← Becker’s Healthcare Podcast
Loading summary
Transcript36 lines
- [00:02]
A
R1 is the leader in healthcare revenue management, helping providers achieve new levels of performance through smart orchestration. With more than 20 years of experience, R1 partners with 1,000 providers, including 95 of the top 100 US health systems and handles over 270 million payer transactions annually. If you want to learn more about how you can transform your revenue cycle operations, Visit us at www. R1RCM.
- [00:32]
B
Hello and welcome to the Becker's Healthcare Podcast. My name is Will Riley from R1. I am joined today by Dr. Jochen Reiser. Jochen is the President and CEO of UTMB. Welcome to the podcast, Jochen.
- [00:48]
C
Always a pleasure to be here. Thank you, Will.
- [00:50]
B
Thank you. So start us off. Tell us a little bit about yourself. Tell us a bit about UTMB and some of the work that you do.
- [00:58]
C
Wonderful. I grew up in rural Germany in the Black Forest. And as part of my education as an MD, PhD at the University of Heidelberg, I was granted a stipend to do research in New York and never left the U.S. but while I was doing my residency in internal medicine at Albert Einstein Montefiore in New York, I also then moved on to become a nephrologist at Harvard Medical School, Mass General Hospital and the Brigham Hospital. Went to University of Miami as a faculty member and Chief of nephrology. Learned a lot about innovation at that time and then moved to Rush University Medical center and from there, after a little bit over a decade, to University of Texas medical branch in 2023. UTMB is a legacy institution in Texas. It's the oldest healthcare institution with the oldest medical school, the oldest nursing school, and some really great missions that we cover. For example, 82% of prison healthcare in the state of Texas, 6 hospitals, 100 clinics, big pharmacy enterprise, and also quite an interesting mission around aerospace medicine and of course the National Laboratory. Only two labs in the United States are biosafety level four. We are the larger one and we are leading that as well. So very interesting place with a lot of excitement for the future.
- [02:35]
B
Fantastic. And a great place to talk about innovation and advancing the role of technology in healthcare, I think. Which is where I'd like to start.
- [02:44]
C
Yeah. In fact, well, you hit the nail on the head. I was always quite a fan of innovation. And by innovation I mean innovation around biotech, but also health tech, technology around AI, educational technologies. And we officially made innovation the fourth pillar of utmb. So when we talk about our mission today, we have education, research, clinical care delivery and innovation. Okay. Opened an innovation center in 2023 and have a few Startup companies now and have also changed how we organize and govern innovation at UTMB and also made it part of some of our curricula. So an exciting topic to talk about.
- [03:35]
B
Okay, good, excellent. Well, I'd like to dig in on a lot of that. Firstly, you can tell me if I'm wrong, but it seems to me that healthcare providers have often been very cautious when it comes to technology and innovation. But we might be seeing that change with the adoption of AI and some more modern contemporary technologies. I'm curious as to whether you see that happening, whether you see this shift in approach to innovation because of AI.
- [04:08]
C
I think you formulate that very well. Typically innovation around biotech is a long term commitment. You need to have a lot of misses to have one or two hits. And that was seemingly not the right place for universities and health systems because we had to struggle with finances anyway to begin with, with small margins on your largest income stream, which is healthcare delivery. But with AI and health tech, this seems to be not quite the case because the needs are so vast that it's almost clear that technologies that are good and rooted in sound science will ultimately and probably a lot faster, make them to the market. You're also avoiding potentially long clinical trials that you do have in biotech. It's much more actionable and also you can utilize those tools in pilot programs already at the inception and creation phase. So it's a much better suited innovation tool at the university. So we see a lot more of it and not just at utmb, but also at other places. But we are proud to say that at UTMB we are very technologically advanced around AI and have probably more programs running today than most other places will have.
- [05:39]
B
And you talked about there about research, but it seems that, that this benefit of AI flows down the food chain too when it comes to the provision of clinical care, a healthcare setting, when it comes to the administration of healthcare as well.
- [05:55]
C
Yes. And so we have ambient listening, like many others have talked about here at the meeting. And some have it in small pilot components of the health system. We already have it rolled out for our entire faculty. So it's a routine component of care at UTMB which as you know, focuses the interaction back to the doctor and the patient, which we want to have takes that pressure off of documentation. It's been really a big differentiator in terms of happiness for the patient, but also for the provider. But you're right, when you look at revenue cycle, when you look at billing, when you look at compliance, those are all areas contracting where you can utilize AI. And in our innovation domain, we're working together with many partners to develop customized programs for health systems that we then pilot at utmb, but ultimately also can spin out or be part of a larger company effort and hopefully get an alternative revenue stream with that as those technologies become mature.
- [07:09]
B
So let's talk about that. You've got mentioned partners and innovation and new revenue streams. So in healthcare innovation, I think you hear about two archetypes.
- [07:21]
C
Right.
- [07:22]
B
Incumbents, large.
- [07:24]
C
Right.
- [07:24]
B
Established health systems, established payers, established technology platforms. Right. They have the data, the infrastructure, control the system and insurgents, let's say. Right. Newer entrants, people who are coming in with more of a disruptive agenda.
- [07:40]
C
Yes.
- [07:40]
B
Trying to change things. How does innovation play out around AI with those two participants in mind? And where do you sit in in that as utmb?
