The Future of Healthcare: Data, AI, and Value-Based Care with Marc Ryan, Chief Solutions Officer at Lilac Software

[00:00:01] Welcome to the Chalk Talk Jim podcast, where we explore insights into healthcare that help uncover new opportunities for growth and success. I'm your host, Jim Jordan. Welcome back to the podcast. Today, we are joined by Marc Ryan. He's the co-founder and a Chief Solutions Officer at Lilac Software.

[00:00:27] Now, Marc brings decades of experience from leading health plan administration at MHK to navigating government healthcare systems in Connecticut. His deep expertise spans Medicare Advantage, Medicaid, and healthcare technology, making him a thought leader and driving innovation. In this episode, we delve into how healthcare organizations can overcome legacy system challenges, leverage digital transformation, and embrace value-based care models.

[00:00:56] Marc shares insights into the intersection of data analytics, artificial intelligence, and real-world healthcare delivery, highlighting strategies to close care gaps, improve outcomes, and reduce costs. Whether you're curious about the future of precision medicine, interested in interoperability, or the evolution of aging policies in America, this episode promises to inform and inspire. So Marc, tell me and the audience a little bit more about yourself.

[00:01:25] Marc Ryan Sure. My name's Marc Ryan. I'm the co-founder and chief solutions officer of Lilac Software. Just prior to helping found this company, I was the president of MHK, which is a health plan administrative and clinical workflow system. Marc Ryan I have a great deal of background in health plans.

[00:01:48] I've been at health plans large and small, have started up some health plans in Medicare and Medicaid and even in the exchanges along the way. I cut my teeth on healthcare when I was a government official at the state level in Connecticut. I was the management secretary, which sort of oversaw the management of all government. And a small state has really all of that healthcare stuff centered at the state level.

[00:02:15] No county healthcare agencies or things like that of any significance. So that's really where I learned a lot about federal healthcare programs, social welfare programs in general. And I parlayed that into a career in health plans later and then into healthcare technology as well. Marc Ryan Now that you're on that tech side, what is it that the lessons from those other experiences that are providing you the most value?

[00:02:43] Marc Ryan I hate to say it, but, and I think things are changing, but if you look at government's investment in healthcare, as well as even many health plans investment in healthcare, it really is not terribly mature. I think things are starting to change and we'll probably get into some of that here. But I think one of the things really holding health plans back specifically is a lot of legacy technology.

[00:03:06] I found in my dealings over the years with various health plans, contracting with them for software that in some ways, the smaller guy or the startup guy actually may even have an edge on the bigger guy who is saddled with a lot of legacy technologies, multiple silos, and they almost can't even fast enough think about getting to digital transformation.

[00:03:35] Marc Ryan So that's why you see some of these insure techs, although they're still struggling to earn money because they're new, do seem to have some very good digital transformation agendas and good interoperability and great ways of interacting with members and providers. Marc Ryan And I think, what do you think ICD-11 fits in for changing this conversation?

[00:03:59] Marc Ryan I think part of the challenge is that the definitions of these data sets is so diverse. I have a dear friend of mine who is from the tech industry. He was the CIOB, obviously very smart individual. And at some point he said, I'm going to go into healthcare and they started an insurance firm and unfortunately they were able to sell it for a little bit of profit. But I think he learned how hard it was because the standards just aren't there.

[00:04:27] And we're still in ICD-10 world, aren't we? Marc Ryan We are, yes. Marc Ryan When's 11 supposed to come in and maybe explain to the audience what 11 even is? Marc Ryan Yeah, basically these code sets. But first thing I'd probably say is that I think another big problem with health plans is they have to manipulate and understand so many different types of code sets, right? Marc Ryan On the pharmacy side, you have NDCs, but you also have what's known as GPIs and GCNs.

[00:04:56] And depending on the type of PBM you have will dictate what code sets you're using on the pharmacy side. On the ICD side, of course, you have diagnosis, you have procedure codes there. And the United States is one of the entities that has never quite kept up with the international classifications very well. I remember even helping move MHK along, the company I referred to earlier.

[00:05:24] And we had a combination of people that were still using ICD-9s as well as ICD-10s. And we were keeping for our clients multiple sets of codes. And even within those codes, you have those codes expiring at certain times as well. And so I think really health plans and even providers have just acclimated to ICD-10 when we should be moving toward ICD-11 as well.

