Welcome to the HealthCare Theory podcast. I'm your host, Nikhil Rady, and every week, we interview the entrepreneurs and thought leaders behind the future of health care to see what's gone wrong with our system and how we can fix it. Today we're speaking with Joshua from TMA Precision Health, a digital health company using AI to improve quality of care. So hi, Joshua, welcome to the HealthCare Theory and thanks for coming on today. Yeah, happy to be here. Of course, and I'm also excited to speak to you today and learn more about TMA. But before we get into that, I know you had quite a bit of background in research. Can you speak more to that and what kind of variety to health care in the first place? Sure. I mean, I got interested in science and research from a really early age. I was a big sci-fi nerd, you know, probably like a lot of people who are who are in the space. And when I got my undergrad in biomedical engineering, I always knew that I wanted to go into helping patients. Originally, I thought that was going to be through creating organ prosthetics. So I really like this idea of printing organs. And I ended up working for a woman named Sangeeta Batia at MIT doing that, but on a very micro level. So a long time ago, I was very early into what's called microfluidics. So that's really the idea of using microchip technology to capture single cells so that we can interrogate how those cells work on an individualized basis, because biology is pretty messy. And so as that scaled up, right, the idea of being in the translational space, things that would always have a patient impact, that's always really where my career has been focused. And so that carried with me through my work at Tufts doing work on cardiac patches, resellularized cardiac patches, to ending up at the Vis Institute for Don Ingber at Harvard Med, working on organs on chips platforms to make sure that we can more rapidly create solutions for human patients that don't need animal models that fundamentally just don't work in the first place. So I mix a lot of sense. I know you kind of had a little segue where, well, first of all, at Harvard, I mean, can you speak more to that research? Like, what kind of sparked that and what was their project like? To the research at Harvard Med? Yeah, so the foundational paper was by a guy named Dan Hu, who has gone on to become a professor at the University of Pennsylvania, I believe. He demonstrated that you could use a substrate called polydimethylsiloxane to create an optically pure rubberized template that you could grow cells on, right? So my graduate work was in extracellular makes its composition and how it impacted stem cell fate. Sorry, this is getting a little wonky. Do you want me to stay a little high level or is this, this is okay for you? We can do high level, I guess. Yeah. So look, the idea, the idea is that you could take microchip printing technology, so control at a very, very finite level and create structures for cells that look like organs. And so when you put cells into those structures, they, they function like organs, all right? And you can do it in a way that you can now image through. So you can take videos and you can do laser microscopy on all of the interactions that those cells have. And so we took that original work that heated in lung and translated it out into 10 or 12 organ systems and then built a machine to connect those organ systems together. So we really had a human on a chip, we called it. At the time, I think it was the largest DARPA grant ever given for this kind of technology. And so my personal work was on lung and liver and gut and making sure that those chips grew and worked and were developed correctly. That work eventually spun out into a company that is not called Emulate Bio, they're down in the seaport. And I think they're still the world leader in organ on chip technology. So really exciting work. It was really, really fun to do. That's awesome. That's really cool. And secure, that'd be kind of interesting to follow them as it spun out. But I know for you, I mean, you kind of started with this research, really enjoyed your time at Harvard and with the professor you studied on, but under, but eventually you started your own company, KAPO. I mean, can you speak more to that and what kind of happened there and what like, market has changed from like research and kind of in the lab all the way now to starting your own company, raising money. What was that transition like? Yeah, so that's a funny story. So, so during grad school, my girlfriend had a, had a fermented foods business. And so we used a lot of mason jars just around the house. And I would drink coffee out of mason jars all the time. I spilled a bunch of coffee on myself one day in the car and I was like, man, someone must make a lid for this thing. Nobody did. I Googled it. Nobody did. My, one of my best friends was a, was a medical device engineer. So he literally was like a freelance guy that people would call up to help them build really, really complicated medical devices. So had him over, you know, for pizza one night and I was like, Hey, I want to build this thing. This little lid that goes on top of this jar. And