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An Iron Man Suit for Healthcare | Cardiologist & FDA Digital Health Chair Dr. Ami Bhatt
33 Plays2 months ago
In this episode, I’m joined by Dr. Ami Bhatt, a cardiologist who practiced for years at MGH and now serves as the Chair of the FDA's Digital Health Advisory Committee and CIO of the ACC Dr. Bhatt brings a rare perspective of how digital health can navigate clinical care, AI, and regulation.
We explore why modern healthcare feels increasingly strained despite unprecedented advances in data and technology, and how the explosion of information has pushed medicine beyond what any human can reasonably process alone. Dr. Bhatt explains where AI is already creating real value, particularly in administrative workflows and diagnostics, and why its most important role is augmenting clinicians rather than replacing them. Finally, we look ahead to what the future could hold: from AI-enabled clinicians and personalized health baselines to digital twins and a more patient-centered, preventive healthcare system.
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Transcript
Introduction to Podcast & Guest
00:00:00
Speaker
Welcome to the Healthcare Theory Podcast. I'm your host, Nikhil Reddy, and every week we interview the entrepreneurs and thought leaders behind the future of healthcare care to see what's gone wrong with our system and how we can fix it.
00:00:16
Speaker
Today, I'm joined by Dr. Amit Bhatt, a cardiologist and national leader in digital health. She practiced for years at Massachusetts General Hospital as a cardiologist, and today she serves as an advisor and the chair of the FDA's Digital Health Advisory Committee.
Dr. Bhatt's Perspective on Healthcare
00:00:31
Speaker
Dr. Bhatt has seen healthcare transform firsthand and brings a rare perspective that spans clinical care, innovation regulation, where she has a direct role in helping new technologies get evaluated and adopted across the healthcare care system.
00:00:45
Speaker
So in today's episode, we talk about why more data doesn't necessarily mean better care, how AI can be an Ironman suit for clinicians, and how the future of technology will shape healthcare.
00:00:57
Speaker
So hi, Dr. Bhatt. Thank you so much for coming on today. Super excited to have you and welcome to The Healthcare Theory. Thanks so much for having me. Of course, it's it's really great to have you on and I want to start off at the beginning of your career.
Evolution of Digital Health
00:01:09
Speaker
Of course, back then pen and paper was a predominant use of technology. Then we saw more rudimentary tools and now AI has been used across workflows. But starting off as a cardiologist, how have you seen digital health evolve as a platform in the past couple decades and what inspired you to join digital health and become an innovator here instead of just cardiology?
00:01:31
Speaker
Oh, my goodness. So first of all, that makes me feel so old, but it is true. um we started writing notes with pen and paper. and We didn't have an electronic health record. And the charts were like alphabetized on the wall with a little sticky by last name, first letter. You know, it was um very old school. um But I will say the thing that I kind of prized or or thought about most when I first started was how much time we had with patients and how relaxed we were.
00:01:58
Speaker
And that's something that changed over my career. ah The volume of providers or clinicians to actually care take care of patients went down and the number of patients as the baby boomers and now Gen Xers became older, especially in cardiology, became greater.
00:02:12
Speaker
So then you started having 15, 20 minute visits or if you went over, then other people were late. and so Really, that pace and volume of medicine changed a lot. and And that's oddly enough what brought me to digital health, because the opportunity to let my patient sit in a room in their own house with a laptop, right? And then if I'm running late or early, and and they're also comfortable, they're not some...
00:02:37
Speaker
um blue paper gown sitting in a, in a sterile room, right? They didn't have to drive three hours to Boston and Mass General to see me. So it was really, I think my patients and the idea that, gosh, like we're bringing them all this way for this experience when think what a better experience they could have from the comfort of their own home. If we would just go to them, you know, instead of them coming to us, um, for a while we went physically to them, you know, we did, I used to do home visits. Um, but, but,
00:03:05
Speaker
eventually you realize that there was a better way. And that's kind of how I started in digital health. Yeah, and
Data Overload & AI Solutions
00:03:10
Speaker
it's very interesting. like Digital health in some ways can make like it's digital can make the process more human, which I really like.
00:03:16
Speaker
And but I'd love to go into a little bit about like the problems behind this that would make digital health so important. Obviously, there's a huge like supply demand issue in health care. But I would love to hear like, what have you seen, like fundamentally change that makes this issue so im like that creates a so much put so much strain on providers and put so much strain on patients? Like what have what's the evolution been like over the past 10 or 20 years? You know, the main part of the evolution is a really good thing.
