Turning Data Into Life-Saving Insights w/ Truveta CEO, Terry Myerson

In this episode of the HealthBiz Podcast, host David Williams sits down with Terry Myerson, CEO and co-founder of Truveta, to explore how aggregated electronic medical record (EMR) data can drive groundbreaking insights in clinical research, drug development, and healthcare innovation. 

Terry shares his journey from mechanical engineering to tech entrepreneurship, his years at Microsoft, and how Truveta is using data to improve patient outcomes and save lives.

🎙️⚕️ABOUT TERRY MYERSON
Terry Myerson is CEO and co-founder of Truveta, a leader in EHR data and analytics, led by a growing health system collective that together provide more than 18% of all daily clinical care in the US. Truveta is trusted by more than 50 leading healthcare and life science customers to study safety and effectiveness, improve patient care, and train medical AI. Across these leading organizations, Truveta connects data, people, and ideas to pursue the mission of Saving Lives with Data.

Terry previously enjoyed a 21-year career at Microsoft, leading the development of Windows, Xbox, and the early days of Office 365. As Executive Vice President, serving on the Senior Leadership Team, Terry played a pivotal role in developing the strategy for Microsoft alongside CEO Satya Nadella. Terry excelled at managing large teams at scale, tackling complex software challenges, and driving growth in partnership with a global ecosystem.

After leaving Microsoft in 2018, Terry joined the Madrona Venture Group and the Carlyle Group as an advisor to their investment teams and portfolio companies. He enjoys learning about new technology, particularly at the intersection of data, AI, healthcare, and life sciences.

An entrepreneur at heart, prior to Microsoft Terry cofounded Intersé, one of the earliest internet companies, which Microsoft acquired in 1997. 

Terry is a graduate of Duke University and a current member of the Duke Engineering Board of Visitors.

🎙️⚕️ABOUT HEALTHBIZ PODCAST
HealthBiz is a CareTalk podcast that delivers in-depth interviews on healthcare business, technology, and policy with entrepreneurs and CEOs. Host David E. Williams — president of the healthcare strategy consulting boutique Health Business Group — is also a board member, investor in private healthcare companies, and author of the Health Business Blog. Known for his strategic insights and sharp humor, David offers a refreshing break from the usual healthcare industry BS.

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Transcript

David: 0:00

Aggregated data from electronic medical records can provide great insights for clinical trials, drug marketing and academic research, but what's the most cost effective and secure way to bring all this information together? Hi everyone. I'm David Williams, president of Strategy consulting firm, health Business Group, and host of the Health Biz Podcast, where I interview top healthcare leaders about their lives and careers. My guest today is Terry Myerson, CEO, and co-founder of Truveta whose mission is saving lives with data. Do you like this show? I hope so. And if you do, please subscribe and leave a review. Terry, welcome to the Health Biz Podcast. Thank you for having me. Outstanding. Well, let's talk, we're gonna about to talk about Truveta, but I wanna talk about earlier days. Before that. I'd love to hear a little bit about your, your childhood, your upbringing, and any influences that have stuck with you throughout your career. Oh gosh. I

Terry: 0:56

mean, that's such an open-end question. I mean, I was born in Connecticut, but I grew up in Florida, uh, made my way to Duke University. Uh. Proud Blue Devil, big Duke fan. My, uh, now two boys, one boy graduated Duke, my other son's playing soccer there now. Um, and I'm majoring in mechanical engineering ironically. So that was, um, I. Where I didn't start out, I just didn't start out pre-med, but I found organic chemistry a little hard, a lot of memorization that I wasn't that so good at. So then I made my way into mechanical engineering, which I haven't really used in my career. But, um, summer after my sophomore year, I got an opportunity to, at the US Environmental Protection Agency and did a bunch, started doing scientific data visualization and fell in love with computer graphics and computer programming. And that's. Some, somewhere from premed to mechanical engineering to to computer programming. But you know, this was, this is the eighties, so it's, um, dating myself. It's different areas. It sounds

David: 2:03

pretty straightforward. I, I think I'm slightly older than you anyway, so, uh, I get that we can, we can talk about acoustic couplers and all that stuff if we want to, but. Also Duke was, uh, hard to get into then, but, but harder now. And I, I have a friend who got, only one of his two boys got into Duke. So if you had two, you're, you're ahead of him. Yeah. Sounds He's, he's a fellow listener. So after, after, after the EPA, I saw you did some sort of super computing in North Carolina back when that was a thing and then started your own company. Is that right? This is

