Medsider: Learn from Medtech and Healthtech Founders and CEOs - Why Finding Investors Who Understand Your Product is Critical: Interview with EchoNous CEO Kevin Goodwin

Episode Date: February 22, 2022

In this episode of Medsider Radio, we’re sitting down with Kevin Goodwin, the CEO and founder of EchoNous. Kevin has already been part of a medical first. In 1998, he founded Sonosite, which released the first point-of-care ultrasound device (PoCUS) the following year. It was a classic startup story: Sonosite hit NASDAQ immediately after launching, with no product, no approval from the Food and Drug Administration (FDA), and no revenue. Sonosite was acquired by Fujifilm in 2012, and Kevin left the company in 2014. In 2015, Kevin co-founded EchoNous, to make a handheld PoCUS device that cost significantly less than the models used as the standard in hospitals. Even more ambitious, it would use AI and ML to not only deliver more accurate results, but to improve the detail and accuracy of those results over time. The company’s AI-powered Kosmos platform was approved by the FDA in 2020. In this episode of Medsider, Kevin explains the pros and cons of public versus private investors, what happens when a competitor with an inferior product dominates the headlines, and why the toughest potential customers are the ones you should try to win over first.Before we jump into the conversation, I wanted to mention a few things:If you’re into learning from proven medtech and healthtech leaders, and want to know when new content and interviews go live, head over to Medsider.com and sign up for our free newsletter. You’ll get access to gated articles, and lots of other interesting healthcare content.Second, if you want even more inside info from proven experts, think about a Medsider premium membership. We talk to experienced healthcare leaders about the nuts and bolts of running a business and bringing products to market. This is your place for valuable knowledge on specific topics like seed funding, prototyping, insurance reimbursement, and positioning a medtech startup for an exit.In addition to the entire back catalog of Medsider interviews over the past decade, Premium members get exclusive Ask Me Anything interviews and masterclasses with some of the world’s most successful medtech founders and executives. Since making the premium memberships available, I’ve been pleasantly surprised at how many people have signed up. If you’re interested, go to medsider.com/subscribe to learn more.Lastly, here's the link to the full interview with Kevin if you'd rather read it instead.

That is the biggest problem I think small companies have, which is you have investors that, particularly the venture site, they think they know more than they know. And they try to manage the company. I don't have that problem. KKR invested in me, the operator, my team, and then the technology and the ideas. They don't get involved in that. They certainly provide guide rails and such. So it's actually a better condition to be in. And they give us time to go do our job and they're supportive. When you have board members meddling with management, they better know what they're doing. And if they don't, what they think they do, that's a big problem. Welcome to MedSider Radio, where you can learn from proven med tech and healthcare thought

leaders through uncut and unedited interviews. Now, here's your host, Scott Nelson. Hey, everyone, it's Scott. In this episode of Medsider, I sat down with Kevin Goodwin, who's been involved in the ultrasound space for over 30 years. Well, at ATL technology, Kevin founded spinoff company Sonocite, which launched the first point-of-care ultrasound, or Pocus device. and was later acquired by Fuji Film.

A few years after that, Kevin co-founded Equinoos, with the aim of creating a portable Pocus device powered by machine learning and AI to give even more accurate and detailed results. Here are a few of the things that we chatted about in this interview with Kevin. First, know your worth and stand by it. Investors who don't have a deep understanding

of the MedTech space can be blinded by buzzy competitors with flashy, low-quality products. Don't let them force you to lower your price or compete directly. Two, when you're looking for customer feedback on your prototypes, find people who are open to new ideas, but take a lot of convincing. If you can make something that even the skeptics buy into, you'll know your product has a market. Third, there are pros and cons to both public and private investment. Public investors can sometimes work faster, but they also expect quarterly revenue predictions, which can be hard for startup.