00:03:42
Speaker
It's that we know so much more about the human body. Back when I was starting, you had maybe a chapter about certain topics in a textbook in the medical library, and that was how much information we had. And now the rate of doubling of information is actually very hard to measure, right? Because we're just constantly increasing the number of research papers and investigations and what we learn in depth about any given healthcare process, not just in cardiology.
00:04:11
Speaker
And so now just the sheer amount of information that you need to know, it kind of surpasses the human brain's ability to understand all of it. Then take like measures and metrics, even you know things like wearables, to then actually testing and imaging, right, to the electronic health record.
00:04:29
Speaker
There's so many things you need to know about the patient themselves. And we realize now, which we didn't before, social determinants of health, right, drivers of health, those are really important. And so I think it's really just the sheer amount of information that makes healthcare care so hard nowadays.
00:04:47
Speaker
It's the reason that we first started thinking about, can we push care to the communities where people live? But I think, and we'll get to it eventually, What's really made digital health more possible is the the advent of AI, right? The advent of the ability to gather information that you need faster at your fingertips. We don't have it right exactly yet, but we're getting there. I think that's really been an accelerant because if you ask those of us who worked in like telemedicine or digital health before now, over the past 10, 15 years, we would all tell you it's kind of like you know hitting your head against a wall. like We couldn't get anything done. right but now with AI, we have a little bit more power because we can use a lot of that data in a more facile way rather than saying,
00:05:28
Speaker
one human has to check out all that data. Hold one second. I'll go back for a little bit to that. So now what we realize is with the advent of AI, all of this data that kind of grew over time, and again, it's a good thing, knowledge growing, data about patients growing, that's all good.
00:05:46
Speaker
But we got to this place where there was more data than an average human could ingest. And now this advent of really not just machine learning, but in the form of generative AI has allowed us now to be able to say, wait, I can take all this data and make sense of it. And so I think the more the the greater acceleration is more recent, whereas the past 10 years, those of us working in digital health were kind of, you know, banging our head against a wall. Yeah. And it's interesting. You'd think that like more information is always good. And in theory it is, but there can be problems with it.
00:06:15
Speaker
I mean, one thing I've heard about is like interoperability is also an issue. You might have like a lot of data and that's great for AI to help process decisions alongside clinicians. But if the data is like in different silos, it's going to be very hard to access it and get like a streamlined output. Yeah, I can't just work as well as it should. But I would love to hear your perspective on this. I know you've spoken on it before, like what progress are you seeing on this front? And like, why is there this interoperability in the issue in the first place? And i mean, with your position at the ACC and FDA, like what progress is there actually is happening in this area?
Challenges in Data Interoperability
00:06:46
Speaker
So so here's the thing. We in the United States really have a largely fee-for-service system. There is some value-based care. This week, we just announced the access program through CMMI. That's again, looking at payment for outcomes, but by and large, we are a fee for service arena.
00:07:04
Speaker
And so in that arena, people own data when they own a system, whether that's an electronic health record, whether that's some sort of an imaging system, whether that's a patient themselves saying this is my data. and so There is no mandate then to say we should make all of this be shared because we don't really have a good model for it. And we're still pushing this idea that the you are charging for what you're doing.
00:07:30
Speaker
And so that's really hard. If you look at the and NHS in the UK, now I'm not saying all other countries have a great plan, but those who have you know one health single payer healthcare care system,
00:07:41
Speaker
Now, the waits for care may be significantly longer because they don't have the competition we do in the state. So there's, again, always a plus or minus. But there, you can really start to force people to say, hey, you got to be interoperable. got to be on the same system because we're all on one system.
00:07:56
Speaker
And so I think that's part of the inherent struggle. And you know a variety of administrations, even as administrations change hands, always are trying to address interoperability. And some programs are successful, some aren't. But I think that's an underlying problem that we have.
00:08:10
Speaker
Yeah, i think there's trade-offs with every healthcare care system and ours leads to like a patchwork or like fragmentation. and like and so far, we have probably one of the best like healthcare care delivery systems in the world, like the best hospitals.
