Eric Horler: New Technology Transforming Oncology Decision-Making

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Eric Horler, your CEO of AIQ Solutions.
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Welcome to the Root Three Pointers podcast.
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Thanks for coming on.
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Eric, you know I work with mostly healthcare businesses, and I believe all of them are impacting healthcare and patient lives in amazing ways.
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But at AIQ, like you're really, really close to seeing the impact on patient lives.
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And I'm kind of jealous of you for that.
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Tell me about AIQ solutions and what you're doing in oncology.
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Yeah.
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So, well, in fact, through what the limited launch we did over the last couple of years, we've already really seen the impact on patient lives and heard about it from our customers who are the oncologists who are using this technology and getting our reports.
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Right now, we're just on that verge of commercializing because we just got a billing code approved.
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So we're at that inflection point.
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But again, already seeing what a huge difference this can make for patients.
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Specifically, we're in an area that is really not well served by other technologies.
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We're helping oncologists better manage treatment of patients that are at late stage or metastatic.
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Metastatic is just when the disease has started spreading throughout the body.
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And there's particular issues with the fact that as that disease spreads, it changes.
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So you don't, the patient doesn't have one disease.
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They have a lot of different diseases, and the oncologist needs a different kind of information to be able to optimize care for that patient in that situation.
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We provide these reports to the oncologist.
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They're really easy for the oncologist to understand.
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The oncologist makes the decision.
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These are really complicated patients.
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The oncologist makes the decision based upon this additional information on how they're going to optimize therapy.
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You know, because these patients, unfortunately, in many cases are beyond being cured, the oncologist is managing a chronic disease.
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And we always say our goal is to keep that patient as healthy as possible for as long as possible on the least aggressive treatment possible.
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And that's what we're doing.
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So I'm going to try to simplify this for our billions of podcast fans out there.
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The way it works today is that the oncologist is making a decision on whether to keep you on a drug, change to drug, stop treatment altogether based on a very small number of lesions that they're monitoring.
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These patients have metastatic cancer.
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There's a lot more going on.
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You're giving them the full picture so that they can really see what's happening, and it's changing.
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What was the the stat in one of your research reports?
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It's changing, like in almost three-quarters of instances.
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It's it's influencing or changing how they're making treatment decisions.
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That's a big number.
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Did I get that right?
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You did, you did.
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And I'll I'll build on that.
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I'll guess.
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It's way too simple.
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I know it was too simple.
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No, I mean, ultimately, that is the general idea of what we're doing.
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And that one study that you're referring to that was just presented at the American Society of Clinical Oncology meeting last week in Chicago, is a prospective study looking at the value that our reports provide in terms of therapeutic management when they're added to standard of care.
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And I'm going to touch on that standard of care piece because that's the other piece that you were hinting at.
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And we were targeting in that study for 25% of the patients to have a case where our information would change their therapeutic management.
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And the study read out at 65%.
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So I mean we were.
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25% is a huge number.
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25% is a huge number, and yours is 65%.
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And we read out at 65.
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And we're repeating that.
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That particular study was in metastatic melanoma.
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Our product is being used across a very broad range of cancers, essentially all solid tumors plus lymphoma.
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And so we're repeating that exact same study design now in prostate cancer, which is, of course, you know, one of the highest prevalence cancers out there.
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But it really speaks to the uniqueness of this information.
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So going back to what you were saying, you know, the typical standard of care is after the patient is diagnosed with late-stage cancer, then the oncologist is going to start monitoring that cancer as they're treating it, and just normal standard of care is they're going to get radiological imaging for that patient as part of that process of monitoring.
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Radiologists do phenomenal work.
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And one of the things that even though we use the raw data from those radiological images, we are doing a very different thing because what they're doing is extremely valuable.
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The radiologist looks at the images, they do their assessment, they write a report that goes to the oncologist.
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The practical reality, though, is that when a patient has dozens or even hundreds of different tumors throughout their body, the radiologist is not going to be able to go in and do three-dimensional quantification and measurement for every single one of those.
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And they're certainly not going to be able to look at how every single one of those different tumors is changing over time.
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And that's what our technology does.
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It's so we supplement what the oncologist is getting from the radiologist, which ultimately is looking at sort of a higher level, more diagnostic sort of approach.
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We supplement that with treatment response information for every single one of those individual sites of disease throughout the patient, looking at change over time, that allows the oncologist to treat the patient at a more granular level.
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And so I'm going to borrow a quote from a big name prostate cancer researcher from a really big name institution out in New York who was very fond of saying that when you're treating patients with metastatic cancer, you have to treat every single tumor like a separate patient, which means you need information, rich information about every single one of those metastases.
