← Becker’s Healthcare Podcast
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- [00:01]
A
Philips is a health tech leader focused on innovation that improves the health and well being of people. Our healthcare technology and informatics solutions help care teams diagnose, treat and manage more patients with greater precision, speed and confidence. Across the care journey with Philips, clinicians are empowered with streamlined insights in the moments that matter for every patient. Better care for more people Philips.
- [00:29]
B
This is Gracelyn Keller with the Becker's Healthcare Podcast and we are recording live at the 10th annual Health IT Digital Health and RCM meeting. I'm currently joined by Jason Hill who is the Innovation Officer at Ochsner. So Jason, thanks for being here. Let's have you start off by sharing a little bit more about yourself and your work in healthcare.
- [00:46]
C
Yeah, thanks so much for having me. This is really amazing and I love the opportunity to come and talk about it, AI, healthcare, all the things. So a little bit about me. So I'm a practicing physician, I'm a hospitalist, I was trained in internal medicine, pediatrics. I started doing work in clinical informatics probably a decade or so ago, doing implementations for electronic health records over a long, long time, then building a lot of things, then got into predictive modeling and traditional AI ML work probably about five or six years ago, pre pandemic. And then this little thing called generative AI sort of hit the cultural zeitgeist in a coup years ago and our leadership, in their wisdom, or lack of wisdom depending on how you look at it, decided that I would be a good person to start implementing some of the early use cases and then started thinking through how we can integrate this into healthcare as a novel technology. And so I am engineer by trade as well, so engineer before I was a doctor, so I have a lot of work and background in computer science and those kind of things.
- [01:51]
B
Well, thanks for being here and let's start our conversation talking about AI as that is the hot issue right now. Nearly half of medical practices reported using AI in some capacity in the last year and it does remain a key topic for health IT leaders. So from your perspective, what are the use cases that are making a difference right now and how are you leveraging them in your organization?
- [02:12]
C
Yeah, so I would say that this is probably going to be very similar to a lot of other people that you've talked to. I think the real Cinderella story that's come out of generative AI most recently is ambient listening. And I think that to me that represents a really foundational use case of how we can actually really train, change the platform of clinical care and use voice as not just a way to Scribe and create a note, but also to create voice as a platform. And I think that's one of the things that you'll see ambient listening morphing into Very early it was describing technology which is okay but not really transformational now that it's actually turning into lots of other things like vocal biomarkers, like the ability for us to do automated revenue cycle, all of those things. And tying those point solutions that are really AI driven technological solutions into platforms that drive business and care practices. Those are the big opportunities that exist. If you could add a couple of other things to that, I would probably say and again this is probably very similar to what other people have said. Prior authorization and revenue cycle management, highly amenable to automation. Those are really good. I would say denials also. That's something that doesn't get as much attention. But denials are actually a key part of the revenue cycle. And to be honest, they are the source of where you're going to actually find a lot of where the waste in your health system is. And so being able to look at that was something most people are looking at prior auth on the front end. I think denials on the back end are actually more important. And those are things that we're looking at right now. And then we're dipping our toe into sort of these sort of voice and agentic AI work with, you know, doing some proof of concept, working with Hippocratic, doing some other voice agent work both on the non clinical and clinical side and then lastly clinical decision support. You know, I'm sure if you got just look at the article recently where open evidence scored 100% on the U.S. medical licensing exam. That's more than I scored by a pretty long margin. But looking at that, you now have something that can provide really high level of clinical knowledge base. How do you then integrate that into the practice is going to be something that the question that I'm actively thinking of addressing right now.
- [04:40]
B
Absolutely. And going off of that. As virtual care expands from AI enabled tools and remote monitoring to broader digital health platforms, introducing new technology does bring challenges. So what advice do you have for leaders navigating everything from governance to patient engagement? And can you share an example of how your organization has balanced innovation with operational constraints?
- [05:02]
C
Oh, operational constraints in health care. Yes, that is something that is really challenging. And I think the first part of that is really a successful innovation company or innovation enterprise is an enterprise that is prepared to fail. And I think when you're running on a 1 and a half, 2 and a half percent margin as a company, your willingness to take on risk, to fail in innovative technologies is somewhat constrained. You really have to be very thoughtful about how you apply your capital and how you apply your humans and how you apply the people in that. So I think those constraints become very challenging to improve at a transformational level. You can make little incremental changes by adjusting things in the electronic health record, by doing other things like that, but really making a big leap. It's very challenging to do that in this environment, in healthcare. And if you're a healthcare system, I think the other thing that you think about is that there are certainly big ethical challenges. Right. And give a couple of examples of that. You know, behavioral health. As you read the, read the headlines recently, you know, there are a lot of people using ChatGPT as their mental health companion. It's probably not a good idea because although it does a pretty good job of making you feel good, it can easily make you delusional into delusional thinking. Right. And AI tends to feed into that because of the way that it's designed to sort of riff off of people and move down that those rabbit holes with them. There's a lot of people, and myself included a couple of years ago that thought that behavioral health is a pretty useful application for AI. I still that that's true, but there were huge ethical challenges to do that, particularly if you're a healthcare provider and your whole goal is actually bettering health to people. So you need to be very careful about that. And as you reach AI into the clinical realm, the other big thing you'll find is liability. Right. So If I have 15 agents that are working for me, talking to my patients, adjusting their medicine, doing all that stuff, that's great. That makes me 15 times more efficient as a doctor. But then what does my role become? Am I just the lightning rod for liability so that if anything goes wrong I can be sued? Right. Or am I how do I then get doctors out of that role and try to actually, but still manage to leverage the power of Magentic AI to make me more efficient than I could ever possibly be? And I think those challenges around liability, around ethics and application, and around cost constraints, to me are the primary areas where I feel like health systems are going to struggle in those barriers.
