Hello, everybody. Welcome to the Healthy Enterprise Podcast. If you're a first time listener, welcome. I hope you enjoy the show. And if you're back for more, thank you.
Heath Fletcher:I appreciate you returning as a listener as well. So today, we're gonna be talking with Kevin Harris. He's the president, CEO, and cofounder at CureMetrics. Kevin has led teams for twenty plus years in tech, and he's gonna talk about how AI is changing the game in health care, especially in radiology and breast cancer detection. So let's have a chat with Kevin.
Heath Fletcher:So, Kevin, welcome to this episode of the Healthy Enterprise podcast. Thank you for joining me. I appreciate your time. And so, why don't you introduce yourself to our listeners and tell us, a little bit about what you're doing?
Kevin Harris:Well, Heath, thank you very much for hosting me. I really appreciate it. It's a pleasure to be here. So my background is kind of interesting. I've been in technology in one flavor or another for about thirty almost well, I guess now thirty five years, so a bit of a stretch.
Kevin Harris:Actually, I have a degree in what's called cognitive science from, the early nineties, late eighties. And Okay. So that was kind of the the that was AI before AI was really AI. Right?
Heath Fletcher:Yeah. No kidding.
Kevin Harris:Because way back then, the compute power was was not real strong. I mean, I mean, this was before Microsoft Windows even came out. Right?
Heath Fletcher:So Right.
Kevin Harris:I think have you to go in the way back machine to really think about that. Yeah. Like a
Heath Fletcher:hot tub time machine.
Kevin Harris:Yeah. You know, and so over the ensuing years, I worked for a whole host of different types of companies and and ran different types of companies. And then about eleven and a half years ago, we decided to start Curemetrics. We were seeing some trends in industry. We were seeing that the cost of storage was coming down.
Kevin Harris:The cost of compute was coming down. The speed of compute was going up. And, you know, so we've been working with some data scientists who had some ideas on how to apply this. And so we decided, okay. Let's let's move into health care and bring online a company that focuses on doing AI in health care.
Kevin Harris:And so that was really the genesis of, of CureMetrics.
Heath Fletcher:Why health care? What was the motivation for that? What was the what was the who cared on that one?
Kevin Harris:You know, sometimes the stars just align. So for me personally, I had worked in a whole host of industries. I had worked in consumer information, real estate, construction project management. I was a c level exec in a large scale insurance company. Uh-huh.
Kevin Harris:I I mean, I've been kind of all over the board.
Heath Fletcher:Yeah.
Kevin Harris:And I live here in San Diego, and San Diego is one of the biotech capitals of the world. And I I just thought to my it was really interesting. You put things out to the universe sometimes. Right? I thought to myself, okay.
Kevin Harris:I've never done anything in biotech. I think it's time. And and literally within about a month, I I got a call. This other company I was I was working with running, we had pitched this group of VCs and they're like, no. We're not gonna invest in that.
Kevin Harris:It doesn't sound like a good a good deal for us. They called me up about a month later and they said, okay. We didn't like that other idea, but we like you. We're starting this company in, you know, in the health care space using AI. Would you be interested?
Kevin Harris:And so I was like, it just came together.
Heath Fletcher:It just pulled you pulled you together in that this one common moment. That's pretty interesting.
Kevin Harris:Sometimes you just gotta follow where it leads. Right?
Heath Fletcher:So That's that's exactly it. I mean, just a quick side story just to relate to that is that the last year and a half, my wife and had an actually serious health issue, and so we were kind of, you know, air dropped out of our life into a completely new life for about eighteen months. And I spent a ton of time reading, and I'd never really spent any time in health care as a patient or otherwise. And now I was immersed in it. I was basically at the hospital every day, and it kind of intrigued me in in in health care.
Heath Fletcher:I got I got kinda interested. And then we started reading books. I'm like, oh, yeah. This is really interesting. There's there's nothing in this realm that isn't interesting.
Heath Fletcher:And then next thing you know, I got asked to be a a host for a a health care podcast. So you're right. You never know what's gonna happen if you gotta be careful what you ask for, I guess.
Kevin Harris:That's right. So I've been I've been thinking a lot aloud about winning the lottery and Yeah. Me too.
Heath Fletcher:Yeah. That's great. So let's go listeners are probably going, okay. What's CureMetrics? Tell us more about that.
Heath Fletcher:Where is where did what is this? And and and and so they're the one they came to you and said, this is what we got and what how can you help us?
Kevin Harris:Yeah. So there were there were a couple of data scientists that had doing work for, NASA in space weather prediction. And so they had some interesting algorithms. They were really brilliant data scientists, but nobody was really clear how to take what they were doing and apply it into health care. So part of the first genesis the the first couple of rounds, first couple of months with the company was actually, you know, looking at what these data scientists had from a tech perspective and looking at health care and seeing where the the alignment lay.
Kevin Harris:And, you know, we look we did a lot of stuff looking at predictive modeling. Could we predict patient outcomes? Could we predict sepsis? Could we predict which patients who enter an emergency department will end up needing a bed, you know, so we can help hospitals with operations management. We were looking at a lot of those things, but nothing was really sticking.
Kevin Harris:And so I went back and really sat down with the data scientists, really tried to absorb what they were doing. And the work that they had been doing was not just an algorithm. Right? It was computer vision, image analysis, predictive modeling. And so when I started to look all that, I was like, okay.