he basically said, you know, that's a dumb idea, but like, let's do it. And so it was just two friends who were like two guys in a garage. Like we got, we were both super nerds, right? So we got really deep into like patents and engineering and specking it out. And we made a thing that we just wanted and launched it. And this is, this is, I mean, I'm going to date myself here a little bit, but this is back before you had to buy ads on Facebook or you know, Instagram had ads on it. Those things weren't even the links back then. And we went viral, you know, so we made 500 to start. We sold those 500 in the first 72 hours, we'd sold 3000 by the next week, 5000 the week after. And it just became this like MBA by fire over the next year. What was crazy is that I was, I was working at the visa. So I was doing a lot. So I'm doing 60 plus hours a week at the visa. Because if anybody who knows biology and wet work, like, you got to go in every day to feed yourselves, right? So it's a lot of work. And so I basically go to our warehouse in Summerville. And Union Square, this is for Kapow, from like six to eight, I'd ride my bike down to the visa, work there until about six, come home, do some dinner, and then go back and work, you know, until like 11 o'clock at night. And it was just like the only way we could scale. And what I found was that I really liked it. I really liked the commercial side of things. I liked, I liked figuring out the numbers and the distribution and all the logistics. And so I really made this transition off of the bench into commercial world while doing both simultaneously. But it really parlayed for me into a career of building businesses. And while I did go run that business for a while, it actually let me come back into the commercial side of biotech and health care. And that's where I'm at today. Yeah. Yeah, that makes a lot of sense. And I think at TMAM right now, it's nice because you get that technical understanding, even if you're not like doing research day in and day out, you're always working with a more technical product and I got some more impact pack full space because the commercial side too, for you're helping develop the product, grow, get more customers. And so I think there's a lot of nuances there. It's nice. But I assume you didn't just go into TMA just to kind of go into the commercial side of biotech. There's, I know there's a new birth story behind that. But if you're comfortable with it, we'd love to hear your story and kind of share more about what got into the space. Yeah, no, very happy to share. Something that I, I forbid I were to talk about a lot. So rare disease is, is a thing that affects 30 million Americans. So most people, people hear rare and they think it's like, you know, one in a billion. But the reality is they're genetic diseases and the effect, depending on what stats you look at, one out of eight or one out of 10 people. So in the States, we assume there's about 30 to 35 million people who have a genetic disease. Like a lot of people in the space, I am here because one of my kids has a rare syndrome. And so despite being born really healthy and normal, about a year and a half into life, my second son, Shiloh, started having fevers. And we went through what is called the diagnostic odyssey, where we spent two years bouncing through a couple of pediatricians. We were in and out of the ER on a monthly basis. His, his is basically a hyper inflammatory issue. So he goes from 98.6 to 105 in about 10 minutes. And so, you know, between, between walking away from him to go like, make some chicken nuggets for like a two year old and coming back to the couch, he's just a mess, right? The syndrome does not respond to NSAIDs, like Ibuprofen or Tylenol. So you actually have to pack a kid in cold compresses and two year olds do not like to be put into cold compresses to keep their temperature down. So it's, it's a huge struggle. We are incredibly fortunate, like in the rare disease jackpot, we, we're on the good side. I talked to families on a weekly basis who have kids who are terminal, who have kids who are catatonic, who are kids who are just never ever going to live a normal life. And it's incredibly frustrating to see healthcare fail them, not just, not just on the pharmaceutical side, right? Where we're not developing treatments fast enough, but even just on our health care application, right? We have all of these amazing technologies, primary one being whole genome sequencing that are not deployed early enough. So the sort of broad picture here is that your average rare disease, your average genetic disease patient takes seven years between first symptoms and diagnosis. And during that whole time, they're bouncing between specialists through referrals, they are getting sicker, they're diseases exacerbating, they're spending a lot of money, they're spending a lot of time, there's a lot of frustration for families. We looked at this problem and decided the place that we could be most impactful was to decrease that diagnostic odyssey in a way that made sense for health insurance. And so we built a tech-enabled solution that gets that seven year timeline down to three months today. And