00:08:20
Speaker
I mean, obviously, you're in Boston, so you would know. Great hospitals, but there's there's this issue of like it just different silos. And how are you kind of seeing about how we can like take these silos down? Because now like data is more valuable than ever. Everyone wants to own their own data. and like You're not really want to give it out for free, but it's very important that we can train these models well and just have the data to get the best insights. But from your view as a regulator, like what can we do to encourage like this interoperability and encourage data sharing almost? That's a great question. I think the the first thing we have to do is recognize that this tertiary care system based health care where we say um you know, ah care starts in the hospital, goes to the clinic and goes to the patient. And then people call that the last mile of health care. yeahp That's absolutely wrong.
00:09:08
Speaker
The first mile of health care starts with the patient in their home where they live, either in a preventative care model or chronic disease management. And then if things are out of range for those individuals, there should be an approach to the rising risk patient. And that should still happen in the community where people live. People live. And then finally, you get to the tertiary or quaternary care, which should therefore be something that's easier to access, right? Where all the relevant data has streamed to you from the chronic management to the rising risk, now getting the right patient to the right thing at the right time. If we change our model to say the first mile of healthcare care starts with a patient, that's not the last mile. Now, naturally, companies oriented towards that
00:09:56
Speaker
are going to aim towards sharing that data because it's in their best interest. Because then we say prevention is the most important thing, in which case you have to have a holistic picture of the patient. um In which case payers are going to want to make sure that the patients are healthy in the communities where they live. And you can see examples of payers saying, hey, the healthy you are at home, ah the more benefits you get in our system, right? You can see them starting to try to do that. And at the same time, trying to curb costs in the larger centers.
00:10:29
Speaker
The other thing that happens is interoperability. So being able to get all the data you need for somebody with a complex disease is so important. I did, I practiced in a field called adult congenital heart disease. And this is ah kids who are young when they have heart disease as a youngster and they grow up, there wasn't really a group of people who knew how to take care of them. And so I was kind of in that first group of people who said, Hey, they're living past 18 now, let's take care of them.
00:10:58
Speaker
And literally the hardest job for an adult congenital heart disease doctor was how do I get the records of this person who has gone so many different hospitals? since they were born and then they were a young adult and now they're adult and maybe there was college in between and there was no clear way to do that.
00:11:12
Speaker
That still continues, that lack of ability. And I think, and a lot of people will say, if you want to bring that kind of data together, you have to remember that patients own their own data.
00:11:23
Speaker
And we need to find a way for patients to be able to carry their own data in a safe way and bring it with them wherever they go. And there've been starts and stops of this idea of patients owning their own data from everything from a USB drive to like, something in their Apple health. But I still think that that's the direction we're going to have to go in eventually the patients own their data wherever it was, you know, affected. And therefore, at least on a personal basis, you can have everything you need for yourself. I think that's the first direction we need to head in.
00:11:50
Speaker
Yeah, I think you want to be able to like empower patients to have their own data, id like to share it too. But like to empower them to also understand that data is like is also very, very important. Because right now, I look at like i like electronic health record, like There's probably not nothing super incredibly complex on there, but I can imagine for a patient who's going through some traumatic process, like it'd be extremely confusing to understand what's going on. And I think that's where I can step in. And um I mean, you're the chair of the FDA's Central Health Advisory Committee, which is really cool. And I'd love to get your idea on what
00:12:21
Speaker
what you're kind of seeing on the ground floor there and what your thesis on AI can like what your thesis on AI's ability to
AI's Transformative Potential in Healthcare
00:12:28
Speaker
impact this area. yeah I know like the FDA has authorized, I think, over maybe a thousand medical devices using AI, but not as much in mental health at least.
00:12:36
Speaker
But how do you balance like innovation and patient safety in this area? You know, I think the Digital Health Advisory Committee has been a fantastic experience. There's also the TAP program at the FDA that I participate in, which gets to companies early. and gives them advice about how are you going to scale once you have a good technology? Because having a good technology is just step one.
00:12:56
Speaker
Getting into clinics and hospital systems or to the patient's hands or provider's hands, that's kind of step two. And then scaling and getting more to them to a couple people, that's probably the hardest step.
00:13:06
Speaker
And so we work really hard at thinking about it, but I would say AI has three buckets, right? One is the administrative uses, back office hospital, prior authorizations, permission from the insurance company for what's happening.
00:13:20
Speaker
scheduling, understanding supply chain, right all those things that make you know any business run. So I think administrative is one, taking some of the load off of patients off of clinicians and the administrative burden they have to do.