Kevin Harris:What if we could take this and apply it to radiology? Right? I mean, there's, countless radiological images generated every day all around the world. And for the most part, at least at that time, they're all read by eyeballs. Yeah.
Kevin Harris:And so is there an opportunity to optimize this process, to improve outcomes for patients, to make it easier and better for the doctors, to make it less expensive for the health care industry in general? And so that was the first epiphany, but then, okay, now you have all of radiology. Right?
Heath Fletcher:Yeah.
Kevin Harris:So now what do I start looking at? X rays of ankles or MRIs of brains or right. Where where do you go? And so, you know, we started looking around, and interestingly enough, it's just some things were happening in our in my personal life. My my wife's mother, my mother-in-law had breast cancer, so there were some things going on on that end.
Kevin Harris:We were looking at some of the variety of images to see where this could apply. And Mhmm. Again, things just started to fall in place that mammography might be an interesting path to look at. And from a business model, it made sense.
Heath Fletcher:Mhmm.
Kevin Harris:In The United States, there's 40,000,000 mammograms a year that happen year over year over year. Right? So there's plenty of volume and repeat volume. Women come in or they're supposed to come in every year, but women come in over and over and over again. So you have longitudinal data for individual patients, and you have all this data.
Kevin Harris:And then it's not just a US problem. Right? So you have mammograms happen everywhere all around the world. So that was the first thing. The second thing was we looked at how how well is mammography performing today.
Kevin Harris:We looked at true positive rates, false positive rates, false negative rates, and what we found was that there were a lot of challenges with mammography. If I break down some of the numbers for you, and I realize your listeners aren't taking out an Excel spreadsheet, but
Heath Fletcher:Well, some might be. If
Kevin Harris:you start with a thousand mammograms, in the current practice today, radiologists will recall about a 100 of those patients. Meaning, they looked at the mammogram and they saw something and they said, you know what? I I want you to come back for additional imaging. Of the 100 patients they recall, they might biopsy about 20 of those patients. Right?
Kevin Harris:So that means 80 of those patients were recalled for imaging but didn't need it because
Heath Fletcher:Didn't need
Kevin Harris:whatever they found didn't require biopsy.
Heath Fletcher:Right.
Kevin Harris:And of those 20 biopsies, they're gonna find about five cancers. So, you know, the when you look at that cancer detection rate of five six per thousand, it's a pretty low detection rate.
Heath Fletcher:Low. Yeah.
Kevin Harris:But when you look at the biopsy rate and the recall rate, it's pretty high.
Heath Fletcher:Right.
Kevin Harris:And so we thought, okay. Maybe we can help here. Maybe we can do something better. And that was really how we got launched into this whole effort.
Heath Fletcher:Very interesting. So that's taken you to this up today. And and so where is CureMetrics at now? What stage of development?
Kevin Harris:So, you know, we spent a bunch of years building out this AI for breast cancer detection. We have a whole suite of products around that. And one of the things that we found that was super interesting is when you're training an AI algorithm to find breast cancer, you have to train it to find everything that isn't breast cancer and throw that stuff away to reduce your false positive rate.
Heath Fletcher:Oh, okay. Interesting.
Kevin Harris:One of the things that we were finding, scoring, and throwing away are something called breast arterial calcifications. It's basically a stiffening or hardening of the arteries of the breast. They're not breast cancer. They're not tied to breast cancers, but they throw a bunch of false positives.
Heath Fletcher:Okay.
Kevin Harris:So we were finding these things, scoring them, and throwing them away. And at the time I was doing that, we as a company, we were doing that, you know, I'm always reading medical journals and papers and seeing what's going on. There started to be more and more chatter around that these breast arterial calcifications may be linked to downstream adverse outcomes in patients.
Heath Fletcher:Oh,
Kevin Harris:really? So we started doing a bunch of research. We started collecting more and more data from our our hospitals and our partners to say, okay. Let's let's look at these breast arterial calcifications and see, are they indeed tied to these adverse outcomes? And, in fact, we we put out a paper, with UC San Diego in the JACC advances, which is a, you know, a journal.
Kevin Harris:And we looked at eighteen thousand women about over an eleven year period of time. So a lot of women, long period. And we said, okay. What happens when women have these breast arterial calcifications? What are their outcomes?
Kevin Harris:And in that paper, we talked about finding almost a three times higher odds if you have breast arterial calcifications for coming out with MI, myocardial infarction, MI, heart failure, stroke, or some mortality or some composite of those.
Heath Fletcher:Right? Really?
Kevin Harris:So just the presence was was increasing the outcomes for these patients.
Heath Fletcher:It was an identifier. Wow.
Kevin Harris:Yeah. And then we we, you know, we looked at younger women too in that study. Right? Because you got 18,000 women. And what we found was when you looked at the ten year survivability of patients, and there's these things called Kaplan Meier curves.
Kevin Harris:But when you look at these two curves, in younger women, the women who survive versus who don't, the curves separate, for those who have breast or chest calcifications. So survivability goes down, just for the presence of having these things. Yeah. So it's it's been a really powerful learning journey on this this indication in the human body.