as we move forward, a lot through AI, some through vendors and partnerships, we're going to be able to get that down to closer to like four weeks in the near future. That's awesome. And before we get into then, how you guys have kind of created that not really changed the space so far and getting it down that much. I mean, what do you think this kind of stems from with the rare diseases, of course, in the name of the rare, so it's kind of this less resource being diverted to them. But I mean, in the US, we have a huge focus on acute care, a lot of money spent on this research. Like, why do you think some people are kind of left behind? Is it like a lack of funding, awareness, or regulations? Where do you think that falls into? Well, it's kind of a loaded question and I might get a little trouble here. But the problem, the sort of global problem is that A, these patients are called rare. It's a terrible name, right? There are technically more genetic disease patients in the states and there are cancer patients, but you think of cancer as being a very expensive place and a lot of innovation happens around there. And cancer is an umbrella term, right? There are literally thousands of types of cancer because they're all really mitotic issues at the end of the day. But people think about it as one sort of block. Rare disease suffers from the fact that there are seven to 10,000, what we call seven to 10,000 different mutations that fall into the rare disease category. They have to, by definition, only affect one of every 200,000 people. 30% of rare disease are in fact cancers, right? The challenge, to answer your question directly, the challenge becomes that the market opportunity historically has not existed strong enough to interest either pharma or research dollars, right? These become fairly esoteric spaces for people. And, you know, admittedly for physicians, if they're seeing heart attacks all the time, if they're seeing diabetes all the time, if they're seeing people at the end of their life who have developed cancers all the time, more of their brain space gets taken up by addressing those issues than by the one kid with a really high fever who comes in every few years, right? Or the kid who has really severe epilepsy. They do their best for that child, but, you know, your your your quest is to do as much good as possible. So you're going to focus on the bell curve. And I don't fault them for that, but we're at a place technologically where we have the ability to go off and address these other diseases. And so that's really what we're trying to enable. That makes sense. I guess the health system can be like pretty utilitarian in that way. And I mean, you see, you've seen this problem now, right? Like that there aren't any resources being diverted here. And of course, it's a hard home to tackle because for most people, they don't have the regulatory ability, financial ability to kind of solve this. But you guys did a TMA precision or solving with TMA. And you're going to walk through like, not only like, what, what, how did you kind of come up with the solution? What kind of went into ideating that? But also what the solution is and provides some background there. Yeah, sure. And I just want to address something you said, Nikhil, because I think it's really important. Like you talked about the health system being pretty utilitarian. I think that we really want it to be, right? Like it's not about the fact that we want to take resources away from cardio patients or from diabetes patients. The idea here is that we're also capable of doing more now for all patients. And that's really about like democratizing healthcare to make sure it works for for all kinds of patients, wherever they are. So it's like well known that if you live in a city, you're going to get better health care. If you're if you're close to Mass General or Stanford or the Mayo, your healthcare is just better than if you live in a rural area, right? It that dichotomy might still exist a little bit, but we can flatten that curve a bunch, right? So the people ever working get better healthcare. Part of the way we're doing that part of our approach to TMA is to make sure that we can both rapidly identify those patients and then track them to the right expertise that they're getting the right treatment insights. And so again, engineer, you know, in Boston, engineer friends, engineer minds around me, when we well, I'm a systems guy, right? So like, there are amazing efforts on patient advocacy, pushing to make sure patient voices are here, to make sure regulation gets changed. Those efforts are completely needed and necessary. When we looked at the challenges across the general health care infrastructure, the system of delivering care to these kinds of patients, the bottleneck that I was able to identify is that payers are the ones who ultimately control access to these kinds of next gen technologies, right? Because they're the ones who have to cover the bill. And, you know, is it the best system in the world? I'm not here to comment on that. It's the system we have, right? So like, we got to make sure that we work in the system we have so we can meet healthcare