Breaking Barriers in Clinical Trial Design: Insights from Faro Health CEO Scott Chetham

[00:00:16] Hello and welcome to The Uprising Show. And today we have a very special guest, Scott. Scott, could you take a minute and give a brief introduction to our listener, what you do, and then we go into deeper conversation throughout your journey and professional journey as well. Scott Chetham, The Uprising Thanks for having me on the show. My name is Scott Chetham. I'm the CEO and co-founder of a company called Faro Health. And what we do is really help

[00:00:46] We do a few things, but what we, the crux of what we do is we bring data to teams that design clinical trials to help them make better decisions about, you know, can this trial actually be operationally performed? Could patients actually participate in it? Could the hospital system or the care system actually deliver it or, you know, enable it to happen? Rather than what is present today, which is more of an opinion-based thing. And so that's the genesis of the company.

[00:01:16] All right. We are going to go dig deeper into this more, but before we do that, can you take us back in time and tell where were you born and raised and was either of your parents an entrepreneur? Yeah, it's an interesting question. The direct answer is, I don't think my parents, either one of them was directly an entrepreneur.

[00:01:40] But I'm going to start at a different, a different place. Cause I have a photo that you can't see it sitting to my left. It's one of these bittersweet photos. It was taken at Christmas. It's bitter because it will never happen again, but I'll explain the nature of it. And that's why, you know, and I'll explain a little bit of the journey.

[00:02:00] Okay. So it's the picture takes place in a cattle property in Calvert, Texas. So, you know, prizes to anyone who knows where that is. It's halfway between Houston and Dallas. It has one single traffic light that you stop at. Um, it's a partly abandoned town. Um, it's been there, you know, a bit over, I guess, a hundred years.

[00:02:22] So I met my wife's family who've had their, uh, this is the property that they've had in their family for generations. They were former slaves. Uh, and they, they're some of the first, um, African American property owners in the United States and particularly in Texas.

[00:02:37] And so there's about 30 people in this photo, um, mostly African American. There's myself holding my son, uh, with my wife and then my parents, uh, from Australia in the far corner, um, of this shop. And that's the part that is going to be hard to reproduce because they flew over particularly for this. So as you can tell, it's how, how does an Australian end up in that photo and with their family?

[00:03:07] And their career here. And it kind of, if you'd asked me when I was back, even in training, when I was 20, if you showed me that photo, I probably would have been completely shocked. I was like, explain, like I trained as a clinical scientist. It's like, you're a CEO of a software company living in the United States. And by the way, all your family's in Texas. I would have really have gone, what on earth are you talking about? I don't believe any of this.

[00:03:32] So how did you kind of get there is I come from originally, I was born on a place called the Gold Coast. Uh, it's a spectacular place. There's a hundred miles of pure white sandy beach. Uh, the little sub area where I'm close to is called surface paradise. And it literally is what it sounds like. It's one of the, it has some of the best surf breaks altogether on the planet.

[00:03:54] It's a great place if you want to be born and to grow up in it's a tourism industry. So when you start to think of biotechnology and clinical trials and all sorts of those things, there's none of that, particularly when I was growing up, growing up. So my, my dad is a cabinet maker, uh, and a shop teacher and my mom, uh, it was an accountant, but when I was very young and I think this is, this might explain some of it.

[00:04:23] Um, my, both, my parents come from pretty poor families. They didn't have the opportunity to go to university. Uh, as I said, my dad was, took up a trade and I think it was around when I was three. My mother decided she wanted to go to university. And so my dad took a weekend job. Uh, and even as probably at four, I remember driving an hour and a half North to the big university in the capital city a couple of days a week.

[00:04:49] And if I was really good, uh, and quiet, uh, in lectures and things, so I attended university at a very young age, more as a guest, we'd sit around this lake and have, uh, lunch, this huge duck pond, uh, on this beautiful campus. Um, I ironically ended up going there. So it was very surreal. I turned up, it's like, I already know this campus layout because I was there from a very young age.

[00:05:15] And so my, I don't know how my mom did this. Now I have kids is she managed to get a degree with two young children. Uh, while my dad supported her through this, she started at a big five accounting firm. Um, and so, and she did that until the day. I think I finished high school. I'm the youngest. And then she quit and she took a job as a founding bursar at a high school, a private high school.

[00:05:45] And she ran all the funding and helped build, uh, what I call a non-denominational or non-religious private school that focuses on quality education and helped the headmistress who founded it, uh, build up this thing into what is now, I think it's like a thousand students from, um,

[00:06:03] um, two years of age all the way to 18. And she built that over a 15 year journey. So I think some of, well, neither one of them was directly an entrepreneur. The thing that I saw is, you know, it takes a couple of people working together to achieve someone's dream. Like my mom couldn't have done that without my dad. You know, he supported her through that journey, but also just seeing how hard she worked and juggled so many different things to do this. Like, I don't think I could do it.

