← Becker Private Equity & Business Podcast
Becker Private Equity & Business Podcast
How AI Is Reshaping Healthcare’s Next Decade with Manav Sevak, Venkat Mocherla, & Malinka Walaliyadde 6-29-26
00:43:49
Loading summary
Transcript48 lines
- [00:02]
A
Ahora mascolos Hill va cada projecto al sigente niver ahora quince porciento nuna selection de puertas personalizadas.
- [00:20]
B
Escrita.
- [00:25]
C
Sigues.
- [00:30]
B
This is Scott Becker with the special episode of the Becker's Healthcare and Becker Business and Private Equity podcast. We're joined today by three remarkable healthcare technology founders, all at the forefront of sort of technology, artificial intelligence and healthcare. We've got the founder of acasa, the founder of Midstream Health, the founder of Memora Health, all with us today. And I'll ask each to take a moment and introduce themselves. I'll start with Malinka from acasa. Take a second on sort of introducing yourself, the organization and I'll ask each of you this question about the problem in healthcare that you've been most passionate about solving or where you're most focused today. Let's do that as part of your introduction. Then I'll ask Manav and Venkat to do the same thing and then we'll pivot to different questions for each of you. Malinka, can you take a moment and introduce yourself?
- [01:29]
C
Of course. Delighted to be here, especially with other folks that I've known for a long time and respect greatly. So I'm co founder CEO at acasa and what we do at ACASA is we develop AI for the healthcare revenue cycle with a particular focus on the mid cycle where the most complex interactions between clinical and financial data occur. And the way we think about this is American medicine is the best in the world, but the American health care system is not. And the gap that exists there comes from how complicated we have made paying for health care in the U.S. that's what we call revenue cycle, right? And, and if you think about it, revenue cycle at its core it is how do you capture and communicate the full patient story to a pair if you do that correctly, claims don't get delayed, things don't get denied. Health systems get full credit for the care they've delivered. But that's been really, really hard to do in the past because of the complexity of understanding the clinical record. So what I am passionate about is solving that problem, closing that gap and having, you know, the way we deliver care be as world leading as the quality of care itself.
- [02:37]
B
And Malinka, let me ask you a question there. Currently estimates are all over the place, but the US spends about 5 trillion out of 26, $27 trillion a year of its GDP on healthcare. So about 5 trillion spent a year in healthcare revenue cycle I would assume is a good 10% of that? Somehow or another, yes. That means we're sending 500 trillion on revenue cycle. Is that inevitable with the amount of different payers and payment systems that we have going, is that sort of going to be the case going forward? If we've got 50 different Medicaid systems, the Medicare system, the VA, dozens of different payers, including the big four, plus several others that really focus on Medicaid managed care, can that number be improved or is that destined to be that we're going to spend 10% of 500 trillion or 5 trillion 5 trillion, not 505 trillion on revenue cycle?
- [03:32]
C
It can and should be lower than that, is the quick answer. Right. It does not make sense that we spend so much on the back end to still deliver not a great experience. Right. So that the we're spending a lot and the ROI we're getting is not great. And the incredible thing is with the, with these new generative AI capabilities through large language models that we're getting today, you can do things that used to be impossible because of the complexity. You can do things much faster, much more efficiently, much more accurately. And we anticipate and are working towards reducing that cost to collect across the board.
- [04:06]
B
Thank you. We'll come back to you in a moment. Manav, you're a serial entrepreneur in healthcare, started a company when you were in college, exited at some point that company starting something else. Talk a little bit about yourself, introduce yourself and talk about what you're most passionate about in healthcare today.
- [04:27]
A
Yeah, thanks so much for having me Scott and great group of folks. So really quick introduction on myself. My name is Manav Sevak, I'm a scientist by training and former founder and CEO of a company called Memora Health. And big picture what we do is we build workflow automation software for health systems to specifically help automate a lot of different follow up tasks and making sure that care teams are able to manage their patients after they've left the walls of the hospital. Scaled the company for over nine years after starting it in college and it was acquired in 2024 by another business in our space called Commier. And now spending some of my time investing and sort of working with early stage founders and spending some of my time starting something new as well in healthcare.