Finding private investors is a slog, but if you can bring in people who understand your product and give you the space to work, the opportunity is worth it. Okay, so before we jump into the discussion, I want to mention a few things. First, since you're listening to Medsider, you're probably aware of how expensive it is to run clinical trials. Anyone who spent time in the MedTech space knows that you typically need to commit hundreds of thousands of dollars, oftentimes millions, towards clinical research. But it doesn't have to be that way. And here's why. ProofPilot is a new kind of hybrid clinical trial platform that enables you to run decentralized studies at costs that are 40 to 80% below traditional approaches. This is how they do it.

First, you can easily design a trial and the ProofPilot Visual Protocol Designer using their extensive library of templates. Next, you can launch those trials to participants and virtual staff without any technical development. Skip the integration of disconnected providers because ProvePilot pulls it all together seamlessly. For example, you can recruit, consent, and retain participants, then schedule, remind, and collect data, often with minimal manual labor, manage site data in real time, query adverse use data, defense quickly and review data and preliminary analysis within hours, all in one compliant platform. Get up and running quickly with an annual license fee and launch as many trials as you

like with an unlimited number of participants. To get started, visit medsiderradyo.com forward slash proofpilot. Again, that's medsiderradi.com forward slash proofpilot. For the medsider audience, with an annual contract, ProvePilot will provide IRB approval for your first study at no cost. Some exclusions apply, so visit Medsiderradiot.com. forward slash proof pilot to learn more. Okay, second, if you're into learning from proven MedTech leaders and want to know when the new content and interviews go live, head over to Medsider.com and sign up for our free newsletter. You'll get access to gated articles and lots of other interesting healthcare content.

If you want even more inside info from MedTech experts, think about a MedSider premium membership. We talked to experienced healthcare leaders about the nuts and bolts of running a business and bringing products to market. This is your place for valuable knowledge on specific topics like seed funding, prototyping, insurance reimbursement, and positioning a MedTech startup for an exit. In addition to the entire back catalog of MedSider interviews over the past decade, premium members get exclusive Ask Me Anything interviews and masterclasses with some of the world's

most successful MedTech founders and executives. Since making the premium memberships available, I've been pleasantly surprised at how many people have signed up. So if you're interested, go to Medsider.com to learn more. All right, without further ado, let's get to the interview. All right, Kevin, welcome to Medsiter. Appreciate you coming on. Oh, thank you.

Happy to be here. Yeah, looking forward to this conversation and learning a little bit more about Echo News, as well as just your kind of background leading up to starting the company. So with that said, let's not get too far into the weeds, but what's your kind of elevator pitch for your professional background, you know, your professional background, you know, prior to Echo News? Well, I jumped into private sector health care in 1980. I originally went to college, a liberal arts college with an idea of going into hospital managers, so working on the provider hospital side, but I decided I didn't want to do that.

And then I went into private health care, joined American hospital supply corporation back in 80, which was at time, arguably the Amazon of health care. They had the first automated systems purchasing that you could automate purchasing through technology. And so that company was actually very well respected. And then I went into technology after that. I went into a medical imaging. And then after that, ended up in ultrasound. So I'm 34-year veteran of ultrasound. That's really my life in private sector of healthcare.

And in particular, 21 years ago, founded the first ever point of care ultrassound company called Sonocyte. So that company created what is now known as the point of care ultrasound space or Pocus. And that's moved from zero of annual revenues in global ultrasound to 35% of global ultrasound hardware revenues. So back in 98-99, global ultrasound hardware revenues were 2.8 billion. Today, they're 8 billion. And 3 billion of that is focused, Planned Care Ultrasound, which is basically analogous to the movement of the technology out toward

the patient to bedside and away from the centralized lab. So that is me. I arguably the founder of that movement. Got it. Great. I love it. And so those I think most people that are listening are familiar with the company. And just so I understand, I know, like on your LinkedIn profile as an example, which I've linked to in the show notes for this interview for anyone that wants to check out Kevin's LinkedIn profile. But I know you were the CEO, but did you also like start the company as well? Yeah.