[00:06:30] Well, you know, I went, you know, I did my 10 years of study, you know, at a young age, completely single. I don't know how I would go back now and do that. But I think for me, it also showed the power of, you know, a formal education. I mean, there still is, I mean, there's, it's an interesting thing, but there's still a lot of value in it, particularly in a, you know, in a clinical field. So that's kind of, so how did I get to the U S from there? Um, I went to university, um,

[00:06:59] ended up training in a hospital and this was my introduction into, um, clinical trials. So I was very, very lucky and wound up in a highly specialized area called cardiac electrophysiology, which is people, when they think of the heart, there's the plumbing side. That's a heart attack. When you go and put tubes, catheters in, when there's a blockage, there's also the, the electrical side. So the joke is, you know, um, a lot of cardiologists, some of them are plumbers and some of them are electricians.

[00:07:27] I worked on the electrician side. And so what we did was we'd been map out, uh, if there was an arrhythmia, the heartbeat too quickly or too slowly. Um, and so I was training in that, but we were this, what we'd call a tertiary referral center. So my state, uh, Queensland, you know, the population was several million. You had to come to this hospital.

[00:07:48] And so it's the perfect place to run clinical trials because people can't disappear. If they needed advanced, uh, cardiac care, you had to come to us. So we ran a lot of the first in human trials for, uh, companies for Medtronic and certain cardiac drugs. And that's how I kind of got into this field is through the people that were training me, um, was that basically kind of involved me at a very early age, even while I was doing, uh, still studying.

[00:08:17] And so I've done almost every aspect in clinical trials now from a coordinator, from somebody who fills in paperwork to an investigator. So you enroll your own patients in clinical trials now to kind of like actually helping, uh, big companies design better clinical trials. So I've moved through the whole journey now on this kind of spectrum.

[00:08:37] And how I ended up here is, um, your choices, if you want to, uh, work in some of the most innovative, innovative areas in, uh, medicine or, uh, molecule development, uh, San Francisco, San Diego, uh, Boston. Uh, uh, we've got, I guess, London in, in Europe. And so in the choice of where I would have to end up, I'm a surfer. So I chose San Diego. Um, and that's kind of how I ended up here.

[00:09:06] And then this, I came for a two year trip. I'd like, and you know, it's the typical story. Met my wife, married. And so now I've been here 18 years. So my, my two year trip kind of is, is, is on gum. Um, sometimes I threatened to go home, but, and my wife actually, even though, you know, she's born and raised here is always like, why don't we go live in Australia? And it was like, no, I have to finish this thing. Thanks for sharing.

[00:09:36] Uh, it's funny. Uh, it's sort of like, uh, uh, I, I had the similar way. My wife is American. I met her and I was like, well, that's my reason to stay in us. And that's how it turned out. It's already been a decade. So, so that's how it is. Uh, but, uh, fascinating. So yeah, I kind of like, I would say like, you did say like your parents were an entrepreneur, but I think they certainly sowed the seed of this, like work ethics and how to make things.

[00:10:04] Uh, you know, like what it takes, like how you're saying to make anything work, especially entrepreneurship or anything you need kind of that, uh, um, not to say like, uh, the saying on raising a child, right? Like it takes a village to raise a child. It's a similar way for companies too. You need whatever support in parallel to do stuff and building companies, building businesses. And that's what it is.

[00:10:28] I feel like that, uh, you got that, uh, taste of, uh, partnership between your mom and dad to like how to, you know, what somebody has to step up. Somebody has to play this support fiddle and get the things done kind of, uh, taste of it. Yeah, exactly. And I think you've just said something that's really, I think important that I've learned as a founder, uh, multiple times and I've gotten better at it as I've gotten older is asking for help.

[00:10:56] I think that the top, the hardest thing for me to learn was it's not a sign of weakness when you ask for help. It's actually a sign of strength and now, and leveraging, you know, there's a lot of people who will help you if you ask. And I think people underestimate that particular as a founder. I mean, it's amazing how many people I encounter who, you know, offer to give product feedback. And, you know, often the message is, is you could take is like, I just really want you to succeed. And so here's my perspective.

[00:11:25] And that's usually how they give it. And so I think it's just learning to like one listen and two is like, accept help. Not all that's good, but you know, that's, I think that comes down to judgment, but it's, yeah, it's learning to let people help you. Yeah. Yeah, I agree. What, let's, let's talk about Faro Health. So what was the initial insight or pain point that sparked, especially the idea for this company?