00:13:32
Speaker
But then there's this whole arena of how do you get the right information to the right clinician at the right time? Because we don't we started by talking about not having enough doctors or nurses for how many patients we have.
00:13:44
Speaker
But if you expand the definition of clinician to include our pharmacists, Right. And we include our other nursing colleagues and we include people with technical skills and we include community health workers. Now we have a fantastic health workforce and a good sized one. That's not as hard to get into as like the 15 years it took me to get to be a subspecialist. Right. And so now we can actually grow the number of caregivers we have at a rate that matches the number of patients.
00:14:13
Speaker
In order to do that, the ability of AI to help upskill people, to give them the right information at the right time, really important. So we call that the AI-enabled clinician. And it's really anyone who's providing care, how do you help them use AI to give the highest level care they possibly can before they kick it up to the next level?
00:14:32
Speaker
And so I think that's a big area where we're waiting to see kind of what happens, what kind of regulation it needs, what the infrastructure looks like. And we're still in that governance phase of how we're going to do this.
00:14:44
Speaker
um And then the third area, so we have administrative, we have this getting information decision type support, but really I like calling it navigating to knowledge, not decision support yet, right? You have to have a human in that loop.
00:14:57
Speaker
And then I think the third area that's really awesome, and this is the area that's been passed through a lot of the FDA, right? Is the one that started with radiology, now it's in cardiology, ophthalmology, other places, which is really machine learning for imaging interpretation, right? or AI off of EKGs, or scans on your phone, or other things, right? Your fingertip on your phone. All the different technologies that are really software as a medical device that now have AI in them.
00:15:24
Speaker
I think that's the biggest growing area, which is often diagnostic and screening. Can I find disease earlier? Or triage. Can i tell you that this is a high risk or low risk CAT scan? Can i start to tell you what I think the diagnosis may be and you can work from there?
00:15:42
Speaker
I think that area is growing really nicely and people are practicing it well. So I'd say administrative on one end and then machine learning on another end, pretty rapidly being adopted and well used.
00:15:53
Speaker
That generative AI part, still we're being careful about it. There's a lot of governance and we're trying to understand how to do that safely. Yeah, that definitely makes a lot of sense. And I think like, yeah, you want to, I think it's harder in the middle because you want to like, you don't want to replace doctors. We don't have the technology too, but you want to get like, give them like an Ironman suit. I think you've talked about before, which is definitely hard to do. But I mean, with these startups you're working with at the FDA, like what are the main things like you're advising them on and like the main piece of advice advice that you're giving them? because I know like you start off pretty early and then there's many paths you can take, many,
00:16:27
Speaker
problems startups go through, but what do you see as like the main levers that you've been able to provide? so When I look at these companies, number one, I just look at the founder. Right. um Are they dedicated? Why are they doing this? Do they understand the depth of it? Do they have a passion for it? because being you know a startup entrepreneur is hard and there are more down days than up days. So you have to have a passion for what you're doing and you have to build a small, solid team around you who you think is smarter than you.
00:16:54
Speaker
So if I have a founder who has a real passion and dedication, who understands how hard it's going to be, and they have a small team around them where they really think the people around them are even smarter than them, like that's step one, right? Like that's how you want to build.
00:17:07
Speaker
And then you take that team and I think you have to focus And you have to go deep in one area. The most common mistake I see is people say, oh yeah, I can do this in this field. And now I can also do it there. And I can also do it there. And they're not even at series A yet.
00:17:21
Speaker
And you're like, no, no, don't, you're not building a platform yet because you're not deep enough in one area, build depth in one area, get really good at whatever the thing is that you do and you provide.
00:17:33
Speaker
And then from there, you can use your kind of stepwise grow if it's diagnosis specific or You can say, hey, I finally built depth. I understood. I understand how to build this. And now it can be a platform and I can apply it in a million different places. But young companies need to have you know a good founder, a good close team, good depth. Now, once you get out of that stage, iterative time, most important thing to me, when you get out into the wild, how quickly can you iterate?
Advice for Healthcare Startups
00:18:03
Speaker
in response to what happens. If you think about it, doctors, nurses, we're really used to like, if you give me a valve, the valve should work in the human period. If you give me AI enabled software,
00:18:15
Speaker
It is not going to work in my practice the way it did in the first trial that you showed me, because my practice is unique with unique individuals and unique inputs. And so I have to be willing as a clinician to say, OK, here's what you need to do differently. It didn't give me the answer. And so part of my job at the ACC is to teach clinicians, hey, it's not going to come out of the box ready. You're going to have to work with it. We call it collaborative intelligence.