Heath Fletcher:Interesting. You have this throwaway data that you think is really streamlining your focus so you can actually pinpoint what you're looking for, and you find, actually, there's a there's a connection from that to a completely different, health issue.
Kevin Harris:Yeah. Exactly. So yeah. So then we started thinking. Okay.
Kevin Harris:With this understanding and by the way, it wasn't just us. Right? I mean, if you look at if if you look at the body of research, there's hundreds of papers published all over the world and and more coming online every day. We're we're publishing some, but with partners as well, and that that talk about this. And they you know, there's ties between breast arterial calcifications and chronic kidney disease or cerebral vascular disease or diabetes.
Kevin Harris:And and if you think about it, you know, the stiffening of the arteries is a vascular condition, and so it's not surprising. You may be we may be seeing it in the breast, but it's an indication that there may be something going on systemically in the human body, and we're just seeing a certain manifestation of it.
Heath Fletcher:Right. And this exam is something that happens for women, ideally, every year to get this done. So this is as you go, you're you're collecting ongoing data. And that's interesting thing about AI and the data processing stuff is that the more data you get, the more information and the and the wider the, the reach of that information too. Right?
Kevin Harris:That's right. Yeah. So it's it's it's been a fascinating journey. Right? These these retrocalcifications are something called an incidental finding.
Kevin Harris:But if our thought process was if we can take these 40,000,000 mammograms a year and give women more information that's more important for their health, that's powerful. Five times more women die from heart disease than breast cancer.
Heath Fletcher:Is that right?
Kevin Harris:Right. I mean, everybody's incredibly scared of breast cancer as you should be, and it's something that's managed and treated and dealt with. But heart disease is kind of a silent thing in in in women's health. And, I mean, I know from my own experience with my wife and her doctor, I mean, doctors don't always react the same to women as they do to men when they come in with different complaints. So we we saw this really as a huge opportunity to have an impact, again, for the patient, for the provider, for the health care system.
Kevin Harris:If we could help get in front of what was, impacting these patients, we may be able to change lives. And that's really the mission that that we've been on for a long time.
Heath Fletcher:And so when this information comes back so who who who actually receives the information back? Is it the primary primary care? Who ordered the mammogram?
Kevin Harris:Yeah. I mean, so it all starts and ends initially in the radiologist's office. Right? They conduct the mammogram, and they do the first read of the mammogram. So any information that's coming out of the mammogram is provided to a radiologist.
Kevin Harris:Right. They make the determination based on what they see and what's in the patient's chart, etcetera, about what's to do next. But most typically, they refer back to what's called the referring physician, which could be the primary care, the OBGYN, you know, who then takes in that patient and says, okay. I see something going on here. Let's run some additional tests.
Kevin Harris:Let's do some additional evaluation and figure out, you know, do you need diet and exercise? Do you need statin? Do you need you know, where where do you fall on that spectrum of of care for what's going on in your body?
Heath Fletcher:So where does where do you insert CureMetrics in that in that trajectory, and who does that?
Kevin Harris:Right. So what we did is this is going back a couple years. When we figured out after all this research that breast arterial calcifications had these impact, we then I mean, that's the I don't wanna say that's the easy part, but that's only step one in the journey. Right? Step two in the journey now, and definitely a hard part, is taking these concepts and ideas and making them into a product.
Kevin Harris:There's a very big difference between a project and a product and even a product and a business. Right? So productizing something like this requires going to the FDA because this is what's called software as a medical device. So we had to collect more data. We had to rigorously test and evaluate the data, and then build a product with appropriate labeling that tells the radiologist how it works, and what it does do, what it doesn't do.
Kevin Harris:So now we have this product called Centimeters Angio, and Centimeters Angio lives in the radiologist's office. Actually, let me just say it. It lives in the cloud, but the process lives in the radiologist's office. So a woman comes in. She has her mammogram just as she would today.
Kevin Harris:Nothing is different. There's no new radiation for the patient. No different effort at all for the patient. It's totally transparent.
Heath Fletcher:Yeah.
Kevin Harris:Yeah. Completely transparent to the patient. And when the mammogram is captured, a copy of the mammogram is automatically de identified and encrypted and sent to us. We process it, and we return results to the radiologist who then, when they're evaluating the mammogram for breast cancer, has a scorecard now where they can evaluate this patient for the presence of these breast arterial calcifications. So our scorecard not only indicates whether or not it's absent or present or present bilateral, but the Curometric C.
Kevin Harris:Mangio scorecard also localizes those arterial calcifications and shows on thumbnails on the scorecard where they are and what they look like.
Heath Fletcher:And the radiologist is the one that's, you know, evaluating and reevaluating or assessing and checking the the the scans and and, you know, doing their job that they're supposed to do. This isn't gonna eliminate that that that person or that step of the procedure.
Kevin Harris:Doesn't eliminate them at all. In fact, this is intended to be used by a
Heath Fletcher:radiologist. Yeah.
Kevin Harris:Right? So as I said earlier, this is actually an incidental finding today. Mhmm. Though even without our software, radiologists, as they read the mammogram, they can see these arterial calcifications, and they're supposed to see them, evaluate them, and make a determination on what to do next. But what our software does is it it helps objectify and standardize that process.
Kevin Harris:The AI sees all mammograms equally, so to speak. Right? Yep. Yep. It doesn't get tired.
