. In a private model. And it's being trained on everything within that you have to interact with, , , billing payments, , scheduling. , physician credentialing systems, you name it, it just pulls all that information together. And then essentially. , they rolled out this thing called the assistant notable assistant, and it is a natural language front end to that backend that I just talked about it and just all those things. And I saw a demo of this yesterday and it's live it's out there and notable clients can start utilizing it. But it is a natural language. Front-end translatable in multiple languages. That you could essentially query. The health system. Now there's an authenticated experience. There's a non, non authenticated experience. The, , not authenticated experiences. What do you think it is? Which is essentially, Hey, I'm looking for an appointment. , , I. I have this problem. And then it gives you a list of, , physicians and appointment dates if they're made available. And you can say, no, no, I'm looking for somebody in this zip code. I'm looking for somebody who's a male. , physician or a female physician, and it'll narrow it down for you until you get to exactly what you're looking for, but it's natural language. Think of all the work we've done in, in, on our homepages. Of figuring out the navigation to get it just right. So people can know what to click on and that kind of stuff. Now imagine the difference between that and a single box, like a Google front end. To your entire health system, except it's better than Google because what Google does is like a librarian. You say, Hey, I'm looking for things on presidents of the 20th century. And then they go and say, well, here are the 15 bucks and they hand it to you. That's what Google does. What. A natural language gen AI solution does, is this essentially you ask it a question. It answers in natural language back. Now, it was interesting when they were doing the demo. The gentleman who was doing it for us. I spoke multiple languages. And so I said, well, , can this translate? And he said, of course, and he did it in French, in French. Okay. So this wasn't like, , Hey, we need to translate this into French, French. This is what large language models do. He asked it in French, it responded in French. Then we asked it in Spanish, it responded in Spanish. And it was really kind of fascinating. Of course the whole thing is ADA compliant. So you could literally ask it. In your language, it haven't responded in your language. And think about what that does from a navigation standpoint and the challenges we have in making our health system accessible. , you've gone from, , The, the person at the other end of the keyboard, being the navigator to a. , essentially in a conversation with your health system. You know, what is the place that's closest to me and those kinds of things. So I'm talking about the non authenticated experience, the authenticated experience. It goes even further. Hey, what make it medications in my aunt? Hey, what, , , , I don't know. Is this visit covered by my health, my health insurance. Is there a doctor I should see in the house of some that, , is covered by that health insurance? Hey, explain my bill to me. And those kinds of things, because the authenticated experience uses OAuth SAML, you name it. , all those different. , ways of authenticating to essentially authenticate to my chart or authenticate to the EHR. Authenticate, whatever your method is. I mean, you could have split it out and done something different, but you authenticate to the health system. You've verified. You are who you are once it does that. Now you can ask a questions. Pertaining to you, your medical record, your interaction with the health system, your next appointment. , all those kinds of things. Now the fascinating thing for this is because it's ingested all that information. , there's just a ton of use cases that are available to the health system internally that are really interesting. Cause so you can see assistant as a patient facing front end, but. It could also be an administrative front end, as well as you're trying to automate things like prior authorizations and, , and scheduling there's internal scheduling. There's external scheduling issue. Now that can be automated as well. And so you can have this assistant scattered throughout the, the health system. , but not only that notable also has the ability to do outreach and those kinds of things. So, , and it's, it's intelligent outreach. I remember we used to do. , outreach population health tools, and we used to do outreach to a patient community. So let's, so this month is breast cancer awareness month. And let's assume we wanted to increase the number of mammographies across a certain population. And what we would do is we'd go out and we query. , our systems and we'd find the likely populations that we needed to reach out to. And then we determined what it was the best way to do that. We can do that through snail mail. We can do that through email. We could do that through texting and all those kinds of things. , and this is why we're bringing CRMs. And this is why we had all these other tools that we had to bring in. In order to do this, it was interesting to me to watch the, , you, you could do that with notable and it, it will know which method is the most effective for reaching certain populations. And it also has a way of smoothing the process. So you, you texted them about mammography. They say yes. It gives them a list of, , it comes right back in the stream is assume it's a text that comes right back and says, , , here are the places you can go for that mammography. Would you like to schedule an appointment? You click. , yes, I would. It gives you the times you click on that and it says your appointment is scheduled. Boom, here's your information. So again, taking that friction out of the process is all part of it. The reason I'm talking about this, it sounds like a commercial for notable, but the reason I'm talking about this is this is what an AI enabled health system looks like. Now you might