00:18:40
Speaker
If you give really smart clinical feedback, these engineers can fix it for you. So for the companies, what I tell them at the FDA often has when I meet with them, especially through the TAP program is quick, iterative time, be willing to listen, be willing to iterate. And and that's going to be really important. If that means you need to have a smaller marketing budget, but a larger data scientist budget, then that's what you need to have when you first get started. So that's some of the early advice. And then the late advice once they get into practice is figure out what the metrics are that need to be measured for you that
00:19:15
Speaker
tell you that you're continuing to create good outcomes for patients while you're safe. And that's where we really need to start helping as the FDA, people understand what is that infrastructure with guidelines.
00:19:28
Speaker
And I think that's really important. Like infrastructure, follow guidelines, have guardrails, understand what you're putting together. Yeah. And it is like, I think as a term, as a definition of clinician expands beyond just like people like yourself, it's especially social workers and people pharmacies, I think obviously your ability to iterate will matter a lot more and your ability for technology to be like,
00:19:52
Speaker
personalized and flexible while still being like specific is going to be more and more important. But I mean, part of that has to come from clinicians like ah actually adopting this technology. And what do you see as like the main barriers there? Because I know, for example, a lot of clinicians are used to using their EHRs or even pen and paper, and it's like very difficult to not only get like an idea of like where I can help, but get like get AI in the workflow.
00:20:17
Speaker
But we'd love to hear from your perspective, like how can for the admin problem and especially the decision making problem, where can AI, where is this hesitancy coming from and how can we kind of alleviate it? Yeah. So depending on what it is, right, if it's an administrative issue, then the AI needs to be almost silent in the background. It just needs to be effective.
00:20:35
Speaker
we We can't be looking at it all the time. Right. And so that's different. If it is in front of the clinician and it's this navigating to knowledge, then you need to have a very fast, responsive, iterative time. And your technology probably needs to have a active feedback mechanism where people can immediately tell you this worked, this didn't, and you get that feedback back. so You can see signals where people may not enjoy your product anymore or trust it and immediately be able to go back out there and do some customer work.
00:21:04
Speaker
right while you're fixing whatever that issue is to make it better. And so that's different. And then I think the key for the third group. So we talked about administrative is like be in the background. Don't bother anybody. Have a great user interface and get it done.
00:21:19
Speaker
Navigating to knowledge is generative AI is be able to work with the clinicians and make sure to be uber responsive to to what they need. Right. um demonstrate safety, demonstrate efficacy constantly.
00:21:34
Speaker
And then the third group, which is the kind of machine learning group or the early diagnostics group in that group, I think the key is really how can you emphasize where you're best in the workflow?
00:21:50
Speaker
So do your market segmentation, understand who's going to benefit from you, who's not, understand where you can realistically fit where you might not be able to. And for that, you really need to think of, okay, who are my five different personas? Whether it's a clinical persona, whether it's a hospital, whatever it might be.
00:22:07
Speaker
Who are the personas that I'm aiming for? What does that workflow look like for them? And what is it creating? Let me give you an example. If I say, hey, my AI can tell you that you have liver disease on your EKG. By the way, that's a real thing.
00:22:20
Speaker
there's somebody out there with a really great algorithm for liver disease. Okay. Lovely and horrible at the same time. Right. It's lovely. It's like, Whoa, you can do that.
00:22:30
Speaker
And it's well, because you're like the cardiologist is not the person who wants to know there's liver disease. This is not a great setup for scale. Right. And so you have to understand that just because you have a technology doesn't mean it's necessarily going to be the right next step, or you need to make sure that there's a receiving end. Even if you say,
00:22:47
Speaker
hey, my AI can tell you 100 more people who have heart failure. That's great. But if I have one heart failure doc in the middle of Nebraska, and you unleash this ai and now I have like 3000 patients instead of 1000 patients, and none of them can get care, that was not an effective technology, and nobody's going to pick that up.
00:23:06
Speaker
And so you have to think about where do you fit in the infrastructure? What else can you do? And so I think that's, that's really the challenge. is you can't be a standalone, hey, my AI is going to totally help. You have to understand where are you fitting, how, where. And at the American College of Cardiology, that's partly what we do is we meet with these companies and say, where do you fit?
