Hello, everyone. This is Erica Spicer-Mason with Becker's Healthcare. Thank you so much for tuning into the Becker's Healthcare podcast series. Today, we're going to talk about driving economic sustainability in value-based care through aligning data, AI, and care delivery. And joining me for this conversation, we have with us Michael Mucci, the president and CEO of Arcadia, and Sanjay Dadamani, founder and CEO of GuideHealth. Sanjay, Michael, great to have you on the podcast today. Thank you so much for being here. Thanks for having us, Erica. Well, thrilled to have you both. And before we get into our conversation, I'd love if you could both share just a little bit about yourselves and your work in the healthcare space. Michael, if you wouldn't mind kicking us off, that would be great. I'm happy to. I'm Michael Mucci. Arcadia is a business that focuses on partnering with health systems, health plans, and accountable care organizations to help them transform the way they deliver care with data to activate success in new payment models and ultimately move to a care model which focuses on prevention and proactive care and focuses on success in alternative payment models. So really topical for the conversation today. Absolutely. Thank you so much, Michael. And Sanjay, I would love to learn a little bit more about you too. Yeah, it's great to be on, and especially with my good friend and colleague, Michael Mucci. We actually first met many years ago when they were implementing at UT Southwestern and Texas Health Resources over their clinically integrated network using Arcadia. So it's nice to come full circle now and have this podcast opportunity. Guide Health is the organization that I founded and am chief executive officer. We are an AI healthcare platform that uses clinical intelligence and human empathy to close gaps and make great healthcare affordable for all. We're physician-led, technology-driven and have been working with health systems, clinical networks, and now employers to scale value-based care, combining AI and human-centered care management into a model that reduces administrative burdens on physicians and allows us to lower total cost of care at a far different price point to scale value-based care. Sanjay, thank you so much. It's great to learn a little bit more about both of the perspectives that you're bringing to our conversation today. And I know we've touched on how your organizations operate in the value-based care space or just care models that incentivize preventative care. And as we're looking at those care models, gain traction in healthcare, there's a lot of conversation about alignment, whether that's aligning provider incentives and payment models. So I kind of want to start at a high level here and ask how you're both seeing payment models and technology aligning in healthcare right now. I think we're at a really interesting inflection point. I would say the pandemic rewinding five years proved at broad scale that virtual care is a viable pathway to deliver comprehensive care, not for all services, but for a lot of services. It had also checked the box and helped a number of healthcare consumers use virtual care in ways that I don't think that they had thought they would before because they had really no other option. And so it was a really defining moment for the role that technology could play in extending the care team and in many ways virtualizing the care team. What happened, though, after the pandemic was a lot of the telehealth waivers, a lot of the virtual care waivers expired, and you started to see a renormalization to the way that care had always been provided. What I'm excited about today, and Sanjay and I have spent a lot of time talking about this, is a lot of the new payment models that are coming out of CMMI, whether it's the access model or the lead model, really put a focus on delivering care through multiple modalities and welcoming digital and virtual solutions into the care team model as part of the integrated care team. And that, I think, is really necessary. You need payment models to not only promote, but permit the role that virtual and technology plays in care delivery. And I think what you're seeing more and more, especially as there are continued access challenges across the country, you're seeing more consumer demand for virtual care. You're seeing a number of health systems and health plans and, frankly, startups and large established technology companies come to the table with solutions to address those access challenges. And so you're starting to see a lot of alignment between the traditional healthcare stakeholders, the technology companies, the payers for care, saying, yes, virtual has a role. And you see consumers demanding it. So I think it's a really interesting time where the stars are aligning to really allow us to change the way that we think about how to deliver care. To add on to what Michael said, I look at this from the lens of one wearing a previous hat, having worked at the front office in CMMI on payment model reform and on payment models. And the second is from the view of our customers We just hosted co a panel of three great health system leaders at varying stages of value care maturity that was moderated by Anish Chopra who has a strong lens on the digital healthcare environment. And this was on economic sustainability. And if you look at where these payment models have finally created an opportunity, it's on where demand signal has now allowed for better infrastructure and more creativity that rewards activities. But value-based care still requires longitudinal visibility, very practical things like attribution accuracy and measurement. And so historically, there was a fundamentally data problem. Now, as we've overcome that, we need to take these insights and make it a scalable action problem. And so it's really about now taking insights into execution and integrating opportunities through digital infrastructure and AI so that human trust won't be eroded and we can have still continued progress on accountability in driving to greater value. I think what's interesting on that point, too, is we as consumers of health care, which we all are, I think are frustrated with the experience. And I don't I don't want to say that that's anyone's fault. But I was talking to Sanjay about this the other day. I had a routine, a routine health care event needed a prescription. And I wanted to be able to access my care team virtually. I just it wasn't available. It wasn't supported by my health plan. There