Dr. James Fox: Hey, Dr. Fox. How are you? Hi. Good. Thanks so much for, uh, for giving me the opportunity to have a little chat here.

Ram Yalamanchili: Yeah, I'm excited. Uh, so I think, uh, to begin, I'd love to hear your story. Uh, you know, like, uh, tell us more about yourself, your practice, obviously, your, uh, research program.

Dr. James Fox: Yeah, so I trained, uh, initially I trained, uh, my residency with, at the University of Missouri.

Then I trained with, uh, Ike Ahmed up at the University of Toronto. And at that time, uh, really that, that fellowship gave me, uh, a real interest in research and real interest on doing things on the cutting edge. And so that's what attracted me to research. And always, uh, behind any, uh, research site that, that has any chance of being good is a, is a large number of people who are putting forth good work, whether that be research coordinators or even, quite frankly, front desk, the technicians and, and, um, so forth.

So I, I've joined Icon Eyecare out here in Grand Junction, Colorado, approximately nine years ago, and we started a research, uh, site. Started off with. One full-time, uh, study coordinator. That really wasn't full-time, but that's what we told. Uh, that's what we told, uh, the first sponsor. Yes, this person's full-time, you know, and I think everyone who's done research had started it from the ground up can relate to that kind of a thing.

And, and now we've progressed to doing over 25, maybe 30 studies. I, I don't know, I don't count them. They didn't count them in preparation for us at Hangout today. But, uh, uh, we've done a fair number of studies. Uh, we have three and a half, uh, study coordinators that are full-time, and that's legit. We actually have that many, uh, now.

So, uh, um, so yeah, that's kind of where I've come and, and where I am right now. Um, in the process of our, our research, uh, our research program,

Ram Yalamanchili: you know, I know we spoke about this, but. Why did you get into research? What's, what's like the, uh, I mean you have a pretty strong, I mean, pretty busy clinical practice from uh, what we know and what we've seen.

But I'm curious what was like the original thought around why get into research?

Dr. James Fox: Yeah, I think ultimately there's very few doctors that wouldn't feel like, um, that wouldn't feel like we went into this to help patients, to help people. And I think we oftentimes start that way in direct patient care. And then I think over time that evolves to how we think about, uh, what we, where we can contribute to our part of, uh, uh, of improving patient care.

And so I've always loved doing new things. I've always loved taking on new surgeries, taking on challenges in that regard, I. And I'm always excited. I've always been excited to take on, um, a, whether it be a new surgery or a new procedure, a new device or, or new medication. I've always been excited to try it.

Um, and so for, uh, myself to not only have the opportunity to try these things earlier, but be part of the group of individuals who. Um, bring, allow, uh, products to be fully evaluated to, to make sure that it is an appropriate thing for all of our patients to have it, uh, uh, available to them. That was like a major driver.

So pretty much just wanting to be on the cutting edge and, um, and, and contributing to patient care in a more than just a one-on-one level. Cool. Cool. I see.

Ram Yalamanchili: And from, uh, the, you know, when you said you had to start with one coordinator, did you have prior experience before that were, were you doing any research in your residency or, uh, uh, maybe part of your training prior?

Prior, yeah.

Dr. James Fox: In my, in my fellowship research was a very, was very emphasized, uh, both on, uh, in investigator initiated like trials, um. Large multispecialty or multi, um, clinic trials and also FDA studies, uh, and registration studies. Uh, it was up in Canada, so there are studies through that as well. Health. Um, so yes,

Ram Yalamanchili: it was big time in the training.

So, uh, I think that's an interesting vantage point you, so what you're also telling me is you've been seeing research or at least working with, uh, within the research framework for quite some time. Outside of that nine years, you've, you've been at Icon, right? Uh, through your practice, so. Mm-hmm. Um, maybe like from your own words, how would you describe things, uh, evolving?

If, if any, uh, which you can share. Like what was it like when you first got into it, to today? Right. I'm talking pre Tilda, not like after we started Optic. Yeah. Talk about, but uh, prior to that, how's, uh, how's it been, um, from your perspective?

Dr. James Fox: Yeah, I mean, I haven't considered that question before you asking me, but, uh, I actually think if we're looking at what we're researching and what we're, um, looking into, there's been massive changes, like incredible improvements.

If we want to look at processes that go behind that research, there's actually been very little change. Uh, and, and that necessarily isn't. A bad thing. I mean, if you, if it ain't ain't broke, don't fix it. Uh, and I think that, um, research has worked. Research is something that's a discipline in, in monotony oftentimes.

I mean, there's the, uh, very intriguing, exciting part of research and there's the monotony side of

research and you can't have one without the other. And, um, so. The behind the scenes stuff, the binders, the regulatory documents, the keeping up with things has had very little, there's very little changes.

Even going back now, how long has it been a good 12 years or plus? Uh, from when I was training in my fellowship, those changes do I do, I have not seen significant changes in the behind the scenes component of research much. Got it. Okay.

Ram Yalamanchili: And, uh, right now, uh, you know, given that you've, you've got a very active research program, um, what, what would you say is sort of like, how, how would you describe your research program from a volume perspective?

Like, you know, um, and what percentage of your active clinical patients are, are usually in research? Is that, is it like, have you like, looked at such numbers just to see like where things are, um, when compared to like your clinical practice?

Dr. James Fox: At any given time wherein we've found ourselves to be in anywhere from eight to 15 studies at any given time.

Uh, I don't know what the percentage of of patients is. That's a little hard. We have a co-management network and, and we have a constant filling up of patient, uh, different patients and, and bringing patients or sending patients back to referring providers. So that'd be a little difficult. I will say that a strategy that we've had is to make sure that every.

Main, um, every main service line that we offer to patients has a research study within it that patients have the option to participate in. And so oftentimes we do fairly well in enrolling. I. We are one of those practices that's fortunate enough to, you know, just have a, a, an extreme stream of, uh, of patients coming our direction.

Um, so the percentage of patients who are actually in research compared to the number that we actually see is, is relatively small. Uh, but, uh, that the amount that we're actually seeing from a research side, I think is fairly sizable.

Ram Yalamanchili: I see. That's interesting. So you are, what you're also saying is from a patient's perspective, if they're coming to your clinic or your, your practice, that every service plan has some option outside of just standard of care.

And that's like, I'm assuming that's, that's an awesome thing, right? From a patient perspective in uh, it could be.

Dr. James Fox: Yeah. Yeah. Most certainly it is. I mean, I find that oftentimes patients do want to contribute. Um, I think also there are times where studies are fitting a niche where. The only thing a patient kind of has available to them may be a study.

Uh, so there's a wide range of of why a study might be beneficial to patients. And our tact is that if they are within the inclusion exclusion criteria that it's presented to them. I mean, we are not, we are not a a a site and I don't think there's many of them around, but they're not, we are not a site where we're trying to GM research down people's throats and at the same time.

For patients to have that opportunity, I think is, is shows great value. Um, I think it's valued in our community. Uh, yeah. So it's totally,

Ram Yalamanchili: that makes sense. So, uh, given that amount of extensive experience, I'm very curious to hear about what are the challenges you've seen? Um, clearly one, I think we, we sort of touched on staffing and such, but, uh.

You know, like if you were to just describe all the different challenges which you might have encountered or are encountering, um, you know, knowing what you know, what would you, what would you tell, what would you tell a younger yourself or somebody else who's trying to start, uh, in research?

Dr. James Fox: Yeah. Yeah. I think that even people are more experienced than me and, and have a bigger program than I have would definitely start relating to, I think the number one issue is scalability.

And so. Also, there's ebbs and flows in research where maybe you might have 10 studies actively recruiting at one moment, and then you have three that are actively recruiting and you're kind of closing up some studies. And so this is the thing where staffing is really critical. Research is built on trust and, and so we, as surgeons who are not in research, we're trusting that the FDA and clinicians that are involved in research are doing things properly so that then when we use products, we can have confidence in those products.

The FDA trusts, uh, in a way that they regulate and they monitor as well, that the, the, the sponsors and the sites and so forth are, are putting forth good data and, and running this study as well. I think ultimately us as private principal investigators, we are trusting our staff to run the study appropriately, and there's only so much resources on a fundamental level when you're in at the site level.

There's only so much resources that one person can do. So I think probably the biggest jump is to go from one full-time coordinator to the second full-time coordinator, but. When you go from one to two, you have one training, the second one. Okay. And when you go from two to three, you're busy enough that that training sometimes gets watered down compared to when the first person got trained to the second spot.

And so that scalability of staffing, because I, for those who are in research, they know that your trustworthiness of your study coordinators is probably the most. Important thing along with them being really having a good attention to detail and making sure they're doing things the right way. Trust is a massive part of this and, and so the bigger your circle is of trust, the more and the more humans that are involved, the more human air can take place and.

So I think scalability is the number one challenge I have for research. I'm fortunate enough to have the volume of patients and the interest in the community, uh, for research. Uh, but, and the ebbs and flows make that scalability necessary, not just, uh, not just, okay, now we're ready to have a third study coordinator.