Kevin Harris:It looks at at what's going on, and it does this detection process and then presents this scorecard to the radiologist. It's then their job to go, okay. Here's what the AI saw. Do I see the same thing? Yes.
Kevin Harris:Confirmed. Now what do I wanna do with my patient? Right? And so then they make the determination, and they manage their patient.
Heath Fletcher:Just gives them that extra layer, that extra level of of information and about and and validation, I guess. Right? That Yeah. What they're seeing is is accurate and that yeah.
Kevin Harris:And what we find actually, it's interesting. I mean, I I don't know how we would do this on the podcast and since it's an audio, it probably wouldn't work. But if I showed you pictures of what of what a respiratory calcification look like, imagine, like, these white earthworms on an x-ray. Right? You can give an artery that traces through an image, and it's, you know, calcifications like white spots within the artery.
Kevin Harris:Mhmm. Looks like an earthworm. The the bright, long, egregious ones, doctors can see today. And and they've been able to see with or without our software. One of the benefits that the software brings is it can find the faint ones that they may either be overlooking or missing.
Kevin Harris:Again, the number one primary purpose of a mammogram is screening for breast cancer. And the doctors work really hard, and they've been trained to scour that mammogram for breast cancer Mhmm. And disregard everything else.
Heath Fletcher:Right. Of course.
Kevin Harris:Just like our software was.
Heath Fletcher:Right. Yeah. Right. Right.
Kevin Harris:So so now as an incidental finding, they can still do that, and then we can bring after the fact the scorecard that helps them assess. Oh, okay. CureMetrics found this. It's faint. They can then go back and look.
Kevin Harris:Oh, yeah. There it is. That that's right. Okay. Now I know what I wanna do with this patient.
Heath Fletcher:Interesting. So they're using this process of elimination just like the just like your, yeah, your algorithm was, but now you're bringing that back and saying, oh, by the way, yeah, we've eliminated all that, and you can you can now see exactly what we're reaffirming that. But here is another layer. There's something else to bring to your attention. So they can still focus on what they've been trained to do, but this gives them a little extra benefit of a
Kevin Harris:Absolutely.
Heath Fletcher:Cause they can go only go on the information that they have, and that just gives them more information to work with.
Kevin Harris:That's right. Wow.
Heath Fletcher:Cool. Wow. That's really cool. And so this is, so let's go back to so you've started working. What was your first role with CureMetrics when you came on board?
Heath Fletcher:What what were
Kevin Harris:I you was the CEO. I was hired on
Heath Fletcher:You're
Kevin Harris:right on
Heath Fletcher:the yeah. Okay. And so tell me about your your experience moving, transitioning from where you were into this. And what did you kinda tap into? Like, did you you you had to take on the you had to learn a lot, obviously.
Heath Fletcher:But for yourself and and becoming a leader of this organization, what did you tap into for for, you know, fulfilling that role?
Kevin Harris:Yeah. It's a good question. I I had to break this down into a couple of different categories. When anytime you jump into a new I've been I've run other companies before. Right?
Kevin Harris:So Right.
Heath Fletcher:It wasn't your first new new, rodeo. Yeah.
Kevin Harris:Right. So, I mean, there's there's a variety of things that have to happen. One, I had these new I had these data scientists. Right? They came with the package.
Kevin Harris:Right. So I had to learn who they were, what their technology did, what it didn't do. And so I had to spend a lot of time with them really getting deep. And, I mean, I'm technical to a certain extent. Right.
Kevin Harris:Not as technical as
Heath Fletcher:them, but
Kevin Harris:Not no. I mean, these people were, like, genius level. In fact, one of them still works in the company every day. Love him to death, and he is amazing. And I will often say, to this day, he is always the smartest person in the room.
Kevin Harris:I mean, just he's he is just he's just an amazing man and just knows so much and sees things through a lens that nobody else can see things. So I had to start by trying to see things through his lens and trying to understand the technology that he built. So that was that was one thing to do. Because, again, project, product, business. We had a technology, but we didn't we had a solution, but we didn't have a problem.
Kevin Harris:We didn't know what we were gonna solve with it yet. So that was my job is to figure out what product
Heath Fletcher:That's right. You didn't you hadn't discovered that, the area of focus yet. Right.
Kevin Harris:Right. The next thing I had to figure out was, okay, health care and radiology. Like, I I'm not a doctor. You know, I've had x rays. I've been to a doctor, but I don't I and I don't have a career in health care, so I had to figure out how to learn.
Kevin Harris:So I went in a couple of directions. Here in San Diego, I went to the chair of radiology at UCSD and talked to him and asked him to be a mentor and a guide to me. And Oh, wow. He ended up actually becoming our first chief medical officer. No kidding.
Kevin Harris:He liked what we were doing so much that as he was retiring from UCSD, he came on board to be our first CMO.
Heath Fletcher:No kidding. Wow. What a find.
Kevin Harris:Yeah. So I went and, you know, I got a lot of clinical knowledge from him and and he just dumped a brain full on me.
Heath Fletcher:Yeah.
Kevin Harris:I also went on the health care executive side. So I talked to a bunch of VCs who eventually I would wanna have fund us, and I said, who do you know? Who's done this before? Who can you recommend? And they had connected me with a gentleman who had actually taken one of our competitive companies and sold it to another company and had been a kind of a known quantity in the business, but at the time was retired.