wasn't a payment model. And so the alternative is you take two hours out of your day, you get in your car, you drive to your provider's office, you pay to park. It's really disruptive. And so you start to think about why people put off taking care of themselves. And it's because there's a really high burden to do that. Whereas in my case, if my plan supported a virtual option, if my PCP permitted that, it would have been a much easier experience. And so I think you're starting to also see consumers start to say, I want to use services like One Medical or Teladoc more frequently. And you're starting to even see pharma companies try to disintermediate that experience by going direct to consumers. So I think that there's a lot of opportunity to redesign the system around data, around agents, around AI that's supporting the care team, not replacing it. Such helpful overviews from both of you. And as we're seeing these evolutions of more digital infrastructure and virtual infrastructure for care, Michael, as you put it, you're seeing kind of, I think you said the stars are aligning in terms of the alignment between payment models and technology. So it seems like an exciting time with a lot of opportunity. But I'd also love to know how this is all creating perhaps new risk. And how does risk look different today than it did in the past? I'll take that first, just because we've been deploying AI at scale in a very big way. And so historically, risk was retrospective and mainly financial. You were putting in your investments, having care managers and others and your technology out there and measuring performance in the rear view mirror. Today, that is not the case. We can prospectively predict and use machine learning and data science models in the work we're doing together with Michael on what will happen, what are we going to do about it and right now, and then taking that not just actuarial risk, but behavioral risk, operational risk, and driving it into helping to better manage populations. Now we can take on hypertension and uncontrolled hypertension at scale, or medication non-adherence and drive to better medication adherence, or missed follow-ups and lost attribution and bring patients back into clinics or close gaps and help with better documentation. So the new frontier is really execution risk. And it's, I guess, the developing comfort around the safety models of integrating AI into real-time workflows. I agree with that. And I think, you know, since we've really been focused on how to integrate technology and AI into the care model, a lot of the work that we've been doing has been based on large language models and how good they've gotten at predicting the next token in a sequence, the next event that might happen given a pattern that we've trained these models on. We're on the cusp of seeing the next generation of models, world models that are really focused on, you train these by building an internal representation of how the world really works. if X happens instead, then Y might follow. It's about predicting the next word, the next scenario. And I think that the opportunity that sits in front of us is really thinking about how we plan for the next evolution of risk based on the path that technology is taking. What I'm really excited about is I think that with the advent of large language models and AI agents and world models we can move into a place where moving into capitation where we know patients get better care more high care incentives are aligned is very reasonable And to Sanjay point the execution risk is starting to be mitigated or minimized Yeah, I appreciate the add-on there, Michael. And while we're on the topic of AI, I'm curious, as you're seeing more models and infrastructure incorporate AI, large language models, do you see AI creating a net increase or a net decrease in healthcare costs overall? I think right now, AI is very inflationary for healthcare. We're applying a lot of AI to use cases like the revenue cycle or payment integrity. And I think that you see a lot of really mature use cases there. You've got a pretty broad deployment of AI and ambient listening and coding and documentation. But I would say on a long-term basis, it becomes very deflationary. And as we continue to deploy AI, I think you're going to start to see us chip away at the administrative burden of healthcare, the administrative cost burden of healthcare. And I also do, I'm very optimistic that it's going to allow consumers to become much better individual advocates for their own health. And we'll see a net long-term benefit of health status as individual consumers have access to smart health coaches that can help them maintain their health and overall reduce long-term costs. But I think in the near term, it becomes very inflationary. I'm curious, Sanjay, what you're saying. Well, a few months ago, earlier in this year in Fortune, there was an article by a Wharton professor who was actually entitled Wharton's Great Contrarian Says AI Isn't an Easy Excuse to Cut Jobs. and the article was about how inflationary AI was and they gave a few examples. In our own use of AI, we've realized that previously unscalable tasks, especially in the short term, are now far easier to deploy. So the example I give is at the end of the year, we had two customers, one where we needed to make all these calls to obtain blood pressures from the patient and report them. And, you know, similar such task with another customer, similar numbers, ironically, about a thousand outbound calls. It took us four hours with AI. And I think we were able to complete, you know, get to the goals of, you know, successfully documenting several hundred of these calls end to end. We actually, for the other customer, used our call center. And we paid time and a half after 5 p.m. And we got the work done by like nine o'clock at night. We pulled 22 people off the grid to do all this extra work. And you can only imagine the staggering costs. So I think there's already evidence of scalability. The question is long-term sustainability to ensure that the overall costs are managed and that it ultimately becomes deflationary. Sanjay, thank you. And what you're hitting on here, and Michael as well, is something that we hear across leadership conversations when it comes to AI. Leaders are under a lot of pressure from their boards and their peers to prove long-term value. not that a quick pilot AI project is working, but in the long term, what's the value it's generating? So that leads me to my next question. I know