So what happened was in 97, I was appointed to head up at the vision, which was inside of ATL, which at the time was an independent NASDAQ listed company. And the CEO for whom I worked asked me to take over the project and figure out commercialization. So that was in 97. I used to call it the handheld systems business group. And then after a year of study, going around the world and talking to people about what would be the value of a hand-carried ultrasound device that was quote-unquote credible. And the reasons for that were technological. So the company ATL had a history of driving the shift from ultrasound image formation using analog circuits to digital.

When they went digital, they got on Moore's Law pathway, which allowed them to compress more and more circuitry on. to a silicon chip, basically using line density improvements akin to what Moore's law predicted. And by the end of the 90s, they decided you could make a credible ultrasound device in a package that might be four or five pounds. And so we did. And so the question is, was there a market? And a lot of people were doubting Thomas's. I wasn't. I talked to a lot of people and used instinct, but ultimately what happened was we spun the company off. I led that spin off into a net. We went straight to NASDAQ. So in April 98, I was born on NASDAQ before I had a product. before we had an actual market before we had revenues.

So we didn't get FDA approval until September 99. And then we started shipping revenues in the final four months of 99, which we did 10 million. And then we did 32 million in 2000. We did 305 million in 2011. Wow. And it sold the company for just under a billion dollars. But the enabler was the shift to digital, the shift to ASICs, which are application-specific ice season.

And parallel with that, a lot of work and material. science on the ultrasound probe. So that market now has had classic market behavior because what everybody did, we started off with a five-pound product. We put in place a product ladder of, you know, good, better, best, kind of like you see at General Motors with a rack of products that go from bottom to top. And then at the time I left the company, our final product was called the export that weighed about 65, 70 pounds, and priced out at about $60,000. Whereas the original product was $21,000 and weighed five pounds. So it's interesting to say this because that marketplace all went up in size, trying to optimize performance and, of course, price to the user

and larger screens and all that stuff. And now the wave is shifting again, so I believe, which is that Moore's Law enabled us to, with this new company, match up and performance with these middle, upper middle market products in terms of engine size, which is the channel count of an old percent device, and put that in an eight-ounce package. So my first product at Solisite 99 was one-fifth as powerful as my current product of Echo Nose and priced out at five times more expensive. Okay. So, and then we have the arrival in 2012, the inflection point of deep learning, where you had tools you could actually use with deep learning that could work. And so,

ultrasound device has always been made up of probes of hardware, signal processing, and software, usually for the sake of operating the machine and also features. Now we have software in the form of AI that organically grows, gets better. And there's been some fascinating stories. So we're the first ones to take the lead in cardiopulmonary algorithms on a device, which is eight ounces that has now been benchmarked against the $60,000 to $120,000 instrument. And basically found to be equal. So, you know, ultrasound is user dependent, it's patient dependent and all those things. But we have a machine that people are starting to change their behavior, which is very exciting. So we've got people at UCSF, Mayo, London, all over the country now that are taking my machine to the bedside as a normal course of business, not when they need it, but they always want it.

So it's at every patient, every day model versus in the past, it was walk up, examine, and say, I think I'm going to do it, ultrassan, I'll get one to order. So we're causing another shift. I mean, this new company, ECHO, is really about disruption 2.0. So everybody's going up and trying to maximize price. We went down and added AI. And we've got a product ladder now between 5 and 15K with AI that can proliferate up and down the ladder. Got it. That's super helpful.

I'm sure we'll get into some of these sort of the key lessons you learned, building and exiting sonocite over the past 10, 15 years. But let's talk a little bit more about Echinose. What can you can you give us like a high level kind of origin story if you will and what made you, you know, what caused you to kind of like actually want to dig in and want to work on this on this project after kind of leaving. Great question. I left the I left Sotomay post acquisition in March of 2014. Six months later, I wasn't saying to myself that I was going to go back into the medical device world. I wasn't sure what I was going to do. It was kind of just going to cruise it along. And then I got a phone. call because I made a comment on a blog and the blog comment had to do with butterfly claiming they were going to bring out all this fancy AI and hardware one year later to which I said, yeah, right, good luck with that. Well, it turns out they never did. It took them another four years

to come out with a product and they had no AI. But what happened was a deep learning group down in San Francisco saw my comments and called me and said, what do you think about using deep learning and ultrasound? I said, well, what is deep learning? And I spent six months studying deep learning and I said to myself, this is going to drive the next 20 years of Point of Carol percent. So that's where I, that's probably where my strength is, just predicting the future because of my experience in the industry and an understanding of trends and needs of users, you know, political needs. So what happened was I called up a guy from Sunnicide, happened to be a Greek guy.