[00:05:52] Another part of that legacy concept, these codes have to be programmed in various claims and other systems, including a workflow or a prior authorization or utilization management system. And the cost and the speed is just not, the huge cost and the speed to get there is just not there. And then you overlay interoperability on top of that.

[00:06:17] One of the big things that I think providers and plans are grappling with are the formats within interoperability. I'm happy that we're moving toward more defined formats like FHIR. And I think that will open the door to really having better communication. But I think the reality is that we are probably, and I hate to say it, but probably five, seven, ten years off

[00:06:42] to really leveraging interoperability the way we need to because of legacy technology on each side. And frankly, the different languages that providers and payers talk. And so you can set out a standard like FHIR, but how that's populated and ultimately interpreting that data is going to be very difficult, I think. And I think maybe we should pause our nerdiness for the audience and just speak to two important things that are in here.

[00:07:10] So first of all, the standard formats, which you're talking about, the FHIR format. But with ICD-11, there was a partnership with SNOMED. And SNOMED does the clinical terminology. So the electronic health record industry very quickly realized that the input comes from physicians and we need to figure out a way to get our clinical terminology into it. And so this, I think, would be the first time where terminology meets format meets electronic health records.

[00:07:38] Now, to your point, it's probably five to seven years away. Absolutely. Absolutely. Yeah. There's just so many different things that are different on the provider side. And leveraging and getting that data in to a provider or to a plan is just going to be a significant challenge. I always use the example of HIPAA formats. There's an 834 enrollment format. There's an 837 claims format.

[00:08:05] And there are other various HIPAA standards out there. But the truth is, they are only about as good as a plan interpreting them, right? Consequently, you need unique companion guides to actually integrate even via a HIPAA format today. And there are still plans using legacy formats, right? Because they've never fully adapted.

[00:08:30] I think we're going to have some of that same fallout and learning curve on the FIRE side as well. But I am optimistic because I do think if we do use FIRE as a standard and we really invest in inoperability,

[00:08:47] I think it will pay huge dividends in quality of data, in lower cost, and I think improved outcomes because you will be opening up a cache of data on each side for the other that can be leveraged in very meaningful ways. And then I think the other piece of this is that the concept of real-time health care record or real-time health system is being morphed a little bit in the past few years. But historically, what it meant is we're going to tie the consumer all the way through to the other end.

[00:09:17] And to do that, I think the consumer end of things needs to have these standards. You need to plug into something, right? And so the question is, how do you plug into something? Given that concept or that background, where does your organization fit in the continuum of health care today? And how are you fitting into this future? Sure. So Lilac Software is a new venture, and largely what we are building is what we call a sort of a state-of-the-art cloud-hosted data analytics platform.

[00:09:46] And it actually will consume a lot of this data. It will consume vast amounts of payer data. It will also consume vast amounts of provider data. And we will essentially take the mystery out of these formats. We will normalize that data. And it will be a one-stop place, almost like an enterprise data warehouse.

[00:10:09] But I think with advanced logic and algorithms built into it, to allow a health plan to communicate with providers really in a foundational way, to really open up transparency of data. Overlaying this sort of data aggregation and analysis tool will be some best practice interventions.

[00:10:29] We're starting out with star scores, where we are working to bring in that data to inform providers about open care gaps, whether it's a drug gap or a medical clinical gap, like a mammography or colorectal cancer screening. But those same data sets, honestly, can be used throughout the enterprise for other reasons. So our next agenda item would be cost of care.

[00:10:54] And to use those same data, the plan in providers about where cost of care can be lowered, for example. It gets into areas of site of care as well, a network and provider performance and things like that. Those are more farther down the road. But we see in the VBC world, the value-based care world and the value-based payment world, more and more focus on efficient care as outcome-driven care.

[00:11:24] And that's really foundationally what LILAC seeks to help health plans and indirectly downstream providers as well. Maybe because some of our audience is from different aspects of health care. Can you explain the STARS system and its purpose? Sure. So the STARS system is on really two programs, the Medicare Advantage program and what we call the Standalone Part D drug program.

[00:11:48] It's most important in the Medicare Advantage program because bonuses to the plans are tied directly to about 40-some measures and their outcomes in your overall rating. And for those who are less familiar, Medicare Advantage is a private managed care option for seniors and those with disabilities in the program. The other option, of course, is what we call the traditional fee-for-service program.

