Transcript
Speaker: Hi, Dr. Min. Thank you so much for coming on the podcast and welcome to The Healthcare care Theory. Thanks, Nikhil. It's a pleasure to be here. And I go by Jim. Of course. Yeah, it's great to have you on again. And I think that before we get into a little bit about Clearly Health, I think it's helpful to take a step back and get into your background because you've had a pretty long academic career, including roles at Cornell Medical College, New York Presbyterian Hospital, and you had some imaging-focused prevention work there from what I know. I'd love to hear what made you go from like, this is an interesting research area, I want to be doing research, to actually understanding that something needs to change about the standard of care within
Speaker: um cardiology Yeah, thanks. like That's a good question. like ah So I was sort of where you were in Hyde Park in Illinois. I went to college at the University of Chicago and I did the majority of my training, internship, residency and fellowship at the University of Chicago as well. So it's pleasure to talk to you today.
Speaker: um While I was a fellow, I had planned to do interventional cardiology because coronary artery disease is both my clinical and my research focus, um but then was introduced, this is way back when, to one of the first sort of successful images of all the heart using a CAT scan, which is just a fancy x-ray, and thought that that was going to fundamentally change our understanding of vascular biology and risk of heart disease and heart attacks and sudden coronary death. So, you know, in my last year, fellowship are
Speaker: switched over into imaging to focus on that modality, mostly at that time as a research tool to better understand heart disease in the context of large scale clinical trials. So, you know, as you alluded to, like after I finished fellowship, I took a job at the Weill Cornell Medical College and the New york Presbyterian Hospital in New York City and stayed there for about 16 years or so. um and and i was sort of the quintessential physician scientist guy who, you know, I saw patients and then i did research and my research was mostly focused on coronary artery disease in the, you know in the form of large scale outcomes research. So clinical trials, registries, and so on. um We ran those trials for many, many years and because we had to do follow-up on the patients to see what we were seeing, how that impacted somebody's outcome, and whether or not we could um positively influence that outcome to prevent heart attacks and and things like that. Learned a lot of new lessons around vascular biology using this non-invasive imaging tool and realized that You know, we were never going to actually be able to see it um translated to clinical practice unless ah we made a product that would actually be able to be used by doctors and advanced practice providers. So left that job in 2000 at the end of 2020 and have been with the company full time since.
Speaker: Awesome. I think that's interesting. Around the same time, of course, or maybe even a little bit before after, we had like in neuroscience, fMRI, EEG kind of technology taking off. But I think you see similar problems there where it helps with kind of understanding the, like, you can see and image things a little better, understand diseases better and disorders better. But it's hard to necessarily use that in in clinical settings. But um I think that the traditional tools back then were, a lot of them were like stress tests from what I know.
Speaker: And I saw this like study, a real-world analysis of heart attacks. They covered on 4.6 million. I found that only around half actually had like documented symptoms. Whether that's a function of them not going to the doctor or enough or whether symptoms are unobservable, I guess it didn't really observe as much. But I'd love to know a little bit from you. I mean, can you walk a little bit more into...
Speaker: what the standard of care was like how did people evaluate risk for cardiac cardiac heart attacks and um cardiac arrest like what was that looking like and why are stress tests without fear-mongering i guess like why are stress tests kind of something that can coexist with meaningful plaque burden and future risk risk indicators yeah it's a great question like i mean it You know we've been doing stress tests for over 50 years and there's many different kinds of stress tests. There's and treadmill stress test. There's ultrasound stress test. There's MRI stress test. There's nuclear medicine stress test. There's CT stress test and so on. um You know, for 50 years, we tried to figure out which was the best type of stress test.
Speaker: What is a stress test? That means that the the goal of that test is to measure the heart physiology at rest and then to measure it again at stress.