Well, sometimes, as far as the budget's concerned, it would be good if we had two, and then it would be good if we had four. And so you hire three. And on the whole, that's a good idea. But you know that from a budgetary standpoint, it'd be great if you could furlough one for a few months until you need one again.

But that's not how it works. I mean, you've got to retain your staff. And so that scalability is, I think the number one challenge we have in research.

Ram Yalamanchili: What has your, um, methodology been in terms of hiring, training, you know, just like evaluating staffing, right? How, how do you build this, uh, um, uh, your current practice?

I would say, uh, your, particularly your research program, right? Have you noticed some things which work and have there been challenges which you ran into, which, uh, you know, uh, clearly did not work, for example.

Dr. James Fox: Yeah, so we've hired people who have ophthalmology experience. We've hired people who don't have ophthalmology experience, but have extensive, uh, clinical research backgrounds.

I think that it's potentially easier to train people who understand ophthalmology, to train people on research than it is to train people who are actually really good at research. On how to do ophthalmology. One of our research, uh, search coordinators right now did fit that mold of, they came from research that was outside of ophthalmology and they've, uh, joined our, our, our group and it took growing pains and it was great.

I mean, he, he did, he really has done a great job at, at developing into somebody who understands ophthalmology well, but I think. Certainly if you're starting off on research, I think, uh, my, my first, my, uh, main research coordinator was the lead technician of our clinic. And so somebody I already trusted, right?

Very important. Somebody already trusted and knew they had the skill of meticulous attention to detail. Uh, and so you

Ram Yalamanchili: took somebody who you already trusted mm-hmm. And also has a ton of experience in ophthalmology and essentially. Trained, uh, that lead coordinator to be, uh, I guess more aware of how research, uh, is performed,

Dr. James Fox: trained, and grew together.

You know, I mean, since we started, I did not, we did not take this program over from a, um, an experienced investigator. We started from the ground up and, uh, so there were a lot of things. We listened to mentors and to, uh, people who would help us in the process, most certainly, but. At the end of the day, her and I were in a room together oftentimes figuring things out for ourselves.

So, um, so yeah, that's, that's a, that's a, that's a little bit of a tough place to start with, but also we know the nuts and bolts it took to get here. And so, um, I think that allows us to really have a firm grasp on how to take things to next levels. 'cause we know the foundation of the levels that we've created.

Right.

Ram Yalamanchili: And having met your staff and also, um, your lead coordinator, I, I think one of the very interesting thing I've noticed is when I first met you and, uh, you, uh, your staff, uh, you're very open about the challenges you've had. Uh, I think there were some active, uh, challenges around, yeah, maybe some staff.

Uh, there was some staff churn. Uh, there were some things, uh, which you would've said we could have built more efficiently. Um. Even with like such a trusted, and, uh, I guess my point is that even with a really strong program and a staffing model, which you already have, uh, it was very clear that she was very much, uh, inundated with work.

Mm-hmm. And there's just a lot of overhead, uh, which was being spent on essentially like, um, you know, tasked, which otherwise would've been spent on maybe patient care or new, new program. New, new, new, new studies, that sort of thing. Can you give us maybe your version of how, um, you know, what that looked like?

Like right before, you know, we started working together, which again, I'd like to touch on as, as well, but I kind of want, want, give, give a sense of like, what was it like, uh, on that day when we met and sort of describe your, uh, your practice and,

Dr. James Fox: yeah, yeah. We had several, sorry to interrupt, but Yeah. We have, we have several, we had several studies that were rapid fire.

Two to three month to six month studies where the inclusion exclusion criteria was fairly generous in relation to how many patients would have access to these studies, which led to a, a large amount of recruitment in a very short time with overlapping studies that were similar, maybe not in the. Areas that they were being studied within, but um, but, but in their intensity and in their ramp up and.

So at some point people only have so many resources that they can, they can deal with. I think all of us can relate to that. Like there's no one who's listening or whatever talk about this stuff that doesn't understand that. And I know you. Yeah, yeah. That's new stuff, right? You've never been tapped out.

Right. So, um, I mean, when we were dealing with. Our secondary, uh, or, or, or our secondary research coordinator having approximately 20 hours of overtime, uh, a week and them saying things like, I don't want this overtime. When can we get a solution? Knowing we needed an additional staff member right then and there, but if we hired somebody new, we'd have to train them and actually slow down our processes.

You then, as a, as an investigator. Start wondering, well, should I not be presenting these options to patients? And that's a disservice to patients, uh, when you come from a perspective of giving patients opportunities to participate in these studies. And so also you want to make sure that you are con contributing to studies well, uh, to, you know, help these products be evaluated fully.

So we just were at, quite frankly, a. A breaking point psychologically, we hadn't yet hit a breaking point physically because my team was spending way too much time, uh, compared to what they should carving into their personal lives and their personal dedication to, uh, the, the research program and also to the patients within it, and also to the data and the.

Uh, the, the making sure things were done well, so I I, you know, when you, when you go through that with a program, you never, and you actually care about your team, you never want to see your team in that position. And so, and that just deals with that scalability, man. I mean, you never want to turn down a study because you don't have the staffing for it.

You want to turn down studies. If you don't have the patient population for it, but you never wanna turn down a study if you don't have staffing for it. You never want to turn down or actually neglect to discuss anything with somebody. Give them the opportunity to participate in the study because you know, your staff's overwhelmed.

And, and so at times it seems to be that there's a zero sum game between those where, um, really that scalability is the part that puts a ceiling on what you're able to do or not able to do. Yeah.

Ram Yalamanchili: You

Dr. James Fox: know, I, and that's where we were when a, when we met, that is definitely where we were. And that, hearing my concerns, some, somebody, a mutual, uh, acquaintance of ours, colleague of ours, you know, heard my concerns and, uh.

That's definitely why you heard those concerns very early on, uh, is because that's how we, uh, that's how we had the opportunity to connect with one another.

Ram Yalamanchili: You know, I just a quick rito, right? I, it's very interesting hearing your perspective where, where you are and because I walked in that first time and, uh, you know, fortunate enough to sort of like personally come in and meet you and your staff on that day.

Right. Uh, I have a very interesting perspective. I think hopefully, uh, you know, you see that. So the way I remember it is we spoke on the phone and I, I immediately was, I was kind of pitching you, hey, there's this whole thing called AI teammates, which we're building and we wanna build this for clinical research.

And I was also very open to you saying we're not there. Where, you know, everything we do is like 100% right. We want, we are building right now, but we want to work with somebody who's like excited about this. Wants to make a difference and can take it somewhere. And, uh, even though what you're describing right now seems like, man, I've got a lot going on.

I shouldn't probably be doing something completely out of there. Uh, I don't remember. You like giving me that vibe. You are very much like, dude, this is super cool. Like, yeah, I'd love to like learn more about it. Let's, let's, let's like unwrap sort of like, you know, what's happening. So if I, if you recall, I came in and we spent like the whole evening, uh, work, talking to your staff with you, um, taking a tour of the facility research program and I saw this board where you had like, I don't know, 15 studies, a whole bunch of them act, a whole bunch of them recording.

Yeah. And uh, I was just looking around. I'm like, wait, but I only see two people. You know how doing all this work. I remember your, uh, uh, lead, uh, coordinator saying, yeah, like, we're just like, you know, we're all in on this thing. We do. We take care of all this work, and we, we do it well. And, uh, it was very clear that they're, they're capable, they're very, very capable in taking on a lot of mm-hmm.

A lot of work and doing this. But at the same time, I think, like you said, um. It wasn't like, you know, this is fine for the rest of our lives or like, you know, future, right? We, we do gotta do something about this. And, and, and so there, and then we started talking about it. Um, so I want maybe like, I'd love to hear your perspective on what it was like to sort of hear what Till was building and you knew this was early.

We were, we were doing essentially a, a collaboration of sorts where we both were agreeing on, you know, we're gonna keep building, we're gonna keep making it better. And at some point AI will get there where things are materially, uh, you know, impactful than, than where it is today from an accuracy quality.

Yeah. And I'll argue over the last, you know, four or five months of, you know, doing all this, I think we're starting to see quite a bit of that turning out to be true. Mm-hmm. So what I'd love to understand is how, how was your initial reaction and why, why did you say, like, why did you even like try this out?

Because given that you knew we were early, like, you know, uh, like late, late last year, right?

Dr. James Fox: Yeah, I mean the reason I'm in, uh, research and you know, is innovation. So there's a lot of different perspectives that I think people have on ai. I think there's people who think that it will not pan out to what it could be, and it'll fade away or it will not be a part of our lives.

And if it does, it may be a part of our lives where we can ask it. Silly questions, and it'll respond to us in a way that's slightly better than what Google can do. Um, or if I ask it to paint a picture, it'll paint a picture a little bit. That picture may have six fingers on each hand, but it'll do its best.