[00:12:14] There's a lot of controversy surrounding Medicare Advantage today. Some people will argue it's overpaid. Some people will argue that it has too much of an advantage, so to speak, over the traditional fee-for-service system. But I'm a big believer in Medicare Advantage. I think it's the best way to probably rein in costs over time and to drive better quality.

[00:12:39] There's a lot of evidence that plans do save and Medicare Advantage plans do save and they do drive better outcomes. That said, Medicare Advantage probably needs a little more accountability. And even though I'm a big defender of it, I do feel like the risk adjustment process, which rewards plans for higher risks, may be being abused by some plans. And we do need to rein some of that in.

[00:13:07] But overall, I think MA is where we need to go. Accountable to the government, but private sector driven to bring efficiencies and better quality and innovation along the way as well. So that STAR program really is the foundational way for CMS, the government regulator for Medicare and Medicaid, to actually gauge how well a plan is doing. And STAR ratings are getting tougher and tougher.

[00:13:35] Right now, MA plans have had a very difficult time of it. They've lost sort of some of their high ratings. That's probably a good thing, frankly. Health plans have to be more diligent. Some of the problems may be related to how CMS administers the program too. But I think health plans also have to be a little more accountable and really look at how they're administering the STAR program and really keeping on top of changes so that they can maximize the ratings and gain bonus revenue.

[00:14:04] What's interesting about the MA STAR program is that your rating determines ultimately how big your rate is. And the added dollars are required to be sent back through to member benefits. In essence, it augments the traditional benefit.

[00:14:23] And that's why it's become, I think, in my view, the single greatest social safety net in the country because it literally is saving hundreds and sometimes thousands of dollars for people that really very much are on fixed incomes oftentimes. And I think this is one part of multiple quality systems that have been installed over the past decade or so. And it starts with you can't improve it unless you measure it, right?

[00:14:50] And I think that I'm thinking as you're telling that story of one particular program that when you realize your STAR rating is costing you income, you start getting creative as to what you can do to improve it. And so in this particular program that I was involved in, it was women's exams.

[00:15:08] And what was happening in gentrified cities is they're pushing the poor out of the city where they have infrastructure to be able to have babysitters and trains and buses and things like that. And so the falling of the STARs rating made them recognize that they could put a lot of these tests into an RV, bring them into the neighborhood. Instead of saying your pap smear is this week and next month is your breast exam, you're doing them all at once.

[00:15:37] They serve coffee and the families were playing with the children outside and they had daycare and everything. And I think this is the community-based intelligence that can be the end of the cycle. The beginning of the cycle is obviously a bit of a challenge. I think when a lot of people understand in drug discovery and in finding new drugs, you need a lot of data to find the predictive variants.

[00:16:01] And it seems to me listening to what you're doing that your aggregation and helping the individual facilities could also start having some insights on public health and various disease management organizations and disease approaches. Is that a fear? Absolutely. So just to give you a little bit of the background on what I see as happening in healthcare today, I think it's a positive trend.

[00:16:26] It really, the industry, especially among the health plans, is shifting from what I call utilization management or prior authorization to wellness prevention, care management, disease, keeping disease states in check. Regulatory regimes have driven that. CMS recently passed a new rule on Medicare Advantage. I don't favor it. But nonetheless, it begins this process where prior authorization is very much restricted.

[00:16:55] I tend to think a fair and accountable prior law system is still a needed part of the system. But they are slowly but surely ripping that apart. There are some state and federal laws that have passed or will pass that will do the same thing. So necessarily health plans are now looking to what I think is the better way of doing it and really pivoting to what I call care management. And that's the care of the individual.

[00:17:21] The way technology is today, you can gain a lot of insights into the needs of individuals. Some of us may be healthier than others but still need help and reminder about preventative screenings and things like that, whereas others may consume a disproportionate share of healthcare. And they will need active case management and very close tracking.

[00:17:45] And I think this digital transformation that we're in, this interoperability, will allow us to successfully move to this kind of care management engagement model. And within it is really member engagement and provider engagement. Today, without the right data, you can't really maximize that member and provider engagement. The data isn't there. If the data is there, it oftentimes is too stale and you've lost your opportunity.