- [05:13]
B
And
- [05:15]
A
I would say distillation of all the work with my prior company and this next one is specifically focused around solving navigation. I'll build a little bit on what Malinka said that you know, the US healthcare system is unbelievable in the sense that for the highest acuity cases, it is able to perform extraordinarily well. And the breadth of therapies that we're able to give to patients is extremely, extremely high. But there's a ton of last mile issues that exist in how people are able to actually access those. And it's very, you know, there's quite a bit of cognitive dissonance between on one end of the spectrum, people coming from all over the world to the US to access very, very rare treatments and therapies. And on the other end of the spectrum, the average American, when they're not feeling well, not being able to get basic information on finding the right provider and being able to just understand what's wrong with them. So spending a lot of time thinking about how not just AI, but generally advances in the world are, are changing that dynamic with the nation.
- [06:24]
B
I'll ask you a question similar to what I asked Malinka. Is Madiff, with a country of 350 million people, again third largest in the world, are we inevitably going to see a regression to the norm in terms of healthcare quality compared to other large nations, or is there something the US can do to continue to or to move in the right direction? It seems like currently cost is going in the wrong direction. Access is really challenging. Quality is good in certain spots, but certainly challenging in other spots, just in part due to access problems. But can we bend that curve, not even talking about the cost curve right now, but can we do better in getting better healthcare to more people, or will we regress to the norm and have more challenges?
- [07:18]
A
Yeah, great question. The short answer is I don't think that we're on the path of regressing to the norm, but I don't think that that has to be the case. I think one of the biggest levers, not just in healthcare, but in every industry, from all the new sort of advancements in artificial intelligence, is this concept of abundance. Not only are there exceptional therapies, but we now have a lot of the tooling that allows us to scale really excellent clinical resources, access to those therapies. And it's really a question of whether or not people in the industry choose to use those tools the right way. So I don't think it's inevitable by any means. I do think that there's a lot of perspectives that healthcare over time should become deflationary as the percent of GDP shrinks. And I am not entirely sure that that's also what will happen. I think we'll probably get closer to healthcare growth being in line with inflation, but on the quality side, I think it's more around using the tools to make sure that we stay ahead of the curve and sort of course correct on the direction that we're heading in.
- [08:23]
B
Thank you Venkat. You've had this fascinating career both as an investor where you originally, I think, got to know Malinka at a 16Z and now as a founder and entrepreneur. Talk a bit about yourself and the biggest problem in healthcare that you're passionate about trying to solve.
- [08:43]
D
Yeah, yeah, absolutely. This is a fun reunion. And healthcare is so small and the world of healthcare entrepreneurs is even smaller. So it's a joy to be on this podcast with these guys. Yeah. Just to introduce myself, as Scott mentioned, I'm the co founder of a company called Midstream Health, focused entirely on how do you think about this AI native platform for some of the largest health systems in the US all around automating financial actions and in this current margin environment where every dollar has always mattered and there's a tremendous amount of excitement and opportunity. As Manav and Malinka have mentioned using AI, I think my own career, one way of looking at is maybe I've not stuck to one job. I've had the privilege of working at starting new companies inside a Fortune 500 company called DaVita. We started new companies in primary care. I've worked in 13 countries globally for what's now part of Optum. And then I got into AI and ML about 12 years ago on the founding team of a company called qventus. And so it's been a pretty amazing thing. I'll just sort of say, just to reiterate, I think there's no short of optimism between the three of us. If people are looking for pessimistic views on the future of technology is probably this is not the right podcast to dial into. Madha mentioned abundance. I just think that whether it's Midstream Micasa or Common Spirit or Houston Methodist or New York Presbyterian or any of these health systems, I think the excitement now is that these organizations and sectors like healthcare, where productivity has always been a challenge, finally have the framework around a 10x teammate with a 10x stack. And I think that that's the excitement around all these new tools and superpowers we're seeing and creating that sort of 100x advantage for a sector that's been plagued with productivity challenges for a long time, with high cost issues, low quality, all the things we know about. So I think, Scott, this is probably the most, you know, I got into the AI space around 12 years ago it's probably the most excited and optimistic I've been. If you ask Malinkin's amount of. I'm sure this except. And obviously they've been. But, but yeah, that's sort of a little bit of me in a nutshell and my viewpoint.