And, and so, and I think there's then the people who are all in ai and that's all they see, and that's all they do. Uh, and, uh. I think I come from a situation where I really, truly believe that with the things I've looked into, ai, that AI is going to make a big difference in our world and that it, I also am very aware that AI requires training, uh, and that it requires frameworks that are made by the people who are designing how it's trained, and what information and data that it can be trained on.

I saw that our problem, though it may not immediately in one second be solved by what you were offering. Uh, it would, working with Tilda would allow us to see what AI was capable of within research on a very practical level, on helping us with our immediate problems that may not immediately lead to solutions.

Uh, but seeing the long game with it too, and knowing that these problems that we had, if we put our heads in the sand and keep doing things the way we always do, did them, it would be suboptimal. I think we'd still be able to get by. I think we'd be able to do things the way they always have been done.

But, uh, if AI has the potential. To change this industry behind the scenes the way, uh, we kind of discussed that possibly it could, that could be a real benefit sooner rather than later to what we were trying to do and what we, what challenges we were facing. Um, yeah. Mm-hmm. Mm-hmm.

Ram Yalamanchili: So, uh, you know, segueing into.

Sort of, it's been what maybe, uh, we've started working with your team, uh, since November. So it's been about three, four months right now. Yeah. Not long. It feels

Dr. James Fox: like longer 'cause we've done, I mean we've done a

Ram Yalamanchili: lot,

Dr. James Fox: but, uh, but yeah, not, you guys have a

Ram Yalamanchili: great time working with, uh, with each other, so. Yeah, that's

Dr. James Fox: right.

Ram Yalamanchili: Sometimes that that counts.

Dr. James Fox: Time flies when you're having fun and sometimes when you're doing a lot time doesn't fly as fast, even if you're having fun doing it. Yeah.

Ram Yalamanchili: It's super fun to work, uh, with you and your team, you guys. That's great ideas and yeah. Collaborative. It's, it's awesome. And, and most importantly, you understand the process, right?

Which is, which is also important. Mm-hmm. Process of development of something new and innovative and, uh, how that goes. So what I was gonna ask you is looking at the, uh. You know, the, the, let's call it before and, and, and current. Um, what's, what's your feedback? Like, you know, how, how have things panned out so far?

Um, I mean, I always say we're on the second innings of the AI revolution, right? So we're not, we're not there, there, but we are, we're certainly not at the starting point. There's several things which are already, uh, happening and we do a good job with, but, uh, I'd love to get your thoughts on what is the material impact?

What does your staff think? What do you think? Um. And, uh, and then I would like to follow up with, you know, what is, what is the feedback? Like where are things where we fall short and or as an industry or our ourselves, we need to focus on and get, get, uh, to a better place. Right? So both like current impact and areas where we can potentially be better at.

Dr. James Fox: Yeah, I think that. I laughed when you said that we've, uh, been working together for three months. I mean, that feels three and a half months, whatever. That feels pretty absurd with, with how we've, what we've been able to accomplish and how we've been able to test this. And I guess that can speak to the fact that we've seen real tangible benefits in such a short period of time, and I do think that there's a lot more benefits that we can achieve over time.

Uh, and we can go into specifics of that as, as is necessary. I think we're, I think the problem we were having on, uh, scalability and I just gotta close a little email there about us, another study, I suppose so, but, uh, the problem we were having with scalability that was addressed fairly quickly, it did take some extra time for our staff to learn the system and the process.

It took more work on the front end than it would've taken to just keep doing things the way we were. But as we've worked well and it has led to savings of time and it's been able to lead to, and I know you had shown me some of the data, uh, was interesting. It's been leading to faster query response times.

It's been leading to. A lower number of manual queries that are necessary. Uh, the inputting of, of, of things is cut down on time. And so there's been real time savings. And that doesn't mean time cutting corners. It, it means literally things that AI on a very basic level can do for us that otherwise we're spending resources, valuable resources of our finite staff doing.

So that's been the benefit. Now, I think one of the things that could be, or a, a challenge that I did not recognize at first is how quickly some studies can, like, get going, you know, and, and start, start up, you know, and, and how quickly you need to kind of pivot from contract to startup of the study. And it takes a little bit of time to line everything up from a.

From a, uh, in this case, we're working with you from an, a ai, uh, assistive research program, uh, to, to help us get everything in line, and I don't think that's a drag on the basis of what you are or what the sponsors are. It's just that there's not a habit yet formed. Of how these two groups, including ourselves, those three groups really, um, can make that process more efficient.

And so that's, I think, something that, uh, will be important in the future. You don't wanna hold up on recruitment 'cause not everything's in line. You also don't wanna get ahead of using a system that is AI driven. You don't wanna start doing it without that and then play catch up later. That really, uh, takes the.

It creates redundancy of how you're going to do things right from the start. And so this is the learning that I've found. We've, uh, we've done. Uh, and I'm curious, what are things that you have found in learning in, in how we've worked together, uh, and, and what challenges you've seen and also what things you've been encouraged by.

Ram Yalamanchili: Yeah, I, I think there's been several, frankly, I think over the past, uh, uh, three, four months, I think we've, uh, learned quite a bit. Uh, we've been very active and busy on our RD and engineering side to address the concerns and, and frankly, there's some very good ideas which came out of our collaboration right.

On how we can address this. So what you just spoke about is study startup. You know, um, there is no reason study startups should not be measured in hours to a day or two rather than weeks. Uh, you know, I can totally relate to what you're saying. Uh, it has not been a huge focus when we first started because we had quite a few things on our plate, and that is something we are actively working on, and we have, uh, made tremendous progress.

So this is going to be. Cutting edge in terms of where we are gonna be, uh, in a matter of weeks right now. So in the next few releases, we'll be a place where things will be instant, right? Like from setting everything up. Uh, so that experience was definitely. Unique in the sense that you have a pretty prolific research program and that is, uh, sort of unique, right?

Like, it's not, it's not everybody where you get a study, a CTS sign and you're ready to go. Uh, you know, once you're initiated, you are ready to go the next day. And that's the personality and the. Yeah, essentially like the bar you hold yourself That is true. Is very, very like different and unique compared to everything else I've seen.

Yeah. Uh, in the industry, which is wonderful. Right. I think the world would be a better place if we had like more people and more programs, like how you are describing

Dr. James Fox: it. I'm sure we have many programs like that. To be fair, for any of anybody who's watching this, you know, somebody's watching this right now and they're thinking.

I mean, none of us surgeons are competitive. You know, none of us surgeons are competitive. I'm sure there's someone watching this right now and being like, man, I'm, I gotta be better than this guy. I gotta be. But, but no, I mean, I think we should hold ourselves to high standards. Yeah.

Ram Yalamanchili: Yeah. I, I, I mean, I'm saying relatively speaking Sure.

But, you know, you asking what is, what was unique and.

And then we started realizing, you know what, we, we, we gotta, we gotta get there. You know, we gotta get there quick to, to meet with that sort of expectation. Yeah. And uh, that means going back to my engineering team and going back to product and sort of, uh, you know, look at our backlog and see what, what is it that we need to do to kind of like get there.

Right. And, uh, so that, that was super interesting. Uh, another set of things I think, which are really fascinating is ophthalmology as a research like vertical has some very interesting problems. Uh, like for example, you and I spoke about how ie criteria matching for certain types of studies can be much better.

Uh, a a as in like you can, you can derive insight from a recruitment perspective, which, uh, could potentially save a lot of time, can help the patient ma be matched to that trial. I think there's a bit of that. Uh, on the patient id part, uh, perspective. Uh, we've also learned a lot about how your staff's current procedures work.

So that's, that's every, every practice has something unique in that sense, like the workflow itself. And so we've, uh, we've like learned about how reg documents get done in your, in your practice, how finances get managed, how stipends get managed, how is patient communication managed. Yeah. And these are all sort of nuanced, right?

There's small changes which we have to be okay with, and we have to train our AI teammates to say, well, Dr. Fox says program, this is their SOP, and this is the style in which they work. So instead of being like. This is what we do and this is how you gotta go. Allowing some amount of like trainable team in the, in the process upfront.

That was another learning for us. Uh, and so we had to build some, uh, tooling so that we can allow that not only for you, but for any anyone else who we're working with. Right. We're working with multiple sites, so, so I think we've learned nuances about ophthalmology for sure. We've learned nuances about site practices and, uh, investigator preferences.

Um. And then just from the fact that your pro, because your program is really prolific, uh, we've learned a whole bunch about how, um, like where you hold yourself to from a standards perspective. And I think all that kind of goes back into our day-to-day because we, we, we, we love working with, um, uh, this sort of a practice because there's just so much to learn and so much to improve on.

And I'm sure like once we build it, then, you know, everybody benefits. Right? So it's, uh, yeah, a hundred

Dr. James Fox: percent. Yeah. I think, I think you mentioned something like, we have worked together. And, uh, I think right now AI isn't such that it can figure everything out on its own. And so having that responsiveness on you guys' side, we're responsive to things that we need to be better.

