Chirag Patel Podcast Transcript
Chirag Patel joins host Brian Thomas on The Digital Executive Podcast.
Brian Thomas: Welcome to The Digital Executive. Today’s guest is Dr. Chirag Patel. Dr. Chirag Patel operates across sectors and systems where innovation meets infrastructure, and where performance, health, and capital intersect with lasting impact. As a physician, founder, and strategic advisor, he has spent his career building and guiding ventures that sit at the convergence of healthcare, investment strategy, elite athletic performance, and global enterprise development.
His work spans both clinical and corporate domains, integrating hands-on precision with long-range execution to deliver outcomes that scale and endure. Well, good afternoon, Chirag. Welcome to the show.
Dr. Chirag Patel: Thanks, Brian. Thanks for having me.
Brian Thomas: Absolutely, my friend. I appreciate it, making the time. I know you’re in New York right now. I’m in Kansas City, so I appreciate you making the time. It’s hard to navigate calendars and time zones a lot of times. So again, thank you. And Chirag, let’s jump right into your first question. You were born in London, moved to the US in 1998, and built a career that now spans clinical podiatry, sports medicine, investment, and enterprise development with patients ranging from Peewee League players to pros across NFL, NBA, MLS, and Olympic disciplines.
What was the through line that took you from foot and ankle medicine to this much broader ecosystem of performance, capital, and business? And was there a defining moment that set you on that path?
Dr. Chirag Patel: Yeah. So, the, the, the foot is, a very complex structure that we tend to forget about, right?
Each foot has twenty-six bones, thirty-three joints, over a hundred ligaments and tendons. And, every step, every stride, every cut, every, every thing that we do with our feet and ankle this is a– this is where a force is meeting a ground. So you start to kinda see this whole system from the bottom up.
Everything that I was seeing was coming from this, this downstream to upstream effect. A knee problem is often a foot problem. A hamstring problem is often a hip problem that the foot created. You’re working with the smallest lever with the largest downstream effect. So the through line is, is that I kept following that constraint, right?
In the clinic, the constraint was time. One person at a time and only, only the ones who could physically get to me. In professional sports side of things, so from the sports medicine aspect, information was never a constraint. Teams have mountains of data and, and there’s almost no way to act on it in the moment that matters the most.
And then in business, that constraint was capital and distribution. So even though this all looks like different problems, they’re actually the same problem just wearing different, different hats or different clothes. So knowledge, there’s knowledge that exists, and there is a gap between that knowledge and the person who needs it.
So an example, to answer your question, I’ll give you an example of an actual situation. Same ankle issue, two different people in the same week. One was a pro athlete, the other one the other patient is somebody that worked two jobs. The pro athlete never needed surgery. Why? Had– they had access.
They had access to being able to text somebody at night had an entire team around them that was able to provide insight and make sure they’re staying on track with what they needed to do. Whereas the other patient received a printed sheet of exercises and a follow-up in six weeks. Tried his best, used resources available to him guessed wrong a few times.
Nobody caught the bigger issue, right? And so he ended up in the operating room. Same injury, same medicine. One of them just had better access and, and someone to talk to. So this is when– this was when the question kinda changed, right? It stopped being, “How do I fix this ankle?” And it became more about, “How do I get this to people who will never be able to physically get to a doctor or get into an office?”
And everything since then has just been different attempts at trying to close that gap and doing it at scale. So tapping into my network, tapping into my access, tapping into the, the ecosystem that I’ve built on the medical side with the colleagues that I have, and working that problem from, from, I guess you could say foot up.
Brian Thomas: Thank you. And that really, your foot is just as I like to say, foundation, right, for your body, and I liked how you kinda went through that journey your backstory as a physician but your foot is a complex part of the body. And as you said, foundationally, a lot of times issues that you have with other parts of your body stem from a foot problem or a foot injury, and I thought that was interesting.
You talked a little bit about business and how that correlates, obviously access to capital, finance, et cetera. But again, there’s a parallel here with what you’re doing in the medicine world. But I love the example two patients, same diagnosis, and had two different outcomes, and again, comes down to that information that you talked about.
So thank you. Chirag, as ESPN’s resident injury expert, you translate complex sports injuries for a national audience, analyzing outlooks, recovery timelines, and return-to-play risk. What’s the hardest part of taking a nuanced clinical picture and making it accurate but accessible for millions of fans? And how do you approach commenting on athletes you’re not personally treating?