think, oh, this is only available to the, , to the large health systems, to the academic medical centers and whatnot. , but my interview yesterday, I think was the most fascinating in that it is available to those health systems. And there are large, very large health systems that are using this type of solution. But my interview yesterday was, was with, , Kristin , who was with north Kansas city hospitals. , , and, , Ameritas. And, , I might get that name wrong, but I know there's north Kansas city hospitals. And. , it's it's as big as it sounds right. It's it's not very big. It's it's , it's. , It's local, it's regional, it's maybe 500 beds. And that has got that. Those kinds of things. And they've implemented this platform, this AI platform. And what was interesting to me is by implementing this platform, this small health system has implemented an LP OCR. , large language models. They've implemented all this kinds of technology behind the scenes. And I think this is the way a majority of health systems are going to implement AI. And I think it's, it's interesting. Cause I asked her, , Hey. Talk to me about the adoption of AI within your health system. And she's, she looked at me and she said, Well, it's not about the AI we're solving the problems that the health system has. We had this problem and we brought in. AI. And we brought in, , this solution set to it. And, and people aren't really asking, , how are you getting this done? How has the technology figuring this out? This was the promise of digitizing the medical record from the get-go to be able to query the system and get information back. This is the next evolution of digitizing the patient record that we've always been talking about. And so these use cases are available to this very small health system. And they keep expanding what they're doing and they're a very smart health system and their outreach yields benefits. Right because they're outreaches and Hey, just for cancer awareness month, reach out to these people around mammographies. It's intelligent and it's constantly doing that. And it's constantly doing that. Not in big batches. It's constantly doing that in small batches. Hey, this group of, , this group of two people, , are, , should be coming in for their mammographies boom text appointment. More followups, colonoscopies jet. You get the picture. And so this is a way to, , Build healthier populations. It's also a way to. , essentially keep the volumes. Where they need to be in order to sustain the health system. , in a profitable manner. This is the promise. This is what a house was and it's available to small houses. Sometimes it's available to large, also something. Now a lot of people are going to sit back and say, well, we're going to wait for epic to do that. And you might wait about three years or so, and epic will implement parts of this as they move forward.
No doubt that that's going to happen. , you might be an Oracle shop and saying, well, Oracle is going to take us in this direction and you might wait three years for Oracle to get this right. , Or you could actually start implementing this now. And as we struggle through the next three years with, , , inflationary pressures, , we struggle through, , the erosion of services through competitive. , the competitive landscape and increased competitors and people buying up primary care. , practices in our markets and those kinds of things. , you could actually put the intelligence of your entire health system to work for you by implementing these tools. I also asked, , , Kristen. , how many AI experts to do. Have to hire internally. And she just looked at me and said, none, we couldn't, we don't have the wherewithal. So you have north Kansas city, hospitals and health systems. Who essentially. Has, , As has become a smart health system powered by AI. And you have very large health systems that are still trying to figure this out or sitting in a room, trying to figure out how you're going to do governance and who's going to do governance and how you're going to do this. And you're going to have those conversations for the next year, year and a half. , meanwhile people are making, this is an inflection point. People are making progress. On these kinds of capabilities and they're making them very quickly and they're going to have an edge. Over your health system. And so I guess. The hope is that the health system down the street. As an implementing these kinds of things. , and you are not because, , I think it will be a significant competitive advantage. , and one of the things that Christian talked about was not, Hey, we're letting people go, but Hey, we're not filling, , positions we used to have to fill in order to do these things. Are being automated and we're, we're not having to hire in. And the problem for them was they couldn't hire. They couldn't find enough people in the markets that they were trying to hire in. To do the work that they needed to get done. I think that's more and more the case. And so I don't think the case is, oh my gosh, we're going to eliminate jobs. Although I I'm, I'm honest on the show. I believe there will be job elimination as we move forward. But it's also those very difficult to fill positions are going to be things that can be automated and moved away so again i i think that's a glimpse into what an intelligent health system could look like i think it's a look Into what the promise of digitizing the health record all those all this pain that we've gone through over the last 10 to 15 years This is what it leads to a smart intelligent Health system that has unlocked their knowledge be it structured or unstructured in order to serve the community the physicians and provide better health just for everyone So i thought i'd share that with you i thought it was incredibly fascinating that's all for today don't forget to 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 to develop the next generation of health leaders Short test artist site parlay ads, certified health notable ad 📍 service now check them out at this week health.com/today. Thanks for listening That's all for now