Speaker: And stress meaning when you're running up the stairs or when you're running up a hill or sprinting for the bus, that kind of thing. And the goal of that test is really to determine whether or not you have aberrant flow or reduced flow at stress due in theory to a blockage in the artery um that slows the blood down, prevents adequate blood from getting to the heart muscle, which prevents adequate oxygenation of the heart muscle, and that causes pain or symptoms.
Speaker: It can cause shortness of breath, it can cause chest pain and so on. That's a very specific question that we're asking, right? Is does this patient's chest pain or shortness of breath come from their heart arteries.
Speaker: um And then historically, if you had an abnormal stress test in general, you'd go to the CAT lab and somebody would squirt some dye, iodinated dye into the heart arteries to determine whether or not there was a blockage. And then to determine whether or not if opening up that blockage through a stent procedure or something like that would help relieve the blood flow impediments um and reduce the symptoms.
Speaker: So over time, um, the field of cardiology became a very symptom driven field, right? We waited for people to come in with chest pain or shortness of breath. But, um, as you carefully and accurately articulated, the majority of people who will die of a heart attack will never have any symptoms before their event. So, you know, there's the question of in somebody with suspected heart disease because of symptoms. What tests should I use? And then there's another question of, you know, independent of symptoms is somebody's disease so severe that it puts them at increased risk of having a catastrophic heart event, such as a heart attack or sudden cardiac death.
Speaker: That was the one that we were interested in that question. It turns out there's some overlap between those two, but but there are Venn diagrams that don't overlap completely. And we really needed to better understand the biology and then also um create safe tools, non-invasive tools that could be used to answer the second question over the first. I think over time, we're gonna still see that we're gonna use both types of tests, but I think the vast majority of people who are at risk don't have symptoms. And so we should probably transition beyond a solely symptom-driven approach to a disease-focused approach and really focus on how we can prevent those catastrophic events from happening. The simple analogy is, you know, somebody came in and had like a lung mass that was suspicious of lung cancer and um was coughing up blood.
Speaker: you'd worry about that person because they have symptoms and signs of lung cancer. And another person who had the same mass um in their chest and that's also suspicious of lung cancer, you wouldn't ignore them. You would want to treat them, right? Even if they were asymptomatic. And, know, if you think about it, all disease is asymptomatic until it's not, right? So the best and the easiest time to prevent something bad from happening is early.
Speaker: So what we'd like to do is really enhance prevention and couple that to the way that we've been doing things. That makes a lot of sense. And I think the example of cancer is an interesting one I want to get into because it's great that we have better technology for cardiology then. But when just thinking about it back when you were doing your research, it's interesting to hear that um I can understand that it's like hard to – these stress tests aren't sufficient.
Speaker: But when we have in cancer, for example, or oncology, we have like better we have a lot of prevention or preventative mentions methods that are like – we're pretty good at risk scoring there, at least compared to cardiology. We're not perfect. um but it's not really super symptom driven like it is in cardiac um and get it is in cardiology and i think part of that is of course we didn't have good tests back then but i guess this question is a little trivial but like why do you do you think the heart it was like a something the organs is a little harder to study is there something else going on with the actual biology that made it so difficult because like what is the roadblock that prevented us from getting these high quality tests like CT imaging like you guys are working on today, what prevented us from just figuring that earlier like we did in oncology and some other segments, like even lung cancer, we got a little bit ahead there. Yeah, it's a great question. I mean, the first time that we could non-invasively image the heart arteries at scale was with a CT scan. And that CT scanner came out in about 2005. So it's been ah around for about 20 years. It wasn't
Speaker: perfect then. It's gotten a lot better over the last couple of decades. and But that was the first time that we were able to actually image the heart arteries themselves to look at the actual primary disease process, which is the atherosclerosis or the plaque that builds up in the in the walls of the artery.
Speaker: Why was that so hard? um If you think about like a lung cancer, it's sitting in the lung. um It's big. It's static. Right. And in contrast, like when you're trying to image the heart arteries, they're small. They can be as small as half a millimeter up to about five millimeters in diameter. And they're in perpetual motion. So think about like your iPhone or your Samsung Galaxy. Like when you try to take a picture of somebody running really fast, if the shutter speed of that camera isn't fast enough to freeze that person in motion, then you're always going to get a blurry picture.