Uh, and then you guys have been very responsive to figure out how that is. Not just immediately, but sometimes it requires some conversation. Like, why is this? What are the things about it? You know, and it's helped us look at our own processes as well. Some of the questions you guys are having, uh, for us helps us look at things in a different way.

And so AI is not good enough right now to take out collaboration with humans, and it is the collaboration with one human to another, but make no mistake. You are gonna need to, if you're, if you're a growing practice or if you're a growing, uh, research program or if you're a growing company. I mean, this is a business thing.

You're gonna have to be training somebody at some point. The more you grow, you're gonna be training a lot of somebodies. And so, um, right now we're working together, we're training one another. And you are in control of like training the AI to, to learn. We're not gonna have to train AI again, we'll be modifying how we train it, but if someone moves along from our practice, uh, uh, uh, you know, they have another opportunity or they go elsewhere or retire or what have you, we will always have that training that we put into ai, uh, behind us, and we'll be growing off of that.

I fully do not anticipate artificial intelligence to replace our research team. Anytime soon and soon, I can use hand gestures to say not anytime soon. But what I can do is I can keep our core group small and manageable and more nimble and more, uh, able to take on challenges, have less bureaucracy that can occur when you get larger and larger systems.

I, I haven't experienced that in research, but I certainly have experienced that to some degree. As a practice grows, you, you get into that ult, uh, um, uh, at some point or another. It just is how it is. So I can keep my group of trusted people small while training a, a, a partner that will not go away, whose resume grows by the inputs we put in it.

Um, and so while maintaining a staff and quite frankly, being able to retain a staff better, who is not so stressed out, who they have somebody to fill in to chip in on certain things and take things off their plate. So this is the, this I think is the thing that I would say for people who are either questioning how AI could help, doubtful that it might be able to, or they don't wanna put the time in.

I think recognizing that you're going to have to take time training somebody no matter what, uh, until artificial intelligence is fully aware and. What is it a GI? What does that stand for? I'm not an expert. What does that stand for? General intelligence. Yeah. Yeah. Artificial general intelligence. Once it can kind of be better than humans from the start without training, I mean, boy, we're into something completely different.

Uh, but until then, it is the collaboration of humans who are working with the AI to, uh, to, to get it done. So has it been work to work with you guys? Yes, it has. Would have it been work for us to be trying to train somebody from scratch? Yes, it would. In fact, the work that AI is doing for us has allowed us to train another individual because we have time to do so.

So we had three, uh, study coordinators. Uh, we started adding a, a part-time study coordinator that study one of the study coordinators that was full-time. It just didn't work out and we needed to part ways and we couldn't. Think of adding another study coordinator because boy, that would take too much time training.

So literally the work that AI has taken from our coordinators has helped been able to go towards, um, towards training of a new study coordinator. Uh, you need room to breathe in order to train. And, and that has done that.

Ram Yalamanchili: Yeah, I mean, uh, that's really well said. Uh, it's essentially. The, the, the few things you were mentioning as challenges, right?

You want to get to scale, but then your staff is overloaded and you're trying to solve for these problems. And the better way to perhaps do it as you have your AI teammates, they're doing part of your workflow. And you know, from our perspective at tilda, we have been through the research program ourselves.

I myself have built a biotech company. I've run clinical research programs as a sponsor. And so having that trust, I think, and building a team which deeply understands research, and then coming to you and saying, you know, we're not just talking technology. We understand the concept and the, and the importance around process for research itself.

And you know, like, you know, tying it all together from core competency and engineering and product building along with core competency in delivering research as a service right in, uh, from our team. I think those need to be there together. Um, our, our idea is that, you know, you can build trust if you are just one or the other.

I think especially in this domain, if you're talking about taking a sort of like a, a, a big bet, right? Where AI is going and you're building something really innovative, uh, you kind of have to have both perspectives, both from a clinician or clinical research perspective as well as from the technology perspective and, um.

Um, yeah, I'm, I'm really glad to hear like what you're talking about. I think that's exactly what we, we have seen multiple times, not just with your practice, but many other collaborations we've had or we're working with right now. Uh, they very quickly tell us, you know, this, this is great because I'm getting a breather for the first time.

I'm seeing my staff actually saying, okay, like, if things are gonna continue working in this direction, I think we'll be in a better place. At least I'll be in like personally, which is great. Right. I. That gives you a lot of other benefits. Your staff is happier, you have better retention, you have better trust.

Like you, you start to build a much more productive team actually from, from that perspective. Mm-hmm. And uh, I think one of the things I realized building, uh, my previous biotech company was that I. Because research methods have been evolving as in like there's new discovery mechanisms, there's new products, there's new vectors in, in terms of what type of research you can perform.

Mm-hmm. But the methods haven't changed. Like you go to A CRO, it's the same exact methodology. You go to a site, same exact methodology. So you do end up running into these pretty big bottlenecks, essentially like large brick walls where innovators on the biotech side will come. And say, well, I have this amazing set of product innovation, which I'm bringing into the market, and then you hit this like brick wall because there is no, no way to, you know, accelerate past that, uh, bottleneck, right?

You have to go through what everybody else does. And I personally think that's, that's, you know, really not, not the right place for us to be as a society. I think, uh, you know, there's so much innovation waiting to happen in, in, uh, in medicine and uh, um. And, you know, there's only so many great individuals who are dedicated and would love to do great research, sort of like you and your team.

And, you know, we should empower them in whatever we way we can, right? So you kinda have to give them the right tooling and, and bring them there. Um, so yeah, it's definitely like AI plus a trusted team, delivering it all together. Working with companies or, or practices like yourself, um, you know, collaborating, fine, tuning it, and then bringing it into value.

Dr. James Fox: So, uh, us Lemme, lemme say one quick thing on that if you don't mind, please. So it's not like research has been run wrongly. We've found out how it can be run well and I think I. You when, I mean for me, I wasn't thinking about AI as being a solution to our challenge because I had the brick walls around us, you know, and it's not through lack of care or lack of trying.

I, I knew that the solution to the fact that the brick walls were there were to further reinforce those brick walls or to build on top of those brick walls. But when we have the opportunity, and this goes in medicine, this goes in anything, when we have the opportunity to move those brick walls. And I think the second we think, oh wow, we've, we've improved enough.

We don't have these brick walls anymore. We'll run into another set of brick walls. Uh, and it may be that you run into it for years and then, and then blow through those brick walls eventually. But I think right now we have an opportunity to move through some of the brick walls that we have. Rightfully stayed within, but may not be necessary to stay within and still produce quality research.

That's one thing. Another thing I wanted to say is that oftentimes people will call me Dr. Fox. Oh, expert. I'm the expert, I'm the whatever. And this is certainly the, Hey, oh, this is certainly the case, right? This is, uh, for sure the case. But I mean, our study coordinators are experts, man. I mean, they're experts in their domain in a way that.

I'm not even an expert in what they do to the level that they are. And quite frankly, if you have a great study coordinator, they better know more about, they better be getting to the place where they know more and then they need to excel in a way where they know more than you, they should. That's, that's what you should be, uh, developing and, and growing and, and in inspiring people to do and, and, and have people that are capable of that.

But you do not wanna bog down experts with things that, uh, are not necessary for their level of expertise. And so we would bring in temps. Uh, we had brought in temps to do some of this, these lower level tasks as it were. But then that trust factor attempt doesn't necessarily know what they're putting in, or doesn't, uh uh, uh, you know, may not recognize that, ooh, that doesn't sit quite right, or that doesn't make much sense.

Um, and so. To add skill to that level, that lower level of things. And by the way, uh, that skill has not always been uniform in our experience just yet. And we're three months, three and a half months in. So, but we've seen that skill increase over time and we know that that. As we partner together, that AI model is gonna train better and better and better.

And, and we are going to get the point where we have high quality, you know, temps working for us, quite frankly, uh, and allowing our experts to be experts

Ram Yalamanchili: that, yeah, absolutely. I, I, I, I personally think. At least in the domain of ai, there's only one way to go. You know, it's gonna be more and more intelligent, right?

That, that is just the trend. And, uh, we have not hit a brick wall just yet, no where that, that ceiling is. So it's exciting. I think things will get materially better, um, in the near future. And, uh, you know, this is all going in just one direction, in my opinion. Um, so I think my, one of the last, uh, areas I wanna discuss is.

Let's just say, you know, everything you and I are envisioning comes together or comes true, right? Like in terms of where AI can go. We have AI teammates, which are, uh, not only doing, uh, you know, in, in your case we're doing data regulatory finance. Mm-hmm. Maybe they do more, maybe they do more workflows within each of those categories.

Dr. James Fox: Mm-hmm.

Ram Yalamanchili: Where do you see things going? I mean, not maybe start with. Your practice, but I also would love to understand what, what do you think will happen to the space? Because you've been in research in ophthalmology specifically for the past 12 years. Yeah. And you've seen where it goes. You've seen the pace and you've seen how innovation happens in the industry.