you've both shared a couple of examples of how AI is generating efficiency, augmenting teams, but is there a specific example that stands out to you where AI is decreasing costs right now? Yeah, I mean, I'll touch on it and then I'll turn to Michael. But we had a great example where, and Michael spoke to this, engaging consumers is a really great opportunity in value-based care. And no better example than making a series of calls to patients using AI to get them comfortable with the chronic medications that they were taking for their underlying chronic conditions and converting those to 90-day prescriptions as part of a med adherence quality exercise actually led to many more patients understanding their medications, wanting their pharmacists involved, and where their pharmacist pended those prescriptions to 90 days. We had fewer gaps in therapy, as well as some really beneficial impact on getting patients to take their medications. And so there are far greater opportunities, I think, using AI to engage patients. And patients are very ready to do that. So there's a huge growth of use cases and different applications. I just say that we're at a point now where we can translate directly into improved quality scores, millions of dollars in value-based performance, and actually better engaged patient care. I agree with that. I think one of the most exciting examples that I've seen is solving for some of the highest cost parts of healthcare When you think about where we rack up a lot of costs in healthcare it oftentimes around transitions and either entering into an inpatient facility for a procedure or an acute event or exiting from that. And we've been able to orchestrate systems where when we see those transitions happening in real time, deploying a set of agents to message and communicate with patients and members of their care team, creating visibility around the event, because oftentimes it's the visibility that's lost. To Sanjay's point, a lot of healthcare is done driving in the rearview mirror. And in addition to that, deploying an agent to engage the patient to help them understand what role they have in helping navigate the transition and offering them resources, whether they're AI-driven resources or escalated to a human, to help them navigate that transition out of a hospital into a rehab or from a rehab to home or from the hospital home with home therapy. Those programs have been both really supportive of helping patients at times where they often don't know what the next step is, and they're overwhelmed and they're transitioning, but also helping to manage next 12-month cost. And we've seen dramatic reduction of next 12-month cost for those patients who engage with those resources because they're able to be better activated in helping manage their health and their team is better activated as well. Yeah, I appreciate you both sharing those examples. So sounds like improved quality scores and the significant amount of money that can come from that, as well as supporting patients through care transitions are two great use cases that you're both seeing. And as we close our time together, I wanted to zoom back out again and just kind of reflect on the fact that healthcare is the country's largest industry and employer. And so I'd love to know from you both how you think AI will transform and really impact the financial sustainability of healthcare, especially in rural communities. Well, we've certainly come to that inflection point that Michael described. Operating margins are exceedingly low. There's healthcare inflation that has been uncontrolled. And we've risen in degrees of complexity. And so there has to be something that's powerful enough to unlock these increased operating margins, reduce the administrative burdens, and allow us to scale up. And so that's where we're betting on AI. And I think nowhere more than we know that this can support scaling for work that's vital in rural as well as underserved communities, even in our urban areas. We don't have the luxury of time or the luxury of adding more clinicians. We really have to ensure that AI becomes a force multiplier. there. That's why we've taken the approach of integrating this into our own services to help scale up our services, to have really good data, and then to be able to drive those insights into actions, to extend the capacity, maintain access, and improve access overall, and then shift the resources towards high touch, as Michael described, so that those high needs patients can really get timely resources for their management. So I'm a big fan of this move in this direction. And I think the patient populations that are most in need, those medically underserved as well as in rural areas, will be the greatest beneficiaries of our time. I think you said that exceptionally well. You know, when we think about healthcare being one of the largest employers of our economy, we do not see a reduction of demand coming for healthcare services. We also can't can't ignore the fact that today we don't have enough capacity in care teams or really even in the administrative side to meet that demand today. We have patients across the country who are waiting far too long to receive care. So as we think about AI transformation, I really think about this as a shift to the left. We're going to allow every member of the healthcare workforce to work at a higher license. People may not have the same job tomorrow that they have today, but I am almost certain that there will continue to be a large and robust and very viable healthcare workforce across the country. The worst thing for rural communities is the closure of a hospital. I think that AI and technology transformation can save a lot of these facilities, especially as you're able to participate in new payment models. And that supports the vitality of these rural communities that need to be propped up. And I think that we owe it to ourselves and to all healthcare consumers to really think about how to drive that experience and support these communities. It's an important sentiment to end our conversation on. Michael, Sanjay, I just want to thank you both again so much for your time and your insights today. A lot of great thought leadership throughout this discussion. So I just want to thank you again for making the time. Thank you. It was great talking today. And we'd also like to thank our podcast sponsors for today, Arcadia and GuideHealth. Listeners, be sure to tune into more podcasts from Beckers by visiting our podcast page at beckershospitalreview.com.
