The Pulse by Wharton Digital Health
00:16:02 · Caroline Xie, Lindy
[00:16:02] S2: Yeah. So I I think there's a a a number of different ways to approach this, and it all... It comes down to what is convenient and accessible and also of high quality. And, ultimately, when consumers desire to take more control of their health, you wanna understand... You know, you could use a number of solutions like remote patient monitoring and other wearables to continuously track how you're doing as well as ability to incorporate maybe more diagnostics from genetics, blood, other other areas to really inform how you're doing on a continuous basis. And I think the question is, how does that or does that integrate into the provider setting? Right? There's... Some of us have relationships with primary care physicians. Some of us don't. And are there more modalities of care in which there could be an easier access to somebody from a provider standpoint who is... Has a relationship and who is looking after after you. And so how do you take all of these more consumer driven data and and wellness about yourself? And does that does that need you? Or maybe it doesn't. But does that... Do you want that to be ultimately integrated into your provider primary care appointment visits or other doctor visits? [00:17:32] S1: Awesome. And the other topic that I wanted to definitely hit on was your last point around AI and how 2025 is going to be an important year. I think you hear a lot of people saying that health care is where the biggest opportunity for more transformation lies. What applications of health care do you think we'll see the biggest winners in?
00:17:54 · Caroline Xie
[00:17:54] S2: Yeah. I I think health care is one of the most obvious applications of AI and literally has the ability to change or save lives, and and there's so many exciting use cases, as you mentioned. So, you know, maybe starting with, a little bit on the life sciences side, there is increased ability to discover novel biomarkers. You can prioritize targets for research. You can really more deeply decipher the disease pathology. And there's increased ability to look at somebody's medical health history for patterns and identify even certain aberrations in the genome. And we're able to do that now in a much more specific and individualized manner than ever before. And, for example, we're also already finding that cancer is likely a much more heterogeneous disease than we thought, and you could say similarly for Alzheimer's and cardiovascular. And there's potential to have more targeted therapies for the individual patient and and have these treatments that could meaningfully improve efficacy while also limiting the side effects, which is, I think, quite exciting. And if you look at the health systems and payer side, US... In The US, we spend a trillion dollars annually on administrative processes. And payers have, you know, significant opportunities to improve patient satisfaction and experience while streamlining their workflows. Health systems, as we know, are struggling with provider burnout, increased turnover, labor shortage, health care has to fill nearly 2,000,000 jobs each year and is, I think, the only major US industrial vertical with negative productivity growth over the last, over the past generation. And so I think AI has significant potential to really augment providers and address a number of these burnout and and labor issues. And if we can imagine a better provider and patient experience from pre visit to post visit, future follow ups, that that could be really exciting. And and I I think to comment on the adoption curve and behavior change as it relates to AI in health care, you know, I think it would be very intuitive and and natural to start with the lower risk use cases on the administrative side, including call center support, for example, and, you know, perhaps moving towards more clinical clinical decision support over time.
00:20:45 · Lindy
[00:20:45] S1: Got it. That makes a lot of sense. And I think the adopt... AI adoption on an administrative side is definitely an area that a lot of start ups these days are focused on. I'm curious to hear your thoughts on how these hospital systems and providers will measure return on investment for a lot of these AI tools? Because I think you see them wanting to balance the cost of adopting these new tools versus the actual returns that they're getting.
00:21:16 · Caroline Xie, Lindy
[00:21:16] S2: Yeah. So I I think the ROI will... You know, you... If you think about ROI from a revenue driving or cost efficiency standpoint, generally. And I think the ROI will exist when you have continuous and sustained engagement and usage of of these tools, and the tools themselves have to be good enough. And I think as... If you break down on the administrative side, some of the tools are closer to revenue driving depending on what they are. If they happen to be in revenue cycle management, you can likely more clearly pinpoint where the tool is and what the tool is doing to driving revenue or or finding gaps in care and ability to bring patients back in to have the visits that that they they they would want to and have the ongoing checkups. I think there is also a number of tools that can really help from a provider burnout perspective. And it could potentially lead to more visits with patients. But I think it's important to just even address from a burnout perspective and retention. And honestly, some of these tools may become such status quo that if you are if you don't have those tools, it may actually be challenging to attract providers to to your health system. [00:22:45] S1: Got it. And I wanted to quickly touch on the piece that Iconiq published recently on its AI predictions for the future at the firm level. So one prediction is that there will be an increase in domain or industry specific models. And how do you see this translating in health care?
00:22:45 · Lindy, Caroline Xie
[00:22:45] S1: Got it. And I wanted to quickly touch on the piece that Iconiq published recently on its AI predictions for the future at the firm level. So one prediction is that there will be an increase in domain or industry specific models. And how do you see this translating in health care? [00:23:02] S2: Yeah. I think it's highly relevant, and and we recognize that domain specific models that are trained on specific data can provide more accurate and contextually relevant outputs than general purpose models and is also more cost effective. Hallucinations are not acceptable in health care, and good enough won't cut it. And the ability to conduct deep, biased scientific exploration with level of reproducibility and agency is critical. The solutions here have to really earn the trust of medical and scientific communities. If it's not good enough, they may not see the ongoing adoption, that's required. And and you don't get that many chances to earn the trust of providers and scientists. The the other thing I would mention is health care generates approximately 30% of the world's data volume, and this is growing faster than a number of other industries like financial services, manufacturing, media, and entertainment. And at the same time, health care data is significantly underutilized. I think, in fact, vast majority of this data is not used today. And so having this rich longitudinal patient data has the potential to inform ongoing treatments and and be also a lot more personalized and targeted as we we chatted about earlier. And so I I see a lot of potential here.
00:24:33 · Lindy
[00:24:33] S1: Yeah. I really love your point about how solutions really have to earn the trust of the scientific communities, which is definitely at a higher bar than across other industries. And I just wanna pivot a little bit more to a specific investment in your guys' portfolio. So Iconiq just exited QGenda last year. Congratulations. Healthcare workforce management has been a big focus area in recent years, similar to what you were talking about addressing burnout with physicians and these growing staffing shortages. So can you talk a little bit about what this journey looked like for the investment?