[00:18:15] And I really think that this is where digital transformation, interoperability, all this data gathering comes together to support this pivot from you, as I call it, to CM and health and wellness. And really allows the plan probably to save dollars rather than cents as they're saving right now in the prior authorization focused world. The health plan world has been focused on UN for decades.

[00:18:42] And their investment in the data analytics care management side has been very much lacking. But you're starting to see that change largely because of those regulatory rules as well as some state and federal legislation that is starting to close the door on that upfront gatekeeper UNPA process.

[00:19:05] And I think your interoperability conversation is important here too, because I think that as electronic health records became scaled and for all intents and purposes is less than five major survivors in the hospital world, they start behaving in a way that competitively defends themselves.

[00:19:25] And I think there was a period of confusion where they towed the interoperability line and the inclusion of other software to the realization now where the analytics and predictability skills that a company like yours brings is a very different skill set from the electronic health record organization. They're both necessary skill sets.

[00:19:50] But it's not like you would have a general practitioner replacing your knee, right? You want to have both experts. And so I think that your company is at the forefront of this transition. And what competitive forces do you see as you're trying to get yourselves funded and get yourselves new customers in scale? Yeah.

[00:20:42] That's right. That's right. That's right. That's right. plans recognize that pivot I talked about from utilization management to care management is really the secret to their success and that they can no longer think really short term. They have to start thinking long term.

[00:21:11] So as this evolves more and more, you're starting to see plans say, I get it. I have to make these seminal investments in technology and analytics in order to really meet expectations down the road. And it also is very much tied to their margin because if they can't do that upfront process of prior off and you went effectively anymore, they do need to find other ways to reduce costs

[00:21:41] and improve quality to gain that quality revenue from star as an example, but to ultimately keep the bottom line, the medical expense low. So I wonder, I'm curious, and this is again, just your best guess, but it seems to me when you're struggling with margins, a lot of the first moves have been to vertically integrate, bring hospitals and insurance companies together.

[00:22:06] And I get it from a profit perspective, but when you think about data mining and the task at hand, they're actually very different tasks. Do you think the availability of software like yours can actually in the next decade change that trend to vertically integrate, to save themselves to the point where they now have enough intelligence to be able to do it on their own? I do. I really do. Because I think if you can essentially get the data in real time in the right place and

[00:22:34] share it, suddenly you open up a whole bunch of new opportunities on the outcome side and on the cost reduction side. I also just as an aside, feel like even with President Trump coming back into office and the concept of him being much friendlier toward big business and these concepts of these vertically integrated colossus like UnitedHealthcare or other large healthcare companies, I do think

[00:23:02] that these vertical integrated companies are going to be closely scrutinized. They clearly drive investor margins and things like that, and they are very successful. But there's also some evidence that the vertical integrations of PBMs, health plans, providers within these things, long-term care facilities, other things all centered within one entity actually

[00:23:30] could drive up cost because there are intercompany relationships that are not arm's length and things like that. I think the combination of getting to the right kind of technology as well as scrutiny of these vertically integrated organizations could in fact over time lead to what I think are more agile, smaller organizations that are truly transformational.

[00:23:55] I don't know that you could today say that these very large vertically integrated companies are truly transformational. I would argue they are really more big business oriented, and I don't think they ultimately help the healthcare system better itself and reduce costs and improve outcomes. I am very optimistic that technology will play a level of the playing field in essence for big, medium, and small players.

[00:24:22] I think change healthcare, the cyber attack was a good example of the danger of too much vertical integration and too much consolidation of healthcare power in certain entities. It's clear, and I think it'll become even clearer, that when they integrated change healthcare into Optum and United Healthcare, they did very little to modernize the architecture of what

[00:24:49] change healthcare did, and they did very little to ensure security. And I think you can repeat that throughout many of these vertically integrated organizations, to be honest. So it strikes me too, and just maybe for the audience, we're talking about insurers and providers, but we also have new healthcare IT models and precision medicine that strikes me that might benefit from a connection to your data set.