for 350 million Americans. What our system does that's so impressive for, you know, when people see it, is we make a financial solution for payers to scale precision medicine for patients with genetic disease. So payers already believe in precision medicine. They've seen the effects in oncology, right? They know how effective it is at not just delivering better care because ultimately payers do really want you to get healthy, but also saving them money, right? Because they are commercial businesses, too, whether we like it or not. They have both interests at heart. But you can't do one without doing the other, right? Like, unfortunately, you can't just come up with a, if it cost a billion dollars to save one person, they can't say yes to that because then they can't help 100 million other people. And so you have to make it work for them. And that's what we've really been able to do. So through software that we have developed, some software that developed was at Mass General, we're able to identify patients who would likely benefit from whole genome sequencing from payers claims data and from EMRs, but that's a little easier to do. The claims data is the root insight. We then go talk to those patient families because we deal with children mostly. So we talk to a lot of parents. We offer our services. We explain what's going to happen. We have a 90% opt-in rate. We provide whole genome sequencing kits to those families. We get those kits sequenced through what's called a CLIA process. So it's a diagnostic ready process that gets reviewed by a genetic counselor. And then that ultimately goes to a disease expert. So in this case, when we focus on pediatric patients with genetic epilepsy, they end up at a pediatric neurologist to get treatment insights and all of that information flows back down to that local care team so that family doesn't have to travel hours and hours to get the kind of care they need. Also importantly here is that we provide all of the information back not just to the care team but to the family because fundamentally patients deserve the right to own their own data. As a parent, I believe that. And there's too many stories in the industry and in the academics about people gatekeeping data from families because they won't know what to do with it or that. So we've really tried to make a system that adds value for every single stakeholder along the process. And that's really where we found the market response to what we're doing. That makes sense. Yeah, because I mean, you're doing the genetic sequencing, understanding the biomarker is almost kind of going on. But on another side of the solution is also using AI as a clinical decision support and not only aggregating that data but interpreting it. And with you with the kind of a biotech background, more in research, what kind of led you to use software? What made you know that software is the best way to solve this using AI? And how are you guys doing that right now with AI and kind of the CDS platform? Yeah, so it's so I wouldn't call it CDS, right? Like you can think about you can think about TMA is really like, so we keep humans in the loop on purpose, right? So we ultimately we always want a genetic counselor or pediatric neurologist or whatever the disease expert is to be to be reviewing and ultimately signing off on what that treatment inside is, right? People, I think correctly, don't want to trust the machine's output. I mean like, you know, go ask Gemini or Chad GPT, how many Rs are in strawberry? And it will still give you the wrong answer, right? So there's a there's like a trust but verify aspect of this. But you know, this idea of using AI, like AI is not a silver bullet. It is just an accelerant. And so you use or I use software tools all the time, PubMed use Google Scholar use all of these different things to source information. And then you have to read it and make sense of it. So AI in this point is a way for us to just vastly accelerate the ability to interrogate millions of mutations against all known data from HBO terms from varying interpretations, things that are clinically relevant, things that are research relevant, and put that into a report that we can put in front of an expert. So we get them 80, 90% of the way down the road incredibly fast, right? So we use AI to give humans superhuman power. We don't ask humans to trust the AI to make their exact right call every single time. Yeah, that makes sense. Because I think a lot of the issue of AI is like, that lack of trust, AI black box is always an issue. You never really know what's going on. And even if you do, I think I think the reason why yes and necessary, because you have so much data and so much going into this now, like people getting more data driven than they as needed to kind of understand what's going on, and build that framework. And I mean, working with like, you guys work with clinicians a lot and are almost like reworking the standard of care. How are you guys ensuring a solution not only is helpful to patients, but kind of fits into the doctor's workflow and it's something they're willing to adjust to. Like, what does that adoption then look like, look like and have you