Terry: 2:30

going back, you know, we're talking No, this is 89, 90. We're talking, you know, the, so that the EPA, uh. That evolved into a project to study acid rain and then do scientific data visualization of acid rain, which was the, you know, today we talk a lot about global warming, but if you roll back to the eighties, acid rain was a big concern. I remember doing a demo to Al Gore, who at the time, you know, was a senator, but uh, uh, the North Carolina Super Beam Center had the simulation models. They had the, the big silicon graphics computers that were doing the big. Amazing videos. And so I made my way through there and then I made my way to Washington DC to actually do scientific data visualization for NASA ames and the Naval Research Lab and bunch of National Institute of Health. And from there I got exposed to the internet and, uh, myself and a group of other folks co-founded the company in 94 to do, again, data analytics and visualization of. I'm sorry. Oh, website build websites. And then Microsoft acquired that company in 97. And then I joined Microsoft in March 97, and I really had no plan. I, you know, moved to Seattle and, you know, met a girl, had a family 21 and a half years later, left Microsoft. So there's a lot 21 and a half years too, but it's just kind of a, a.

David: 4:01

So if I get this right, so the idea is you're supposed to like have a charmed upbringing in various places. Go to a top school, find some interesting stuff along the way, even if it means it was like a hard class you dropped out of in order to go and, or that didn't drop out of but went to change majors and then just ended up finding your way along and just go to like a, do interesting stuff and then work for great company at the right time. And is that basically it?

Terry: 4:23

Well, I mean if you go back to 94, uh, the customers that I was working with. I remember distinctly a meeting at National Institute Health where I was showing the Silicon Graphics, Silicon Surf website, and I went back to my company and said, Hey, we should build a website. And they're like, no, we, we don't know what that is. We don't do websites. And that, uh, frustrated me and my colleagues. And so we started this company to go build these websites. And then, you know, this was a, we were living, I was living in Northern Virginia at the time, and so. No, I mean this is sort of a wander, this is a random wall all and passion and interest and uh, that led me to Microsoft. And then within Microsoft it was, you know, a whole variety of careers. I think that started with internet services. Email was a leading internet service. I mean, we gotta really roll back the clock now, so. Uh, outlook and Exchange competing with Lotus Notes. And then, you know, Blackberry was the mobile device. And then, so that led to connected the email and exchange. And so that led me to mobile, the mobile efforts inside Microsoft and the mo. And then every device became mobile. And so that led to Windows. And then every device is leads, computer graphics, and that led to Xbox and Windows and you know, so it all, it's all connected until really. You know, 2020 when the pandemic hit and that, that, I would say that was really a, you know, I developed this interest in data science and life sciences in my post Microsoft. So after Microsoft, I did join a venture capital firm to sort of learn and stay fresh with people and ideas. And you know, when this pandemic hit, I just. Opened my eyes that we don't have the data to, to make public health decisions. An old colleague had become Chief Information Officer of Providence Healthcare, and so exposed me to how inside Providence, they didn't have the tools to quickly ask and answer questions about how to best take care of patients. And, uh, you know, rod Hockman, CEO of Providence, kind of talk to me about some of the industry complexities and, you know, of trying to achieve. You know, and how himself and other leaders of other major healthcare systems had been talking for some time about building a consortium to put their data together. And, um, outta that came veta, you know, which kind of a, you know, he could see, you can look through my life and train and see how there's these threads of connecting to it, but healthcare, life sciences, this is a, this is a, I'm learning so much every day. I'm so fortunate to be working with these incredible colleagues at the healthcare systems and at our customers to apply, you know, all the data and analytics experience I've built previously to the domain.

David: 7:12

I like to connect these long arcs. So to me, I, I'll take it back to organic chemistry, right? Like, that's really hard. You need to do that for going to medical associates. Put that aside for, let's say 30 or 40 years, and then you come back and say, now you've solved these problems at, you know, Microsoft and. Now you wanna get into something which is pretty hard, which is how do you make sense and any value out of all this healthcare data? Nevermind not just with one institution, but across the whole consortium of them. So I think you found a good problem to hit your head on, but that one is worth solving or at least, uh, solving a piece. Definitely. I

Terry: 7:45

mean, this is I the, the mission side. The technical challenges are fun and, and in and incredibly hard, but the mission of what we're trying to achieve and the purpose of we're training, that was very grounded for me in those early days of 2020 when we didn't know how to take care of the patients in the communities. And it's, it de definitely feels like the most meaningful thing I've ever worked on. And I'm working with the most mission driven people I've ever worked with. And so that's just an incre. I feel incredibly lucky for that.