[00:11:55] And can you give us a brief exactly also what Faro Health does and how is it different from everything else in the market? Yeah, well, the, the, the pain point that I'm trying to, I should say we initially try to solve, we go on to do other things, but the pain point is a pretty simple one. And it's happened over, I've been very, very guilty of this. And then you see, as you grow in your career and you start managing other teams, you see the same, I see the same pattern of behavior repeated.

[00:12:24] And what it is, is when you're a scientist and you are trying to answer a question, particularly if you have some new molecule and you want to prove it's both safe and effective in somebody, you immediately gravitate. Fear kind of grabs hold of you. So you tend to think, well, I need to collect so much data. I need to collect all these things. And there's a lot of motivation behind that. It's one is being scientifically rigorous.

[00:12:52] But the other one is like, well, what if somebody asked me this question? So I need to be able to collect that thing. Oh, and I should do this. And then somebody else asked me once I saw this thing. And so I'm going to collect that. And so what tends to happen is you get these clinical trials where the data collection is so intense, you can't actually operate. You can't run them. And what's really interesting is Ken Getz at Tufts has done a phenomenal amount of work on this. So you don't really have to believe me.

[00:13:19] You can actually just look up all of his publications where the strongest predictor of trial failure that we have is actually a measure of complexity and the complexity of the amount of data and how much we're collecting. And it directly correlates with the cost increase of trials and the failure rate. It doesn't mean complexity is causing the failure to be clear. It's just they're highly related.

[00:13:45] So the problem that FIRA is trying to fix is how do you use data and provide it to people while they're making a decision to say, is this the right piece of information to collect from the patient at this particular time, at this visit, at this phase of what we're doing and the questions we're trying to answer? Because up until today, it's a completely opinion-based field. It's based on medical judgment.

[00:14:11] You put it all in a table in Microsoft Word and you email it to your colleagues and you have a discussion about it. And that's how it's been done. So the vaccines for the pandemic were designed this way. They put what's called this schedule of activities, of things that you're going to measure in a patient and when you're going to measure them. They put them in a table in Microsoft Word and everyone sat around and argued about it. And so how do you change that experience to become a completely data-driven experience?

[00:14:41] And the other thing is, because this is a very highly specialized field and there's a lot of domains expertise in different areas, we have to spend a lot of time reviewing and writing documents. And a lot of that, thanks to LLMs, can be automated.

[00:14:59] And because we spend so much time creating, crafting, writing, and redlining and editing and reviewing, we lose track of where we should be spending the critical amount of our time, which is, you know, is this the right thing to be doing? You know, how strategically should I manage X, Y, and Z? So what we try to do is use data to help people make better decisions and then automate a lot of the busy work so that people can spend their time on much more important things.

[00:15:27] And you're offering this as like, is it like a software as a service platform or what exactly is the offering? Yeah, it's software as a service predominantly. So instead of a, you know, the core of it is instead of a, I'll give two examples. So instead of just a flat table in Microsoft Word, a lot of our interface, some of it looks like Microsoft Word.

[00:15:49] And so as you actually put things in this table or tables to go, okay, I'm thinking of collecting a chemistry panel on somebody on this day. This system's smart enough to go, oh, do you mean this type of, there's like 50 chemistry panels, 20 more common ones. Know which one you actually mean, because right now you'd have to have a meeting about that because you'd just write chemistry panel. And it's like, oh, hang on, what did you mean? How am I ordering this?

[00:16:16] So it works out that for you, but that can also automatically tell you, oh, you need this much blood. This is going to cost this amount of money. It's going to take up this amount of patient's time. It's going to need this many staff members to do it. And as you can imagine, as you start to build up more and more, this, you get a picture of what it means to be a patient in this trial. What it means for the hospital system or the care setting to collect this data. And you can use that information and benchmark against the industry.

[00:16:44] So this is where AI can, well, a lot of that is done by AI. But AI can come in because it's scraped the performance and the characteristics of all the trials out there. And go, well, trials that look like this perform like this. And so with data, you can start to nudge people into thinking, okay, actually, do I really need this piece of data? Should I be collecting on this day? Sometimes you do need it and then it becomes a different question.

[00:17:09] It's like, how am I going to support my patients and the hospital staff who have to do this? Rather than waiting to be told, I can't do it. Because that is what happens today. You wait until the site tells you I can't do it or no patients are enrolled. And then you react, you know, instead of being proactively getting in front and managing all of this. And who do you sell this to? It's typically the research companies or pharma companies. What's your market here?

[00:17:38] Our market is large is enterprise pharma. So we have a soon-to-be majority of the top 10 who use the platform now. And then through the top 20 and then mid-sized companies. We have a few small startups. The problem is the same. It's just, it's been an odd journey. Normally companies like this launch into small companies, work it to success, work it out, sort the kinks out, and then go to enterprise.