But how, how do you envision things will change? Like is the next 10 years gonna be about the same, will be different? Like, and and what, what's your thoughts on that? Paint a very broad picture.

Dr. James Fox: Boy, very broad picture. I mean, I'm not an expert at AI like you are. You know, I don't,

Ram Yalamanchili: no, no. I'm, I'm asking you.

No, I know. If AI were there, what does that mean? From a clinician's

Dr. James Fox: perspective? Yeah. From a clinician standpoint, I, like I said, I don't have the experience you have. I've just, I'll tell you, I've used chatt PT since rather early, and it's a hell of a lot better at. Uh, helping revise some emails of mine that might come across a little aggressive.

So I'm making fun, kind of mocking myself in this and, and, uh, and, and, and so now, uh, it can be a, it's become a real help. It's not a cheat code. It, you still have to be yourself, but. I think that where AI has the potential to go is, is kind of absurd to what Scap, I think to to, I mean it's fair, I hear you Ram to ask me what I think's gonna happen over 10 years of ai, but I think I.

AI six years from now will be able to tell you where it's gonna go over the next four years better than any human being will be able to. So

Ram Yalamanchili: that is su that is super, super, uh, very interesting comment you made.

Dr. James Fox: Yeah. Yeah. I mean that sentence right there,

Ram Yalamanchili: insight level, right?

Dr. James Fox: That, that sentence right there.

Yeah, exactly. Like, uh, where does a caveman think that it's go, that we're gonna be in society in tens of thousands of years? I mean that in 10 years, things could be a whole lot different. But I think my goal biting off bite-sized chunks, I, I mean, quite frankly in our entire thing, I mean, I think in our whole industry, in in what devices are available, in what, um, I.

Basically, I mean, I don't know. I can't even, it's, it's gonna expand far more than research, of course, but to take up a bite-sized chunk of this for what I see it doing over the next six months, nine months, one year for us, because I think it's hard to project out where AI is gonna be, uh uh, with how.

Nearly limitless things appear. It could be at this time. Uh, I think that where I see it is functioning on the level of, uh, one and a half to two study coordinators. And it will function as more study coordinators. The more busy we are and it will function as less study coordinators, the less busy we are.

So it will help us with the scalability. Uh, I'll be able to keep my. Group tight and small and nimble in learning how to do things better on the areas that we're experts in, and I think it's going to first do all our menial work and then it's going to continue to grow. I don't think it's gonna replace my team, and I think there's fear.

I think part of why AI is not looked into is the fear of what might come. When will I, Dr. James Fox be replaced by ai? And, uh, it's coming whether we like it or not, or our fears are there or not. And so figuring out a way where it can work in conjunction with the, the human beings that are the experts in your program.

That's probably what I'm most looking forward to is we've been three months. I'm looking forward to seeing how our human to human collaboration will create a stronger AI partner that will allow. Are, uh, employees who are research coordinators to be more self-actualized in their ability to do things that they quite frankly, have not been, had time to do, uh, because they've been bogged down by other things so many different places.

I mean, one thing about our, there are some study centers that do mi data mining and look at, uh, look for patients. We don't, we literally look for patients in the room that we are doctor to patient. I'm looking at the criteria of a research study and knowing that, and then, um, and then having a conversation with the patient in that moment.

Uh, we just don't have time to assign to our study coordinators, even though it's an effective tool to, to do that. We got work to do. We got, we have things that we need to do, and so. I, I wanna ask. It's just gonna, it's just gonna, I think our functionality and our, our scope of what we're gonna be able to do as a research program is going to extend far more than what it has already.

And, and I do think we're a fairly successful research site and for us to see, see that we could grow ideologically as quickly as we potentially could over the next six to nine months. I mean, that's, that's exciting. So 10 years, I have no clue, dude. Uh, but six to nine months I think I have a pretty good idea as to where I want to see things going.

Yeah.

Ram Yalamanchili: Yeah. And, and something you pointed out is interesting, right? Which is from your perspective, your team can grow, you're taking on more work. Um, but at the same time, I think if there were more programs like yours enabled. I also think it'll have a, a net, a really big net positive effect on medicine.

At least that's my goal with building. Hundred percent. And, um, you know, another way to put it is when we look at your, uh, program metrics, how efficient things are, how your, you know, you're consistently top one of the top performers on, on many of these studies, and we're talking about from a quality perspective, consistency perspective.

Um, patient satisfaction, like various metrics, right?

Dr. James Fox: Mm-hmm.

Ram Yalamanchili: I would love to have like the world have, you know, 500,000, 10,000 of such like research programs because I do believe there will be that amount of volume of medicine or innovation coming down the pipe. Uh, it's only, to me, it's only a matter of time if we are, you know, if we are building foundation models, which can answer core biology, basic science questions, and mm-hmm.

So on so forth, you know, you are expanding the scope of how many innovations are gonna come down through the pipeline. And ultimately that means having more prolific programs like yourself, being replicated, being assisted by teammates like ours, like AI teammates will be a huge net positive for the industry, right?

For and, and for, for frankly everybody. For all of us. Um, so I, I think it's, uh, uh, I'm, I'm really excited. I think that, you know, what we're showing in, in metrics at the moment, uh, it's early, but like I said, it's second innings. It's not, we are not not playing the game. We are, we are in the game for sure.

And, uh, um, yeah, I'm, I'm super excited to see where things go and uh, uh, how we can basically bring what you're talking about.

Dr. James Fox: Yeah, innovation is a passion of mine, and I looked to this as a, I looked first, we, our first contact was due to a need that we had, and it is, it morph very quickly into seeing how this could be beneficial to more than just.

iCare, Dr. James Fox and the research coordinators within, and the, and other investigators within and so forth. I mean, quite frankly, honestly, I do consider this a privilege to work at this level of where things are. I, I really, truly do. I, I think that I, I don't particularly care that I'm remembered for anything.

I don't really care, but I do. Do think it's a privilege to be in things so soon and to help be part of a solution that could make such a big difference over time. And, and if it's just you and Ro uh, you and Irom over the course of our lives, uh, that's a shame, uh, because, uh, exactly that we need to make this be, have a larger scope of impact.

But it is a, it is a privilege to be a part of it, the early start on things, and I look forward to more and more people adopting because more and more people adopting, quite frankly, will train the AI models better so that my AI model works better. So I, I do look forward to adoption of this. Um, on a altruistic, like, I'm excited for what it can do and on a selfish level as well, I suppose, uh, in, in the fact that that will train AI models even faster.

So. Yeah.

Ram Yalamanchili: Yeah. No, absolutely. That's like, uh, like I said, it takes a village, so we we're certainly, uh, getting there.

Dr. James Fox: Yeah.

Ram Yalamanchili: Um, great. Well thanks for your time. Uh, I think we're good. Um.

Dr. James Fox: So, yeah, thanks so much. I.

Hey, Dr. Fox. How are you? Hi. Good. Thanks so much for, uh, for giving me the opportunity to have a little chat here. Yeah, I'm excited. Uh, so I think, uh, to begin, I'd love to hear your story. Uh, you know, like, uh, tell us more about yourself, your practice, obviously, your, uh, research program. Yeah, so I trained, uh, initially I trained, uh, my residency with, at the University of Missouri.

Then I trained with, uh, Ike Ahmed up at the University of Toronto. And at that time, uh, really that, that fellowship gave me, uh, a real interest in research and real interest on doing things on the cutting edge. And so that's what attracted me to research. And always, uh, behind any, uh, research site that, that has any chance of being good is a, is a large number of people who are putting forth good work, whether that be research coordinators or even, quite frankly, front desk, the technicians and, and, um, so forth.

So I, I've joined Icon Eyecare out here in Grand Junction, Colorado, approximately nine years ago, and we started a research, uh, site. Started off with. One full-time, uh, study coordinator. That really wasn't full-time, but that's what we told. Uh, that's what we told, uh, the first sponsor. Yes, this person's full-time, you know, and I think everyone who's done research that started it from the ground up can relate to that kind of a thing.

And, and now we've progressed to doing over 25, maybe 30 studies. I, I don't know, I don't count them. They didn't count them in preparation for us ha hang out today. But, uh, uh, we've done a fair number of studies. Uh, we have three and a half, uh, study coordinators that are full-time, and that's legit. We actually have that many, uh, now.

So, uh, um, so yeah, that's kind of where I've come and, and where I am right now. Um, in the process of our, our research, uh, our research program, you know, I know we spoke about this, but. Why did you get into research? What's, what's like the, uh, I mean you have a pretty strong, I mean, pretty busy clinical practice from uh, what we know and what we've seen.

But I'm curious what was like the original thought around why get into research? Yeah, I think ultimately there's very few doctors that wouldn't feel like, um, that wouldn't feel like we went into this to help patients, to help people. And I think we oftentimes start that way in direct patient care. And then I think over time that evolves to how we think about, uh, what we, where we can contribute to our part of, uh, uh, of improving patient care.