adjusted? Yeah, it's a great question. I don't have a hard stop. You can go until 10 if that's okay. So historically, that's like the big pushback, right? You hear, and it's one of the reasons why we don't advertise ourselves as clinical decision support. The minute you say that to a Western doctor, they clam, they do not want anybody telling them how to do their job. And again, if I went to med school and then specialty for like 15 years, I want somebody to tell me my job. But the truth is there's just so much information and such a big contextual space to interpret for what are really esoteric diseases, right today. And there's so much nuance that goes into understanding how certain genes interact with different pathways and what the compensatory mechanisms might be, that having a super computer sidekick to hold that space and let you ask questions about that space becomes really valuable. Because we're also dealing with really challenging cases and cases that have frustrated healthcare delivery for a long time, we have found a huge acceptance, if not just overall adoption of what we've been able to put in front of the physicians on both sides, the local physician who needs the help and ostensibly doesn't want to have to lose that patient out to referral. And the specialists who want to be able to exercise their specialty knowledge and help more patients, right? So really, we enable both to do their job better. And through that, we've had a lot, a lot of positive feedback. I mean, I don't I can't think of anybody who said no to us at the local level. The only people who said no to me at the specialty level, frankly, have been blocked by academic appointment. Okay. And so can you elaborate more on that? Why do they kind of say no? It's usually because the medical center that they're working for says you can't do consultations on the side. Yeah. Yeah. So a lot of doctors, and we have a lot of doctors that we work with, no problem. But some places are still they still have this like medieval mentality about like these are my doctors and my patients and only only we should be seeing these patients. And that's that's like a deeper rabbit hole. We could go down just about the fragmentation and the health US healthcare system. But it's a thing that we have to overcome. And you know, frankly, telehealth adoption over the last few years has been a good smoother of this process. A lot less hurdles to do with it now. Yeah. I think not only is like fragmented, but the goal is to kind of miss a line is that everyone's kind of working on their own has their own incentives. And just because the goals aren't aligned, it makes it difficult to get like the best outcome possible. But that's where like democratizing how they're kind of ties in. But it's definitely kind of a long, long-term goal. I mean, what do you think that'll kind of comment to play? I guess I could try self care for everybody. Yeah, I think that could take like, that might never happen because like decades who knows. I mean, if you want my founder answer, give me five years, you know. But no, I mean, look, we start we start with with genetic disease patients because it's personal for us. Everyone on our founding team has been touched by rare disease. So it's it's really a personal mission. They're the highest cost, highest margin patients that we can intervene with. So it's a really easy place to demonstrate that this has application. But the technology doesn't really care what disease you have, right? Like, like understanding the global information space about how to treat a patient. The only place that this technology is really not good for is like broken bones and heart attacks, right? Things that require a physical intervention. If you're just trying to get cutting edge information on what the right application or treatment that you should be considering is, our tech does that, right? We just focus on these high cost patients because that's where the that's where the burden of change relies on lies on. I think that the idea of democratize like, you talked about incentives and it's a really important thing for founders, people who are who are starting businesses to understand because different levels of health care delivery, patient, doctor, payer, pharma, they all have different incentives. And usually what you're trying to do is effectively like take some margin out of one person's dollar and either pull that into your own company or you're trying to reshuffle it around. And one of the things that I think we've done that's so smart is we haven't asked anybody to compromise, frankly, like their revenue stream, right? And so it's it's been a way for us to align incentives from an economic stance around patients and it it stinks to have to talk about patients as paychecks, right? Like nobody wants to nobody wants to think about human beings as dollar signs. But unfortunately, especially when you start to get into health insurance, you have to come to grips with the fact of how the financial implications of your solution make sense. And so that's why when we built the model, it was imperative that it worked for payers. Otherwise, it's just never going to go anywhere. Yeah, I think