[00:18:08] We got pulled in the complete opposite direction in that the problem is so acute now. And our hospital system is so overloaded that we have to really spend a lot more time thoughtfully designing these things. So we got pulled into enterprise and we've been sprinting for the last several years to kind of build the enterprise-level controls that they expect. But they've kind of forgiven us for some of these things and gone on a journey with us to build that part out.

[00:18:37] Because the core offering is extremely valuable for what it does. Because it's kind of unique. And it's a unique thing that we do. The other cool thing that we've, you know, thanks to LLMs is protocols are 1 to 200 pages of dense, highly complicated text. And we have a specific implementation that can leverage multiple models of LLMs to generate a long-form document that is contextually, it reads correctly through it.

[00:19:05] That contains all the appropriate details and that can do it. And, you know, instead of someone spending three months on it, this is about, it's not minutes. It's more like 15 minutes. It's not fast. Having said that, 15 minutes versus three months is fast. It's not like ChatGPT because the sheer amount of information and computation that goes into creating these things is immense. But it's a cool thing to watch because I've spent a huge chunk of my life writing these things with medical writers.

[00:19:34] And to see months worth of work compressed into, like, 15 minutes is pretty impressive. No one's going to lose a job, though. I mean, you still need all of these domains. It's just you don't have to spend as much time on this kind of early, really painful stage of drafting.

[00:19:53] It's like, I feel like the biggest power of AI and especially LLMs is, like, one is, like, at least proven at this point is, like, the analysis of gazillions of sets of data sets, right? And that's kind of, like, has been really great, like, in every field, whatever, irrespective of the industry. And that's, like, amazing. I've spoken to people who are now even doing, like, able to recognize even genome patterns.

[00:20:23] And then they know what to even edit on those sites. And those algorithms are improving. And this is all just because everything was just super manual and took so many months and days. And now it has shrunk that cycle to, like, you know, a few minutes or at least or within a day. And that is a big win in general for especially data intensive pattern recognition, all those workflows that we ever had. And that is, like, a huge, huge win.

[00:20:49] But do you see, and you touched upon this a little bit, about your protocol designs. And let's take it a little bit further. Like, would you think the biggest challenge as, like, bringing those things to the market, which is the trial executions, and that takes too long. Do you think that cycle shrinking with now these things in place? Yeah, no, they definitely will. I mean, I think we're still waiting for these proof points.

[00:21:16] I mean, we have a paper out with Merck about just the cost reduction in designing better trials. And it's, you know, across their programs, it's well over $100 million through cost reduction alone. But the more important thing is can you compress, as you said, the cycle time from the moment you start ideating until you get the first patient in. And then the execution from the first patient into the last patient in, last data point collected.

[00:21:43] In theory, and we all believe it, is that, yes, if you have a much more, I would say, leaner design, an appropriate design, you should lose less patience to attrition because it's taking up less of their time. It's taking up less of the hospital system's time. And it should run faster. But that's the proof point, you know, we're building it. We don't have it yet. I'd love to publish it as soon as we do.

[00:22:12] We're a five-year-old company. So, you know, we're still, these trials run for a reasonable amount of time. So we're still waiting for them to, like many of the ones in our early days to finish enrollment. And then we can come out with some of these publications. But I do think the initial indications is yes. And the one thing I can tell you, but I can't say the name of the company, we have avoided two complete trial failures now.

[00:22:40] Because what our data pointed out is they could precisely get zero patients to ever do this. And what it allowed them to do is alter the way the data collection was going to be in this and successfully argue, actually, to the FDA that the requirement they were putting on them was impossible. Because it was also for pediatrics. And so things that an adult could do are very hard in pediatric patients, particularly two- to five-year-olds.

[00:23:10] I can't imagine my three-year-olds sitting at a hospital for 12 hours and having to take a day off work and then three days in a row. It was like, this is never going to, this is what I'm saying, these things are just never going to happen. And so with data, you can actually go and argue. And the FDA actually accepted their argument. And they came up with something extremely innovative to be able to kind of de-load that trial quite a bit. But that required a regulatory change. But the regulator agreed.

[00:23:40] Because, you know, the other one was like, this will never happen. And so, you know, I was happy to see the FDA made, in my opinion, like the right decision there. Yeah, that's a big deal. So you're obviously running very clinical operations focused company. And this is like, you know, dealing with a lot of regulations, bureaucracy, everything comes with it.

[00:24:09] So what was the hardest institutional or systematic barrier you faced right now with your company, with your experience? Just share that experience. Yeah, no, it's getting people out of Microsoft Word. You know, it's in some ways the enemy.

[00:24:31] Because what it's done is it's, in people's minds, they've conflated the act of designing a protocol with authoring. Like writing. Because that's what Word forces. But in reality, every other field where we do $150 million things, we have like CAD for designing buildings. We have simulation environments. Almost nothing is designed by verbal description in Microsoft Word except clinical trials.