00:23:26
Speaker
You fit here, you don't fit there. Please don't go after that market. That's not going to work for the following reasons. And so I think that kind of research is really important. Yeah, do you think that just requires like a lot of conversations with with people throughout the industry? What do you think that actually takes? I know like there's so many different systems within those systems, the subsystems, and different people, and even like doctor A, doctor B can have totally different ideas on what their workflow is. So it's a very nuanced problem with maybe no right answer. And so product market fits really hard to get to.
00:23:54
Speaker
um How would you suggest like going about that and getting through that process? yes That's my day job. So so that is ACC invasion. We're literally what we do. And I talked to a lot of other societies who are trying to model the same thing, which is at the same time that we advise technology companies on what's your right market? How are you getting into the workflow? What does it look like?
00:24:15
Speaker
We then at the same time actually teach clinicians and health systems. Here's a new technology. This is what it looks like. This is where it would fit in your workflow. And so you have to teach both ends of the spectrum the same thing about each other and then test it and get feedback. And, and I used to, you know, I used to do a lot of this advising for individual companies.
00:24:34
Speaker
This is um similar to this similar to me being a pediatrician. So when I first trained, I trained as a pediatrician and I used to give parents advice about their kids. And then I had my own kids and I was like, oh Jesus, but like what what have I, read like what have I possibly told these people before I knew?
00:24:51
Speaker
I think the same is in a way true when you go to when I was an advisor for companies as a single doctor, I only knew the MGH system. All my advice would be based on the MGH system and then they would try rural Arkansas and it would not work because that doesn't make sense. But you can't fund individual advisors from every practice. If you go to a society that has 60,000 members who come from all these practices Now we can say, hey, we have about seven different personas. Here's what it is. You seem most most well matched for this academic arena or you seem most well matched for an FQHC. Right. Like um and and that's really important is being able to talk to people who have that entire viewpoint. And that's why we're really pushing other speciality societies like our own. Like, let's make a coherent plan to all do this for the tech industry together. I just sent an email about it this morning. Like we need to, we need, and that was my email this morning was to like the nephrology society, the psych society and ourselves. And you would think, what do these three have in common? But it turns out that there are a lot of systematic healthcare care areas where it doesn't matter what the care is specifically, it's the infrastructure that we need to help build together.
00:26:04
Speaker
And do you think like it it'll come from like more communication between these societies or like ah like an overlap between them? like what do you think that will actually look like in practice, like in an ideal world where we have this progress and the ability to like understand this infrastructure? like what do you think that would actually look like, like at least from your perspective?
00:26:22
Speaker
I think for individual devices that are unique to a subspecialty those are going to be different, right? Because that's clinical and we understand how to fit that in, where it grows, what a control trial looks like, where it goes in the guidelines.
00:26:36
Speaker
For things that are more general, like thinking about generative AI-based searching for guidelines, that's something that you should be able to do the same way, no matter which so specialty you're looking at.
00:26:48
Speaker
There shouldn't be a difference in how you get guidelines for psychiatry versus cardiology versus endocrine, right? that That doesn't make any sense. For those kind of interactive things, we should just have one plan. And that's kind of what we're moving towards is things that are very specific to a disease, take it and run.
00:27:06
Speaker
But those that are about medicine, about the delivery of care, those kind of things we should all have the same approach to. And I have one question that's maybe like a little bit interesting. is like there's different like With innovation, for example, there's a proper innovation theory, like small companies like disrupt the market, and which is what we're seeing to some extent. But also, i think incumbents have realized, like Epic Health, for example, are still spending billions of dollars on AI. And I'm wondering how you see that balance of like, incumbents trying to fix these problems, startups trying to fix these problems. like
00:27:39
Speaker
Do you think, um like who do you think is, like better suited in this case? Obviously, it's not just one or the other, but from your perspective, where do you think the innovation will actually come from with solving
Balancing Innovation: Startups vs. Incumbents
00:27:48
Speaker
these problems? Yeah, i am I'll give you my kind of stock answer, and then I'll just give you, like, the off-the-cuff my answer. My stock answer is we probably need a little bit of everything because there are different things that come out of large incumbent systems that know how to tweak and adjust what's happening there, and they're already reaching hundreds of millions of patients. So why would you not innovate?