And so I've always loved doing new things. I've always loved taking on new surgeries, taking on challenges in that regard, I. And I'm always excited. I've always been excited to take on, um, a whether it be a new surgery or a new procedure, a new device or, or a new medication. I've always been excited to try it.

Um, and so for, uh, myself to not only have the opportunity to try these things earlier, but be part of the group of individuals who, um, bring, allow a product to be fully evaluated to, to make sure that it is an appropriate thing for. All of our patients to have it, uh, uh, available to them. That was like a major driver.

So pretty much just wanting to be on the cutting edge and, um, and, and contributing to patient care in a more than just a one-on-one level. Cool. Cool. I see. And from, uh, the, you know, when you said you had to start with one coordinator, did you have prior experience before that were, were you doing any research in your residency or, uh, uh, maybe part of your training prior?

Prior, yeah. In my, in my fellowship research was a very, was very emphasized, uh, both on, uh, in investigator initiated light trials. Um. Large multispecialty or multi, um, clinic trials and also FDA studies, uh, and registration studies. Uh, it was up in Canada, so there are studies through that as well. Health.

Um, so yes, it was big time in the training. So, uh, I think that's an interesting vantage point you, so what you're also telling me is you've been seeing research or at least working with, uh, within the research framework for quite some time. Outside of that nine years, you've, you've been at Icon, right? Uh, through your practice, so.

Mm-hmm. Um, maybe like from your own words, how would you describe things, uh, evolving? If, if any, uh, which you can share. Like what was it like when you first got into it, to today? Right. I'm talking pre Tilda, not like after we started Optic. Yeah. Talk about, but uh, prior to that, how's, uh, how's it been, um, from your perspective?

Yeah, I mean, I haven't considered that question before you asking me, but, uh, I actually think if we're looking at what we're researching and what we're, um, looking into, there's been massive changes, like incredible improvements. If we want to look at processes that go behind that research, there's actually been very little change.

Uh, and, and that necessarily isn't. A bad thing. I mean, if you, if it ain't ain't broke, don't fix it. Uh, and I think that, um, research has worked. Research is something that's a discipline in, in monotony oftentimes. I mean, there's the, uh, very intriguing, exciting part of research and there's the monotony side of research and you can't have one without the other.

And, um, so. The behind the scenes stuff, the binders, the regulatory documents, the keeping up with things has had very little, there's very little changes. Even going back now, how long has it been a good 12 years or plus? Uh, from when I was training in my fellowship, those changes do I have not seen significant changes in the behind the scenes component of research much.

Got it. Okay. And, uh, right now, uh, you know, given that you've, you've got a very active research program, um, what, what would you say is sort of like, how, how would you describe your research program from a volume perspective? Like, you know, um, and what percentage of your active clinical patients are, are usually in research Is is right?

Like, have you like, looked at such numbers just to see like where things are, um, when compared to like your clinical practice? At any given time wherein we've found ourselves to be in anywhere from eight to 15 studies at any given time. Uh, I don't know what the percentage of of patients is. That's a little hard.

We have a co-management network and, and we have a constant filling up of patient, uh, different patients and, and bringing patients or sending patients back to referring providers. So that'd be a little difficult. I will say that a strategy that we've had is to make sure that every. Main, um, every main service line that we offer to patients has a research study within it that patients have the option to participate in.

And so oftentimes we do fairly well in enrolling. I. We are one of those practices that's fortunate enough to, you know, just have a, a, an extreme stream of, uh, of patients coming our direction. Um, so the percentage of patients who are actually in research compared to the number that we actually see is, is relatively small.

Uh, but, uh, that the amount that we're actually seeing from a research side, I think is fairly sizable. I see. That's interesting. So you are, what you're also saying is from a patient's perspective, if they're coming to your clinic or your, your practice, that every service line has some option outside of just standard of care, and that's like, I'm assuming that's, that's an awesome thing, right?

From a patient perspective in uh, it could be. Yeah. Yeah. Most certainly it is. I mean, I find that oftentimes patients do want to contribute. Um, I think also there are times where studies are fitting a niche where. The only thing a patient kind of has available to them may be a study. Uh, so there's a wide range of of why a study might be beneficial to patients.

And our tact is that if they are within the inclusion exclusion criteria that it's presented to them. I mean, we are not, we are not a a a site and I don't think there's many of them around, but they're not, we are not a site where we're trying to GM research down people's throats and at the same time.

For patients to have that opportunity, I think is, is shows great value. Um, I think it's valued in our community. Uh, yeah. So it's totally, that makes sense. So, uh, given that amount of extensive experience, I'm very curious to hear about what are the challenges you've seen? Um, clearly one, I think we, we sort of touched on staffing and such, but, uh.

You know, like if you were to just describe all the different challenges which you might have encountered or are encountering, um, you know, knowing what you know, what would you, what would you tell, what would you tell a younger yourself or somebody else who's trying to start, uh, in research? Yeah. Yeah. I think that even people are more experienced than me and, and have a bigger program than I have would definitely start relating to, I think the number one issue is scalability.

And so. Also, there's ebbs and flows in research where maybe you might have 10 studies actively recruiting at one moment, and then you have three that are actively recruiting and you're kind of closing up some studies. And so this is a thing where staffing is really critical. Research is built on trust and, and so we, as surgeons who are not in research, we're trusting that the FDA and clinicians that are involved in research are doing things properly so that then when we use products, we can have confidence in those products.

The FDA trusts, uh, in a way that they regulate and they monitor as well, that the, the, the sponsors and the sites and so forth are, are putting forth good data and, and running this study as well. I think ultimately us as private principal investigators, we are trusting our staff to run the study appropriately, and there's only so much resources on a fundamental level when you're in at the site level.

There's only so much resources that one person can do. So I think probably the biggest jump is to go from one full-time coordinator to the second full-time coordinator, but I. When you go from one to two, you have one training, the second one. Okay. And when you go from two to three, you're busy enough that that training sometimes gets watered down compared to when the first person got trained to the second spot.

And so that scalability of staffing, because I, for those who are in research, they know that your trustworthiness of your study coordinators is probably the most. Important thing along with them being really having a good attention to detail and making sure they're doing things the right way. Trust is a massive part of this and, and so the bigger your circle is of trust, the more and the more humans that are involved, the more human air can take place and.

So I think scalability is the number one challenge I have for research. I'm fortunate enough to have the volume of patients and the interest in the community, uh, for research. Uh, but, and the ebbs and flows make that scalability necessary, not just, uh, not just, okay, now we're ready to have a third study coordinator.

Well, sometimes, as far as the budget's concerned, it would be good if we had two, and then it would be good if we had four. And so you hire three. And on the whole, that's a good idea. But you know that from a budgetary standpoint, it'd be great if you could furlough one for a few months until you need one again.

But that's not how it works. I mean, you've got to retain your staff. And so that scalability is, I think the number one challenge we have in research. What has your, um, methodology been in terms of hiring, training, you know, just like evaluating staffing, right? How, how do you build this, uh, um, uh, your current practice?

I would say, uh, your, particularly your research program, right? Have you noticed some things which work and have there been challenges which you ran into, which, uh, you know, uh, clearly did not work, for example. Yeah, so we've hired people who have ophthalmology experience. We've hired people who don't have ophthalmology experience, but have extensive, uh, clinical research backgrounds.

I think that it's potentially easier to train people who understand ophthalmology, to train people on research than it is to train people who are actually really good at research. On how to do ophthalmology. One of our research, uh, search coordinators right now did fit that mold of, they came from research that was outside of ophthalmology and they've, uh, joined our, our, our group and it took growing pains and it was great.

I mean, he, he did, he really has done a great job at, at developing into somebody who understands ophthalmology well, but I think. Certainly if you're starting off on research, I think, uh, my, my first, my, uh, main research coordinator was the lead technician of our clinic. And so somebody I already trusted, right?

Very important. Somebody I already trusted and knew they had the skill of meticulous attention to detail. Uh, and so you took somebody who you already trusted, but mm-hmm. Also has a ton of experience in ophthalmology and essentially. Trained, uh, that lead coordinator to be, uh, I guess more aware of how research, uh, is performed, trained, and grew together.

You know, I mean, since we started, I did not, we did not take this program over from a, um, an experienced investigator. We started from the ground up and, uh, so there were a lot of things. We listened to mentors and to, uh, people who would help us in the process, most certainly, but. At the end of the day, her and I were in a room together oftentimes figuring things out for ourselves.

So, um, so yeah, that's, that's a, that's a, that's a little bit of a tough place to start with, but also we know the nuts and bolts it took to get here. And so, um, I think that allows us to really have a firm grasp on how to take things to next levels. 'cause we know the foundation of the levels that we've created.

Right. And having met your staff and also, um, your lead coordinator, I, I think one of the very interesting thing I've noticed is when I first met you and, uh, you, uh, your staff, uh, you're very open about the challenges you've had. Uh, I think there were some active, uh, challenges around, yeah, maybe some staff.