[00:24:58] So you have, we have to, we had to convince people they are separate and that they should be in different systems. And it took a while, but what's really fascinating is the whole industry now is there. And that's, you know, in some ways I consider that a win for me personally, because I can tell you five years ago, people thought I was nuts when I tried to describe this to people. And they were like, no, it's never going to happen.

[00:25:28] And then it was like this, the term of like, we came up for insights for providing insights data to help people design better trials. People are like, what are you talking about? And yeah, it's in some ways validating that, you know, the entire industry, it's almost industry wide now has realized they're separate things and that I need a designer. We successfully have created a competition for ourselves.

[00:25:53] We've got other startups now chasing us, but they're coming late, in my opinion, to the table, because it was a little bit of a left of center idea when we kind of came up, when I came up with this and tried to pitch it. And then it's been a fascinating journey because in the beginning, it was like, well, actually just design in here and then we'll automate a protocol. And there's about a hundred other documents and we're on the journey to automate that. But that was such a big lift.

[00:26:20] This is why we created all the analytics was because, well, I have to give people something for breaking, you know, their experience of doing a Microsoft Word. So what value trade can I give them that's going to convince them to like adopt this part early? You know, just the designer before we've got all this other animation and the connection, automated connections to other systems done. So this is why we invested heavily in creating this, this expanding analytics.

[00:26:45] And that just turned into, as I said, it's a little product and business on it, you know, that can stand alone almost now while we go on to add more and more automation. That has been the hardest thing. And it still is. People will always want to, when you've been doing something for 20 years the same way, it's hard to convince people like stop doing it that way and now do it this way. Yeah. So I guess from a systems perspective, right?

[00:27:14] Like where, where is the greatest friction or waste in the clinical trial operations today? Give us some insights there. I'm going to give a bit of a controversial answer, which is a slightly, slightly, a little bit sideways of what you've asked. And the reason is we've clinical operations. I, in my opinion, is actually for the technology we've had has been extremely well done.

[00:27:38] Because it is, everything is done by hand and it's a massive quality field and people know what they're doing. I think we have a slightly, I would say a slightly different problem. We have a, we have, we've reached the capacity limit for clinical trials almost in the U.S. at academic centers. There's no more space to have more oncology trials. Like we're at capacity.

[00:28:03] I mean, there's more trials or discovery to go in than actually the hospital system can take or our care system can take. But partly the capacity is taken up by what I would call zombie trials. These are trials where a successful molecule has already been brought to the market. It is commercially successful. And they haven't stopped the trial for something that is now out of date, has no chance of commercial success.

[00:28:31] So there's these biotechs around running these clinical programs that frankly should have been shut down years ago, taking up space. And we've got a lot of them. And there's also like the sub-market, particularly oncology, where, you know, you've got what we call standard of care, which is like best in practice. And then you have these kind of drugs that came later that frankly, the data is not that all impressive from them.

[00:28:58] They're not really any more effective than standard of care, but you can probably convince three or four physicians to prescribe it. So you can make a kind of okay business out of it, but they should never have been that. That should just not be happening. But our regulatory system, the way it's set up allows that, you know, allows research to be conducted in this way.

[00:29:19] And so I think we just, we're wasting our limited capacity because ironically it's handled on a, almost a per site or per hospital system level by the, by the institutional review board who's approving it. Because the market access that the FDA controls comes later, like once you've proved it safe and effective and then people can commercially buy it.

[00:29:41] But buy it, but before then, if you meet the requirements, it's really these institutional review boards who are approving these trial designs and these molecules to, you know, to go into a clinical trial. But they're not better than standard of care. So I don't know why we're wasting time and taking up capacity with these kinds of things.

[00:30:02] It's a slightly controversial answer, but it's, it's frankly wasting everyone's time and money just so that somebody can come out with a me too product that could get like 1% of the market. Is it like, is it like, if something is already commercialized, is it more of a regulatory concern or constraint that this need to be trialed more? Or is it just like, because it is some variation on top of that, that's like 1% of the market, different variable. And that's how you're trying to do it.

[00:30:30] Well, it's probably what I'm particularly talking about is a startup. Let's say it's a startup or another company who decides, well, they're not going to, there's a thing like if you thought you were better, you'd do a superiority trial. They're very hard to design. They're very hard to succeed at, to be blunt. But you would say, I am superior to this drug. If you do that trial and you are superior, you'll take their market. But they're not doing that.

[00:30:55] What they're doing is coming in and running a similar trial against what I would say a less, they can choose what they're comparing against. So their standard of care might not be as good as what this new care is. So what happens, see the problem, I'll paint it the picture a different way. The market keeps changing. So as a new drug enters and gets approval, the standard of care improves.