- [10:58]
B
No, thank you very much. And I love that optimism and I see reason and room for optimism in healthcare more so today than I've seen in the last few years for a bunch of different reasons. Venkat, let me start this turn with you. Healthcare organizations continue to face tremendous staffing and financial pressures. I know you work with some of the largest systems in the country. Where do you see the biggest opportunities today for AI and automation to create value in healthcare? Where do you see the biggest opportunities for artificial intelligence?
- [11:30]
D
Yeah, I mean I think for me, I know it sounds very generic but to me American healthcare is so interesting in that most of the health system in the U.S. including our customers and our partners like Common Spirit and Mount Sinai and Houston Methodist others are all not for profit. And it's an interesting dynamic where they all negotiate with for profits. So it's payers, pharma, med device are all Fortune 550 or 5 depending on which company, which bucket you choose. And so I think that the excitement for me is sort of twofold. First is internally some of these large healthcare systems, they've just not been able to solve around some of the core optimization issues around how do we negotiate against these payers the right way, how do we sort of purchase things in a more optimized way, how do we make sure our costs are in line with our revenue, especially because private equity is taking over all the interesting high profit areas. And so for us, I think the excitement that we see in this era for health systems is not just thinking about areas like the revenue cycle which are incredibly important. And there's incredible companies like Akasa right on it. But what about everything else? And it turns out these health systems that are not for profits, which have an amazing mission, are making something like a single digit margin and every day make thousands of financial decisions. And so how do you for the first time have a consistent way of helping them make decisions? I think the way of sort of the operating jump I see is not too long ago I can still see in the Mazda MPV my parents arguing about which exit to take because they had MapQuest printed. And my dad was Dan damn sure about his way of taking directions even though he probably is dead wrong. And then somewhere along the way we have Google Maps and Waze and not too long ago my wife and I went to a remote town in Japan and I, who don't have a great sense of direction, made it there on time. So, you know, I just think that jump in an operating environment with a single digit margin environment, that to me is the most exciting thing. It's not any one thing, but it's how we make every decision that I think will be 10x better, whether it's clinical, operational or financial.
- [13:38]
B
Thank you very much. And I love those examples that we see in daily life and trying to see how they ultimately translate into healthcare. I love that. Manav when you think about healthcare leaders and what they're getting wrong about AI adoption and what separates organizations that seem to be getting this right and really making progress and succeeding from those that might be struggling in trying to adopt and work with AI, particularly at an enterprise level versus a one off individual level.
- [14:09]
A
So I think, you know, I think one big trend that I hear from health systems and health plan leaders is essentially how a lot of people have chosen to use AI tools to essentially just become more adversarial. So the number of health systems that are using tools to, you know, essentially better adjudicate against plans and the number of plans that use tools to find ways to deny more claims is sort of created this. There's always been somewhat of an arms race, but it's sort of enhanced this arms race to some degree. And I think that it's generally the wrong, the wrong framework, especially when they're thinking about implementing tools like that reactively. I think that the best applications of AI that I've seen and the way that people have thought about financial pressure and staffing pressure is from my perspective on right sizing utilization and giving people a lot of confidence in managing their own health. So when AI tooling becomes more and more prominent for consumers and it's easier for people to access more healthcare information, naturally they have a lot more choice in how they think about navigating the healthcare system as a result of that choice for health systems to stay competitive and also manage staffing shortages that they have and utilize their resources as efficiently as possible. It's important for them to be able to not only treat people in cases where they actually need to be treated, but also make sure that they can maintain a relationship with them after they leave. So ensuring that we're sort of filling the gaps between visits and managing people longitudinally I think is really important. And scaling the capacity of providers I think is incredibly important just because ultimately that's probably One of the biggest cost levers for making sure that people are using the health system the right way to. And you're using these best in class clinical resources in cases where people really need them and do it in a way that actually is manageable from a cost perspective.
- [16:05]
B
Thank you. Melinka, let me ask you this question. We're at a casa and you've been at this for how long have you guys been at this now? You were at the forefront of artificial intelligence and revenue cycle before most of us knew what artificial intelligence was. Maybe not Venkat, Manav and you, but the rest of us. When you look at that today, what are you most focused on right now at acasa. And where do you see the greatest opportunities for growth and impact over the next 12 to 24 months in what you're doing with health systems?