Kevin Harris:Right. And so I pulled him in and, he became, like, an a professional mentor on the the med tech side and on this niche part of the industry. So Mhmm. I I pulled in a lot of advisers and mentors to really help me ramp up as quickly as possible.
Heath Fletcher:Is that something you you did in the past is create this circle of of knowledge around you to help support you?
Kevin Harris:It is. I mean, that's just how I operate. Yeah. You know, some people will just go to the library, go online, and order a 100 books and read them all. Yeah.
Kevin Harris:I like to riff with people. I I learn by listening. I learn by asking. Engaging. Yeah.
Kevin Harris:Engaging. So I wanted to have people that I could rapidly come up to speed on. And I wanted introductions. Right? So there's a couple of really marquee industry conferences every year.
Kevin Harris:I needed somebody who would take me around, introduce me, tell me who all the players are, etcetera. So I had these guys, you know, kind of being my tour guide to the industry at these conferences. Right.
Heath Fletcher:Was smart. Yeah. Great. Great way to do that. Because they would have been having had such long careers in the industry, they would have had many connections.
Kevin Harris:Yeah. They knew everybody. And everybody knew them, which built some credibility for me, which I had not. Right? So I had to Exactly.
Kevin Harris:I had to build some.
Heath Fletcher:And then what do you infuse into your team now? You know, what what kind of leadership skills do you lean on now? Is it I mean, it sounds like you base a lot of your decision making on trust because you you sound like you trust your people that you work with. But what other kind of values do you kind of build into your into your leadership?
Kevin Harris:It's interesting you ask. We're actually hiring a couple people right now, and I often get asked, you know, what's the culture of the company? Yeah. I've really focused hard on building a a company made up of adults and professionals.
Heath Fletcher:Mhmm.
Kevin Harris:We don't have a lot of drama. When I hire somebody to do a job, I want them to be the best at that job. I don't wanna micromanage them. If I have to hire somebody to do a job and I have to tell them what to do or tell them how to do their job Mhmm. Then I hired the wrong person.
Kevin Harris:Right?
Heath Fletcher:Okay.
Kevin Harris:I have my job. It keeps me very busy. So I
Heath Fletcher:I need do your job too.
Kevin Harris:I need I need to count on you to do your job the same way that you count on me to do my job. Yeah. Exactly. I don't like to be micromanaged. I don't work well for others.
Kevin Harris:So I try not to micromanage others either. Right? I I you know, every now and again, I have to play the role of making the decision because there's somebody has to arbitrate when there's a lot going on, but I want adults and professionals. No drama. Know your job.
Kevin Harris:Do it well. If we're doing something wrong, fix it. If you need help, ask for it. Like, it's a I'd like to think it's a good culture. Sometime have my team on and you can ask them.
Heath Fletcher:You can ask Brock.
Kevin Harris:Sure. You could ask Brock.
Heath Fletcher:Actually, you talked about problems or challenges. So what kind of challenges have you actually met with Curemetrics at this point? It's particularly with the advancing AI.
Kevin Harris:Yes. I mean, that's an interesting thing. Mhmm. There's been an what I'll call an arc of problems. Though initially, if you go back, I don't know, six, seven, eight, I guess, eight, nine years ago, even though we saw that AI was gonna be exploding Yeah.
Kevin Harris:Doctors didn't. Right? In fact, initially, they saw this as a giant threat. You know, wait a minute. I read mammograms all day.
Kevin Harris:If you're building software, is it gonna replace me? Am I out of a job? You know, there was there was a lot of of pushback and fear, from the doctors, and not just with my product. There were lots of other AI products that came along, and a lot of people had that same concern. But we got past that because I think eventually the doctors began to realize, wait a minute.
Kevin Harris:This isn't first of all, the FDA doesn't clear to replace a doctor. That's the first thing. It's clear to work with the doctor. Right? So now okay.
Kevin Harris:This is the tool for me to use. Okay. Great. It's a tool. Now how do you use it?
Kevin Harris:Right? So this was an ongoing conversation in education, but that was kind of the first thing to get people over.
Heath Fletcher:That's the mindset across the board. You know? It doesn't matter what industry you're in or what job you're in. It's like that mindset of switching it from being being replaced to being actually just a tool to make your job easier or better or faster. That is that's it's very common.
Kevin Harris:The second one, I think, is this perception that AI has to be perfect. Right? We see this in the in the self driving car world. One Tesla has one crash, and it's on the nightly news. Right?
Kevin Harris:Meanwhile, if you look at, like, the per mile auto crash rates of us as drivers
Heath Fletcher:Human makes way way more mistakes.
Kevin Harris:Way more mistakes. You know, and the same thing applies here. Right? If if the AI can run at two in the morning or two in the afternoon, it can run on a six foot tall patient or a five foot tall patient. The AI is doesn't care about any of that.
Kevin Harris:Right? Doesn't It's an caffeine.
Heath Fletcher:It doesn't use melatonin or serotonin.
Kevin Harris:It doesn't have good days or bad days. You know what I mean?
Heath Fletcher:Migraines. Yeah. Like, none of this.
Kevin Harris:Of that. No. Right? So the AI acts in a very consistent manner. That said, it's never perfect.