[00:31:21] So what's happened is, is earlier on a trial with a rival drug entered with the previous standard of care. Now there's patients now enrolled getting what I would say this oldest standard of care. But this new drug has been successful and entered and it's better than the performance this one ever could. But you've still got people now who, from a legacy perspective, are still in this old trial receiving outdated standard of care. But they're there because that's what they got put into.

[00:31:51] Those things should be chopped off. And that's what I'm calling a zombie trial. They should just be ended by the company. But they're not ending them. They're running them out for, you know, for, I would say, commercial reasons. But you have no chance of success and you've got patients trapped in something where they don't have access to now a better drug that's now been approved. See, that's what I'm saying. These things have to stop.

[00:32:14] But this is going to require, it's going to require a level of institutional review board review that they don't do right now. But as I said, it's a bit of a controversial answer. But we are, in my opinion, we are wasting our precious resources on these types of things. Yeah. And because you have, I mean, you're from Australia. So I think this question I wanted to ask, is it more regulation wise?

[00:32:42] Do you see more complex or bureaucratic environment in the US on, especially on clinical trial side versus in Australia? Or is it the same? Like you don't see any different. It's different. And, you know, I've also worked in Europe. So it's different. I mean, if I could sum it up as in a nutshell, the US has, I would say, decentralized payment and a centralized regulator.

[00:33:08] The rest of the world has centralized payment and decentralized regulation. And so choose your pain. So, because the nice thing is like here, and I think this is why you launch in the US, it's the biggest market. But when you get through the central regulator, generally that will give you payment access for a drug.

[00:33:32] The problem you're facing in Europe and Australia is you might get through regulations faster. But to lobby that for reimbursement could take you, that centralized payment system for reimbursement could take more evidence. So either way, you're doing more, you're still doing the same late phase research. The only thing is, it is generally a little bit faster to get market access in Europe and Australia than the United States.

[00:34:01] Because I would say the barrier is a little different. It's not really near the higher or lower. It's just you can get there a little faster than you can here. And that's, again, not even to say the FDA is slow. It's just the way the systems work are just different to each other. But then you encounter the other pain. So, as I said, choose the pain that you want to have because you're going to have it. Yeah, that's funny.

[00:34:31] So, what's your take on the decentralized clinical trial? I've been hearing that there's a hype around it. Is it here to stay or what's your take on that? I think it's just a tool in our toolbox. I mean, as an investigator and as a sponsor company, we were running decentralized aspects of trials for 20 years. It's not new. In my opinion, it's not new. I think the technology aspects are new.

[00:35:01] Particularly when you come from a state, Queensland, where I was from, where it's five times the size of Texas. We couldn't fly people back to our center to do follow-up work. So, we were always doing decentralized visits. We even had a plane that we would fly in to do clinics in the outback. That was always fun when you got that rotation. And so, there's always been decentralized aspects, in my opinion.

[00:35:26] And I think what we need to do a better job of is understanding patient preferences. Because I can tell you with 100% certainty, not all patients want decentralized trials. And not all trials need decentralized aspects. I read a really fascinating piece. I think it was, I've forgotten who it was in. It might have been stat. And the title was The Toxic Nature of Oncology Studies.

[00:35:54] And they interviewed patients about their preferences. And one of the patient volunteers said, I am not waiting at home for the delivery of my study drug the day before. And then waiting for the nurse to turn up in my house. It's much faster for me to just go to the site for it, you know, that day. And not have to wait for drug delivery the day before. And all these logistics because she didn't work from home. And so, I think this is the nature of like, decentralized.

[00:36:23] Not one thing works for everybody. And in the, I preach more from the design, just think from the design perspective. Talk to patients earlier in your disease and find out what works. And enable options. Don't force one or the other. Because it's just, it's what works in one works in another. It's just so different. And it's different for disease. It's different per disease. You have caregivers in some settings. And they have different things again. And so, it's not decentralized.

[00:36:53] You know, it's not going to rescue anything at all if we don't apply it properly. So, again, I think it's just choosing the aspects that work for you. Okay. So, you kind of, your take is, this has been around for several years. It's just more like a tool based on patient preference set up in that way. That's the key. Yeah. In other words, it was overhyped. I mean, a lot of people were doing it for decades already, bluntly. We had been. We have to. I just don't think it was as slick as you have today.

[00:37:24] And then you have all these things like, what is a decentralized trial? Is it just like remotely delivered survey instruments? Or is it literally, which we've done, is put a cold centrifuge in the trunk of someone's car and for a PK analysis and do it, take the blood and run out the back and spin it. So, you know, there's extremes on it. But having said that, the last one is just not practical at scale. It really isn't.