- [16:36]
C
Yeah, so yes, we started the company pre large language model. That's sort of the dividing line for us as a company. And I say that because that is what has made so much possible, I mean for I think everyone on this call and around the world. Because in 2023 we have the advent of large language models. People call it generative AI. It's just such a substantially more powerful form of AI that could do way more complex things and it just made it things that were impossible before are now actually possible and sometimes even easy. And to go from a world where understanding something like a clinical record was impossible for software to do, now software can understand a clinical record better than a human, right? And the opportunities that unlocks is like massive. Right? And so that's what we've taken into now. There's many other unlocks from LLMs and I think, you know, folks here are doing a lot many other things. For us though in our world, the unlock that comes from plugging into the clinical record at scale is the biggest unlock, right? And everything downstream of that dramatically changes all the problems downstream of that in, you know, in, in, in the revenue cycle and documentation and coding and all of these things that were impossible to do with software and AI before now you can do and you can actually do the most complex bits, right? And so that's what I'm most excited for in this upcoming. That's what we've been working on, that's what we've deployed at scale, at our, at our customers. And, and, and just to go back to one of the questions you were asking earlier, right, like what are maybe people getting wrong about AI adoption? Sometimes it is, it is human nature to apply these things to the Simplest tasks, right, because that's, you sort of think, I'll do the simple things. No, just do that. You can actually do the very complex things now. Right. And it's actually useful to start there because that's, that's often where you see the biggest roi, right? And so you can just directly go and address some of the most complex challenges in the domain. And so for us, right, we focus on, for example, inpatient facility in the, in the revenue cycle. That is the most complex stuff. And we do it at some of the most complex health systems in the world. Some of our customers are folks like Cleveland Clinic and Mayo Clinic. And we have seen remarkable, remarkable outcomes there because what you can do with LLMs now is so much greater. And so we're just going to keep doubling down and investing in particular doing the most complex stuff that was historically unattainable because that is where often you see the most value creation, because it is the most complex.
- [19:04]
D
I actually just to double down on that, I think it's so interesting that so much of the way we were making decisions pre LLMs is with Pareto efficiency, like 8020 and everything is like an 80 20. And I think that it's interesting, I think that if your margin is in the single digits, you should work on the 20. You should work on the really hard, really complicated things that are actually applicable for compute and not humans. And I think of, you know, Malinka, what you said, which is like taking it on the most hardest decision is so spot on because when I think about how hospitals on the cost side have more SKUs than Walmart and Target combined. And yet, you know, if you think about functions like supply chain, some of the other legacy functions, they've just been not as native in the past and now they are. And so when you think about that jump, I think you're absolutely right. The focus in the rpa, you know, phase was let's do the simple things because they tend to break down. But this is the actual inverse and I think it's a spot on, you know, observation.
- [20:06]
B
I want to ask you about that because so many of us are prone to take on easy tasks, first get wins. And what I see in sort of the AI world for so many of us is, you know, we use ChatGPT every day for little things. And you've made a different argument that really working towards enterprise and big solutions is the real payoff. How do you move teams and minds towards that? Because that's more complexity that most of us want to deal with. Malika, how do you think about that? Because I think you're right. But I think so many of us as humans are engineered or built to think in terms of simple victories versus big enterprise challenges. And how do you manage it? And I'll talk to Monav invented about this exact same question because both of them are doing this at scale as well and working with huge systems and enterprise efforts. How do you move people towards that thinking? Because that seems so hard for the average person, including myself.
- [21:07]
C
Yeah, it is a mindset shift. But again, the capability sets that we've gotten over the last couple of years are so massive. So. So I mean, what I would say to you is like, do you know, not. It's easy things first get wins, I think made sense in the prelim world. Now it's like, do the hard thing first get way bigger wins, right? And just to give you context, with some of our customers, we're literally delivering nine figures of value a year, right? Like that is the scale of impact that's now possible when you actually take on the very complex things. And the nice thing is when you just start there, it becomes so much more straightforward to increase awareness adoption, right? Like, I heard this, I heard this line somewhere where someone was saying, you know, when you do the simpler things, you see AI everywhere except in the bottom line, right? It was like, I don't know, some pithy thing I heard somewhere and I was like, that's only if you do it. If you try to scale the old way of doing it, if you actually lean into the new way of doing it and actually take these incredible capabilities and actually take time, tackle really complex things head on, you can deliver so much ROI that you will never hear an argument like that from the enterprise. And when you can show wins like that quickly it becomes, it creates this incredible flywheel where people just want more. And so if you. That's part of what is driving. I mean, you've seen the growth of AI be very rapid, right? And it's because people that didn't believe when you tack on a complex challenge, deliver incredible value, and then you start seeing more and more people lean in without really needing to convince them because they're convinced already.