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And that's what we do is we provide that to the oncologist.
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Okay, I'm gonna I'm gonna throw a quote back at you from another amazing um oncologist researcher, Dr.
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Glenn Liu, one of your co-founders.
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I was recently talking to him, and something he really said really hit me.
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He said that I'm looking at the quote the the ability for doctors to optimize when they start stop or switch therapy with your technology, your analysis, has the potential to extend survival almost as much as developing a whole new drug.
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Yes.
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Well, and and we believe actually, probably more than developing a new drug.
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The reality is some of these drugs are getting FDA approval by extending life for a couple of months or a few months.
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And what we're finding is some of the things we can do, and I and I can go a little more detail about exactly the what here, but you could potentially extend patients' lives for many months through iterative optimization every few months, new information, you know, looking at how the cancer is changing.
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So we are going to continue to generate that data as we continue to grow, we continue to get more usage.
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But I really feel that we're ultimately gonna be able to show that we can do even better than a neutral in terms of extending survival.
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Well, the research you partners have, the research partners that you already have that you've published extensive research on.
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Like you're working with some of the top research institutes in the world already.
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Like, go ahead and this is your chance to brag about some of the organizations you're working with.
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Yeah, absolutely.
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I mean, so so absolutely.
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I mean, as much as anything, I'm grateful for the excitement we've gotten from some of these researchers that are on the cutting edge.
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But we've got researchers at places like Memorial Sloan Kettering, Weill Cornell, MD Anderson.
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Shout out to Carbone Cancer Center here at the University of Wisconsin, which it may not be quite as well known of a name, but should be.
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It's a phenomenal research institution.
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So a lot of people who are using it there.
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Um, but then, you know, other places, Baptist Health, gotta give a big shout out to Baptist Health.
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There's gonna be some really exciting data in an oral presentation at the Radiation Oncology Society meeting coming up in the fall that's coming out of uh a research study run at Baptist Health.
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So want to give them a shout out to um, but also, you know, the oncologists who just treat patients and the feedback we're getting from them and the workflow in there.
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Yeah.
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Exactly.
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I mean, workflow and and you know, and and things like how do we optimize?
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So we we are still a young company.
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We are still looking at how do we make this easier for the clinician to use, how do we make this easier for the institution?
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Um, but a lot of times it's that feedback from those those, you know, real world in the trenches on colleges as well at at places that may not be household names for research, that that also helps us move the needle.
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Um, but yeah, a lot of really amazing people that are building this into studies and then incorporating it into their care of patients.
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Um, you you mentioned at the very top, but you skated over it, and we need to spend some time on it.
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You are in a very exclusive group of people who have achieved a CPT code for your type of technology.
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Talk about how you did it, how you were focused on this as your strategy from the beginning and why it's so important.
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So, yes, I mean, in terms of exclusivity, as of December, the last time that I saw the FDA put out numbers, there were about 1,400 software medical devices with artificial intelligence components that the FDA had approved or cleared.
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Out of that 1,400 number, less than 30 have secured CPT codes for the service that covers the use of that technology.
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So it's definitely a small category.
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It's it is a growing category.
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I'm happy to say we're not number one or number two.
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It's really great to be number 20 something because there's a lot of great companies that have blazed some trails and started to establish the process by which the AMA looks at these relative to providing a billing code, which is the CPT code, by the way by which CMS or other payers look at at ultimately paying on these codes.
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But we knew from the inception of the company that for something this novel, the brutal reality of healthcare in this country is there has to be a way for our customer, the hospital, to get reimbursed for the use of the technology.
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And that pathway is getting a code that that is a new code, that is something that the hospital can bill, that's not bundled or packaged into an existing reimbursement, in which case it's a pure expense for the hospital.
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We knew very, very early on that we had to have a reimbursement strategy.
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And so we hired a world-class reimbursement consultant to help us before I even raised our seed financing.
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Yeah.
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At the point in time, I was the only actual employee of this enterprise.
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We had a little bit of revenue coming in from a partnership with a pharmaceutical company.
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And we started off working with this reimbursement consultant, Galileo Consulting, at that point.
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And we built a strategy before we had a product about what that reimbursement pathway looked like, what kind of evidence we'd need to generate.
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And ultimately, it took from the beginning of 2019 when we first engaged with that with Galileo Consulting to February of 2026 to get to that approved CPT code.
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Well, I mean, it sounds like a long time, but in healthcare, that's a sprint.
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Like that's incredible.
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Um I also want to go back to like you spent a lot of your career in the biggest of the bigs, GE, Baxter, like in these enormous healthcare organizations, and then you end up, boom, you're the only employee at this startup, and you built a team, you built a great culture, but like talk to me about how you made that decision to go from you know this giant organizational structure to the tip of the spear.