[00:37:48] I mean, maybe in a rare disease where it's hard to find people, but you can't do that in large, like, cardiovascular outcome studies. The cost is just beyond what anyone could afford. Yeah. Yeah. Now, especially for Farrow Health, like, what, let's say fast forward five years, right? So what will you think would be changed in the clinical research ecosystem? What's your hope?

[00:38:14] Oh, I really think the problem we have to solve for is right now it takes about, what, 10 to 12 years to get a drug to market. And the cost is doubling every nine years. It's not sustainable. It's just not. We have to compress the timelines.

[00:38:34] And what I help, what I can sincerely help companies like Farrow can do, and it won't be just us on our own, is we can help compress the timelines by automating the large amount of busy work that has to be done. And it does require a lot of skill, but can now be automated, which is never, thanks to AI, could just never be done before because it just required a complexity in the automation and understanding that's just not been around.

[00:39:03] And I think the other thing that has to happen is it will be a network of solutions that solve for this. It's not going to be one company that can do it. So ideally, what we'd say is, you know, my vision is you design a clinical trial in Farrow. We can produce your operational documents instead of three to six months. This happens in a couple of weeks. Then you program the electronic data capture systems automatically. Instead of spending, you know, six to eight weeks building, you can now do this in a few days.

[00:39:33] You can push to the lab vendor who can have all your kitting ready to go. Instead of someone having to read a protocol, sort through everything, you can just send them from us. Here's exactly the specification. And they can get going. And I just see a world of interconnected vendors like we have in software via API so that all the systems can talk together so people don't have to waste time having so many meetings.

[00:39:58] Because scheduling a meeting to answer these questions inside these companies takes weeks. And so we're sometimes we're our own worst enemy. Because, I mean, look at the spectacular result of Pfizer and Moderna getting the COVID vaccine to market so quickly. That shows the power if you prioritize something inside these companies. And, I mean, because they don't live on their own. They live with, like, zero sites.

[00:40:26] Once, when everybody prioritized that program and those meetings and made sure everything happened, look at how fast you can move. I mean, it's the proof point. So the thing is, if we can automate that now, and we can automate a substantial portion of it, then we should be able to compress this timeline and reduce the cost of drug development, which everybody wins because that's how you reduce the cost of drugs. You have to reduce the development time. Would you be able to share just traction number for the company?

[00:40:56] How big you've gotten? How many patients? Some stats behind it? Oh, no, we never share that. Because then people ask you the next year, like, how much have you grown? That's a catch-22. You know, we're in several of the top five now at scale, which for a five-year-old company, I'll put it that way, for a five-year-old company is impressive.

[00:41:19] It's not common for a startup to go from doing onesies, twosies in a company to becoming every single trial that they design and on the journey to becoming what I would call the source of truth. For all design, data and analytics. So, we're on that journey and several other in the top 10, and we're growing very, very quickly. Now, I can say in the revenue side, we're growing year over year, we've been growing at about 250%.

[00:41:50] So, year over year now for several years. It's always good, though, because effectively we started at zero. But, you know, for three to four years, we've been growing extremely rapidly. And that's going to play out for another several years before I think we enter a more traditional growth curve. But it is a very fast-moving industry right now. Profitable? Raising? Where do you stand on that? Fundraising? Yeah, we'll go.

[00:42:21] We're not profitable yet. Ideally, next year. We will be. We will go out to do – we're a Series A company right now. We have great venture capitalists in general, Catalyst, Polaris Partners, Section 32, and Zeta. So, we've got an amazing group of VCs around the table. We will go out to do a Series B around the end of the year, I imagine. And we're lucky we have flexibility because of revenue. Yeah, yeah.

[00:42:49] I mean, anything, everything that you shared, it's, you know, lots of VCs here to this show. So, they know how to reach out to Scott and Farahel, just so you know. This is good. All right. So, last couple of questions before we conclude today's conversation. What's the one question you wish more people in the industry were asking right now to you? You know, it's really – it's a great question.

[00:43:19] It's in some ways hard to answer, like, in a very succinct – in a succinct way. I think what's really shown up to me in the last three months, we've had so many, I would say, inquiries about generative AI writing for medical writing, particularly for what I would call clinical development documents. I think there's a misunderstanding in the field that all of medical writing is the same.

[00:43:48] And it's really not because, you know, for commercial writing, like, it's a very nuanced field. And so, for clinical development, the question that I wish they'd ask us is, the data model that underpins the ability for LLMs to write this is highly specialized.

[00:44:07] And I think they need to spend more time asking about how do you understand the complex interrelationships of the thousands of concepts in a 200-page document so that you actually can automate it and have it contextually correct? That is a huge problem we've solved. But people aren't asking that because they want to see a flashy thing or someone from another field.