David: 8:17

Now the company, and you sort of answered my question in a sense, is Truveta, would you consider it a company? Is it a collective, you know, why this sort of organization of it?

Terry: 8:27

Well, at the core we are, we are a collective of healthcare systems and you know, with the shared mission of saving lives with data, I do believe to, to. Very important to build momentum and to create focus, you actually have to have not only mission alignment, but also economic incentives and economic alignment. So Truda is a company and, you know, we are a tax paying company and the, but the governance of the company is the healthcare members that, you know, contribute the data to their company. And, um. I think that sort of mission and economic alignment is critical to, you know, creating prioritization and creating the momentum.

David: 9:15

Now, what has the evolution been like? And obviously it started with some discussions, you brought some organizations into it. How has it evolved and what would you kind of check off as your achievements up to this point? I don't know,

Terry: 9:30

the word achievements is kind of a. You know, the, in terms of, you know, the, the company did start with four health systems, uh, Providence Healthcare Advocate, Aurora Healthcare, which is now part of Advocate Healthcare, uh, Trinity Healthcare and Tenant Healthcare and Northwell Health, you know, right behind and. We have expanded from those five health systems now 30 health systems come. Spirit Health, Henry Ford Health, advent Health Memorial, Herman Health, Taylor Scott, and White Health, Navan Health. Incredible. I feel incredibly fortunate to be working with these just incredible organizations. And you know, I think the, and then also we have this incredible team that's been, you know, been very committed to this mission and taking on the technical challenges of. You know, building this complete timely and clean regulatory grade dataset. And, uh, you know, along the way there's been, you know, I think incredible things we're proud of. Whether it's be contributing to Pfizer's work to track the safety of the Covid vaccine, or, um, contributing to Boston Scientific's incredible work to study the health equity of per peripheral artery disease. Um. You know, the CDC is now a customer, which is just an incredible honor to be, you know, when I think when we started the company, there was this idea that the CDC didn't have all the data it needed to pursue their mission to protect the communities. And that, so now to have the CDC working directly with us and the data is an incredible honor. Um, and there's more, I mean, there's just various things. There's various things along the way. I mean, I, it's hard, you know, it's. Pfizer was our first customer, incredibly honored and humbled. And that, you know, Boston Scientific was right there with them. You know, those visionary leaders that saw the, uh, potential. What we were doing is just incredible. I would say the CDC was another major milestone. And then, you know, we've shared, there's now a hundred customers working with the data, which is. Yeah, I love all of them.

David: 11:39

Good, good. All right. Well I thought for a minute there when you were thinking of, you know, achievements, I thought you weren't gonna be able to think of any of 'em now. It was more a matter if you have so many, you gotta gimme just the top few. So that's how I'll take it. It's just not a

Terry: 11:49

humble word. I just don't, I just, I don't feel, I don't know, you know, I know this is such a, you know, this is a long journey to achieve, you know, to really bring, um. Those really data-driven insights to how we take care of our communities and every single patient and bringing together that complete, timely, clean data set. You know, we're five years into this and we are not halfway done.

David: 12:19

So let's talk about this term that you just mentioned and I was gonna bring up anyway, regulatory grade. So when I hear a term like that, the first thing it reminds me of like the GMC commercials, like commercial grade, you know, but. There's, there's really, there's, there's a big reason why you say regulatory grade, so let's get into that. But also something that is a heck of a lot easier to come up with a good term for it than to actually achieve it. So, can we talk about, you know, what, why this concept of regulatory grade data is necessary and then the things you've done to get toward that?