- [22:45]
B
Let me ask this question. Mahnav, let me spend you on this question. And then if anybody wants to comment on that last question about going through this at an enterprise level versus a one off level, we'd love to have your thoughts. But mad, we talk so much about AI and administrative work in revenue cycle, in logistics, operating management, predictive analytics, which is where I think quite frankly, Venkat started his career. What are some of your thoughts on where we're going to see the most improvement in clinical outcomes or even in clinical navigation? Where do you see this in 3 to 5 years and how do you think this will fundamentally change experience clinically and maybe in navigation too?
- [23:33]
A
Yeah, great question. One thing I'd maybe add to what Malinka was mentioning just before I get to your question is I do think there's sort of this interesting question, especially for health systems and health plans of AI native versus AI enabled. Just because the delta between thinking of building a system entirely from scratch versus augmenting an existing system is actually so large, uniquely in those types of businesses that, that there's a lot of cases where I think it's actually easier to go through design exercises with leaders at those places and say, look, if you were to redesign an entire prior authorization or utilization management function from scratch, or if you were to redesign a care management function from scratch, you'd probably just make very different decisions on what you did and did not support inside of that. So I think that, that also I think has somewhat changed this concept of what's possible for folks as far as on the clinical side, where I think there's, there's the most leverage. I think there's two things that are going to change dramatically. The first is this, this kind of future where people have access to some form of an AI doctor is certainly going to come one way or another. What that means is something different for everyone. In some cases that, you know, people think of that very intensely as actually diagnosing and prescribing autonomously. And in other cases it's essentially as lightweight as high quality triage that's making sure that you're being navigated to the right type of care. And I think that that is going to dramatically change how people understand their health care and it's going to change a lot of the decision making. I do think that the number of healthcare decisions today that are actually well informed is still a fraction of what everyone in the industry would want it to be. So that's one area as people themselves will just become better stewards of their own career as a result of having more access to information. The second is obviously it's going to give clinicians a lot of superpowers. You're already seeing it somewhat in primary care, where the breadth of what a primary care doctor is able to do is starting to seep more and more into secondary and tertiary care to some degree, just sort of on the fringes. And it's because they have access to much better clinical decision support tools. It's because they have access to so much more information in a very, very short period of time that is very easy to query that clinicians are just going to be able to do a lot more in terms of breadth of diagnosis as well as the total number of people that they're actually able to manage. So those would be kind of like the two broad strokes changes, at least on the clinical side, that I think we'll start to see.
- [26:16]
B
It seems as though we're starting to see almost a who moved our cheese type of moment where so much of, for example, the GLP1 delivery, diagnosis, prescribing is done outside of the traditional health system, outside of a traditional doctor's office, where just a couple years ago it was being done. And it seems like 20, 30 million people are now getting their diagnosis, getting their drugs that way. The price has gone down. There might be a human in the loop there someplace, but God knows how you find that person who's actually doing the final prescribing. But. But it does seem like so much this has moved towards almost being automated with a little bit of a person in the loop. Is that where a lot of primary care will end up going over the next couple decades or decade to more and more AI self serve with some help with humans in the loop?
- [27:08]
D
When I think, I almost think that and I'm curious amount of like, I also think it's important, by the way, not to just think about like on this topic, not just from a US centric view, but actually an international view because I think, you know, we've had these conversations, but it might even go faster in some of these other markets outside the U.S. but Utah allowed for, I think Doctronic to have, you know, prescriptions with an AI service. So maybe the genie's out of the bottle on that one. I also think that while, you know, it's funny, enterprise talks about like AI adoption, but meanwhile all your physicians are using app and evidence, all your patients are leveraging, you know, all the latest models to figure out if they can actually understand what's happening to them. So in some ways, like I would argue you're, you're very right, monav, that organizations that are native versus new to it is a delta. But I think everyone is learning how to be AI native just because of the frustration of being a patient in the American health care economy. I think every board deck and sales deck starts with the same thing. You know, America costs more than the German GDP and you know, outcomes suck. But the bottom line is we fail the patient every single day. And I think that that frustration has led to a, an incredible resurgence on these sort of consumer apps, whether it's, you know, for physicians or for patients. And so I would argue like that that argument's kind of done. Like it's, it's. But it's.
