Medsider: Learn from Medtech and Healthtech Founders and CEOs - The Power of Quality Data: Interview with CorVista CEO Don Crawford

Episode Date: November 7, 2024

In this episode of Medsider Radio, we sat down with returning guest, Don Crawford, CEO of CorVista Health. CorVista is developing a non-invasive medical device that uses advanced machine-learned algorithms to detect heart conditions, right in a doctor’s office.Don's career began at Medtronic, followed by progressively senior sales and marketing roles at Guidant Corporation, Ventritex, and Intermedics, including overseeing a $100 million international cardiovascular business. Before CorVista, he led Sapheon, a venous device company, to a $238 million acquisition by Covidien. In this interview, Don talks about the need for focus in startups, the importance of precise data collection, and the value of transparency and respect within teams. He also shares his approach to hiring and building a top-notch group of people.Before we dive into the discussion, I wanted to mention a few things:First, if you’re into learning from medical device and health technology founders and CEOs, and want to know when new interviews are live, head over to Medsider.com and sign up for our free newsletter.Second, if you want to peek behind the curtain of the world's most successful startups, you should consider a Medsider premium membership. You’ll learn the strategies and tactics that founders and CEOs use to build and grow companies like Silk Road Medical, AliveCor, Shockwave Medical, and hundreds more!We recently introduced some fantastic additions exclusively for Medsider premium members, including playbooks, which are curated collections of our top Medsider interviews on key topics like capital fundraising and risk mitigation, and a curated investor database to help you discover your next medical device or health technology investor!In addition to the entire back catalog of Medsider interviews over the past decade, premium members also get a copy of every volume of Medsider Mentors at no additional cost, including the latest Medsider Mentors Volume VI. If you’re interested, go to medsider.com/subscribe to learn more.Lastly, if you'd rather read than listen, here's a link to the full interview with Don Crawford.

Hiring the clinical directly and working clinical directly is because we were, you know, we were collecting data from people who would be our future customers as well. So why would you pay someone else to get the relationships when then you have to base someone to build a relationship four years later? So we built a lot of relationships with clinicians, not all ultimately customers, but they were always customers and advisors at the same time. So we decided to do that for the fact that we wanted to be close to the end user. And we got lots of marketing feedback, lots of things that we got fed back to us on how to do better clinicals.

Welcome to MedSider, where you can learn from the brightest founders and CEOs in medical devices and health technology. Join tens of thousands of ambitious doers as we unpack the insights, tactics, and secrets behind the most successful life-sides. and startups in the world. Now, here's your host, Scott Nelson. Hey, everyone, it's Scott. In this episode of Medsider, I sat down with Don Crawford for a round two. Don's career began Ametronic, followed by progressively senior sales and marketing roles at guidance, venture techs and intermedics, including overseeing a $100 million plus international cardiovascular business. Known for his results-driven leadership and focus on team collaboration, he's now president and CEO of Provista Health. Previously, Don led Sapion, a

Venus device company to a 200 plus million dollar acquisition by Cavidian. Here for you the key things that we discussed in this conversation. First, in the startup world, there's no room for inefficiency. Spreading your efforts too thin can be risky. Choose a focus, one that aligns with your company's strengths and direct your efforts there. Second, precise, clean data is the backbone of any data-driven technology. With high-quality data, everything else, whether solving a problem or training algorithm, becomes much simpler.

Keeping things in-house gives you the control you need to design trials carefully and ensure accuracy and reliability at every step. It also lets you connect directly with potential users, gathering valuable insights, building trust, and refining your technology based on real-world feedback. Third, transparency and mutual respect among team members are crucial for an efficient startup. Be open about goals, challenges, and successes,

and show respect for everyone's ideas and contributions. When hiring, prioritize flexibility and growth, start by bringing in consultants or part-timers to assess their fit before committing to full-time roles. Before we jump into this episode, I wanted to let you know that the latest edition of Medsider Mentors is now live. We just published Volume 6, which summarizes the key learnings from the most popular interviews over the last several months with incredible entrepreneurs like Dan Rose, former CEO of Limfloe, Dr. Stephen Michelson, founder of Ferrapulse, and current CEO of Field Medical, and other leaders of some of the hottest startups in the space.