[00:25:15] Meaning that you think of, we're quickly learning end-stage renal disease, diabetes. These are multifaceted. You've got cardiac, you've got multifaceted pieces of it. And the question is, where do you get the data? So the precision medicine models, the first versions are very much, do you have this thing? Yes or no, what do we do? But I think eventually what we're going to find is that these other pieces of data are going

[00:25:41] to also be predictive, which would in theory be outside of the traditional precision medicine model. So how do you guys connect to that? I think it's a great point, and I think it's something we've thought about. It's on the longer side of our roadmap, but I think you're absolutely right. I think there's many different forms of personal health information, clinical information

[00:26:06] that providers and payers have, which if we can assemble it, we can get a true social determinant picture as well as a clinical picture of an individual. And really better intervene in those cares. There are plenty of risk models out there that do those kind of things very well. But to your point, there is a whole bunch of other statistical data sets that are out there

[00:26:32] that can be very specific to individual disease states and outcomes over time for certain individuals that could even be combined here and provide really individualized care or, at the very least, care that centers on a given sort of disease cohort that becomes very specific. And I think, frankly, that's where the world is going, right? It's going to be a long time.

[00:27:00] But I think you raise a great point about how data and how technology could eventually identify disease early, identify the best course of treatment, and really holistically take care of that member on that healthcare journey. What sort of talent are you guys hiring? Because that's a different historical skill set. What people do you look for? I'll be honest.

[00:27:24] We have a number of product folks like myself who have been in technology and understand healthcare, right? But I'll be very honest. We are really very heavy on very specialized developers that understand the healthcare system and how to build the right data analytic tools and things like that. But most importantly, we are hiring data scientists.

[00:27:48] They are perhaps the most important aspect of what we do, along with those development professionals in healthcare. The combination of the two of them are absolutely essential. And over time, we will need to go even deeper in that data science area where we will have coders and other types of folks that have very specific clinical knowledge from a pharmacology standpoint or a clinical standpoint as well.

[00:28:16] So we eventually will see a merger of data science, clinical, and development expertise that will help us hone these very specific models. We also have some very good AI experts, right? I know that you talk to somebody about AI or machine learning and you can get a different viewpoint of what this is from 20 different people. You'll get 20 different views, right? And I think that's largely because we're just starting out in that direction.

[00:28:46] But we're also introducing AI concepts into data analytics because machine learning allows the quick identification of patterns and potentially best practice recommendations that would otherwise take individual clinicians or health plan professionals years to actually even stumble upon in some cases.

[00:29:09] And I think that maybe, again, for the audience, when you're looking at data science, data scientists are looking at unstructured data and trying to find insights or, as we would say, from our high school science hypotheses and then prove them to be true or untrue or probability true. And that's a very different skill set from people that are trying to help people and use protocols

[00:29:37] a bit today. But the other aspect of this is once you get that insight, you need to convert it into a practical business model for your clients. So there's really two things that you're doing that are very hard for someone who's processing patients every day to be able to do at the same time. It's very different skill set. Absolutely. Yeah. And one of our focuses, at least initially, is what is the best way to close a star gap?

[00:30:03] And there's a bit of a controversy in this field, by the way. CMS does not want plans being out there that are just closing just enough people to move up in a star rating, right? They don't want you to look at who are the what's the small cohort of individuals that have the best way of getting you to that next star score. They want you to look holistically.

[00:30:28] But the reality is, I think you can do both. I think you can outreach to the entire population, but you can also look at the propensity of giving people to close. And so you might be able to quickly close gaps in care, which is a good thing for people that have a high propensity to close. But at the same time, AI and machine learning can help you look at the barriers of other people to

[00:30:56] closure of certain care gaps in allowing you then to create those intervention plans, those best practices to actually get to the other people. I don't believe it's an all or nothing proposition. I think through the right kind of data science and analytics, you can really serve the entire population, close people that have major SDOH barriers or barriers associated with multiple

[00:31:22] comorbidities, but at the same time, close the people that should close and maybe improve your star rating along the way too. And that's something we feel past determinants of health is SDOH. Sorry, SDOH and determinants of health. Because I think that maybe people are starting to see this when they go for their annual checkup now, the doctor's asking about your mental health, your living situation, are there guns? There's not just all these questions. And I ran into a gentleman

[00:31:49] who had a very successful Wall Street career, came from a disadvantaged neighborhood and worked himself up. And he came back and he recognized that your healthcare problem is also a housing problem. And my audience is probably tired of the story, but I remember one day being at my company and we received a letter that was from someone down the street around the south side of Pittsburgh. And just walked down to the brownstone and a woman comes to the door and a walker and oxygen and she