we'd all love for health care to be it is very impactful, but I think we'd all love for health care to be super mission driven. But I think there's nothing with insurance that they want the economic incentive and want to see ROI. And one interesting I saw that you guys are doing it because you're working with pharma companies now to kind of help get them like clinical patients or clinical trials, which is interesting because you have this market of customers that would love to be in these innovative clinical trials and get solutions to their rare diseases. But for the actual pharma companies, they're looking for people with those diseases, which may not be so common. I mean, what sparked a solution? How have you guys approached that coordination there? So, and so let me just be clear that it's a place that we anticipate growing into. It's something that we talk about with pharma right now. But the idea is, to your point, as more and more pharma, so look, the better we understand how human beings work at our genomic and proteomic level, the more all of us will become an individualized patient. Pharma already understands this. And frankly, pharma understands that the ability to create, instead of creating hundreds of drugs that treat thousands of people or tens of thousands of people, pharma is incentivized to create tens of thousands of drugs that each treat hundreds of people. It's just a supply and demand curve. So, making sure that those personalized drugs work effectively so that you can transition from phase one to phase two to phase two to phase four successfully means that you have to capture a trial population that will respond favorably to your drug. And to do that, you have to rule them in and rule them out by specific biomarkers. If you make a genetic therapy, you need that biomarker to be the mutation of interest, right? You want to make sure that the person is going to actually respond to the drug that you've made them. The more specific you make that drug, the more you decrease your patient population, right? So, there's pressures on here. Like, there's all of these pressures. And so, for some rare disease trials, you're talking about like, ends of single digits, not ends of tens of thousands. And so, being able to pre-identify those patients becomes highly valuable to pharma companies who are looking to make super specialized therapies. Yeah, because they don't want outliers, right? They want it to work and for everyone. And, oh, I'm kind of here so you guys are going to this base, but also, I know you guys are expanding on a lot of other fronts and are developing pretty quick, growing people. Can you speak more till you kind of have where TMA is going to go and whatever goal is their long-term vision for TMA is going to be in the next couple years? Because I know AI has been changing everything. A lot of people are kind of adopting AI, but you guys did it pretty early on. So, I'd love to hear your story. Yeah, it's, you know, today we're really focused on genetic epilepsies. We want to be because we don't just capture genomes and longitudinal medical records. We capture genetic counselor thinking. We capture treatment insights from disease specialists. We capture outcomes from patient response to those, right? So, it's not just about capturing patient foundational information. We're capturing all of the information around how to treat those patients the most effectively. And so, we really think that from a foundational training set, that's really where that data starts to become really interesting to even further automate the back end for how to treat these patients. So, instead of putting a doctor 80% of the weight down the road, maybe we can put a doctor 98% of the weight down the road, you know, in the near term, the next few years. We definitely think about expanding into other genetic areas of high unmet needs. So, these would be things like cardio myopathies, neuromuscular disorders, CNS sort of writ large. Some of those can be even in the things like bipolar or schizophrenia, right? And so, ultimately, we want to be the deepest data set for certain types of genetic diseases on the planet to affect healthcare delivery as efficiently as possible for those patient groups. That'd be really interesting. Epilepsy care has a huge market and it's good to hear you guys are expanding. And I kind of want to take it back a little bit. I know. So, you kind of started as a researcher, worked at Harvard, loved your work there, then you started your own startup, and now you're kind of commercializing this one than the biotech biopharma space. And you've had that long journey from a research to entrepreneurship. If you could go kind of back in time and kind of plan out your career, I mean, how would you do differently than what advice did you have in your space? I don't know. I mean, I think my kind of guiding mantra is like, talk to everybody. You know, you never know where your network's going to take you. I think everything important that has happened to me has come through my network, right? Through my mentors, through my colleagues, through my interns, right? Through people like you that I get to just meet because I