Speaker: And that was the problem that we had was that we didn't have the technology to essentially freeze the heart motion with a picture so that we could get exquisite detail about that. Once we had that, then we had to do the requisite research, right? And biology to understand what it is that we were looking for. we We could do that before, but just it required invasive imaging. It required going to the cath lab and getting a catheter threaded into the heart arteries.
Speaker: And then a wire going down the art you can't do that at scale right so, so this was like a great thing, it was a huge advance in the field. And then it probably took about 1015 years to do all of these large outcomes studies to really demonstrate you know its superiority over other ways that we can evaluate heart disease and risk of heart attack.
Speaker: Yeah, that same example is great. I think it really puts into picture how difficult it is. So I read that you first kind of heard about these early CT imaging in Milwaukee, kind of at a conference, I think, and that um you thought you could like, you realized you could actually study the disease process, and which is a little bit different from the downstream consequences of the symptoms, or a little bit more limited.
Speaker: So what did you see in that moment that made you change your for a trajectory almost, almost change your entire role. um and And what exactly is this um coronary CT imaging? Like what is 3D imaging of the coronary arteries actually mean in practice for people that don't have this background? And why can't you simply just infer that from the symptoms alone? Like what's different about it? And what got you so excited when you first heard about this, this new, new process?
Speaker: All of those are great questions. Like the, um, You know, what I saw was the actual disease. um So if you think about what heart disease is, it is atherosclerosis. It builds up silently in the walls of the heart arteries over many decades.
Speaker: Prior autopsy studies in victims of the Korean and Vietnam War demonstrated that we start to build up plaques in our arteries, even in our teens, right? So it's not something that happens overnight. It happens over many, many decades.
Speaker: And we historically have used either, we have used indirect markers of disease rather than looking at the actual disease, right? in the asymptomatic person who, you know, you want to do prevention of cardiovascular disease on, you typically rely upon risk factors of heart disease, such as high cholesterol or diabetes or high blood pressure. For the symptomatic patient, we wait and we do those stress tests, which look for a reduction in blood flow.
Speaker: We can then take them to an angiogram invasively looking for a blockage, but neither blockages nor reduced blood flow are the disease. They're downstream consequences of the disease. The disease the plaque.
Speaker: At some point, the plaque may encroach upon the inside of the artery to cause a blockage. And at some point, the blockage may reduce blood flow, causing chest pain or or shortness of breath.
Speaker: But we've never looked at the primary disease process, only upstream and downstream surrogate markers of the disease process. So our our thesis was that heart doctors should measure heart disease. And if we aren't doing that, then we're doing a disservice to our patients. And that was what our focus was on atherosclerosis. Yeah, that's really interesting. I guess even and even now, for a lot of other indications or areas, we can't even look at the actual disease. So figure out that we could do that in 2005 would really, it's kind of exciting, I guess. You have 21 years to get this product into the hands of people. And it seems like, I think from my understanding, it's kind of changing the unit of measurement for this. I saw there was like a cohort where with firefighters, they had pretty, i mean, they were pretty good. um
Speaker: Like they had a lot of non-calcified plaque from what I understood, but their calcium score was like zero. So... It was pretty different. I mean, can you translate like what exactly are you looking for here and how does that change with the indications or indicators in the other stress tests? Like, do they often correlate or is there something different that we're doing here? I think you've kind of hinted at it a little bit.
Speaker: Yeah, I think you jumped in on a very important point. Like, you know, when we started all of this research in 2005, we thought that all disease was bad for you.
Speaker: And we thought that if you could measure the total amount of disease, then you could identify those patients who are at risk. It turns out we were totally wrong. there were There are many different phenotypes or different types of heart disease. Some of them are the strongest predictors of who will have a heart attack.