[00:32:18] invites me in fatigue. And it's a beautiful day I came in and she tells me the story how her son quit college and came back and is working to help her out and different things like that. And he's going to school for engineering and he's now working at Burger King. And again, there's nothing wrong with working at Burger King. His choice of working at Burger King or finishing school was diminished by the health situation of his mother. And so I think that when we think of risk stratification and economic

[00:32:44] modeling, those are other aspects of things that you probably see for having been on the government side that most people don't see. Yeah, no, absolutely. And I think you raise a great point. Many people feel that you should not make health care a social service, so to speak, right? But the reality is that most studies will show you that the social determinative health barrier is a greater

[00:33:13] predictor of health care spending than actually underlying clinical disease states, for example. And that's just the reality, right? And I do think we have to find a way without going overboard to take into consideration how you're attacking social determinative barriers in health care because you're fighting an uphill stream otherwise if a minority of the predictive cost of Americans is tied just to that

[00:33:43] clinical disease states. I think it's a very important issue that we need to grapple with. I would argue that sometimes certain states go way too far on this issue. But I also feel like many do not take SDOHs into consideration enough. And unless you do, you're fighting a spiraling cost curve in health care anyway.

[00:34:07] And we don't have the data, right? So I taught a health systems course at CMU and we had a very, two people in the class that worked for senators. And one was a Democrat, one was Republican. And of course, I intentionally opened up the Donny Brook, right? Is health care a right or a privilege? And everyone's getting riled up. And so the question came down to the person who thought it was a privilege is that if

[00:34:31] you could bring down your taxes, improve the gross domestic product per capita and productivity of this country by providing that, would you be behind it? And her answer was absolutely. So the issue is that we don't have enough data to have a non-biased opinion on what the situation is. As you're doing your startup, what challenges are you facing in these early phases of getting clients or demonstrating the vision?

[00:35:00] Yeah. With any startup, you always have your challenges, right? And this was repeated back in 2010, when I was one of the founding executives at another startup. And you're always, number one, you're battling making sure you have the right kind of financing from venture capitalists and others. And sometimes you put your own money into it too, right? Basically, because you need skin in the game. And so there's a constant

[00:35:24] cultivation of those that are investing in you because the road is somewhat long. It's years before you get to a point where you're going to be profitable usually. Number two, talent acquisition is always a challenge. We've been very lucky at Lilac because we've hooked up with a number of really great data scientists and developers that believe in the mission and want to be part of it. But talent acquisition is always a

[00:35:51] challenge because you are a startup. And when you think about technology and healthcare, you already have heavy competition, right? So talent acquisition would be the other big one. And then obviously, I think health plans are generally suspicious of new entities, right? They want proven. And so you have to get your product to market pretty quickly. It's got to resonate with the health plan. And generally speaking,

[00:36:20] you probably start off a little slower with a given health plan than not. You may try and solve a piece or two of their problem somewhat quickly. And then they get very comfortable with you. But ultimately, the real thing you have to prove is that your platform and your technology is responding to a real concern out there in healthcare. Data analytics, star improvement, those kind of

[00:36:46] things that we're working right now on clearly resonate. And you got to have some resonance in the market. That's probably the fourth thing. So I think it's financing, talent acquisition, proving yourself, and then the resonance as to the value of what you're trying to produce here. So as you look out in the next four or five years, what's the biggest opportunity and threat for you all?

[00:37:09] I think of it in almost two stages. The first stage is really meeting the expectations of what a health plan executive is looking at day to day. And when I was at health plans, I looked at four things. And these tend to still be the most important things. And there's common sense, but they are revenue, which could be star program revenue. It's the cost of care, which is your medical expense.