Uh, there was some staff churn. Uh, there were some things, uh, which you would've said we could have built more efficiently. Um. Even with like such a trusted, and, uh, I guess my point is that even with a really strong program and a staffing model, which you already have, uh, it was very clear that she was very much, uh, inundated with work.

Mm-hmm. And there's just a lot of overhead, uh, which was being spent on essentially like, um, you know, tasked, which otherwise would've been spent on maybe patient care or new, new program. New, new, new, new studies, that sort of thing. Can you give us a, maybe your version of how, um, you know, what that looked like?

Like right before, you know, we started working together, which again, I'd like to touch on as, as well, but I kind of want, wanna give, give a sense of like, what was it like, uh, on that day when we met and sort of described your, uh, your practice and, yeah. Yeah. We had several, sorry to interrupt, but Yeah. We have, we have several, we had several studies that were rapid fire.

Two to three month to six month studies where the inclusion exclusion criteria was fairly generous in relation to how many patients would have access to these studies, which led to a, a large amount of recruitment in a very short time with overlapping studies that were similar, maybe not in the. Areas that they were being studied within, but um, but, but in their intensity and in their ramp up.

And so at some point. People only have so many resources that they can, they can deal with. I think all of us can relate to that. Like there's no one who's listening or whatever talk about this stuff that doesn't understand that. And that was you. Yeah. Yeah. That's new stuff, right? You've never been tapped out.

Right. So, um, I mean, when we were dealing with. Our secondary, uh, or, or, or our secondary research coordinator having approximately 20 hours of overtime, uh, a week and them saying things like, I don't want this overtime. When can we get a solution? Knowing we needed an additional staff member right then and there, but if we hired somebody new, we'd have to train them and actually slow down our processes.

You then, as a, as an investigator. Start wondering, well, should I not be presenting these options to patients? And that's a disservice to patients, uh, when you come from a perspective of giving patients opportunities to participate in these studies. And so also you want to make sure that you are con contributing to studies well, uh, to, you know, help these products be evaluated fully.

So we just were at, quite frankly, a. A breaking point psychologically, we hadn't yet hit a breaking point physically because my team was spending way too much time, uh, compared to what they should carving into their personal lives and their personal dedication to, uh, the, the, the research program and also to the patients within it, and also to the data and the.

Uh, the, the making sure things were done well, so I I, you know, when you, when you go through that with a program, you never, and you actually care about your team, you never want to see your team in that position. And so, and that just deals with that scalability, man. I mean, you never want to turn down a study because you don't have the staffing for it.

You want to turn down studies. If you don't have the patient population for it, but you never wanna turn down a study if you don't have staffing for it. You never want to turn down or actually neglect to discuss anything with somebody. Give them the opportunity to participate in the study because you know, your staff's overwhelmed.

And, and so at times it seems to be that there's a zero sum game between those where, um, really that scalability is the part that puts a ceiling on what you're able to do or not able to do. Yeah. You know, I, and that's where we were when a, when we met, that is definitely where we were. And that, hearing my concerns, some, somebody, a mutual, uh, acquaintance of ours, colleague of ours, you know, heard my concerns and, uh.

That's definitely why you heard those concerns very early on, uh, is because that's how we, uh, that's how we had the opportunity to connect with one another. You know, I just a quick rito, right? I, it's very interesting hearing your perspective where, where you are and because Ike walked in that first time and, uh, you know, fortunate enough to sort of like personally come in and meet you and your staff on that day.

Right. Uh, I have a very interesting perspective. I think hopefully, uh, you know, you see that, so the way I remember it is. We spoke on the phone and I, I immediately was, I was kind of pitching you, hey, there's this whole thing called AI teammates, which we're building and we wanna build this for clinical research.

And I was also very open to you saying we're not there. Where, you know, everything we do is like 100% right. We want, we're building right now, but we want to work with somebody who is like excited about this. Wants to make a difference and can take it somewhere. And, uh, even though what you're describing right now seems like, man, I've got a lot going on.

I shouldn't probably be doing something completely out there. Uh, I don't remember. You like giving me that vibe. You are very much like, dude, this is super cool. Like, yeah, I'd love to like learn more about it. Let's, let's, let's like unwrap sort of like, you know, what's happening. And so if I, if you recall, I came in and we spent like the whole evening, uh, working, talking to your staff with you, um, taking a tour of the facility research program.

And I saw this board where you had like, I don't know, 15 studies, a whole bunch of them act, a whole bunch of them recording. Yeah. And uh, I was just looking around. I'm like, wait, but I only see two people, you know, how are doing all this work? And I remember your, uh, uh, lead, uh, coordinator saying, yeah, like, we're just like, you know, we're all in on this thing.

We do. We take care of all this work, and we, we do it well. And, uh, it was very clear that they're, they're capable, they're very, very capable in taking on a lot of mm-hmm. A lot of work and doing this. But at the same time, I think, like you said, um. It wasn't like, you know, this is fine for the rest of our lives or like, you know, future, right?

We, we do gotta do something about this. And, and, and so there, and then we started talking about it. Um, so I want maybe like, I'd love to hear your perspective on what it was like to sort of hear what Till was building and you knew this was early. We were, we were doing essentially a, a collaboration of sorts where we both were agreeing on, you know, we're gonna keep building, we're gonna keep making it better.

And at some point AI will get there where things are materially, uh, you know, impactful than, than where it is today from an accuracy quality. Yeah. And I'll argue over the last, you know, four or five months of, you know, doing all this, I think we're starting to see quite a bit of that turning out to be true.

Mm-hmm. So what I'd love to understand is how, how was your initial reaction and why, why did you say, like, why did you even like try this out? Because given that you knew we were early, like, you know, uh, like late, late last year, right? Yeah, I mean the reason I'm in, uh, research and you know, is innovation.

So there's a lot of different perspectives that I think people have on ai. I think there's people who think that it will not pan out to what it could be, and it'll fade away or it will not be a part of our lives. And if it does, it may be a part of our lives where we can ask it. Silly questions, and it'll respond to us in a way that's slightly better than what Google can do.

Um, or if I ask it to paint a picture, it'll paint a picture a little bit. That picture may have six fingers on each hand, but it'll do its best. And, and so, and I think there's then the people who are all in ai and that's all they see, and that's all they do. And, uh. I think I come from a situation where I really, truly believe that with the things I've looked into, ai, that AI is going to make a big difference in our world and that it, I also am very aware that AI requires training, uh, and that it requires frameworks that are made by the people who are designing how it's trained, and what information and data that it can be trained on.

And I saw that our problem, though it may not immediately in one second be solved by what you were offering. Uh, it would, working with Tilda would allow us to see what AI was capable of within research on a very practical level, on helping us with our immediate problems that may not immediately lead to solutions.

Uh, but seeing the long game with it too, and knowing that these problems that we had, if we put our heads in the sand and keep doing things the way we always do, did them, it would be suboptimal. I think we'd still be able to get by. I think we'd be able to do things the way they always have been done.

But, uh, if AI has the potential. To change this industry behind the scenes the way, uh, we kind of discussed that possibly it could, that could be a real benefit sooner rather than later to what we were trying to do and what we, what challenges we were facing. Um, yeah. Mm-hmm. Mm-hmm. So, uh, you know, segueing into.

Sort of, it's been what maybe, uh, we've started working with your team, uh, since November. So it's been about three, four months right now. Yeah. Not long. It feels like longer 'cause we've done, I mean we've done a lot, but, uh, but yeah, not have a great time working with, uh, with each other, so, yeah. That's right.

Sometimes that that counts. Time flies when you're having fun and sometimes when you're doing a lot time doesn't fly as fast, even if you're having fun doing it. Yeah. It's super fun to work, uh, with you and your team. Yeah, that's great ideas and yeah, it's collaborative, it's awesome. And, and most importantly, you understand the process, right?

Which is, which is also important. Mm-hmm. Process of development of something new and innovative and, uh, how that goes. So what I was gonna ask you is looking at the, uh. You know, the, the, let's call it before and, and, and current. Um, what's, what's your feedback? Like, you know, how, how have things panned out so far?

Um, I mean, I always say we're on the second innings of the AI revolution, right? So we're not, we're not there, there, but we are, we're certainly not at the starting point. There's several things which are already, uh, happening and we do a good job with, but, uh, I'd love to get your thoughts on what is the material impact?

What does your staff think? What do you think? Um. And, uh, and then I would like to follow up with, you know, what is, what is the feedback? Like where are things where we fall short and or as an industry or our ourselves, we need to focus on and get, get, uh, to a better place. Right? So both like current impact and areas where we can potentially be better at.

Yeah, I think that. I laughed when you said that we've, uh, been working together for three months. I mean, that feels three and a half months, whatever. That feels pretty absurd with, with how we've, what we've been able to accomplish and how we've been able to test this. And I guess that can speak to the fact that we've seen real tangible benefits in such a short period of time.