Speaker: And some of them are actually protective against heart attacks and associated with a reduction in in adverse cardiovascular events. That was a big lesson learned. You know, you alluded to this thing called the coronary artery calcium score. um There's a lot of people who go out and unfortunately or fortunately, whichever camp you're in insurance doesn't generally pay for that. The reason it doesn't pay for it is because of a couple of things. Like one, there's never been a randomized controlled trial that actually has demonstrated that getting that information actually helps you to to live longer, to reduce heart attacks and and so on. And then the other
Speaker: issue with it is it came out in the 1980s right those were the times where we had black and white tvs not oled or key led tvs and it images only a certain type of plaque the calcified plaque it turns out that that calcified plaque is the stable plaque so you're just imaging it's sort of like the analogy is that you're imaging the sun spot on your hand that's where the cancer risk used to be but it's not there anymore What we've learned and every single large scale outcomes registry or trial has demonstrated that it's the plaque that's plaques that are non-calcified that are ah the the bad actors. And those can only be seen on this coronary CT angiogram or CCTA. So that I think that the calcium score needs to be advanced. You know, it's been 50 years and we, I think we found the advancement through the science. that we should really be focused on the the non-calcified plaques. And in particular, there's a specific sub-phenotype of non-calcified plaque that is largely in the field called low-density non-calcified plaque, which just means it's very fatty and cholesterol-filled. And that is a a really potent harbinger of bad things to have. It's the strongest predictor of who will have a heart attack or not. And the important thing to so think about is that the cholesterol inside the plaque or the fat lipids inside the plaque, that's different than the cholesterol inside the bloodstream. You know, you can have a very low bloodstream cholesterol, but very high intraplaque cholesterol. And we won't be able to see that actually, unless we take an image of the exact plaques rather than just checking bloodstream
Speaker: Yeah, I didn't know that, actually. That's kind of interesting. I guess um it's easy to underestimate how incredibly complex our biology is. um But that's very interesting, and I think that it's good we made progress there. But I think one thing that's maybe equally difficult is also you've had...
Speaker: what could be an industry-changing technology that might be to save costs or revolutionize how we detect for something like um heart attacks. But there's the whole commercial side of things, which is like equally difficult. For example, you tested at one, the whole randomized control trial, building reimbursement pathways, getting with the FDA.
Speaker: And I think that might have been some of the reason why you built a company. I mean, you could have maybe sold off this technology, but I'd love to hear a little bit from you. um In terms of founding Clearly Health, what motivated that? It seems like it wasn't something like, let's build an AI company and do that. It was more like you found something clinically meaningful and powerful and you wanted to scale it from my understanding. But i mean can you walk me through the founding process from Clearly Health? You're basically going from a researcher to a founder and CEO.
Speaker: What did that transition look like and why do you think it was necessary to really take this technology to the next level and even and change the way you do your work as as ah as a person yourself? Yeah, I mean, about 13 years ago, we felt that we had started to understand the vascular biology of the coronary arterial bed in a manner that would really inform us in clinical practice. And so we started a new program when I was at Cornell. My friend Erica Jones and I started that program called Heart Health.
Speaker: And it just took a very different approach to cardiovascular prevention. We wanted to marry... um high quality outcomes evidence, i.e. randomized control trial, large scale registries with personalized medicine.
Speaker: um So the if you think about the randomized trial and you take like 100,000 people on one side and another people on the other, um What you are doing is you're effectively proving ah whether or not an approach works or not.
Speaker: um But you're doing it at the group level, right? Like you're saying, you know, that 100,000 people over on the left is sicker than the 100,000 people on the right. But you don't do it at the individual level because within each group, who you thought was sick was actually healthy and who you thought was healthy was actually sick. And you can't quite figure it out through the RCT mechanism.
Speaker: That's where, you know, this holy grail of doing truly precision heart care or personalized medicine comes in. We thought that we could do both, like um understanding the large scale ah trials that we had run and then applying that to an end of one approach in the clinics.