[00:37:37] It's making sure you understand the risk of your patient, which is a little different than the cost of care. It's really knowing what the disease states are, how advanced they are, and things like that. And then lastly, member satisfaction. One of the areas that most people don't even think about is member satisfaction, but the churn in membership and health plans can be one of your biggest killers. You could have a great medical expense plan, but if you're constantly losing members, that churn is

[00:38:06] very expensive. So we're going to focus on those four things to help plans and health executives really have the right kind of data and the right interventions to keep good on those four principles. But I think the even the greater thing I think we'll do over time is really use data analysis for this emerging value-based care and value-based payment world. I think it is going to resonate. I think plans

[00:38:34] know they need to get there. There's different kinds of payments. There may be episodic and other payments that are associated with providers. And then at the health plan, there are things like capitation and risk arrangements with providers that are a different form of VBC. But both of them, data is so important to analyze whether those payments and that value-based care relationship is

[00:39:01] working. And there's going to be so many nuances. There's going to be nuances on the drug side, on the medical side, on episodic payment sides, on just general relationships between health plans and payers. Data is going to be king in this VBC, VBP world for sure. And that's the most exciting thing because I think that is transformative for our healthcare system. We are still stuck, as much as people are

[00:39:30] trying in this transactional payment per event system, which we know is extremely costly. It's not outcome-based. And by the way, it invites fraud, waste, and abuse like crazy. Today, up to 25% of our expenditures yearly are tied to waste, fraud, and abuse. Traditionally, people used to say it was about 10%. But new models are suggesting it's really more like a quarter of all our spending.

[00:39:58] So literally more than a trillion dollars a year is wasteful, fraudulent, abusive. And that VBC, VBP transformation is going to really take a lot of that out. You couldn't chase enough unscrupulous people down the way CMS and HHS does it today and Medicare and Medicaid to even put a dent into that without true change in how payment is thought about in the United States.

[00:40:25] Yeah, it struck me as a point in my career, I owned a durable medical equipment business that had billing. And when you get to the end of the quarter, all of a sudden everything gets rejected. And the billing employees say, well, that must be the wrong code. They start trying to go find codes. And then subsequently you realize that they shut you off till the next quarter because it's a cashflow issue, right? So they invited, to your point, fraud, right? Because people are said,

[00:40:51] oh, I guess it must be this code or that code. And it's a lack of communication and it's a lack of integrative systems, really, to your point. Yeah. Is there anything else you'd like to share with our audience? No, I think that one other area I would talk about, and we referenced this, it's the meeting of aging and healthcare together. And one of the things that we don't really focus in on is what

[00:41:17] I think is the necessity of an aging policy in America. Today, a majority of long-term care is furnished by Medicaid. And generally speaking, you need to spend down your assets to even get onto that program. And consequently, many people are also taking care of elderly relatives or friends because they don't want to spend down their assets or they don't want to go into a nursing home

[00:41:46] because generally speaking, in Medicaid, that's oftentimes where you would end up. You wouldn't end up in a supported living situation, as an example. But as America ages, as you can imagine, the cost of care medical side goes up exponentially. And if you don't have good long-term care, that makes it even more burdensome on the medical side of things. Back in my day in government,

[00:42:11] I built a continuum of care that was investment-driven and even had some investment from people on the personal side saying, I'm going to buy into home care. I'm going to buy into assisted living, and I'm going to pay what I can afford. The government will pay the balance. There are cost-effective models out there that allow us to probably keep people in the dignity of

[00:42:36] the community rather than institutionalizing them. And unless we get our arms around aging and an aging policy, the health care bill is just going to increase even more dramatically. And I think there is a fairly successful program that's not covering the scope that you're talking about, but our PACE program has shown it's small, but there's some pretty interesting evidence that they're

[00:43:01] basically for the audience, and maybe you can help me with this, but it's a covered life. And they cover aspects of having you maybe even come to an adult daycare center, or maybe your doctor's there or your beast, they help wash things that allow people to stay independent longer and is trying to treat chronic disease and make sure their meds are balanced and surveillance if their meds are out of

[00:43:26] balance. So it's not all over the country per se, but in the few that I've seen, I thought it's been a pretty, pretty good program. But to your point, there are missing systems and not to nerd out a little bit, they're going around with a bus and picking up 15 people in the morning and bringing them in. And they have someone drive out to make sure Mrs. Jones is set up to get her on the bus and they drive three more people down to get Mrs. Smith or Mr. Smith. And one of them's delayed.

[00:43:53] It's the equivalent of an airport flight didn't arrive kind of situation without the infrastructure to figure out how to sort it through. So that's part of the things we need as a country. Very good. Absolutely. Thank you for your time and your intelligence and good luck with this important startup. Well, thanks very much, James. I appreciate the time. Thanks for tuning into the Chalk Talk Gym podcast. For resources, show notes, and ways to get in touch, visit us at

[00:44:22] Chalk Talk Gym.com.