And I said, I think that AI is going to, is going to proliferate the way these devices are used, that you could automate acquisition of the image, you could automate interpretation, you could automate physiological measurements. And then down the road, when big data arrives, you can connect to the big data and bring immense knowledge to the bedside right there and then. For example, you could study and people are doing this correlate the subset of parameters that are most predictive in an ultrasound image and connect that to outcomes. And then when you do that, and we're seeing it happen, you can just get an image and the AI can tell you what you've got to deal with over here on the other side in terms of probable outcome. And that's definitely,

That's very close. We're very close now. So to do AI in ultrasound, you have to have a data-rich image. So you can't do it with crappy or low-cost hardware. Hence, these low-cost products, these men's products like butterflies, they just can't deliver the goods. You need to have a data-rich image. An ultrasound image usually has 80 to 100,000 data points inside of it. So my partner said, well, I could make you a really good ultrasound machine if nobody tells me I can't. And the problem is in this industry, everybody is loathe that self-cannibalized, right? So he said, I can create a machine that's going to have 64 to 128 channels and fully functional. We can make an image really, really well, which we did. So we combined the AI opportunity with the miniaturization, I'll call it

frontier opportunity, and put them together in a product, and then we started showing it to physicians live in August of 19. We got FDA approval in March of 20, and then COVID, you know, cream the world. So we didn't do a lot of marketing and selling that year. What we did instead was product optimization. And then in March of 21 this past year, we had the product kind of right where we wanted it in terms of functionality and quality. And the AI was plugging along. And so we made a tremendous move this year. We've seen rapid unit growth quarter to quarter.

And now we're in Q1 and we're looking at substantial rate of growth in terms of unit sales. What we've done is conquer the part of point of care that no one went after, which is cardiology. predominantly. Cardiology was a was a was not a participant in the point of Carroll-Persand movement. They would buy the cheap products, the GE product, the old one and others just to take a quick look, but those are non-diagnostic, non-reimbursable you know, not essential if you will. They're better than a stethoscope if you're an expert. But if you're not an expert, uh, you have to actually know what you're doing to use these devices. It's not an iPhone. So, uh, nobody tells you what you're looking at and nobody

even tells you whether you have a good image. Now my hardware, you can put the ultrasound probe over the heart. It uses object detection, labels the heart anatomy. And you can move the probe around in various ways that you can't trick it. It'll tell your right, ventricle, left ventricle, all those things. It also grades your image. It tells you how good your image is, and then it guides you to improve the image so you can then move to another algorithm called autostostolic, which is a 17-second workflow, gets a very accurate ejection fraction. Now, it's interesting because we took that hardware over a big hospital in Greece. And they were very suspicious. I'm skeptical, is better word, whether the hardware would perform at the level predicted. But then on the AI,

they're like, yeah, right. You're going to, the algorithm's going to do better than I know how to do for ejection traction. So what happened is they watch the algorithm get better and better with time. And now they use it to check their work. So when they're at the bedside, they say that at that ejection fraction is 55 percent. They'll use our product, which has a 17 second workflow. You take one view, another view, you pull the probe off the body, and there's a seven-second compute cycle. And we're taking advantage of the Qualcomm technology. They're the best in class for mobile AI, NVIDIA being the best in class for large-scale stationary. That doesn't work in mobile. And so we've seen that happen. So you have this trio, which grades, guides, and labels,

and you have then the auto-sostolic. And now we just put in place a product that takes the work of a cardiologist, which is usually as much as 40 minutes when an ultrasound image says take it of a heart, they come in, they do a bunch of point click and measure and they put it into a report and then they dictate an answer. That's usually a 40-minute process. This machine learning tool does it in two minutes. Wow. And it's accurate.