- [28:30]
B
But isn't that true, isn't that true that so much care is being done more and more directly because patients are frustrated? It's why people go back to the er. The hospital administrators will say you shouldn't go there, but people go there because it's the lowest common denominator. And same thing with getting electronic prescribing and dealing with things in this way. Venkat, any comment there?
- [28:51]
D
Yeah, I mean, it's funny, I'm looking at Malinka when he was an investor at a 16Z. I feel like the trope back in the day was that, and this is in venture, not talking about any firm, but the worst way to make, you know, monetize startups in health care was dtc. You never, you know, it's almost like the death kiss, right? But then now if you look at the investments that have gone to direct to consumer, it's like 180. And so I think if you follow the money and capital and how some of these businesses are being funded at the scale they're funded. I, like you could almost comment about the past, but it feels like we've
- [29:26]
C
taken a turn on that just to respond to that. Yes, I, I totally remember that. I think again, what's changed is the products work now. Like they work so much better now. Right. I mean if you look at transcription, right, you've seen like a massive uptick in obviously the, all the transcription ambient companies. Many of those companies were, you know, were around Pre LLM and like the, and they were an okay product. And I think this is like, this goes to why, you know, people in the past thought, you know, healthcare adoption was low and that that was like kind of a thing. But actually turns out the products were just not good enough yet for the complexity of healthcare, which is just higher than most industries. And now the AI is good enough to tackle that complexity. And when you have products that are good and deliver great value, people do adopt those at a rate that is comparable to other industries because they finally deliver meaningfully. But just to go to the DTC point, yes. Like you, you actually we need the technology to catch up to deliver this type of Experience that patients want to use it and feels, you know, safe using it. But it has to happen, right? Like you know, the, the, the, the demand for health care is so much higher, so much higher than the supply. My dad lives in Sri Lanka, right? My dad lives in Sri Lanka and he spends, he's like when I talk to a doctor and she'll actually sell care is not great there. He's like when I talk to a doctor they spend like three minutes, five, three to five minutes with me, right? That's it, that's all the time they get. And he's like but chat GPT will like it's infinite, right? And so it's just going to happen and I think it'll happen first outside the US and then we'll quickly. It's happening here already. Just because the demand is so, so much higher than the supply. It has to happen.
- [31:05]
B
But, but your point is so well taken because people can access this in certain ways that don't require them to go through the entire system. And like you talk about the three to five minute visit now. I could spend as much time as I want diving into symptoms and ideas with ChatGPT or whatever. My, my choice is you could do that and it becomes very easy and you're getting the responsiveness in real time that you couldn't get before without making an appointment, without getting in, without getting out, without getting test everything else. It's fascinating to see that evolution. I'm going to ask each of you the following question and I'll tell you what's fascinating to me and Venkate and Monica. You could, you could, you could come into this if you want. The audience is listening on audio, not video. But it is amazing how much more polished Malinka looks today than eight to 10 years ago when I first met him. And how professional mature he looks.
- [31:58]
D
I mean there's, there's pre LLM Malinka and post LLM Malinka.
- [32:03]
C
So yes and right.
- [32:04]
B
And now he looks so polished and professional. He's built this huge company that delivers nine figure results for clients. It's really remarkable to watch the growth and evolution of both LLMs. AI and the three of you. The question I'll ask and I'll start with you Malinka. Then I'll go to Venkat and Manev. If you were advising a health system CEO today and each of you spend a lot of time in this, we've gotten some hints of your thoughts on this. You'd go big versus small problems. Malinka, what is the one AI initiative You would prioritize first and why, and then Manav and Venkit will ask you the same question for our final round of questions.
- [32:42]
C
So as a C event of a rev cycle AI company, I'm sure everybody here will be shocked to hear me say that they should, they should look at the mid, the, the revenue cycle as one of the corporates. But more seriously, I say that because when you're trying to get people excited about something new and potentially scary like AI, it is helpful to lead with outcomes that are so bulletproof. Right. And then people, you can just build a coalition of support around doing everything else that you want to do. And there are definitely a lot of areas where maybe it feels a little, you know, squishy like the roi and you have to kind of make an argument. But in the revenue cycle in particular, and in particular in the mid cycle, there is such a clean and obvious roi, especially in the very complex stuff that you can start with that no one should ever be saying something like we, we don't see, we see AI everywhere except the bottom line. That, that shouldn't, that should never be on people's minds. And you get people excited to do not just, you know, more in revenue cycle where obviously there's major pain points that need solving, but more everywhere. Right. So lead with complex high ROI areas and it will get the organization so much more excited to do so much more over time. Yeah.
