The Pulse by Wharton Digital Health
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?
00:25:10 · Caroline Xie
[00:25:10] S2: Yeah. Absolutely. So we were introduced by an existing investor at QGenda, and we met with the team virtually in March 2020 and moved very quickly thereafter towards a partnership. Many entrepreneurial stories begin under unlikely circumstances, but the story of QGenda really stands out to us as as quite unique. At the age of 15, Greg Benoit, the founder and CEO, developed the initial idea of a QGenda as a summer project to help a family friend and anesthesiologist, and he created an automated call schedule for physicians at his medical practice. And he received a thousand dollars for completing this project. Many of us may have forgotten the details of our summer jobs, but Greg saw an opportunity to build a business and to solve this really challenging problem of physician scheduling at scale. And so throughout high school and college, he leaned into the opportunity. He wrote the first lines of code, and he personally called on prospective customers to generate the company's initial revenue. And over two decades, Greg and the team grew QGenda into an organization with 700 employees and and really built the company to become the modern SaaS leader in health care workforce management serving 700,000 physicians. And QGenda had transformed from being the scheduling platform for hospital departments and physician groups to really the workforce system of record for entire medical organizations. When we first met the company, it was primarily focused on provider scheduling. It showed early innings of product expansion beyond scheduling, such as time and attendance, credentialing, analytics, but really the the continued success success from product expansion, product growth, as well as go to market execution has been really remarkable. And throughout our partnership, we deeply engage with the QGenda team to understand what their top priorities are. And we facilitated over 40 highly curated commercial and strategic introductions to support revenue acceleration and investment prioritization. And we also supported QGenda on multiple acquisitions to accelerate their product road map.