So it's actually been found to be, in some cases, more accurate than a doctor, meaning it's found disease where a doctor missed it. So that's the promise of AI. The promise of AI is making measurements, accurate and fast, reducing interoperator variance, which is a big problem in healthcare. So if you and I scan the same patient or two doctors scan you, they'll get different answers oftentimes. An AI takes that variance towards zero.

And then it opens the door for a lot of other things. They're going to be very interesting, saving a lot of time, automating physician and other workload and things like that. So we're very happy about this. But we had to have a piece of hardware that was non-trivial in nature, meaning it's just not cheap. It's small, but very, very powerful. So on that note, thanks for that explanation. It's clear like you're an expert in the ultrasound space after having kind of built that FICO movement. But let's talk a little bit about like those formative years when you're,

you know, you're prototyping, right, those alpha and beta units. You mentioned your partner asked you the question, why I can build this as long as you tell me I can't build it, right? How do you know? Actually, the problem was that where he worked and where anyone would work in this industry, if you said, I can match a $60,000 or higher machine in a product that can be hand carried in less than 10K, none of them want to do that. Yeah. They all want to go upmarket and get higher average prices and they don't care of unit sales flag. What we're doing is going the opposite way and disrupting again.

Got it. Got it. So you're, I guess, the company allows you to kind of, I guess, you know, sort of pivot on a, on a more normal, kind of normalized business model, right? when you don't have to be cognizant. Oh, yeah, it was formed for that purpose. So it was formed to create hardware that could scale and then sell AI applications at the point of sale or after. So we've got a whole portfolio of AI applications coming in behind us.

Got it. Do things of value for people. Got it. Yeah. So like take us back to some of those early days, right, when you're building out like those alpha, even those beta units. Like what are some of the key learning, like for other device health tech entrepreneurs that are in that same boat and they're trying to balance like how to do this efficient?

I don't want to like over engineer this thing, but I also want to, I also want to make something that's truly differentiating. Like what are some of the key, like one or two like key lessons that you would maybe speak, you know, or offer offer up to that same. Yeah, what goes wrong. What goes wrong. And we actually had more mistakes at this company that I did at my prior company. What goes wrong is you got to hire the right people who have the skills to do what you want to do with the product. And then to do it in the way you want to do it. So we wanted this device to teach the user how to use it. That was the premise. I don't want to make. just a pedestrian ultrasound machine, even if it's small and good. I want it to help the user

learn how to use it. So that premise, we had some malalignment at the beginning in our company where the wrong people were doing the wrong things and didn't do things the way we wanted to on the software user interface and AI side. However, on the hardware side, the imaging ultimately we had the fundamentals right. We just had to get it optimized and we had to get functionality optimized. And so I'd say that you really have to have alignment. and skill and know-how and belief on what you want to do at the very beginning. This is vital. Okay.

At SunSite, we started off saying we want to create disruptive hot products, hot meaning highly designed value. So not just ugly ultrasound machines, but pretty, you know, attractive ultrassound machines. I want to win design awards. I also want to make them very easy. And we did a great job of that over the years, a simplifying ultrasound from many buttons to no buttons. Now, we're on the verge today with our product of being able to take away with AI any user interaction with the machine. So that's pretty interesting because when ultrasound, you have to push the beam into the body that's called depth.

And you've got to manage brightness on the screen that's called gain. And we can automate that. We can also automate other things like the placement of a Doppler cursor, which our hardware has unprecedented functionality. You can measure a full heart echo, a full echo of the heart with my product, which you can't do. anywhere in the world with another machine. It opens up a new world, and it's interesting because that world's emerging now, which is you can now go into communities with our hardware and the AI we have, and you can find people with heart failure before they have symptoms. You can find people with valve disease before they have valve symptoms. And this is unprecedented. And we're told, like,

for example, the valve disease, one in ten people get a valve that need a valve. They wait until they're sick. And the reason is no one's looking at their valves. And so, we're We're right there now. And then on heart failure, you've got a lot of therapies coming out that can open the door to screening. So having people look at patients more routinely at the beginning of their healthcare journey at a particular age group or whatever. And you can find heart failure either, you know, there's two kinds of heart failure. There's heart failure normal. There's heart failure with preserved ejection fraction, which is even more scary because your rejection fraction is normal, but you have heart failure.