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In addition to every volume of Medsider mentors, you'll get full access to the entire library of interviews dating back to 2010. You'll also be able to see all of our playbooks, which are thematically handpicked collections, of the most insightful interviews covering topics like capital fundraising, early stage development, regulatory challenges, reimbursement, M&A, and much more. And last, considering that fundraising could be one of the most daunting tasks for any startup, we curated a database of over 700 VC funds, private equity firms, angel groups, and more, all eager to invest in medical device and health technology startups.

Access to this database is a premium member exclusive, so don't miss out. Learn more about Medsider Mentors and our premium memberships by visiting MedsiderRadio.com forward slash mentors. All right. Without further ado, let's jump right into the interview. All right, Don, welcome back, I should say. Right. Not welcome to, welcome back to a second interview here on Medsiter, but I appreciate you taking the time and really looking forward to learning what you've been up to with Corvist over the past handful of years. All right. Thanks, Scott. And for everyone listening, I highly encourage you to go back maybe and check out the first interview I did with Dodd. It's been, got six or seven years ago now, kind of coming out of his exit of Safion to

to Cavidian at the time. And for the, this interview will be a little bit more focused on his next startup with, which is Corvista. But let's start there. Donate, you can. I recorded a very short bio at the outset of this interview, but we'd love to kind of hear from you first. Like maybe give us an elevator pitch on kind of your career trajectory leading up to, you know, starting, starting Corvista. Well, I mean, my career trajectory started right after I left college and my very first job out out of college was to work as a clinical service rep for Medtronic servicing the sales reps in South Florida. And so I was an undergraduate chemical engineering from Georgia Tech and actually graduated as a chemical engineer and went into sales. Can you imagine that?

And so I spent the first two decades of my career working in Manhattan, in what we call now cardiac rhythm management arenas and interventional cardiology arenas for big companies like Medtronic and Guyton Corporation, Ventrotex, which was like a small fan club of Venturetex players out there from the very early years of the cardiac rhythm management industry. And then ultimately went through, I don't even completely count them, but maybe three, four or more mergers from one company to the next is St. Jude to Boston

and Boston, the guidance, and guiding, you know, it went on and on. But ultimately, after Boston Scientific bought guidance in 2006, I think, in 2006, I took the opportunity to leave corporate America. I was in sales and marketing for 20 plus years. Started basically as a sales rep and worked my way through or even maybe a little lower or even earlier than a sales rep, but I worked my way through and then went to MBA school while I had a sales territory with Medtronic at Emory University and always had this theoretical business desire and background. I was always running a business, even your own

sales territory and is running a business. And so as I left, Forkidt America, I did what every red-blooded previous executive would do, and that is start consulting. And in that process, I consulted for a group that was evaluating an idea a physician had that we met the physician on the back of a napkin. He scribbled out of his idea in a hotel restaurant. And six years later, in clinical trial, FDA, PMA, approval, we ultimately sold that idea to Medtronic, as you mentioned. And so I was, you know, more or less a free agent, but I thought I was moving into retirement. And lo and behold, I'd get a phone call and you've got to come look at this. This is going to revolutionize medicine.

And so I got a call from an investor from Safion who was on the board of Corvista. And to be clear, Corvista is our U.S. subsidiary. That's the business that we use to promote the product. But the parent company, which is Toronto-based, is Analytics for Life. So Analytics for Life was doing some very early research in data science and so early in the stage that nobody knew what a machine learned algorithm. Nobody had heard of AI in 2013, 2014. But this was in Toronto, and most people, I think I just saw an article that Dr. Hinton, who is

executive, former executive with Google, is the godfather of Aon. And so we did all of our technical work with data scientists coming out of the University of Toronto, Queens University, and other Canadian universities that were very much at the forefront of what is today known as AI, and ultimately that played a big role in developing Corvista's diagnostic system. Got it. I want to dig into kind of what you described as revolutionizing this, right, which is Corvista system. But you're not going to say, and so I will. But one of the things about your experience with Safian that always impressive was how much your team accomplished was such a small amount of people, right? In fact, I remember, I think, even the PMA, I think, was achieved, like, you know, much earlier than expected, which is such a rarity, right, in the world of medical devices.