[00:44:36] And it's like, it's not – our demo for writing is the most – if you saw our design platform, the analytics are beautiful. It's always moving pictures. And you can simulate different scenarios. For generative AI writing, it's the most boring thing to watch on the planet. You hit a button and you have to wait. And you have to wait. And then it gives you checklists and quality, like, some outputs, like, I'm confident here. I'm not confident here. You don't have enough data. Like, you haven't got these things correct and do it.

[00:45:00] But it's not sexy like ChatGPT can have a conversation or interact. But when you anchor on quality, like – so maybe I'll put it this way. Customers should ask, prove to me that you have high-quality writing. I wish they asked that because then it was like, let me show you how we score it and what it takes to do a very accurate, high-quality piece of, you know,

[00:45:27] this type of documents because it's so – it requires so much understanding. And you're not going to get – and you're not going to, frankly, get that out of a generalist company. It's not possible. Because we knew what it took to get – to make it work. And we're not – I'm honestly not sure anyone else has done it. I hear claims, but I've never seen it. It's like, show it to me. Show it to me. And I can show you ours. Like, I'll show you ours. Like, show me yours.

[00:45:55] We've not seen anything back yet. Just think. Yeah. I was saying that, you know, there was a time when I worked outside of healthcare. I actually worked for a documentation company. And one thing that was – I remember at that time is we were just trying to go into more regulated markets with this thing.

[00:46:18] And a part of it was, like, yeah, documentation itself, there is, like, nuances of, like, quality, which comes with domain knowledge. And, like, that needs to happen. And then over that is the second part of it is, like, the workflows, right? Like, and that needs to be very much simulated. Like, who needs to read it? Who needs to approve the first part? Then it goes to the next one in the cycle. And if that's missing, forget about your AI and everything. That's done, right?

[00:46:47] Quality, for sure, it understands the nuances. The quality comes from that. The industry, your model needs to support that. But also on top of it, I think the workflows, that needs to be supported in that way, too. It goes through that cycle of workflows. Yeah, no, that's really important. And the key thing, because the other reason ours is unsexy is it fits in the existing workflow. We sit inside everything they bought. So it's not like how others were. It's a standalone system. It's this little thing that pops up in Word.

[00:47:17] And it just doesn't look impressive. But it was done this way because it fits within exactly the systems they've got today. So you don't disrupt anything they're doing. It just sits and helps them kind of drop very, very quickly high-quality content. But for you, Sage, you have to fit with what they do because you try to disrupt that. But it's very hard because you need all this extra stuff. And as a startup company, it takes years to build what Viva Vault has built. You can't take on.

[00:47:46] You can't out-Viva Viva, the premier provider in this space. An excellent company. And you're not going to beat them at that game. You're just not. So you've kind of got to work with them. Yeah. Yeah. That's it. Well, this has been great. And my last fun question before we end the show is tell us one thing about you that no one in your professional circles knows. Something fun or surprising about Scott.

[00:48:14] And this is your chance to tell the world. What would that be? Oh, this is a hard one. I'm very transparent, so people know a lot about me. I am a terrible cricket player. I mean, tragically bad. And it's funny because people ask me, oh, you're Australian. Oh, you must be interested in cricket. It's like, oh, no. Because otherwise, I'm pretty athletic. You can tell when you look at me and I played a lot of different competitive sports. But man, am I terrible.

[00:48:44] Like, surprisingly terrible. Our school had like an A, B, C, D, E, F, G, H, I, J, K. I was on the K team. I don't know what it is about that sport. I can't get into it. And I literally, I think I'm red, green, colorblind. I can't see the ball. So I've been hitting all over the body. And yeah. And it's just because I run into people all the time because this is an international field.

[00:49:15] And did you watch the test match and other things? It's like, no. No, don't talk to me about cricket. Well, hey, it seems like you did more surfing than the cricket. So, you know, Gold Coast has its own advantage. You were not at the, you know, you were not at Gabba or Melbourne. You were here and, you know, enjoying the beach. So that's what it is.

[00:49:44] Thanks for sharing. So, Scott, if people want to reach out to you, connect with you, how can they reach out where? What would be the best means? Yes, you can reach. The easiest way is come to our website, phyrohealth.com. We have a great form there and a list of addresses. If you want to reach out to different functions within the company, that is the best way to do it. The other way is you can also just connect with me on LinkedIn.

[00:50:14] All right. We will include those links wherever you get this show from. And Scott, again, thanks for your time. This has been great, a great reflection of your personal journey. But also, we are getting a first taste on this show on the clinical trial and clinical operations world. We haven't had any guests in this area of the healthcare world. So this is a good, good first start for us with you.

[00:50:43] You've given us a lot of and based on everything you told us, seems like, you know, all signs are positive. We are going to know about what phyrohealth is doing. And like everyone, they will be raising their B rounds soon. So keep an eye for that and reach out if you are an investor. Thanks, Scott. Okay.