Speaker: So in our heart health program, we performed coronary CT angiograms on people and said, look, we we think that the problem with this approach that we've done for 50 years ah is that it's so symptom driven. and if it's you know And we think that it should transition to a disease focused approach. If you have severe disease and no symptoms, you're still at risk. Like in fact, you're at extremely high risk. You just don't know it. And that's almost worse because you don't know how to prevent it. So we leveraged the coronary CT angiogram to use a very non-invasive approach to characterizing actual heart disease.
Speaker: We understood what was the good findings and what was the bad findings, and we would use that to track whether or not somebody got better or not. When you treat somebody with a drug, say a statin medication or PCSK9 inhibitor, you get about a 20% relative risk reduction.
Speaker: So that means you fixed one out of five. But that also means you failed in four out of five and you don't know who you failed in. And so we we know that the convergence of every single contributor to heart disease is that it forms plaque.
Speaker: And if we could understand whether or not there was these bad plaques or good plaques or so on, based on the research, that we could effectively determine whether our therapies were working in that individual using the imaging. So we went seven years with like five cardiologists and just didn't see adverse events. And so we had, we realized like, Hey, we're onto something. There's just no way that we could provide to other hospital systems. And so that was how Clearly was born. We just wanted to take our personal experience at a New York Presbyterian hospital in Cornell, and then be able to provide automated tools to people so that they could really offer that to every patient across the globe.
Speaker: Yeah, that makes a lot of sense. And I think there's so many interesting things to talk about with what you guys are doing and clearly today and how you've been scaled, how you have been scaling. But just like going through that transition from being a researcher to a founder, what were some of the challenges for you, at least in your own personal story?
Speaker: that required this adjustment i mean do you think it was maybe easier than you expected because you guys had built such a good technology it's easier to sell there was there a lot of other challenges like i know you guys have built a whole ai workflow and it's beyond just a simple test i mean what did that look like for you guys in terms of building this product and how was the transition for you for you um during this time yeah i think the transition was very challenging right or the transition was a challenge Like, unlike you, I was a biology major.
Speaker: You happened to be in biology and economics. I didn't have that business background, right? So um there was an entire new world that I had to learn that, you know, about how you start a business, how you scale a business.
Speaker: And so it's beyond just financial, right? It's also how do you lead a team and how do you recruit well? And, you know, how do you decide what the product roadmap is and how do you, ensure that the people using your tools are actually pleased by the experience and not frustrated by them. So there was just a lot lot to learn, but you I think that we went out with a pretty solid um understanding of what we wanted to create. um Then we realized that there were holes in that that plan and that we had to fill those holes up. but um But the initial couple of products that we put out there, I think were very loyal to our initial view of what we wanted to develop.
Speaker: Yeah, and I think if you're doing a startup and everything is going great and perfect and nothing's wrong, you're probably doing something wrong or not looking hard enough. I think that it's just a normal part of the journey, from least my understanding.
Speaker: um But where'd you guys start? I mean, I know that you kind of hinted at you selling to hospitals, but I'm sure this technology has a lot of applications. You have the commercial side of pharma and biotech, but I think more likely in the the delivery side or even insurers. There's so many different people that would I guess stakeholders that could be invested in this in the long run by getting costs down early.
Speaker: But who'd you start selling to and what was that process like in going about that and and showing them that there's real ah ROI with this use case? Yeah, i mean, one thing that we decided very early was that, you know, we we created a software platform.
Speaker: by Platform's like an overused word, but the the reason I say platform was because that it was really trying to to provide tools to every stakeholder in the care pathway of somebody who is getting treated for coronary artery disease. And that turns out to be a lot of people.
Speaker: um It turns out to be the cardiologist, but also the radiologist who reads the images, also the preventive cardiologist, the interventional cardiologist. it also turns out it's the researcher who wants to do large-scale research, the primary care physician who is the the patient's quarterback doctor, the advanced practice providers within that primary care setting, and then most importantly, the patient.