And so I'm curious to kind of learn if you're, you know, what sort of key learnings, yeah, that you pull from your experiences in Safiom that you're utilizing at Core Vista. But it was always something that stood out to me, right, in the various, you know, from the various, you know, founders and CEOs that I, startup CEOs that I chat with on this, on this program. Well, I think that the key reasons that we were able to work efficiently and get the work done we needed at Safion, I brought all of that learning. I mean, everything I learned at Safion has made Corvista, you know, not easier but richer and more efficient. But the advice which I have given many, many, many times throughout these kind of questions is, you know, What's most important is no matter what start with a quality management system. I mean, from day one, don't fit it in, you know, with a shoehorn at the end or think you can get in half the way. But most importantly, when recently we're able to get through the first company and even with Corvista, you know, from the time I came into a Corvista, everything was done through a quality management system like almost employee, you know, number two or three.

So that was a big part of what allowed us to capture everything and not to have to repeat a lot of things. Yeah. So that helped us in Sanfianning. Clearly, that was a big part of our journey and a big part of an awakening to a group of non-medical device data scientists. They've heard of a quality management system where they thought it was just a box you checked. Yeah. Yeah. What so many of us do, though, right, that, you know, if you haven't made the mistake, you know,

You may think of it or like straight enough of quality of QMS in that sort of fashion, right? Just another other box to check. But yeah, let's get to Corvista, but I admit the remiss if I didn't shout out. Some of your, some of the rest of your team, Gary McCourt and Monty Manson, Dr. Rod Raby, Nate Raby, just a lot of, all those folks are such, not all good people, but have accomplished so much in their careers and are on to other, other, developing other, other cool companies like yourself at Corvista. So, um, it's probably spoken with each one of those. will you mention within the last 30 days. Oh, yeah.

So we built... Bad passing. And I had gone the message as one of the reasons we got together that one million kits had been shipped from on-track manufacturer that we set up with Safion at the very beginning, that they just shipped their one million kit to treat patients around the world. That's pretty cool because, I mean, that too is a bit rare, right? And like a lot of startups, they're, you know, they're acquired by a large strategic and, you know, either shut down or they never really take off.

And to see that kind of experience, that's got to feel good. But I mean, from my perspective, it all kind of just speaks to the team that you built, right? It's so, so crucial in any startup, the fact that, you know, whatever we're sitting on maybe almost a decade later, you know, you still corresponding with that same group. That's pretty, that's pretty cool.

So with that said, let's jump to Port Vista stuff. So we'll get into kind of some cross-functional kind of learnings that you've picked up on and incorporating it at the new company. but give us like a high level overview of like the kind of the pain point that you're trying to solve and how Corvista system does in fact, you know, solve for that issue. Well, so some early work was done with essentially signal processing of physiologic signals, electrical signals, hemodynamic signals, imaging signals with this team of data scientists who had no medical device training, but they'd worked with a doctor, got some signals.

And lo and behold, two or three years later on a shoestring, $2 million budget, they were able to be, they were able to demonstrate some evidence and some excitement that this type of technology could detect abnormalities or disease within the body. And we focused it in cardiology disease, although we have patents. in cardiology, in neurology, oncology, but key to a small company is to stay focused. So we focused on cardiology. And ultimately, what the theory was is that we could collect fairly common or easy to collect physiologic signals from a patient, but do it in a very proprietary way,

meaning that we would create a piece of equipment that would be used around the patient. the world on every single patient, so that was uniform, and we would do it in an incredibly precise way than maybe what's off-the-shelf models of collecting electrical information and hemodynamic information. And once we collected this information in high fidelity, that we would be able to identify patients with, for instance, coronary artery disease is the leading cause of death in America as well as most anywhere in the world, and that you could detect, we could detect from signals that are being released from the body that the body's producing. We could interpret those signals and see a difference between a disease state versus a healthy