Terry: 12:50

Well, I think

David: 12:50

throughout

Terry: 12:51

healthcare you have these, this taxonomy of research grade and regulatory grade. It shows up in. Lab tests, you know, you can do lab tests that are RUO or you can do lab tests that are, you know, clinical grade. And so, you know, with data now, I think we have the same thing. I mean, there's, there's a lot of data out there. We don't know where it came from. You don't know how it was been transformed. You don't know exactly. It is just a data set that's on, on a disc now, what decisions can you make with that? Well, you can definitely query it and maybe learn something from it, but. Can you really make mission critical decisions? Can you, and in the case of the regulat, you know, regulators in healthcare, C-D-C-F-D-A-C-M-S, you know, they have, they have, they're taking responsibility for the taking care of our country. And so for them, uh, they have published very clear and very stringent guidelines in the case of FDA, what they expect to see in data clear provenance, clear, clear audit trails of transformations. And I understand because like today you have these very slow, very expensive clinical trials and registries that are used to make so many, so many decisions, but they have a level of confidence and accuracy and comfort with those, with those data sets that are required very slowly and very expensively. And so we're now creating this data set that, you know, really with just a query, you can you to, you know. Replace a whole clinical trial, replace a whole registry, and what we need to do is build confidence in all the people using this data. They can make decisions with this data comparable with what they could do with a clinical trial. Not, we're not talking all clinical trials. There's still phase one, phase two, the interventional arm of phase three clinical trials, which you know you cannot replace, but the placebo arm for the standard of care for all of the phase four trials to track comparative effectiveness, safety. Off-label use, all those things. Those are not, those should be queries of a dataset. Um, and, but how do you build confidence? How do you build confidence that the query of this dataset can be reasoned against and make life impacting decisions against, you know, it's shocking. Like, you know, wait a second. Instead of spending $50 million in two years collecting this data, I can just run a query. The answer's yes. But building trust and confidence in that, um, is a lot of work, a lot of investments, and it's gonna take some time. But I think that is a big part of the transformation that I think we can bring to healthcare is those slow and expensive clinical trials and registries, not all of 'em, but many of them, can be replaced by a query.

David: 15:45

So if you think back on, you know, the last few decades in healthcare with data, before you had electronic medical records and before you could really connect them, they had claims data. And what you'd see about claims data was that, you know, what, what was valuable about it? You people had to put certain information down so they'd get paid. So those fields were filled in well, and the payment amount, you know, was accurate and all that. But essentially using it for clinical purposes or other purposes is just a purely a byproduct. So you have to wonder, you know, using a byproduct for something Now for the electronic medical record data, theoretically it should be, that's clinical information. That's the kind of information you want. But at the same time, a lot of the reason it's done is for, you know, it's recorded in the first place is for administrative purposes, compliance purposes, and to, and to get paid. And so a question I've always had. Is, you know, can you really get the data? Uh, can you really, how, how much can you rely on electronic medical record data? Or even if you really get to the core of what it is, is it really gonna give you the clinical information that's comparable to a clinical trial? Or are you still getting a byproduct of something that was put in place in order for someone to get paid?

Terry: 16:51

The, you know, the data that's recorded by your physician is, um. That's the data that's being used to make your medic, you know, to chart your medical course. I mean, and so there definitely, you know, the, the claims data has this economic bias to it. I mean, I just don't think now whether it is the, I do believe, you know, there is influence over what gets coded to either maximize or minimize reimbursement. And I think as these claims get. The advantage of claims is you see everything. You see everything. You see everything. Whether it takes place inside a healthcare system or not, and that coating, that structured coding absolutely has economic bias to it, but it has this, um, longitudinal completeness with the medical records. I don't, when I look at the clinical notes, you know, de-identified that doctors are writing that says, you know, Terry's reporting with pain in his left knee. He's been, you know, it's been there for three months and now he's worried about his hamstring. You know, whatever the, the writing there in those clinical notes or in those images or in that genetic test, I mean, those are medical. I believe that's unbiased medical facts. Um, but the coding, the coding of medical records into claim claims are a byproduct of the medical record. You know, there's bias there. There's, I just think, you know, I think all parties try and, you know, o operate with integrity, but there's plenty, you know, there's humans here that, you know, when we say, when, when a payer says we pay more for X instead of Y, the payer's trying to. Get the claim changed to Y and the provider's trying to make the claim say X. And I think that's this whole why revenue cycle management is such a hard problem. But the medical re, the clinical notes, the images, the genetic tests, the lab tests, these are medical facts and none of those are in claims. You know, the lab test results are not in the claim. The clinical notes are not in the claim. The images are not in the claim. The genetic test is not in the claim. And in that, that rich clinical data, I, I think there's just incredible. Insights on how to take care of our communities and take care of each of us.

David: 19:14

So let's talk about genetics for a minute. Uh, you recently announced the Truveta Genome Project not to be confused with the Human Genome Project, which, uh, preceded it. Um, what is this project and, and what are you hoping to, I won't say achieve? What are you hoping to get out of it?