Dr. Chirag Patel: Yeah, great question, Brian. So, the hardest part is that the, the fans or the public, they want the date. When is this player getting back? How long are they going to be out? But biology just gives you a distribution. So everybody wants to hear six to eight weeks. Six to eight weeks is a fiction that fans, would love, and medicine honestly cannot supply, right?
So the discipline is to give the range and the reason instead of just giving a number. So what does this injury do to this tissue or in this situation? What has to be true for it to hold up under load? What would make me more optimistic, and what would make me less optimistic on certain timelines or certain return ranges?
What would we be looking for in, in the follow-up consultations that the, the doctors that that athlete will see will be looking for as well? And just kind of guiding that process and giving that insight of what’s kind of happening behind the doors or what will happen behind the doors. So people are smarter than we give them credit for, right?
An audience can– they can handle a range. What an audience can’t handle is a confident number that then, you know, it, it, it feels like a promise that gets broken. So on the, on the second part of your question, which is the one that I take actually more seriously, my rule is pretty simple: I comment on injuries, I don’t comment on people.
So I do not have that– their imaging, I do not have their labs, I do not have the access in that situation that people on the field may have, their own medical team may have. I don’t have their chart. So I’m not their physician, and they’re not my patient. So I can tell you what this injury pattern typically does in an athlete of that age or at that position or under that kind of a situation or load.
What I can’t tell you is what is happening inside that specific human being. And when I’m estimating, I, I say that honestly, that this is an estimate. This is what would likely happen in this kind of a situation. And that sounds like a limitation, but it’s actually the entire job, right? So the value was never in being certain on, on, on what I say.
The value was in being calibrated and in being honest about exactly where the edge of my knowledge exists in that setting, right? To, to give the fans a deeper look, as I said, behind, behind the closed doors of what progresses after the cameras are off or the player is no longer in that view. So it’s the same standard that I hold with the technology that we’re building now, right?
It’s a system a system that won’t tell you when it does not know is not an intelligent system. It’s just a confident system. So I try to be very particular and very calculated in how I address those types of segments where I’m commenting on a professional athlete Especially someone who is now, their career is also h- in question here for the most part.
Fans want to know, are they coming back this season? How long will they be out? These are, these are, easy landmines to trip over and, and, and the honest answer is always just give what you can, what you can speak about confidently and more on a population level and, and on the backend stuff versus trying to go so deep into trying to define what that injury is for that exact person because you don’t know.
You don’t know until you see those labs, those images, those charts
Brian Thomas: Thank you so much. I appreciate that. And it really resonated with me, Chirag. I’ve worked in healthcare for a major part of my career on the technology side, but I was obviously exposed to that whole industry and, and I thought it was interesting.
But fans are extremely curious, as you know, and they wanna know when their favorite athlete’s gonna be back in the game. But there’s a couple things I, I noted here. You do provide those insights on injuries, not the actual person or athlete, because you don’t have access to their chart. And even if you did, there’s probably a lot of things you can’t say due to HIPAA, right?
So it’s, Right … it’s an int- interesting insights, and I appreciate the answer. Chirag, you emphasize data-driven recovery optimization for elite performers. What kinds of data are actually most useful in guiding an athlete’s return to play, and how do you balance what the numbers say against the human individualized side of recovery?
The part ca- really can’t be reduced to a metric.
Dr. Chirag Patel: Yeah. So, things like limit- limb asymmetry, for example, the difference between the injured side and the healthy one. The body will hide the deficit right up until the moment it can’t, right? So rate of force de- the, the rate that a force develops how fast you produce that force, and, and not just how much because in sports, this happens in, in fractions of a second, right?
So load tolerance across a week rather than a single test because one good day tells you almost absolutely… tells you nothing. So you have to, you have to have this time series almost. And then what gets missed, right? Most return-to-play decisions are still made with two instruments pain and calendar.
Does it hurt, and has it been six weeks? Both are just terrible, right? Pain is a lagging indicator is subjective as well, and the calendar knows nothing about you. And of course, in professional sports, the, the tolerance is, is much higher than a, than a normal person, right? The pain indicator for someone like me, that, that isn’t an athlete the smallest thing might be considered a, a, a excruciating pain, whereas for an athlete, they’re, they’re unfortunately so used to taking these bumps and bruises that that pain indicator is, is, is very different.
So the most useful data is not the most sophisticated data. It’s the data that you actually collect consistently. So thirty seconds every day beats a lab session every quarter. Lab tells you what was true on a Tuesday, for example, on a certain, certain month. The daily signal is what tells you what’s true right now, and right now is the only thing that you can really make the decision on.