- [07:52]
C
Well, I would say what has not changed is that the data that health systems have is the main capital for the innovation. So whether the data is absolutely vast or whether it's meaningful in terms of regional or diversity of data, that doesn't matter that much. But it's making that data available safely and to really work with it, what's making the difference? And I would say that Texas is generally seen as a place where innovations are done typically well. So we've had no issues with attracting partners. But what has changed at UTMB is how we work with our own data sets with partners and making that data available for others to use safely and obviously with a lot of oversight rules, but in a way that we can create these novel tools in our markets and then begin to employ them and see how they work as we are refining them.
- [08:57]
B
So data is a massive advantage for an incumbent who can then facilitate that innovation, network and process. Essentially, yes.
- [09:06]
C
And so our access center, for example, is now really a combination of actual intelligence and artificial intelligence.
- [09:14]
B
Yeah.
- [09:15]
C
So AI Square, I sometimes call it, where we have AI supported components in there, but then we have escalated nursing branches and real life operators that come to augment the AI component and vice versa. And that is very relevant to our own markets because we want to, we want to expand access to our health system. But it's also a great real life working space for a technology company. And there are many who are looking for that type of partnership. So I think that AI is providing finally the type of partnership between industry and academia that we always wanted but never could do because prior innovation like biotech was so much driven by the pharmaceutical industry, by the investors that are giving the money and rather little on the university side in AI, it's different. We are equal partner, if not leading partner in this newly established friendship as we're innovating.
- [10:25]
B
So new friendships and new collaborations maybe require new governance models and governance approaches. I'm curious about that too. You've talked about having this data advantage and putting data to work, but obviously doing it in a way that is compliant and safe and so on. Can you talk about how you've built a governance framework to support the innovation?
- [10:49]
C
Governance framework for AI is complex. It needs to have obviously a lot of technical oversight, it needs to have security oversight, it needs to have legal oversight, ethical oversight, and also compliance. So all the components need to be there. So we have an AI council that is typically the governing board of everything around AI. We do, and that is very helpful to me because I know at any given time what are all the projects that we're working on, because I can go to that council. We also have a newly established center for Artificial Intelligence where we obviously have the work groups that work together with our industry partners on the projects, but are also giving me an inventory and giving me a state of the art of our current affairs. I would say that it's unrealistic to believe that one can establish such an oversight even in a month or so. It's an evolving organ that gets better and better over time, and we have to be careful that we don't take on too many projects before the governing body is actually mature enough to handle it all. And I think at UTMB we are confident we have that maturing governing body ready to go for quite a while now. And that's why we are seeing a lot of projects coming through, being evaluated, and some, not all of them are moving forward to a real creation or implementation stage.
- [12:16]
B
Can you tell us a bit more about a couple of ones that are exciting to you or maybe some of the startups that you're spawning?
- [12:23]
C
So. Well, great. I keep it a little general, but one is around education. For example, an AI tool that screens a large language model that goes through all of our patient inventory and picks out cases that are of particular educational value. So there might be a patient, I'm just making this up, that comes in for shoulder repair, but has a rare genetic underpinning issue, but that's not active right now. So that case will get lost under normal circumstances for the students to learn about, but the large language model will flag those cases and say, hey, bring the students to this case and make them talk with that patient or with the work team about this particular case. So we're using that that goes into customized education. So we are learning how students prefer they get their content managed. And some of our students might do better with virtual content, the other ones more with hands on. Some might have some issues with three dimensional thinking. And so our AI tools will help those students to get their preferential mode of learning delivered to them so they can best fulfill the task of getting to the next level. That's very exciting. I think this is like redefining how we are teaching as a university. Not just content delivery, the way of how we deliver content. And I think that's something I am particularly excited about because our universities, and that's not just utmb, need to be really rethinking relevance for university. Only if we stay relevant and people want to come to university to get their content, we are going to survive as a university. Otherwise we just have everybody learning online from some platform and then go and do a test somewhere. That's not where we want our universities to ultimately go. We want to continue to create the best, most advanced learning environment. And that's why I'm very excited for.
- [14:30]
B
The use of AI as consumers. And patients get better equipped with data about themselves and access to tools that are very powerful but very cheap and easy to use. Do you see that risk as well from the delivery of healthcare? You're talking about it from an academic perspective, but actually patients really empowered to know what to do. From a healthcare perspective, I think it.
- [14:57]
C
Would be not truthful to say that every patient is more educated today literally than perhaps 10, 15, 20 years ago. But there's also a lot of education that's not quite correct. And so we will always need the expert. Right. But the good news are that in today's needs in terms of health care delivery, we have too few doctors, too few nurses, too few health care workers. So by adding on artificial capacity and artificial intelligence to help us manage that, we're not competing with each other. If anything, we need more on both sides and together it will be better health care delivery. I would never want anybody to just rely on a computer telling him or her what it is they are dealing with and what it is that they should or should not be doing. I want them in conjunction with the human brain, artificial intelligence and actual intelligence in perfect harmony together. I think that's the future.
- [15:57]
B
Yeah. Okay. Okay, fantastic. Thank you very much. Is there anything else that you want to add that we haven't, that we haven't covered? It's been really interesting.
- [16:05]
C
Well, Will, you know I was here last year and within one year, so much has happened. Yeah.
- [16:09]
B
Yeah.
- [16:10]
C
I'm just so excited to be.
- [16:12]
B
Feels different.
- [16:12]
C
It feels different. It feels like we are not just talking about AI, we're doing it. We actually have some experience reports and we're starting to really gel with that new topic of that cognitive revolution we are living through right now. So let's see what we are talking about next year. Thank you very much.
- [16:31]
B
Thank you so much. Thank you.