Terry: 19:28

Well, so the data, you know, we've now be working on. Um, collecting this complete, timely and clean de-identified data set of medical outcomes across the us and that's incredibly useful for studying safety and effectiveness. And, um, it's great. I mean, we're on this journey with so many customers now to think about where they can, you know, where can they get insights from this safety effectiveness data that doesn't require these slow and expensive trials and registries. But when you think about moving upstream into drug discovery or. Actually developing new therapeutics, just knowing outcomes is insufficient. Uh, and so for there, you're curious about the, the genetic makeup that might lead to these medical outcomes or the, the, the pro, the proteins in the body that maybe leading to these medic outcomes. But the challenge is that data doesn't exist. It's not collected during our care because it's expensive to do so. And because. Um, we, there's no clinical decision that's gonna get made if you go and do a genetic sequence of one of us in care and the, the doctor's given this 25 gigabyte binary file, doctors go, what do I do with this? This is you. I spent all this money to get this test. And there's, I can't reason against this. There's no tools to process, there's no, there's nothing, uh. It's gonna change in your course of care because we don't know. And so it's, to me, it's this chicken and egg problem that we've had to say, how do we create this data to be able to not only discover new therapies, but but this therapies, but to go figure out how to use the data in care. And what was, what we've created with the Regeneron Genetic Center and Illumina investing to make this happen is, you know, with. These incredible visionary health systems that see the benefit here. They see the benefit of, you know, precision medicine and is let's create, let's go invest our time and effort to go create this data set that maps our, you know, genetics to medical outcomes. So we can learn how to do precision medicine and we can, uh, discover new therapies. And, you know, the way it works is you'll, you'll. When you go to one of our member health systems, you'll be presented with the option to participate within the T Genome project. You will cons, you'll have the option to consent that your leftover blood from the blood draw you are already taking, be used for this anonymous genetic research. So it's an option that'll be presented to you the. There's no incremental prick of the arm. There's no extra blood drawn. The idea is when your blood, your doctor ordered an A1C blood test or a cholesterol test, whatever your Dr. May have ordered during your course of care. There's always leftover, not always, but there's often leftover blood. And that leftover blood today is incinerated and a or some other safe medical disposal procedure. Here we're saying let us use that leftover blood anonymously to create genetic data so we can study the correlation between genetic data and medical outcomes. And what's, you know, for me personally, you know, I absolutely, you know, I get care at Virginia Mason, which is a division of Common Spirit Health, and I get, my wife gets care at Swedish Hospital, which is the division of Providence Health, and I think we're passionately both. Ready to consent and be part of this so that we can discover things that will not only take care of us individually, but our children and our children's children, and, you know, the communities we, this knowledge could unlock so much incredible insights that could just transform how diseases are, you know, detected, prevented, and cured. And so I think it was just incr, you know, it's one of those opportunities, the, the admission incentive is there. Their, uh, economic incentives are there to actually take this and create this. Mean, it's very expensive to do this, but the, but the insights we can create could be so profound for the health of our communities. So it's, that's what we announced.

David: 23:37

So let's talk about, uh, GLP one Drugs. You know, they've kinda taken the world by storm originally for diabetes, weight loss, and expanding into other indications. There's a lot of money that's being spent, a lot of clinical trials that show very good, uh, very good results. But the question is then always, well, what's the real world impact? Especially as we're spending, you know, so much money on these products. Now this is a little different from what we were talking about before, where you can do a, you know, click of a button and it's a lot less expensive than a clinical trial here. We actually don't want a clinical trial here. We wanna know sort of the real world impact. On lots of people. And the impact is that so much money is being spent overall. You've got a new JAMA article that I think analyzes real world impact, perhaps using some of your data. Can you provide some insight on that?