Speaker: And what we realized was that this advanced imaging that we're seeing in you know in all of the vascular biology that we like to nerd out on, um others aren't that familiar with it. And so you can have these very extensive reports on this and that, and it just confuses people.
Speaker: So what we realized was that they one of the biggest things that we tried to do was translate. all of the advanced imaging findings into clinical insights that were easily actionable and understood by each and every stakeholder in the care pathway. So that part was like something that we spent a lot of time on just to experience that practitioners and patients so that they could be empowered by the data rather than frustrated by it.
Speaker: I think the other thing that we we felt was that, hey, this platform, quote unquote, is a software solution, right? We're a web-based solution. and you know, unlike other softwares that are in non-healthcare industries, this one is very highly regulated, right? So, you know, we had to um ensure that what we did was in keeping in direct accordance with the FDA, who looks at safety and efficacy of of devices. And very early on, we said, look, we're not a valve or a stent or some other piece of hardware you put into somebody's body, but we are a medical device and we should hold ourselves up to the rigor of science for all medical devices. So we invested very heavily into large scale clinical trials. Like, I mean, we've probably enrolled over 25,000 patients now with two, three, five, eight and 10 year outcomes. So we we understand what our tools find in human bodies and human hearts and how that affects somebody's outcomes. And the reason we wanted to do that was to prove it to ourselves, right? That what we made was worth worthwhile, to prove it to the clinical community, um but also to prove it to the payers, right? To show that there was real really like a large incremental value to having these new tools that we didn't have before.
Speaker: All of those converge into sort of the commercialization as well, because in order to to be able to sell into hospitals and cardiology practices, you do need reimbursement still. And that reimbursement comes through coverage policies. And those coverage policies come from insurance providers that mandate clinical evidence that beyond a shadow of a doubt, that that with great certainty, you're actually helping people. And, you know, we were also very careful to try to be judicious to the healthcare care system, like You know, there is a finite amount of resources to take care of people. We want it to bend the cost curve downwards. And how do you do that? You find people at a very early stage where things can be prevented rather than waiting for late stage disease, which is always more expensive.
Speaker: Yeah. At first you think that like, I think people generally get a conception that insurers don't want to spend money on anything, but they love spending money on things that save them money. which of course this is one of them, right? You can get people earlier, ideally do preventative methods and then yeah, bend the cost curve down is I guess the best way to put it, um, to make sure you don't have these large scale things and yeah ER visits later on.
Speaker: But I can imagine part of that is like, yeah, the clinical trials. I mean, 25,000 people is a lot. And I'd love to hear a little bit about that. I mean, It sounds like I could see where the demand is coming from for you guys while it's necessary, but how did you go about that in the beginning? I know your TRANSFORM trial is running right now, but those early clinical trials, were they structurally different? Like, what did those look like and what were the early findings um from those that kind of motivated guys to go further? Or TRANSFORM kind of the main the main one you guys are focused on now?
Speaker: No, i mean, we have several, an array of clinical trials. We probably have over 20 clinical trials ongoing. right now. i mean, some of the larger ones, one of them was called Confirm 2. Confirm 2 is a global registry. I think now over 50 centers across the globe um have are participating. We've enrolled more than 13,000 patients with five-year outcomes. So we understand exactly what we're looking at and how that impacts somebody's outcomes, not only in aggregate,
Speaker: but also for men and for women. um Turns out women and men have extremely different um risks of heart disease with the same degree of disease buildup. We understand it from a racial standpoint. We understand it from a health equity standpoint. So we were very... um deliberate I guess in trying to say that not only you know did we ah learn lessons from clinical trials that informed the product, but after the product was made, we wanted to but make sure that we could better understand the disease process um with clinical trials that were applying our software solutions. That's a large scale outcomes registry. You know, everybody talks about the sine qua non of clinical evidence as being the randomized control trial. So as you alluded to, we have two randomized control trials. One of them is in a symptomatic population presenting with chest pain. that It's a trial called Paramount. and We should report it out later this year.