Kevin Harris:Right? It will make mistakes. There will be false positives. There will be false negatives because the AI, one, it's it's only as good as the training data you put into it. And two, even if you had the vastest amount of perfected training data, it's still gonna make mistakes.
Kevin Harris:Mhmm. And so getting people over the hump of accepting that Mhmm. Was the next challenge. But now
Heath Fletcher:we've done need people you still need drivers, operators of AI that can catch those mistakes. Right. That's the key. Right?
Kevin Harris:Right. So then, you know, to get over that, we talked about one of the things we do is if you look across radiologists, there are some fellowship trained breast imagers who do nothing but read mammograms all day. But there are some general radiologists who read a couple of mammograms a week. So if we can help bring the general radiologist up to a level of, you know, a more seasoned senior mammography by giving them more information, more tools, that's part of the benefit that an AI like this actually provides.
Heath Fletcher:Right. Right. Yeah. That makes sense.
Kevin Harris:And I think beyond just those kind of challenges, I think the last big one, I can go on because, you know, our life is filled with challenges. But Yeah. The last big
Heath Fletcher:one How much time do we got?
Kevin Harris:Exactly. Our last big one, I think, would probably be regulatory. Because Yeah. Clearing a product like this is not just about saying, yep. It works.
Kevin Harris:Let's go to market. The FDA has very crisp regulations on what you can say, what you can't say, how you have to test the product, how you have to validate the product, the biostatistical metrics you have to use to ensure its performance. I mean, it's so the good news is, right, I feel very secure when I use a product or a drug or something that has gone through this massive amount of of under a microscope testing and and rigor to come to market because I've gone through it, and it's not trivial. The bad news is it's not trivial. It's, you know, a huge amount of lift.
Kevin Harris:And AI, as it has become more ubiquitous and more products, the FDA has gotten more wise and more stringent on what they ask for, what they seek, how they compare it to other products and predicates and current standard of care. So it's it's a lift. Look, it's a very big lift.
Heath Fletcher:Perhaps the FDA needs to adopt some AI.
Kevin Harris:It it wouldn't surprise me if they already have and just haven't told us, but, you
Heath Fletcher:know told anybody. Or they're not using it to speed it up at all.
Kevin Harris:Hey, Chad. Is this a valid submission or not? Right?
Heath Fletcher:Should we approve this or not? Exactly. That's funny. Yeah. I've heard that before.
Heath Fletcher:That is a that is not a it is not a process for the faint of heart. You go into that, and and in some cases, that is what that's what prevents people from moving forward because it is it was a big step.
Kevin Harris:Yeah. It is definitely a big step.
Heath Fletcher:Yeah. Mean And so if you're
Kevin Harris:Go ahead.
Heath Fletcher:Go ahead. Sorry.
Kevin Harris:I'll say if I had to put one more challenge on the plate, so getting acceptance, getting adoption, getting clearance, now the last part, of course, is getting sales. So convincing somebody to actually open up their checkbook or their, you know, their Venmo or their whatever we have today and pay pay for this. I say checkbook. I've dated myself, of course.
Heath Fletcher:Yeah. Really?
Kevin Harris:My kids have never my kids have never written a check. But
Heath Fletcher:What's a check, Dan?
Kevin Harris:Exactly.
Heath Fletcher:Can we ask for the check at the restaurant?
Kevin Harris:Yeah. So so getting getting the customers to understand the value proposition, the impact to their patients, and and their bottom lines so that they will pay for the software. That's the the final hurdle.
Heath Fletcher:So that's where you're right now. You've gone to market.
Kevin Harris:We have gone to market, and fortunately, we have demonstrated value to customers. So Mhmm. We are, making sales, and we've got happy customers and and a growing business.
Heath Fletcher:How do you approach that? What's your kind of what what's your go to plan for for sales right now?
Kevin Harris:Well, you know, it's a smallish industry. Mhmm. You know, radiology is one sector of health care. Mammography is one sector of radiology.
Heath Fletcher:Yeah. It's very niche. Yeah.
Kevin Harris:It it's pretty niche. So it's it's a combination of outreach at trade shows and conferences, outreach from our business development reps, outreach from channel partners that we have and and places who people who are engaged with and resellers that we're engaged with.
Heath Fletcher:Mhmm.
Kevin Harris:And, you know, once you get in front of the right person, you know, because we could talk to a hospital that has 50 radiologists. And if you're talking to radiologists 49, they're deep in the machine. They don't have a whole lot of influence or power or Right. Not a partner, you know, so you gotta work your way up as as with all sales. Right?
Kevin Harris:There's the influencer, the decision maker, and the buyer. And, you know, you gotta figure out who those people are, get to them, have the right message for them. It's it's definitely a a process.
Heath Fletcher:When did you actually go to market? How long has it been?
Kevin Harris:We got our FDA clearance, our first clearance on this product in October 2023. And we actually, believe it or not, had our first customer signed in November 2023, and they were live at least under a trial agreement in December 2023. So it was a pretty quick thing, mostly because we've been on I go to conferences and trade shows. We've been on the the radar of a couple of these big players for some time. And when they saw that we finally got the clearance, you know, they already knew that there was value for them and their patients.