Terry: 24:24

Well, the, the study that was published in JAMA by, uh, it was a study of the ENTA data. It was, uh, Providence Healthcare, university of Pennsylvania and a, a researcher at truda. And it was, you know, query the data and then studying that data and, you know. Going through the peer review process to get it then jama, but it was basically showing that, um, folks are staying on these drugs. You know, there's for less than a year, you know, we were looking at reasons for discontinuation. Uh, based upon, again, those clinical notes. What did the doctor write in the notes? You know, why did you discontinue? And there was, um. There was variance in rationale between how long people were staying on the drugs, you know, that. And, but there was a correlation between, you know, the household income of the patients and their length of time on the drug. And, um, I would refer people to the actual study to, I'm not the researcher so I don't wanna misquote it, but this was just, I thought this was great to actually look at. What is the real world adherence by patients of different household incomes to the GLP one medication? And obviously, you know, I think it's natural to assume that correlation being led to household income is connected to the price of the medication that people are having to spend out of pocket to get it. That's,

David: 25:51

well, I just think it's a good, I didn't need you to be the, uh, the PI on it, but I think it's just a good example of we have this large scale data that's not in a controlled trial, but is looking at the overall impact because we know there's a lot of spending that's going on and a lot of assumptions being made, but we don't really know, you know, what it's like for all those patients that weren't on the trial and don't conform to those exact inclusion excision criteria.

Terry: 26:15

You know, I don't. Thousands of patients being studied. Uh, one of the GLP one studies was like 18,000 patients. And the, you know, these are patients that make it through with strict inclusion and exclusion criteria, and you're looking at the, and then you're just, you know, creating cohorts to compare. You're, whether you'd be, whether you're comparing ozempic and majaro or comparing, uh, patients of two different, or a, a set of household income stratifications. But that's the kind of work that's very. Easy to do with Tru Beta and you know, we love it when customers dig in and, you know, start, you know, it's the scientific method. You start with a thesis, you then, you know, acquire data to test your thesis, and then sometimes your thesis is proven correct. Sometimes your thesis is proven false, but then you iterate on the question, the, the analytics, the statistics, and. You discover an insight and then share it with the world through the peer review process or email.

David: 27:16

That sounds good. Well, you know, there's a new administration in Washington, and my first thought when you started talking about the CDC was, you know, are those guys still around? But, uh, in any case, uh, what, what has it meant so far, uh, for Truveta and what do you expect, uh, longer term?

Terry: 27:31

Well, I think the CDC plays an important role to monitor the public health. Of the us. So I think, you know, the specifics of what every organization does and the scope, I don't, I don't, I don't completely understand all the things the CDC does, but I think the CDC does play an important role to monitor the health of our country. So I, the CDC is still around. I mean, there's still a customer and my team working with 'em every day. And so I, um. I feel incredibly humble to be working with the CDC or C-M-S-F-D-A, all of these incredible office of Women's Health. There's this incredible people and organizations within health and human services and how I think there's room for, you know, all of us who need to keep learning and improving and growing. And what will change? I don't know. I don't know. I'm not, um. Part of those conversations of what will change.

David: 28:30

Good. All right. I that, that's a solid answer. I'm gonna take it as a true answer, but I'll say word. I,

Terry: 28:36

we are serving their missions and I, I think their missions are, are, are fantastic and worthy. And will the details of how they pursue those missions change? I assume, but it's 'cause we got new administration, but I don't know. I, I don't think the CDC is gonna stop monitoring the health of. United States. I think it's an important mission and we'll see how it, how things evolve.

David: 29:00

Okay. Fair enough. Well, last question for you is about a book recommendation, and I'm wondering if there's any, uh, any books you've read, uh, lately or whenever, uh, anything that you would recommend and conversely, if you have anything that you wanna recommend that people should avoid reading.

Terry: 29:16

I'm not gonna recommend anyone avoid reading anything. I mean, reading is terrific. I actually, I'll tell you a book I just got recommended to me. Uh, I'll, let me go over my email here that I got this really strong, uh, recommendation, the Genome Odyssey Medical Mysteries, and the incredible quest to solve them. That was a book that got recommended to me yesterday. Someone said, I think you're gonna love it so that the. But I haven't read it, but something, because my reading taste said, I think you'll love it.

David: 29:46

It sounds good. And it sounds relevant for your work and for the, uh, the genome project that you're, you're working on, uh, as well. Yeah, no, that's, um, that's terrific. I have had the occasional person that says, you know, they don't, they don't like to read and, uh, just like to watch something. Um, so that would be better. But most, uh, uh. Most people have something that sounds like a good one. I'll, I'll pick it up when I have a chance. Well, my guest today has been Terry Myerson, CEO, and co-founder of Truveta Saving Lives With Data. I'm David Williams, president of Health Business Group, and I hope you've enjoyed this. Terry, thank you so much for joining me today.

Terry: 30:22

It's great to be here. Thank you.