- [07:44]
B
Absolutely. Great points there. And shifting gears slightly, although you touched on it a little bit. How are you seeing recent legislation, both at the state and federal level, affect healthcare organizations and health IT specifically? And have you adjusted strategies in response?
- [08:01]
C
Yeah, it's been a very interesting world in the past year for legislation at both the state and federal level. I would say that at the federal level, we're still trying to figure out what they're going to do with AI. They seem to be really like, hip to AI and really wanting to do it. But at the same time, there's not a lot of thoughts about how to regulate the parts of AI that are not great. And so I think we're still in a bit of a holding pattern. And if you talk to people the average Joe blow about AI, there's generally sort of two prevailing opinions. One is that it's magic and it's amazing and it's the best thing ever. And the other is that it's horrible, it will cause the end of humanity as we know it, and it's going to take my job. And the truth is, obviously between those two extremes, however, the problem is that policymakers tend to think of the most extreme scenarios when they, when they devise policy. So you have to be very careful that as you're talking to policymakers, you help them understand that, that yes, AI is great, it does need to be regulated 100%. No, it's not going to take everyone's jobs. So you don't need to like prevent against that, but you do need to provide some way to help transition our society. Right. And I think of that as like how even educating our own doctors and nurses about how to use AI as part of their jobs, a lot of them are just using it organically. We really haven't provided a systemic level of organization just to our own health systems. So if you take that and then think about it with a broader lens, think of you're now trying to do that for all of America. That's really challenging. And, and I don't view the government's role as just to educate or is just to litigate and regulate. On the back end, government's role should be to provide incentives for private corporations to help to move people to a more positive future for that. And I'm hoping that that will happen. You know, I've got a lot of hope around the rural health part of the big beautiful bill that's come out. We're hoping that actually that large sum of money actually goes towards innovating rural health, because to me, that's one of the areas of our system that is most in danger of dying. And mo, in Louisiana, where I'm from, over half the counties are rural and half the population does not live in a city, which means that if we're only Looking at AI in our top academic medical centers, we're ignoring the vast majority of the people that we take care of. So to me, I feel, I'm hopeful that that money that they've been able to set aside can help support and bolster businesses that will drive better health care for patients that are disadvantaged.
- [10:51]
B
Definitely. And as we wrap up our conversation today, I'd love to know your top piece of advice for health care leaders as they prepare for further advancements in technology and rising demands for care.
- [11:03]
C
I would say listen to your front line. One of the things we learned with ambient listening is that it's very. If you take the ambient listening technology and you took that to the CFO and you said, here's this technology where you can talk to someone and it'll create a note, they would say to you, where's the value? It has no roi. If you ask any doctor that has ever used ambient listening, they will tell you that it is immeasurable, valuable. And I think that's one of the things that leaders sometimes miss you sort of in the world of, like, the spreadsheets and the boardroom, and you're sort of looking at tangible returns and those kinds of things, particularly if your finances are tight like most healthcare systems are. And they should be looking at that. Like, this is no knock on them to be looking at that, but at the same time, talking to the people who are actually delivering care in the. In the trenches, talking to your patients, talking to your. Your frontline clinicians and asking them what value is is a really, really important exercise, because you would have never been able to know how valuable ambient was. And so you actually talk to those doctors. Like, I've been implementing electronic health records for the better part of my adult life. No one has ever walked up to me and have been like, thank you so much for implementing this electronic health record. This has made my life so much better. Literally no one, in fact, quite the opposite. They're, like, all mad at me, right? But the first time I did ambient, I had doctors finding me in big meetings, like, literally coming to find me and saying, this has changed my life. Thank you. And though there's no dollar signs in that value, that is unrecog, that is like, completely recognizable value. So I would say, listen to your people. Think outside of straight dollars to value return, and think of technology not as a way to drive incremental progress, but as a way to transform the game. And you can think a lot of that work in revenue cycle if you really think of it the right way. You could either develop revenue cycle to make an individual person as a little more efficient, or you could create revenue cycle in a way to completely make the person calling the phone not have to do that and instead then spend their time doing higher value activities for your company. Like, if you think of those areas where you can reframe how those humans are doing work, that's where the value lives in a way that you can tangibly leap rather than just crawl your way towards getting a little better.
- [13:34]
B
Definitely. Well, Jason, thanks so much for joining me today on the Becker's Healthcare Podcast and sharing these thoughts and insights again. We are recording live at the 10th annual Health IT Digital Health and RCM meeting.
- [13:45]
C
All right, thanks so much.