- [33:56]
B
Can I get you to weigh in or venk it first? Whichever one is ready to tee up. And what's the initiative that you'd be advising health system CEOs on first that they should attack first?
- [34:08]
D
I mean, I think, genuinely, I think just taking Malinka's point, I think that every Persona inside the C suite views successive AI differently, in my opinion. But ultimately, I think in a, in the current and margin environment we're in, I think the ROI piece just to double down on that matters. And in fact, I would argue that any AI solution or initiative that is not outcomes oriented in whatever way, shape or form is not going to succeed because ultimately you need a clear scorecard of is this thing going to work or not. I think if you think about, there's n number of areas where you can start, but there's two points I'll just make real quick, which is if Malinka is going to go on the revenue side, I'll go on the cost side. I'll take the other side of the bet, which is I think that we put a lot of lip service to the cost of American healthcare. But if you look at the functions that underline the healthcare costs in the health system, these functions have not had the same level of investment that the revenue functions have had. I think the danger that I see inside of boardrooms and set of health systems is that there's a lot of investment going. And appropriately so, by the way, in thinking about how do you get paid for appropriately for what you do. But I think that making sure that AI, you know, native decisions are made on the cost side, I think will also be incredibly important for the future. The other thing I'll sort of say is that the hardest thing inside a large enterprise and health system is a classic case for this because in most parts of America the largest employer is actually the health system. Forget the left hand doesn't know what the right hand sometimes is doing. The left finger, you know, right next to that finger is isolated, siloed. And so I think for me the opportunity to build this second brain using AI, where everybody has shared context, like I think a lot about this, where everyone thinks a lot of time about the org chart, the human chart, but there's going to be an agent chart and agents talk to agents 24 7. And so how do you seamlessly orchestrate the level of intelligence that a Fortune 500 company has when they're negotiating against you? That same level of transparency, enterprise transparency, not price transparency, is going to be critical in thinking about how do you build this organization in the future. And so for me that sort of more esoteric philosophical thing, but building a second brain. But I think it's going to be really critical in how you culturally change these organizations to make, to make the most optimized decisions.
- [36:39]
A
Yeah, those are. Both of you had great points. I think the only thing that I would add is the way that people, similar to what I mentioned earlier, the way that people access the healthcare system is just going to evolve, right. For the same reasons that Malenka was describing of how people have endlessly long conversations with something like ChatGPT. People will just have so much more choice in how they choose to engage with the healthcare system. And, and I think that that's going to create all sorts of new competition for how health systems actually manage their patients that they historically haven't had to deal with. And in many cases like may even be hard to predict what sorts of avenues come up. And as a result, I think ensuring that health systems are spending a lot of time and focus around using all of this super powerful tooling to make sure that patients or leaving visits feeling empowered, make sure patients actually feel as if they're getting the requisite amount of time that they want with their providers, make sure that they actually feel cared for is is going to be front and center and making sure that they actually feel as if they are competing at the experience level rather than just having some sort of service that other people don't and competing, whether it be geographically or on the basis of some scarcity.
- [37:53]
D
And I think the cautionary tale of what Mon have said, if we were, if there was a health system CEO or a board listening to this, I think there's a Mark Andernism Andreessenism, which I think is actually a Jim Clark quote, by the way, from the 90s that all of businesses like bundling and unbundling. I think the risk is we're in a great unbundling cycle right now, given all the trends and the frustrations of the consumer. And so for the health system to think about, which has been an incredible ridden the top wave of bundling for a long time of physicians and all the capabilities and technology for people to access that if we're in a wave of unbundling, I think the health system business model is at risk. And so I think that's the fundamental question that every boardroom's got to ask themselves of how they position themselves the next decade.