Speaker: And one of them was in a completely asymptomatic population, right? So TRANSFORM is the name of that randomized trial. Dr. David Shepard, the hypothesis that we're testing there, I think, is a very critical one for public health, which is, you know, is the evaluation and treatment of actual heart disease atherosclerosis or plaque on a CT scan. Dr. Superior to the evaluation and treatment of risk factors of heart disease, like cholesterol and so on. We know that cholesterol and diabetes and high blood pressure are correlated very unreliably on an individual level. And we think that practicing this end of one personalized medicine to provide precision heart care may offer a superior approach to preventing heart attacks and death. Very, very similar to what you would see with mammograms, colonoscopies, pap smears, lung CTs. What are all of these blockbuster successes in medicine share?
Speaker: It's that they all use some form of imaging to directly visualize actual disease at the individual level. And I think without doing that, we're always going to see that heart disease is the number one killer in this world. um We published a study last year in the European Heart Journal that looked at around 5 million heart attacks across the US.
Speaker: Turns out the majority of people didn't have a symptom before their event. And it turns out about 30% of them didn't have any risk factors for heart disease. And more than almost half of them never saw a doctor in the year before, why should they they don't have symptoms or risk factors and then. David J. About three out of four of them weren't on a single preventive medical therapy so think about you can have the best prevention program the best cardiology program.
Speaker: but we'll never fix this problem because these people don't come to us. They die in the home. And so but i think the only solution we have is to go up into the home and find them. And that's what mammograms do. And that's what colonoscopy does. And that's what pap smears do. They just go when it's at a very early state where people can be prevented or asymptomatic state where people have severe disease, they're about to experience a catastrophic event and we can somehow prevent that.
Speaker: That's really interesting. I think that i mean mammograms are huge. All those other type of imaging methods were really important and foundational. And I think that, I guess to clarify a little bit about the clinical trial, and please correct me if I'm wrong, I guess the idea is that you have different tests you guys try out and over a longitudinal study, you see which is the best predictor of who gets heart disease, or is there something else I might be...
Speaker: No, there's one there's one or two steps beyond that, which is the randomization is for heart outcomes. So it's looking at a ah strategy of looking at actual heart disease versus risk factors of heart disease and to determine whether or not knowing the actual disease and acting upon it actually significantly reduces cardiovascular death, heart attacks, and strokes.
Speaker: Okay, that makes sense. Yeah, that's what's being randomized. That makes sense because I guess, obviously, that's a better – you want to kind of loop in the variable of how much does prevention matter too, not just alert detection. How does that change too? So in terms of these trials, like what would a win look like and what do you imagine kind of changing with these – if transform and all these other trials work out what do you imagine changing and the way this is going to be like reimbursed i saw that um this is now a category one cpt code for um this type of like imaging and plaque analysis but what do you imagine as a long-term thing there and what role do the trials play in getting to you to long-term reimbursement and um getting commercial coverage by aina and these other players
Speaker: for the current reimbursement for Clearlease analysis, Clearlease plaque analysis, that's restricted to symptomatic patients with suspected heart disease, right? That's the way we've practiced. That's the way the insurers currently allow for coverage and reimbursement. So we don't send patients for, you know, insurance-based coverage unless they they have symptoms or we don't send anybody because we're the company, but the doctors don't. um In this case for transform, we believe that there is merit and that it will be highly cost effective if we can take asymptomatic patients, people not actually let me rephrase that not asymptomatic patients asymptomatic people.