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It really comes down to risk taking and and your personal interest and and stomach for taking risk in a business environment.
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I was general manager of a large global business at GE when I left.
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And fundamentally, when you are steering a ship that big, every decision you're looking at, every risk you're you're looking at taking requires a tremendous amount of internal socialization and ultimately buy-in and approval.
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And so you you're you're very, very slow to be able to react to things.
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I frequently tell my team that as an early stage startup, if we don't take risks, we will fail.
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If we do take risks, we may fail.
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But if we don't take risks, we will fail.
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And so we've built a culture that is built around learning.
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So it's built around trying things, taking risks, having a really, really fast iteration cycle on trying something, learning from it, course correcting.
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We you talk about the big companies often talk about encouraging risk taking or encouraging people to make mistakes, but then they can't actually like walk the walk.
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And we really do when you know people make a mistake and we learn from it, we celebrate it.
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Because if you're not trying things, you're taking not taking risks, you're not making mistakes, you're not learning.
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And the faster we can do that, the faster we can actually do something new and innovative.
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And so getting back to the me, why I do this, it's really to be the ability to be in that environment, to be surrounded by people who like to come up with new ideas, like to try things, are not scared to make a mistake.
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And, you know, be able to get into this rapid iteration, it's exciting.
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It's a roller coaster.
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You know, it's you your ups and your downs happen, you know, you know, sometimes on the same day.
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So you have to be able to handle that.
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But just that the ability to be in that fast-paced, risk-taking sort of environment is very exciting for me.
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And I'd honestly be very hard for me to imagine going back to a big corporate structure decision-making process.
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Yeah.
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I mean, I I love the middle market, startups in the middle market.
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It's where innovation happens, it's where change happens.
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Um, and you are one degree of separation away from the impact.
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You're not 50 degrees of separation away from it.
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You're you're there.
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And that that's what I love about it.
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Yeah, I mean, there is no question that there are things that we are trying that that I could never have gotten Bay-in to do at GE or Baxter.
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Sure.
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You know, it just they it absolutely is swimming upstream.
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And um, the fact that sometimes, honestly, sometimes we try things because we don't know any better.
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And if we had a huge team of lawyers to tell us that we shouldn't, maybe we wouldn't.
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But because we don't know any better, we try them, and you know what?
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Some of them work.
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So that is actually the perfect segue into my root three-pointers quiz.
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And it's all about you have a background in biotech and chemical engineering, is that correct?
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That is correct, yes.
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That is correct.
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So, a lot of the biggest advancements in those fields were accidents.
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Um, so this is your quiz um on the greatest and wackiest moments in chemical engineering and biotechnology.
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So, get excited.
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We'll probably put some exciting music under this.
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Like people are gonna love it.
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Um, so first question which everyday miracle product was discovered because a chemist accidentally left a gas cylinder in a cold storage room?
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And I can give you, I can give you some choices.
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So which everyday miracle product was discovered because a chemist accidentally left a gas cylinder in a cold storage room?
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Is it A, nylon, B, Teflon, C, Kevlar, or D plexiglass?
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I'm gonna go with flexiglass.
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It is Teflon.
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Teflon, okay.
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And I don't know if I would call it an everyday product.
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Like did you but you know, it it was an everyday product for a while.
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When we were growing up, everyone was using Teflon pans, and I the world kind of moved away from it a little bit.
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Um, so it might not be quite as everyday, more everyday than Kevlar, I would say.
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Yes.
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He's so this is the the lore is that he opened up the cylinder and said, huh, that's weird.
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And that's his quote on making history and inventing.
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So if you ever accidentally discover something, have a better line in your in your head.
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Um, all right.
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This is but still on chemical engineering.
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Which chemical engineer's discovery caused department store riots when it was first released?
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B, um, sorry, A, nylon, b, polyester, c polycarbonate, or d polyprophylene.
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I'm gonna go with nylon on that.
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Yeah, it was the nylons.
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It was in the 50s, and everybody lost their minds with nylons.
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Okay, we're going into the world of fiction.
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Which famous fictional character is a former chemical engineer?
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I'll give you a second if you want to pull it up, but do you know?
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I do not know.
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Okay, it's a pretty broad question.
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So uh Tony Stark from The Avengers, Walter White from Breaking Bad, or Dr.
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Emmett Brown from Back to the Future?
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I'm gonna go with Tony Stark from The Avengers.
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Um, it's been a long time since I've seen that movie, but that's what I'm gonna go with.