So the other half of the, of the question you asked is numbers tell you the tissue or the, or, or the ligament or the muscle is ready but it doesn’t tell you that the athlete is ready, right? So fear of re-injury is one of the strongest predictors of whether someone’s gonna g- going to get back to their prior level and there’s currently no sensor for that.
I’ve seen athletes who are physically perfect, look like they’re ready to return to play, but mentally they’re nowhere close. And if you’re not mentally dialed in, this is a definitive higher risk of, of potential recurring injury. And so data narrows, the range of good decisions, but it, it doesn’t make the decision.
It’s still a human being sitting with another human being. And so at the end of the day I do not think that that changes, and I don’t think it should
Brian Thomas: Thank you. Appreciate that. Really do. Some great insights there, I know traditionally pain and calendar has been the measurement for athletes to be considered, re- that recovery, right?
But you talked about that data. Unless you consistently collect that data, for example, daily on what’s true, it would narrow that gap whether or not that athlete’s ready. And the one thing I would like to highlight, and I, I think this goes so far so back for, for everybody, is that mentality or, or truly mentally is a big part of this.
Whether you think you can as you know, it doesn’t matter what you do truly determines whether you can or you can’t. So I appreciate that. And Chirag, the last question of the day, as AI, wearables, regenerative medicine, and real-time biomechanical data continue to advance, where do you see elite athletic performance and injury prevention heading over the next decade?
And what breakthrough would most change how the next generation of athletes trains, recovers, and extends their careers?
Dr. Chirag Patel: Yeah, great question, Brian. So all four of those things that you said are advancing, you know, super fast, I would say. And we’re not short on capability. What we’re short on is a decision layer.
The distinction I would put in front of anyone listening is data is a reference point. It’s not a decision point. A number on a dashboard tells you where you are. It doesn’t tell you what to do next. And it definitely does not tell you what to do in an do next at six in the morning or at seven in the evening when you slept badly or you were sore or, or you have a game on Friday.
And that is when the decision actually gets made, and that’s precisely where every system that has been built so far goes quiet, right? So think about Google Maps. Maps doesn’t hand you a traffic report and wish you luck. It tells you when to turn it, and it works for– it tells you when to turn it, and if you take a wrong turn, it adjusts, quietly without, without letting you feel that you’ve made a mistake here, right?
And it works for one reason, and the reason it works is because it knows where you are. It, it’s it’s not telling you where the average driver is. It’s telling you where you are right now, right? And when you miss that turn, like I said, it doesn’t lecture you. It does not make you start over. It recalculates instantly without judgment and still gets you to your, your destination.
This is a piece that health and healthcare hasn’t had in abundance, or it’s still missing, right? Every health product that I have ever seen has a layer of punishment for making a wrong decision, right? You fall off, it shames you, and you delete it. And this is where then that human, The, attention to that, the human capital that’s invested into that, that’s using a product to help them, move their own– bend their own curve to the, to the right direction can be lost because they feel like it’s not working for them.
So real decision layer comes when, when you can assume that wrong turn. It’s built for that, and that’s a gap that we’re, we’re building at Bio to close. So as for the breakthrough, it’s when that layer costs nothing, right? So a professional athlete has 10 people doing this around, around the clock for them, trainers, physios, strength coach, physician.
Somebody’s reading that data before they even walk into a building. A 16-year-old has a coach and a parent with a phone. That is not a talent gap. It’s a staffing gap. So knowledge is power, but general knowledge isn’t, right? So knowing what happens to the average knee tells you nothing about your knee.
Knowing what happens to the average foot tells you nothing about your foot. Your load, your history, your week, your environment in your body, that is what changes a decision. Medicine has spent 100 years getting very good at populations. What changes the next generation of medicine is, is going to be N of 1, going down to the individual.
So extending professional careers is a small version of this, but the big version is a kid who does not have to blow out a knee at 16 in a way that ends a career before he even gets his start.
Brian Thomas: Thank you. That’s awesome. So you talked a little bit about health tech, emerging tech advancing rapidly. We know that.
The good news is it’s gonna provide a lot of that data that you talked about to help improve training, recovery, and, and kind of look at, if, if they’re on the brink of a, a p-potential injury or prevention in any way. But you stated, again, data is a reference point, not a decision point, and generalities won’t get you there.
You need specific data points on that specific individual in order to be successful in that– in this space. So I appreciate that, really do. And Chirag, it was such a pleasure having you on today, and I look forward to speaking with you real soon.
Dr. Chirag Patel: Appreciate it, Brian. Thanks for having me on.
Brian Thomas: Bye for now.
Chirag Patel Podcast Transcript. Listen to the audio on the guest’s Podcast Page.