Kevin Harris:It was that was a pretty easy lift for us. Mhmm. Mhmm. But, you know, every sale is is unique and requires a lot of care and feeding.
Heath Fletcher:Is do you find that education is probably paramount right now for you is helping people understand, what this will do or could do for them? Is that, you know, that why the trade show do you do you guys speak at these organ at these, functions too?
Kevin Harris:Yeah. And that's the purpose of all the research papers, the speaking at trade shows, podcasts like this. Right? I mean, sometimes sometimes, luckily, I get on the phone with a doctor who says, I totally get it. I totally understand it.
Kevin Harris:The science is right on. I've been tracking this for a long time. We just have to convince my CFO to do this. Right? Right.
Kevin Harris:Those are the ones I love because Right. The business model's easy. Sometimes I get in with a doctor who says, yeah. I don't know. I see these things.
Kevin Harris:I don't know why I need AI to help me see these things. I don't know how critical they are or how important they are. We're gonna hold off for right now. And, you know, my head wants to explode when I'm like Yeah. You know, I know the data.
Kevin Harris:You know, I I see what this means for patients and their health. Like, you're killing me. So
Heath Fletcher:Where you're see because you're seeing the patient's bottom line. They're just seeing the bottom line. Right? They're
Kevin Harris:just No. No. No. I don't I don't mean it like that at all. They they see more than I do.
Kevin Harris:Doctors see the patient's bottom line. Absolutely.
Heath Fletcher:But No. I met the CFO.
Kevin Harris:Oh, the CFO. Sorry. Yeah.
Heath Fletcher:CFO.
Kevin Harris:Yeah. But in our health care system today, I mean, our our radiologists are busy. Like Yeah. We had to build this software with no new clicks and no buttons. Yeah.
Kevin Harris:And, like, as little effort for the doctors as we can because they're already overwhelmed with with work. So, you know
Heath Fletcher:So it's fairly seamless for them then. They don't really there there's not much extra for them to do. It's just sand.
Kevin Harris:No. Whole process is completely automatic up until the point when the scorecard is presented to them.
Heath Fletcher:And
Kevin Harris:then what they have to do is look at our scorecard and form an opinion based on what they see and based on what they know about the patient. So no clicks, no buttons, no no nothing extra for the radiologist.
Heath Fletcher:So cool. No wonder they're oh, and when they see it, they're just like, yeah. Bring it. I want it.
Kevin Harris:A lot of doctors are. Right? We're we talk to a lot of doctors who are very excited. This is finally coming out and finally in the market. They know what this means for their patients, and they see this as a huge opportunity to impact their patients' health.
Heath Fletcher:How do you see AI playing a role in advancing care for women?
Kevin Harris:You know, I think it's really interesting. AI is a really broad topic these days. Right? I mean, you have everything from
Heath Fletcher:Yeah. It's pretty wide. Yeah.
Kevin Harris:From from chat, and, you know, we as you and I were chatting before we went on, like, I just spent some time in Japan with my family, and I used chat to help us plan what we were gonna do. Like Yeah. You know, hey. I'm in Osaka for the day. This is my family profile.
Kevin Harris:What are five things that would be interesting for us to do today? You know, so you can have this interactive exchange with chat. Yeah. But, you know, you can think about that also in the health care context. And I think that there's the opportunity to say, hey.
Kevin Harris:I've got this patient. These are some of the things I'm seeing. This isn't the diagnosis that I'm thinking. What might be some other things I should consider? Right?
Kevin Harris:So I think the more information that chat, for example, can digest in that context Mhmm. The more opportunity there may be for those types that type of AI to actually begin to assist patients and doctors in helping to to understand what's going on. Will it become a better diagnostician than the doctor themselves? I don't know. But do does the AI have access instantaneously to a million papers with instant recall?
Heath Fletcher:Real time?
Kevin Harris:Yeah. Yeah. You know, it's it's an interesting it's an interesting thing to talk about. So that's one side of AI. And then the other side is like the place where we live, where when you can take in all of these data points from a patient we we're the image side.
Kevin Harris:But, you know, associated with images, there's other patient data. What's your blood pressure, your heart rate? Are you pregnant? How old are you? Your veteran status, your marital status.
Kevin Harris:Like, there's a whole array of information that we as patients have in orbit around us that again, if the AI can see patterns in that that maybe the human can't, there's some interesting opportunities there.
Heath Fletcher:Yeah. I think so. Well, I think you you just you'd identified one earlier by saying that what you eliminated was something that was became useful and that was able to actually be a benefit from. And and the AI found that, which is really interesting. So when we're talking if we're thinking about what we got some people listening who are maybe, you know, nearing the end of their education or newly out of out of graduating and, or, you know, in the in the, health care industry.
Heath Fletcher:Are there gaps in in women's health care that you see or maybe some of the biggest opportunities for people as they're sort of navigating the AI and how that's impacting jobs and careers right now. What are some of those opportunities for people out there who are moving into health care?
Kevin Harris:Yeah. I mean, I I definitely think if you look at the confluence of AI and health care, this is gonna be something that's gonna be powerful and impactful moving forward.
Heath Fletcher:Mhmm.
Kevin Harris:I the health care system in America is amazing, but it's also very challenged.
Heath Fletcher:Mhmm.