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I mean, he invented everything, so I'm guessing he had every title, so I'm gonna I'll say that's probably true, but Walter White was uh um that so I felt like that I honestly felt like that was too too obvious, to be honest with you.
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That was I I I kind of tricked talked myself out of that answer.
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Yeah, well, um, there's probably uh a theory on doing trivia that we could apply there, like go with your first answer, but I I don't know.
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There's no rhyme or reason to this quiz, and no one is supposed to know any of this information.
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Okay.
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Um you gotta know I it's it's been a long time since I was a chemical engineer.
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Well, we're switching over to biotechnology now.
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So which biotech breakthrough was discovered because a scientist forgot to clean his petri dish?
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And that's CRISPR, penicillin, PCR, or insulin purification?
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Because he forgot to clean his petri dish.
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I'm going with penicillin.
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That is correct.
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That is correct.
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Um okay, this is the last one.
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The first nuclear medicine image was created how photography, hand plotting, or exposing film directly to a radio tracer.
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The last one exposing film directly to a radio tracer.
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It was a hand plotted radiation count.
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It was the okay.
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Yeah, that was the first nuclear medicine image.
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Um before we go, what's next for you and AIQ?
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Commercialization.
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So the the the catch 22 in this world is when you ultimately are in a position where you can have your product reimbursed, now you can make the economics work for everybody, from the insurance company all the way down to the patient.
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But until you get to that point, because of the way the system works, you can't start charging for your product.
00:20:23.839 --> 00:20:35.519
And so we've you can you can get your product out there, you can get people using it, you can learn how to get it into hospitals, you can generate information about how clinically useful it is, you can do all of those things which we've done.
00:20:35.839 --> 00:20:46.960
But until there is a way for our customers to unite the entire economic value chain, again, for everybody's benefit, from the payer on to the patient, we can't sell.
00:20:47.119 --> 00:20:49.039
We are now at that inflection point.
00:20:49.200 --> 00:20:51.039
And so we are actively raising money.
00:20:51.119 --> 00:20:54.400
If anyone listening to this podcast is interested in learning more, they should ping me.
00:20:58.160 --> 00:20:59.119
We're not allowed to crowdsource.
00:20:59.200 --> 00:21:00.799
That came up at a conference I was at this week.
00:21:00.960 --> 00:21:02.400
Crowdsourcing is not what we're looking for.
00:21:02.559 --> 00:21:04.559
We're looking for venture capital button.
00:21:04.640 --> 00:21:06.160
Um, you're ready for it.
00:21:06.799 --> 00:21:07.200
We are.
00:21:07.279 --> 00:21:13.519
And and so we were raising the money so we can go build that commercialization team.
00:21:13.680 --> 00:21:18.960
We can take all of these users who love it and actually turn them into true customers.
00:21:19.200 --> 00:21:20.400
That's exciting.
00:21:20.559 --> 00:21:26.559
Um, now having said that, we still have a whole pipeline of RD.
00:21:26.720 --> 00:21:38.319
We've got a Gen 2 product, we've got a Gen 3 product that's in the pipeline, we've got a whole pipeline of engineering work that we want to do that's all around, you know, making the machine run smoother with less friction.
00:21:38.400 --> 00:21:42.400
So there's a lot of other stuff that we want to do that'll happen in parallel.
00:21:42.559 --> 00:21:46.640
But the really exciting thing is, you know, and I guess I'll leave it with this thought.
00:21:46.880 --> 00:21:54.640
Um, when you work with these researchers, you know, researchers are really good at doing the science, innovating, publishing.
00:21:55.039 --> 00:22:01.119
But I really strongly believe the only way to change the world with innovation is to commercialize.
00:22:01.440 --> 00:22:03.200
It's the only way you're gonna change the world.
00:22:03.440 --> 00:22:09.599
So I want to come back in a few years on this podcast, and the the title can be How AIQ Changed the World.
00:22:10.400 --> 00:22:11.599
I think that's a great goal.
00:22:11.759 --> 00:22:15.599
You're you're I'll I'll send you an invite right now for two years out.
00:22:16.799 --> 00:22:17.519
It'll be perfect.
00:22:17.680 --> 00:22:18.000
Excellent.
00:22:18.160 --> 00:22:20.240
Never the whole audience here will be waiting for it.
00:22:20.400 --> 00:22:23.119
Eric Horler, CEO of AIQ Solutions.
00:22:23.200 --> 00:22:24.160
Thank you so much for being on.
00:22:24.400 --> 00:22:25.759
Oh, thanks for giving me the opportunity.
00:22:25.920 --> 00:22:26.480
This was fun.
00:22:26.640 --> 00:22:27.759
All right, take care.
00:22:28.000 --> 00:22:28.640
All right.