And heart failure is a big problem from an economic and health care standpoint. So anyway, that's what is exciting is when you put that technology together, what it should do if it's really, really truly affected. It should open up new markets, new market expansion opportunities. Got it, got it. And circling back to your point around alignments, right, like some of the challenges that you had to work through, you know, at Echo, was it was it was an alignment around like what you wanted this, what you wanted this device to look like? or was it alignment around like the high level like strategy for the company? It was what I wanted the device to look like because that was a core element of a strategy to the company.

What happened though, the engineers did what engineers often do, which is to try to drive to the MVP, the minimum viable product as fast as possible, which is not altogether bad. So what we did after that, we got to that base probably by August of 19 and then we iterated like crazy through March of 21. And now we're in a great position. but it took that call it, 15, 16, 17 months of iteration to get us there. Got it, got it. All right.

Well, let's shift a little bit and talk about, you know, raising capital. I know you've raised a couple rounds for Echinose. Talk to us a little bit more about your experiences in capital raising, you know, throughout your, you know, your 30 plus years and kind of. Yeah, yeah, that's a good question. Yeah. Yeah. So when I was sput out in Sonocite, 1998, it was not a good time.

for a small cap medical device. And we were pre-product, pre-revenue, pre-market. So it was pretty abstract, even though instinctively and research-wise, I knew we had something. You had a lot of people telling me over the year and a half, this is going to change everything, which it did. But we were born on the NASDAQ. So we went out and raised money in April 19 into a headwind. We had a lot of so-called experts saying the deal will never get done.

And we ended up overselling the deal initially and then did a pipe later. that year. So that was my first experience in public money raising. So I raised 160 million collectively in equity dollars at sell the site. I never needed more than 60. So we excessively raised, but that's what you tend to do when the money's out there and they're able to tend to load up the bank. So that was one experience, but then when you raise equity publicly, you have to sign up for the dance with Wall Street every quarter. And that's detrimental to startups. That's not really good for startups because you're trying to predict the future 90 days out that's highly unpredictable.

So, for example, in our first full year of revenue, we tried to do $50 million in 2000 of revenue. We did 32, which I'm told is the upper 5% of the med device companies, and we were slitting our wrist as if we failed. So we went from 32 to 45 to 76 to 100 to 300 in a cycle. So, you know, that's not so bad. I mean, but you would have thought we were, you know, deprivation. because the quarterly calls can be depressing when you miss, you know, and when you make, it's great, but we postponed profitability, which was a model that was somewhat controversial

back then. Amazon was doing it, and rightly so. We built the business and we built everything but the profit engine, and then eventually the profit engine was expected to arrive. Now, in the private sector, I was fortunate KKR knew of me and the company, it's on a site previously. They found out that I was raising money for this new venture, took a good hard look at it, and they said, we'll take the whole deal. So they'd give me $120 plus million to get us this far. We did a debt deal in May with Kennedy Lewis, so a little unusual for a company of our size. And then we did, we're right now looking to close a C round. It's been harder than I thought it would be to close a C around, given what we have and who we are. In our environment, you have companies like Butterfly that have raised a boatload of money and who went into the market via spats.

Now, their business model in my view is highly questionable, given what they're doing, and their stock price is going from like 26 to 6, which happened to me. It's on the site. That happens. And my stock price went from like 32 to 11 or something. It just happens. You know, Wall Street's fickle. So that's the experience that I have today is private sector money raising is harder because

you don't have the pure liquidity of the public markets. You know, you don't have the ability to get out in an hour. Right. But I think that there's a lot more value creation potential in this situation because private company benefit if you have a good owner like we do. KKR is excellent. 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.

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