state or a non-disease state. And that sounds easy. And for the most part, going through the process and collecting the signals was easy and then identifying, let's say, signatures of the signal in a incredibly sophisticated, mathematically complex process and procedure in three-dimension and fourth dimension and fifth dimension. I mean, there's all kinds of mathematical manipulations that you can place on data now once you have. a good core set of data. And the key is that in data science, you know, data is like garbage in, garbage out. But if you put in really rate data that you collected, you know exactly where it came from and that it was clean and clear. All the records are there. Then you can actually

obtain or decipher information about that data very well. So with that, we were able to for coronary artery disease, it probably wouldn't surprise people that if you take the energy, the three-dimensional electrical energy produced by a heart, that you would see, and you can measure that energy field with precision, that a heart that is suffering from an area of the heart

that suffering from low blood flow and low oxygenation would probably look different Then a section of the heart, the energy released from the section of heart was perfectly normal. And ultimately, we developed a process and a digital health machine. Even before it was called digital health, that's what we were doing. And we designed a collection device, capture device, to capture the signal, just that before the patient would get a gold standard test. so that we would know what the gold truth is, is if disease or not with precision,

and we got our signal at the same time. And then after thousands, and in our case, 11,000 patients collected under FDA sanctioned clinical trials, that we were able with, you know, some of them getting left heart casts and some of them getting CTAs, or CT angiograms or echoes. In other words, we would have another test to learn from that we could decipher the difference between a patient with a blocked artery and that is suspected to have a blocked artery versus one

that is pretty assuredly doesn't have a blocked artery. And ultimately, we are able to do that now with a patient sitting in a doctor's office so that we can detect significant coronary disease as defined by the guidelines of the American College periodologies, and we can detect the lack of significant blockage with a 99% negative predictive value, so that if you're negatives, that's actionable by a physician. I know now you don't have coronary disease, and now I can do something like not send you for other tests. Got it. Got it. That's super helpful overview. And so I'm

looking at your website now, which is corevista.com, for everyone listening that doesn't get a chance to read the summary article. It's Corvista, kind of just as it sounds, C-O-R-V-I-S-T-A.com, Corvista. And you've got this little workflow, right, on the site under the page that describes your system in a little bit more detail. But the normal workflow for a patient is much more complicated, it's a much more convoluted, right, to sort of diagnose coronary artery disease. And if I'm, if I'm hearing you right, I can, with Corvista system, if I'm in a primary care office, as an example, right? It's a very simple test and the results are immediate, in essence, right? Yeah, within, well, in the same patient's sitting, the physician and the medical assistant can

apply the test, get the information, the physician will get a report and can consult with the patient in the same sitting. No, count. Got it. Ultimately, create a care plan. Care plan could be that the test is showing what I kind of system. that you do have signs of significant point of disease or hard failure or pulmonary hypertension.

For instance, those are three separate clinical indications that we have either gotten approval for or that we have discussions with the FDA ongoing. So that with a very simple three-minute test in a doctor's office with the patient at rest, no nothing injected into the body, no energy, no nuclear radiation, no exercise and a rest. We can collect this data packet of 10 million data points. The way that we are able to project it to the point of care is that through cloud services,

and to me, that's one of the most innovative part is we collect the data at point of care. We're able to push it automatically into a cloud service. server that's secure for patient health for private health information. And in the cloud, we do a massive amount of computing, more so that you could do on a desktop computer. It takes those 10 million data points. It processes them through FDA validated algorithms. It produces an FDA sanctioned diagnostic report that the physician can interpret and as an aid in diagnosis of coronary artery disease, significant corny artery disease, pulmonary hypertension, which affect over 10% of the patients and greater than age 65. And then we are in discussions with

another product we have developed that measures metrics of heart. failure. Got it. Sounds like you're doing a fair amount of compute. I'd say to say that maybe you're a big customer of Invidios then?

Yeah. We're really a big customer of AWS because all of really power is in the cloud. Yeah. Got it. Power in the cloud. So is the Corvista system?