00:28:09
Speaker
However, the fast moving small company is going to come up with things that you never would have thought of because you are held back sometimes in a larger system. And so I think those fast moving like quit iter quick iteration or like, i have a new startup, it died. I have another new startup, it died. And then finally, my third one hit because I understood and I learned something. You have to have that kind of an ecosystem for real success. And so we we need to see that, right? This is like Michael Jordan, right? How many shots do you take, right? You're not going to get every basket, but you got to take thousands of shots to get the number that he does. And I think that's really true with innovation too.
00:28:46
Speaker
um And then I'll tell you what I tell right now, although I'm changing now, but for the past few years, I've been telling most of the young people that come to me that have a new company. Right now, it's really hard to survive in the current fiscal environment.
00:29:02
Speaker
So if you can build until you exit, And that exit is not an IPO, but it is let one of the big guys take you and your awesome technology. And then go build another thing and let one of the big guys take you again. I've been saying that for a couple years to people. Like, build deep, get it right, find somebody to buy you, get out, come up with a new idea. Because the IPO road is really long.
00:29:28
Speaker
I'm just now coming back off of that hump to say, hold on, i think the innovation environment is changing again. i think payment models like the access model that just came out that's you know paying companies directly to provide digital healthcare care in the communities where people live.
00:29:45
Speaker
I think maybe we're reaching a little bit of an inflection point where the potential IPO of a new young company maybe kind of back in for me. Right now, other people will say, hey I mean, it's always been all three of them. Some are going to make it, some are going to exit, some are going to die. And that is true. But I'll just say I, for a while, was feeling really tense about any company aiming for IPO and hoping for most of them to like get bought out by someone big.
00:30:13
Speaker
And I'm now feeling like that's opening up a little bit. And I think see a thousand flowers bloom again. And so I don't know what's giving sense, but I'm in the arena a lot, so I might as well say it out loud. I think it's starting to happen again. Yeah, and I definitely agree. I think there's a lot of virtue in the original path of like selling your startup, and maybe it's a bit smaller, selling your product, I should say, to a larger company because, of course, they have the distribution. But also, a lot of times, there's great ideas that like a larger company just can't build, whether it's politics or the infrastructure or them cannibalizing their own product.
00:30:44
Speaker
it might require a startup to build something that's necessary. So think there's obviously and hopefully we'll see more maybe platformization of these startups and like see innovation kind of come together. But it is really exciting. I think we're seeing like a little bit of a changing prime. IPOs are picking up a little bit, but yeah I would love to look ahead. I mean, this next five to 10 years down the road, like what are you most excited about? I know we've talked about it a little bit. what are you most excited about the realm of AI and health care? And what are some breakthroughs that you're maybe envision that might become reality soon enough?
AI in Personalized Medicine
00:31:13
Speaker
Oh, gosh. So I think one is the use of AI to really look at physiology. We have a lot of imaging that looks at anatomy in whatever field you're in. Like, what does this thing on your body look like?
00:31:26
Speaker
And I'm excited for AI that tells us how is your body acting with this thing in it, right? I think that's really important. And so I think this idea of physiology, I'll give you the cardiac example. It's looking at plaque in your arteries. And we all say, do you have plaque or not?
00:31:42
Speaker
And now we're moving towards, is that plaque going to rupture and give you a heart attack or not? Right? And I think that's really important. Can you hear the dog in the background?
00:31:54
Speaker
Yeah, but I can just edit it. She stops. I can do it. I can just edit it out. This shouldn't an issue. I can try and let him in and see if he'll be quiet. okay yeah
00:32:04
Speaker
music make a establish Yes, no, I have a dog at home too. So like when i if I record podcasts back home, like that happens all the time. So it's not. um So looking to the future, i think I'm really excited to see the use of AI for physiology, not just anatomy and imaging.
00:32:26
Speaker
I am excited about the possibility of really personalized medicine. um And, you know, the really complex kind is I took your imaging and your genetics and your, you know, daily life and I put it all together. Maybe that's a little further away. But even just being able to know like, hey, my blood pressure is usually this and today it's out of range rather than people tell me that my blood pressure should be blank. You know, we always have these static numbers and things. You should be blank. No one knows what you should be.
00:32:55
Speaker
But if we can run in your own data, know what your baseline is, and then you could know, hey, certain things make me feel worse because these numbers change. Certain things make me feel better. Or I think I'm getting sick.
00:33:06
Speaker
I think that kind of just personal knowledge is going to give patients such patient agency. And I want agency over my own body. I want to know what's happening and how I feel. And so I'm really excited for it those two things, AI for physiology, and then really thinking about kind of personalized medicine.