Speaker: They're not patients they're just the people that you're you see in the grocery store walking on the street like you know everybody knows somebody right who. went out for a run and never came back or who went for, you know, went to sleep and never woke up and died of a heart attack. The reason that everybody knows somebody is because it's the number one cause of death. And if you look at the math, people talk about the number needed to screen in order to save a life. Like it will be wildly cost effective if we, if this trial is positive, right? Like we have to see how this trial pans out, but everything, you know, everything points towards
Speaker: measuring actual disease to be superior than measuring indirect markers of disease. Yeah, that's interesting. And I also think that because you're able to do this, of course, there's so of people that are untapped, but from a business perspective, it also expands like the TAM, the total addressable market for your entire solution. And then that allows you to save i ideally much more lives. And one last question that I'm a little curious about is that, I mean, with this technology, I think there's a larger debate I've had with a lot of guests lately is that whether the role of ai what's the role of AI in healthcare? care It seems like for you guys right now, it's often like relatively procedural with workflows, but also more and more, I think it's going to get involved in the analysis itself.
Speaker: And the FDA right now currently has a stance where they don't they wanted to empower doctors, but not necessarily replace doctors and do make decisions on their own. Where do you think that will come into play long-term? I mean, how do you ah see clearly as a platform, as you put it, evolving in the next few years? and where do you think AI will change that? Do you think AI will inevitably be able to provide these finalized imagings that people and all these different stakeholders can use? Or do you think there's going to be more there that needs to be orchestrated that clinicians need to take up work for, for clearly to scale?
Speaker: Yeah, I mean, a great question. I don't know if I have really the answers. I don't have the crystal ball. Yeah, think anyone does. Yeah, what I can say is this, that our interactions with the FDA have been extremely positive. Like, I think that they are extremely dedicated people who have a vision to promote and make sure that they approve or clear devices that are safe and effective. And, you know, the AI boom.
Speaker: think about AI, like, I mean, I'm in and applications i'm an I'm not a methods developer, right? So when I think of something like AI, right, whether it's what kind of machine learning it is, like there are people who develop those methods and then there are people who apply them.
Speaker: I apply them, right? So for me, AI, like we started the whole AI thing in 2011. It didn't work all then. 2016, it was getting better. by two thousand and sixteen it was getting better um obviously now it works really, really well, but I still think we're in the first inning of this ball game. Like the kinds of advances that you're going to see from AI yeah is going to a dramatically and fundamentally change our world, but it's just the tool on how it changes it. Then you have to figure out how to apply it and how to apply it safely. And so like, I can't tell you how it's gonna happen, but I think the marrying of different disciplines into one,
Speaker: and That's what a machine is really good at, right? It's taking disparate data in an unbiased fashion and then integrating it into a common output. So think about it like they say, i don't know if it's entirely true, but they say that half of your risk of heart attack is due to genetics, but it's also environmental and it's also social and it's also psychological.
Speaker: And it also is due to cholesterol and blood pressure and ApoB and Kidney disease, and like you can go on and on and on and on, but they all converge to form disease, right? So if the AI can simplify things so that we can take all these different fields of data, aggregate them, integrate them, and output it in a way that's very actionable for a doctor and a patient, I think that's where the future is going to be.
Speaker: um Is it going to replace doctors? Invariably, there will be tasks that doctors do today that will be intrinsically replaced. How quickly? It'll go slower than the rest of the world because healthcare care is such a regulated environment and changing medicine takes time. So i think it's going to be you're gonna whatever you're seeing in other fields that's probably going to happen to healthcare just at a slower rate yeah i think that the bottleneck in healthcare isn't really the model quality just yeah mean an extent it is but a lot of it's like the data the orchestration like the regulations and things like that that are preventing that but it's really cool to see technology like clearly health NCT imaging kind of take a step forward. I mean, it's been doing that for so many years. And I think you can kind of see how commercially there's, you have the ability to scale through that by doing clinical trials and everything like that. So it's really exciting. I think that what we want to see is kind of the, yeah, almost like the mirroring of like you have this basic of science and this understanding of the research and taking it to commercial aspects and having someone that can do both and just one company or one lifetime is really, is really exciting. So um thank you so much, Dr. Mann, for coming on today. Really excited, really appreciated you coming on, just kind of sharing more about your story. I'm super excited to see where clearly goes in the future. Great. Thanks so much for having us, Nikhil.