Kevin Harris:It's expensive. Oftentimes, there's long waits. I think that there's a huge opportunity to to stratify the work of health care and begin to identify which pieces could actually be safely automated. And I think that that's a place where people can start looking. That doesn't mean we're eliminating doctors.
Kevin Harris:It doesn't mean we're eliminating nurses. But I do think it means that we should look for opportunities to put these folks into their highest and best use and and at the top of their license, basically. You know, doctors should be operating at the top of their license at all times, and anything we can do that is not in that realm, that AI can be used to better filter patients as they're coming in, to better treat patients from home, to all these types of tools so that doctors can operate at the top of the license. I think that is the a big opportunity, and I think that will improve care for everybody.
Heath Fletcher:Yeah. Yeah. Because some of that those areas that you mentioned are very top heavy, and that's what that's what kinda bulks things up, slows processes down, and makes things more expensive. Right?
Kevin Harris:Yeah. Interesting. Think, you know, for example, if you look at at screening, like, in radiology and images, for example
Heath Fletcher:Mhmm.
Kevin Harris:It would be my hope that at some point in the not too distant future, softwares like ours could be used as almost a preread for the doctors. Mhmm. You know, if if the doctor's gonna read a thousand mammograms, and as I said, let's say there's five cancers or six cancers out of that thousand, today, the doctor's gotta read all 1,000.
Heath Fletcher:All of them. Wow.
Kevin Harris:What if there was a better way where doctors only had to read 900? Let's say that AI let's say you only trusted AI 10% of the time. Let's say
Heath Fletcher:Eliminated a 100 scans. Yeah. No kidding.
Kevin Harris:But I
Heath Fletcher:mean Yeah.
Kevin Harris:You know, I I honestly believe you could probably eliminate four or 500 of those scans with the right AI in the right place, tested in the right way. Mhmm. Nobody trusts it yet to do that. Right? You know, the patients, I don't think trust it trust trust it.
Kevin Harris:The doctors, I don't think trust it. I think the FDA trust it. No. But I think that there is that opportunity. If you thought about, well, if doctors had to read 30% less scans, let's just say, because the AI preread and with 99% certainty said there's nothing here in these thirty percent.
Kevin Harris:What could doctors do with that 30% of their time?
Heath Fletcher:And
Kevin Harris:that's It's an opportunity, but I don't know the word there yet.
Heath Fletcher:Yeah. But that's the future.
Kevin Harris:That's the future.
Heath Fletcher:Awesome. If, people want to learn more, where should they go if they wanna learn more about QMetrix, Kevin?
Kevin Harris:Yeah. So, I mean, obviously, we've got a website, curemetrics.com, and it's with an x on the end, curemetrics.com. And that's probably the best place to go. I mean, obviously, there's there's journals and and lots of other things. But if you're interested in any of our papers or research, that's also on our website.
Heath Fletcher:Or becoming a customer.
Kevin Harris:Or becoming a customer. Absolutely.
Heath Fletcher:Absolutely. Right.
Kevin Harris:I would love to talk to you.
Heath Fletcher:Okay. And you're also on LinkedIn if people wanna track you down there as well. Yep. But, any other, any other last points or things you wanna mention before we sign off or maybe even, some resources that people could tap into if they're looking for that?
Kevin Harris:Yes. I mean, I guess what I would just say is I'm I'm super excited to be on, right, and and to be speaking to your audience. This is as I think you pointed out earlier, it's a journey of learning and a journey of education. So every opportunity that we get to be out there and making people aware of this, you'd be surprised how many women I talk to don't even realize that breast cancer or calcifications are a health issue that they should be paying attention to. They should be asking the radiologist about.
Kevin Harris:Yeah. And then when we get into the radiology sector, most radiologists are aware of it, but even there, some some docs are this is somewhat new to them. And and so, yeah, I I definitely think that we need to do a better job of educating the public and and, I mean, if only I had a a celebrity spokesperson in the middle of the Super Bowl who could, you know, tout this. But, first, I've gotta find a $10,000,000 check to write them. So I'm working on it.
Heath Fletcher:Maybe they're listening right now. You never know.
Kevin Harris:Maybe they're listening right now. They wanna come and and help us out.
Heath Fletcher:Well, awesome. I really appreciate your time today, Kevin, and I've enjoyed talking with you and learning about you, CureMetrix, and Centimeters Angio. It's been a pleasure to, to hear your stories. And, yeah, I wanna hear more another time and catch up, and and we'll do this again.
Kevin Harris:Thanks, Heath. I really appreciate it.
Heath Fletcher:Here we are again talking about AI in health care and how, CureMetrics was founded to leverage AI in health care. And so cool that they were actually using that technology to identify cancer, breast cancer in women's mammograms. But through that process of elimination of pulling away the information that they knew wasn't what they were looking for, they determined that they actually found other indicators of health risks with the Centimeters angio, breast arterial calcification. So that's really cool that they were able to sort of discover that they had, another stream of information that was gonna be valid to radiologists and doctors in that in that patient care process. So very cool.
Heath Fletcher:So that's where future opportunities exist in automating health care process based on Kevin's experience. So if, you're coming out of school looking for a place to, focus, that would be a good place to go. So, be sure to visit their website, learn more about CureMetrix, and I wanna thank you for listening to this episode. Please stay healthy, and we'll see you again soon.