- [38:44]
B
I want to take one more second of that. I know we're ready to wrap up, but I think there's so much truth to that as I watch some of these kinds of care and I talk about this in the who moved my cheese type comment move outside of the traditional health system. It's not the disruptors that people feared, such as Walgreens, Walmart, CVS that people were so fearful of a couple years ago. It is all this artificial intelligence and digital interchange and different teletype services. How much concern is there that the traditional health system model changes much more dramatically than expected over the next few years? Venkate, can you comment on that? Just as a last comment and then Malinka or Manav, if you want to comment. And I'd love to hear your thoughts too.
- [39:30]
D
I mean, look, I think that, I think a lot of health systems will argue that, and this is true, if any loved one really was in trouble, they would want to go to any one of these institutions we've mentioned on the podcast because of quality and because of the brand, et cetera. But I just think if you read a chapter of Innovator's Dilemma and How things Work, which is it's always death by a thousand Cuts. And I think that, and oh man, I'm like just Internet memes today from Silicon Valley. But like I'm thinking about the paranoid survive from Andy Grove. And I just think, you know, if you're not, if you're not evolving with what is possible and partnering, I think that even in Silicon Valley, if you take some of the companies like Salesforce, they have arguably avoided the walled garden strategy, which I think they tried, and now it's, it's the, it's the headless model. Right. So these open ecosystems, I think we give a lot of grief to EPIC for the innovative ecosystem. How do you open that up? And I think of same thing of a health system, which is, I think my push for health system CEOs is what does it mean to be much more partnership friendly, partnership forward than ever before because of the rate at which these apps are today. They might look laughable and funny and tiny and niche, but I think that very much quickly compounds over time. So I do think it's a thing you have to worry about and I don't think enough attention is being paid.
- [40:54]
C
Yeah. And I think just to highlight that the, again, the amount of value creation now for any one of these areas is so much higher than it used to be that people should just forget their priors on these things and like be willing to explore an area they may have looked at a couple of years ago. Like the rate of change is so high that the thing now might literally be 10x better than it was when you last thought about it. So and if you aren't prepared for that, like you will be surprised and disrupted by, you know, someone who is looking into that seriously. But I think just being aware of the rate of change, which is a very difficult thing for humans to do, I think is something to be really, really cognizant of.
- [41:36]
B
I think it's such a good point. You made a point earlier about how much better the tools are today than they were 10 years ago. And your point resonates here as well. I mean if you. I'm much older than you folks, which is embarrassing, but years ago people would try and dictate with Dragon, naturally speaking. And it was so bad 20 years ago that it made you shy about using it again. And then recently I do more and more things dictating through the phone in other ways. And the pickup today is brilliant compared to that time ago. But it took a long time for me to restart really using it again because it was so bad some time ago. And I love your point and how quickly so much. This is accelerating and you have to be open to it in looking at it or you're going to miss the moment or fall behind. I think that's so right.
- [42:26]
D
Yeah. Your son's an engineer, Scott, and I think of engineering as like anybody who's. And this is probably why, you know, there's an arb of paranoia if you go, you know, into the west coast, because if you follow the arc of engineering, software engineering, I actually think it is, you know, depending on your point of view, a canary in the coal mine or the doomsday scenario. But I actually think it's a canary in the coal mine of how work has changed. You know, I remember two years ago when someone called me and said, you know, what's the best way for job security? They're about to graduate. And I was like, you're fine, you're a software engineer. You're going to be good forever. And by the way, for all the automation that's happened, actually there's more jobs now in demand we're seeing. So all the job scare is. Is overblown. But I think at one point you were writing all your code. Next point you were using cursor, then you're using Claude code. It feels like every three days you're reborn. Today you don't even code because you're telling agents to code and do your work. And if you just. That's just in the last two years. Some of this stuff is in the last three months. And so I just think that if you apply that to Malinka's point, that rate of change to all jobs, not just one particular type of job, I think that order of magnitude, people underestimate the change that's going to occur in the next decade. I just implore people to look into the arc of engineering. If you're not fixated like some of us every day in this world.
- [43:51]
B
It is a fascinating discussion. I want to thank Manav Venkat and Malinka for joining us. Really a pleasure for me. I hope you three enjoyed it as well and as importantly, hope our audience loves it too. Thank you all. You're at the forefront of what's going on in the change in healthcare and just remarkable thinkers and entrepreneurs and people. Thank you so much for joining us.
- [44:14]
C
Thank you, Scott.
- [44:15]
A
Thanks, Scott.