Is it, where are you at right now? We're recording this in Q4.24. Are you actively commercializing the system? Is it available here in the U.S. or in other geographies? Yeah. So our Pornary artery disease,

which was kind of our very first focus from 2015 or 16. It was going after, you know, kind of one of the, well, the most common cardiac disease is coronary disease, which causes heart attacks. And then the more we got into it, the more we understood standard of care and the way testing, we recognize that, you know, there's a lot of room for improvement. And we have a board of directors who have, joined our board who Tim Adderberry, who tells us, he's a pioneer in cardiology himself, former resident and CEO of the American College of cardiology, who says that, you know,

in 50 years, we really haven't been able to reduce the deaths due to coronary artery disease, or we haven't made a big impact. So, you know, this is an area that we always recognize as being an area that there's right. There's room for improved lateral improvement. And improvement helps the patient. Improvement helps the payers, the hospitals. And ultimately, the physicians are there to take care of their patients. If you can give them a tool that allows them to do it, it's incredible to see the physician, cardiologist, or primary care physicians, how good they feel about being able to do something or they're paid right when the patient's there. That's what we developed, but we did get approval for coronary disease, and it was a long and winding road through the FDA.

We were very early, very early, with machine learned algorithms, taking the patient populations appropriately, explaining things to the FDA, FDA explaining things to us. And we ultimately did get, after a de novo review, we got a 510K approval of our Poinary artery disease system. So it's the equipment, the server system, the report, as well as the algorithm that's embedded in the system for quinoary disease. And then along the way, we developed with a support from J&J, a large support to study pulmonary hypertension, which was a whole new group of patients that we needed to do clinical trial. And you've been around the block and know that cardiovascular clinical trials are not

cheap. But we were able to do 11,000 patients under FDA sanctioned trials to collect the data on 11,000 patients. And ultimately, that was the information we used for the FDA. And it probably costs close to $40 million for that activities. But we did it ourselves even without a CRL. Oh, no kidding. Yeah, we managed that trial internally because the data was so important, we had to make sure that data was pristine. Got it.

And so with that, we submitted for coronary disease and with the help with J&J, we had patients that we were able to submit for pulmonary hypertension. And the test was so promising during our development. admit that we were awarded breakthrough designation. You've heard of that before, but many people don't know that to this day, only 90 is, you know, less things have changed in the last four months, only 90 companies have gotten breakthrough designation and actually gotten an approval. We were 90 from what I was told.

So that's a pretty rare event to do something that is that impactful. the designation breakthrough means you're doing something that is badly needed and there isn't already something they're doing it. Yeah. Yeah. And you asked me 10 years ago and what I thought of breakthrough designation. You know, you'd see various headlines about company A or company, you know, B getting a breakthrough designation. I thought, oh, that's kind of cool.

I didn't know. But it's a big deal, right? I mean, there's not a lot of technologies that get that designation. So congrats, congrats to your. It's hard to get the designation even harder to, once you get there to get approval because it's so. So we now, since April 15th, are commercial in the U.S. We're doing it in a very focused rollout, commercial rollout,

such that we're not trying to take over the world at one time. What we're really doing is focusing in a sector of the U.S., the southeast, mostly. And we've been now selling for about four months, five months. only three sales executives and one clinical support. But the idea is we want to go deep on a group of customers. We've already signed up about 15 or 16 customers with these service contracts. But this is where everything changes is we're not selling the equipment. We own the equipment.

We run a diagnostic data testing facility. and we collect this physiologic signal and create the reports, manage the quality, manage the cyber security, transfer all of this data in the cloud, get it back to the physician, all at the speed of light, so that the physician can talk to the patient when the patient first complains of symptoms. And we're not a screening device. We are only used in patients who have symptoms of cardiovascular disease. chest pain, shortness of breath, tightness, fatigue, which is the same complaints you get, whether you have coronary disease or heart failure or pulmonary hypertension or probably

dozens of other cardiac diseases. The symptoms are all vague, but significant. And we have the technology that's able to sort out up to 75% of the presenting diseases. It's the, you know, It's the prevalent disease that one should say. Hey there, it's Scott, and thanks for listening in so far. The rest of this conversation is only available via our private podcast for MedSider Premium members. If you're not a premium member yet, you should definitely consider signing up. You'll get full access to the entire library of interviews dating back to 2010.

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