get to say yes to doing a cool podcast. I think I don't know that I could have like overthought it or mapped a different way here. You know, if I didn't have a kid who had a challenging sickness, maybe I'd still be on the bench, right? Like, if I didn't have the experience of like having a viral startup that went on Oprah's blog, you know, like maybe I never would have made the jump to commercial, right? So the same way that like looking back at all fields sort of deterministic, like all those experiences added to my skill set and my quiver to let me be effective at the job I have today, I don't know that I could have proactively assembled that skill set, you know, and I think it's just like stay open to what comes in front of you and be ready to pursue opportunities because it's not like I started this company yesterday and it's been a really easy ride, right? We've been working on this for almost five years through the pandemic for very little money, dragging my family along with me. Like there's all of these other costs that go into starting a business and if you just want to make some money, like there's way easier ways to do it, you know? So I think for me that the biggest driver has been having something that I want, that I'm passionate about, and that has really kept me, kept my grit in action, right? To make sure that we're going to get it and we're about to close our first financing, our first equity financing and really start to scale. So it's been a great journey. That's awesome. And one last question, it grabs only the equity financing closing the round, you know, it's kind of a headache dealing with like VCs and other equity partners, angels, but I'm curious. I mean, you kind of had like with most reps, it's not revenue generating or it takes a lot to run a profit if you're doing what it does after a few years. And at what point did you realize that like, this is really going to work and really going to scale? Or did you always have to have that belief that this is going to work in the day of the World War? Yeah, it's a great question. So, we joke around that my rare disease is being pathologically optimistic, right? So I think if you want to found a company, you have to believe it's going to work. You also have to stay open to throwing out all of your preconceived notions about what is going to work or what is not going to work and be willing to listen to experts and take advice and internalize that and pivot where necessary, right? So like when we first started the company, we were going to solve all rare disease, you know, and ultimately like our technology still is built to do that, but we can't do that at once. So we have to focus, focus, focus. But yeah, you need to care about the thing that you're doing because you're going to have a lot of tough times and people told me this and you know, maybe I listened, maybe I didn't, but it's the truth. And being passionate or being driven about the thing that you're doing is going to be the way forward. And it's 2024, it's not 2019, like money is not just falling off of trees anymore. And so you better be sure that you want to take the ride that's in front of you because there's going to be a lot of ups and downs. You know, the ups are really great, the downs are really bad, but ultimately if you can go affect the thing that you want to deliver on, I mean, hopefully, hopefully it's not only rewarding for you, you know, emotionally and financially, but hopefully it also makes a big difference in the world around you. And I think that's the biggest impact any of us could ever ask for. 100%. And that's what, yeah, I definitely agree on it. It's definitely difficult to be listening to the market, listening to others say it, but also stay quarterly or believes in your vision. But I think once that really happens, the stories are really great. Yeah, I mean, look, I won't, I will end on like a fairly corny note because I'm a heavily tattoo guy. But right here, it says be here, do good, right? That's just for me, like that. If you can make even a little bit of positive change in the world, I think that's a life that's really worth living. 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. 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 going wrong with health care and how you can help, check out our blog at the healthcare theory.org. Repeat the healthcare theory.org. Again, I appreciate you tuning in and I hope to see you again soon.
We're joined by Joshua, CEO of TMA Precision Health, to explore their innovative approach to tackling the diagnostic odyssey for rare disease patients through tech-enabled solutions and AI-driven insights.
TMA Precision Health is revolutionizing rare disease care by reducing the diagnostic timeline from seven years to just a few months, addressing gaps in accessibility and healthcare equity. Joshua shares his journey from research at Harvard to founding TMA, his personal connection to rare disease through his son, and the company's mission to democratize healthcare. Discover how TMA leverages precision medicine, whole genome sequencing, and AI to create a scalable, patient-centric system that bridges the gap between innovation and care.