00:33:23
Speaker
Yes, the complicated kind where we put in 100,000 things and we find stuff about you, but also just the daily thing. Like, here's my baseline. This is where I'm supposed to be. this is where I feel healthy. And here are the things that affect me. So I think that patient empowerment i'm really excited for.
00:33:37
Speaker
Yeah, it is almost like it's so hard to imagine, but so exciting that like we can get to a future we can personally like personalize to do the energy genetic therapies, can personalize like almost any cure. Maybe it's very far down the line, but it's it's it's amazing. And I think one other thing is that we've seen like a very transactional relationship between entrepreneurs and doctors.
00:33:55
Speaker
entrepreneurs believe that doctors are just like they're selling something to doctors. Doctors feel like they're just paying somebody. But excited for a future where can kind of see more collaborative environment where doctors realize that entrepreneurs are trying to empower them. Entrepreneurs realize that doctors are provide value that AI often cannot bring. And I think once you see that intersection, I'm excited to see how yeah, I can not just build good technology, but we'll actually work in like real clinical usage. So um yeah it's great. That's exactly right. No, no, I don't want to interrupt, but I want to tell you, I don't think it's just the entrepreneurs and the doctors. I think we have to think about the governments being part of that plan and yes yeah involveded what we're doing. And I think we have to think about investors. We have to think about those who are pouring money into it, not just looking at it as a like money making opportunity, but also as a,
00:34:45
Speaker
giving back, making the world a better
Introduction to Digital Twins
00:34:47
Speaker
place. And so I think we really need what you're looking at, Nikhil, is a breakdown of those silos and understanding that like if we all work together towards those same goals, we'll be better off. um I'll tell you one more thing that I really love though, but before I forget, which is I love the idea of a digital twin.
00:35:04
Speaker
I love the idea of a digital twin. It's one of my favorite things, to be able to take a digital version of my patient and test what might happen with a med before I do it.
00:35:17
Speaker
Gosh, I mean, that, I think that's one of the areas that I really love the most. We didn't touch on it today, but for your listeners who are interested, it is worth looking up a little bit of stuff about digital Yeah, I hope we can do an episode on the future with another guest. I think like digital twins are just are really exciting, especially like, i know you've talked about longevity as a problem before.
00:35:33
Speaker
Whether it's for that, increasing health span or just in general, i mean, I can't imagine like a yeah future where you can just have like, you digitize like everything you need to know about a person just be to like work off of that could be really really interesting um but ultimately i think there's so many different applications from my systems and infrastructure perspective to a more technology perspective and it'll be cool to see how they all come together so i'm glad had a very optimistic episode today um it was super exciting but appreciate coming on today dr bod and just like walking us through your journey, but also just to see like what your perspective is on know what it takes to succeed, both as a clinician and also as a entrepreneur too. So I really appreciate your time today. Well, thank you for having me. And and if there's one last thing I can say, it's um, you gotta enjoy what you're doing. Um, and if you really have a passion for it, if you enjoy it, then even the hard days, you wouldn't want to be doing anything else. Um,
00:36:25
Speaker
Oftentimes we get stuck into this rut of, well, I'm supposed to do this. And I will just tell you like life is short. So if you're not enjoying it, find something you really enjoy, you're passionate about. And then even the bad days seem like, okay, days, which is good.
00:36:39
Speaker
Yeah. And do you think like, I think it's like, from your perspective, passion is like one of the probably strongest differentiators of a good entrepreneur or a good clinician, right? Like how much they love doing it when the days go, when like the days or months look a little bit distraught.
00:36:52
Speaker
That's exactly right. That's exactly right. I think we all need to have a little bit more of that in health care. So, yeah, really appreciate the time again.
Podcast Conclusion & Resources
00:37:02
Speaker
Thanks for listening to The Healthcare Theory. Every Tuesday, expect a new episode on the platform of your choice. You can find us on Spotify, Apple Music, YouTube, any streaming platform you can imagine.
00:37:14
Speaker
We'll also be posting more short-form educational content on Instagram and TikTok. And if you really want to learn more about what's gone wrong with healthcare care and how you can help, check out our blog at thehealthcaretheory.org.
00:37:27
Speaker
Repeat, thehealthcaretheory.org. Again, i appreciate you tuning in I hope to see you again soon.