And I do think that there's a lot more benefits that we can achieve over time and we can go into specifics of that as, as is necessary. I think we're, I think the problem we were having on, uh, scalability and I just gotta close a little email there about another study I suppose so, but, uh, the problem we were having with scalability that was addressed fairly quickly, it did take some extra time for our staff to learn the system and the process.

It took more work on the front end than it would've taken to just keep doing things the way we were. But as we've worked well and it has led to savings of time and it's been able to. Lead to, and I know you had shown me some of the data, uh, was interesting. It's been leading to faster query response times.

It's been leading to a lower number of manual queries that are necessary. Uh, the inputting of of, of things is cut down on time. And so there's been real time savings. And that doesn't mean time cutting corners. It means literally things that. AI on a very basic level can do for us that otherwise we're spending resources, valuable resources of our finite staff doing so.

That's been the benefit. Now, I think one of the things that could be, or a, a challenge that I did not recognize at first is how quickly some studies can, like, get going, you know, and, and start. Startup, you know, and, and how quickly you need to kind of pivot from contract to startup of the study. And it takes a little bit of time to line everything up from a, from a a, in this case we're working with you from an, a ai, uh, assistive research program, uh, to, to help us get everything in line.

And I don't think that's a drag on the basis of what. You are or what the sponsors are. It's just that there's not a habit yet formed of how these two groups, including ourselves, those three groups really, um, can make that process more efficient. And so that's, I think, something that, uh, will be important in the future.

You don't wanna hold up on recruitment 'cause not everything's in line. You also don't wanna get ahead of. Using a system that is AI driven, you don't wanna start doing it without that and then play catch up later. That really, uh, takes the, it creates redundancy of how you're going to do things right from the start.

And so this is the learning that I've found. We've, uh, we've done. Uh, and I'm curious, what are things that you have found. In learning, in, in how we've worked together, uh, and, and what challenges you've seen and also what things you've been encouraged by. Yeah, I, I think there's been several, frankly, I think over the past, uh.

Uh, three, four months. I think we've, uh, learned quite a bit. Uh, we've been very active and busy on our RD and engineering side to address the concerns and, and frankly, there's some very good ideas which came out of our collaboration. Right. On how we can address this. So what you just spoke about is study startup.

You know, um, there is no reason study startups should not be me measured in hours to a day or two rather than weeks. Uh, you know, I can totally relate to what you're saying. It has not been a huge focus when we first started because we had quite a few things on our plate, and that is something we are actively working on and we have, uh, made tremendous progress.

So this is going to be cutting edge in terms of where we are gonna be, uh, in a matter of weeks right now. So in the next few releases, we'll be at a place where things will be instantaneous, right? Like from a study startup perspective, setting everything up, ready to go. So that learning experience was definitely.

Unique in the sense that you have a pretty prolific research program and that is, uh, sort of unique, right? Like, it's not, it's not everybody where you get a study, a CTS signed and you're ready to go. Uh, you know, once you're initiated, you are ready to go the next day. And that's the personality and the.

Yeah, essentially like the bar you hold yourself That is true. Is very, very like different and unique compared to everything else I've seen. Yeah. Uh, in the industry, which is wonderful. Right. I think the world would be a better place if we had like more people and more programs, like how you are describing it.

I'm sure we have many programs like that. To be fair, for any of anybody who's watching this, you know, somebody's watching this right now and they're thinking. I mean, none of us surgeons are competitive. You know, none of us surgeons are competitive. I'm sure there's someone watching this right now and being like, man, I'm, I gotta be better than this guy.

I gotta be. But, but no, I mean, I think we should hold ourselves to high standards. Yeah. Yeah. I, I, I mean, I'm saying relatively speaking Sure. But you know, sure. You asking what is, what was unique and.

And then we started realizing, you know what, we, we, we gotta, we gotta get there. You know, we gotta get there quick to, to meet with that sort of expectation. Yeah. And uh, that means going back to my engineering team and going back to product and sort of, uh, you know, look at our backlog and see what, what is it that we need to do to kind of like get there.

Right. And, uh, so that, that was super interesting. Uh, another set of things I think, which are really fascinating is ophthalmology as a research like vertical has some very interesting problems. Uh, like for example, you and I spoke about how ie criteria matching for certain types of studies can be much better.

Uh, as in like you can, you can derive insight from a recruitment perspective, which, uh, could potentially save a lot of time, can help the patient ma be matched to trial. I think there's a bit of that. Uh, on the patient id part, uh, perspective. Uh, we've also learned a lot about how your staff's current procedures work.

So that's, that's every, every practice has something unique in that sense, like the workflow itself. And so we've, uh, we've like learned about how reg documents get done in your, in your practice, how finances get managed, how stipends get managed, how is patient communication managed. Yeah. And these are all sort of nuanced, right?

There's small changes which we have to be okay with, and we have to train our AI teammates to say, well, Dr. Fox says program, this is their SOP, and this is the style in which they work. So instead of being like. This is what we do and this is how you gotta go. Allowing some amount of like trainable team in the, in the process upfront.

That was another learning for us. Uh, and so we had to build some, uh, tooling so that we can allow that not only for you, but for any anyone else who we're working with. Right. We're working with multiple sites. So, so I think we've learned nuances about ophthalmology for sure. We've learned nuances about site practices and, uh, investigative preferences.

Um. And then just from the fact that your pro, because your program is really prolific, uh, we've learned a whole bunch about how, um, like where you hold yourself to from a standards perspective. And I think all that kind of goes back into our day-to-day because we, we, we, we love working with, um, uh, this sort of a practice because there's just so much to learn and so much to improve on.

And I'm sure like once we build it, then, you know, everybody benefits. Right? So it's, uh, yeah, a hundred percent. Yeah. I think, I think you mentioned something like, we have worked together. And, uh, I think right now AI isn't such that it can figure everything out on its own. And so having that responsiveness on you guys' side.

We're responsive to things that we need to be better. Uh, and then your guys have been very responsive to figure out how that is. Not just immediately, but sometimes it requires some conversation. Like, why is this? What are the things about it? You know? And it's helped us look at our own processes as well.

Some of the questions you guys are having, uh, for us helps us look at things in a different way. And so AI is not good enough right now. To take out collaboration with humans, and it is the collaboration with one human to another. But make no mistake, you are gonna need to, if you're, if you're a growing practice or if you're a growing, uh, research program or if you're a growing company, I mean, this is a business thing.

You're gonna have to be training somebody at some point. The more you grow, you're gonna be training a lot of somebodies. And so, um, right now we're working together. We're training one another, and you are in control of like, training the AI to, to learn. We're not gonna have to train AI again, we'll be modifying how we train it, but if someone moves along from our practice, uh, uh, you know, they have another opportunity or they go elsewhere or retire or what have you.

We will always have that training that we put into ai, uh, behind us, and we'll be growing off of that. I fully do not anticipate artificial intelligence to replace our research team anytime soon and soon. I can use hand gestures to say not anytime soon. But what I can do is I can keep our core group small and manageable and more nimble and more, uh, able to take on challenges, have less bureaucracy that can occur when you get larger and larger systems.

I, I haven't experienced that in research, but I certainly have experienced that to some degree. As a practice grows, you, you get into that ult, uh, um, uh, at some point or another. It just is how it is. So I can keep my group of trusted people small while training a, a, a partner that will not go away, whose resume grows by the inputs we put in it.

Um, and so while maintaining a staff and quite frankly, being able to retain a staff better, who is not so stressed out, who they have somebody to fill in to chip in on certain things and take things off their plate. So this is the, this I think is the thing that I would say for people who are either questioning how AI could help, doubtful that it might be able to, or they don't wanna put the time in.

I think recognizing that you're going to have to take time training somebody no matter what, uh, until artificial intelligence is fully aware and. What is it a GI? What does that stand for? I'm not an expert. What does that stand for? General intelligence. Yeah. Yeah. Artificial general intelligence. Once it can kind of be better than humans from the start without training, I mean, boy, we're into something completely different.

Uh, but until then, it is the collaboration of humans who are working with the AI to, uh, to, to get it done. So has it been work to work with you guys? Yes, it has. Would have it been work for us to be trying to train somebody from scratch? Yes, it would. In fact, the work that AI is doing for us has allowed us to train another individual because we have time to do so.

So we had three, uh, study coordinators. Uh, we started adding a, a part-time study coordinator that study one of the study coordinators that was full-time. It just didn't work out and we needed to part ways and we couldn't. Think of adding another study coordinator because boy, that would take too much time training.

So literally the work that AI has taken from our coordinators has helped been able to go towards, um, towards training of a new study coordinator. Uh, you need room to breathe in order to train. And, and that has done that. Yeah, I mean, uh, that's really well said. Uh, it's essentially. The, the, the few things you were mentioning as challenges, right?

You want to get to scale, but then your staff is overloaded and you're trying to solve for these problems. And the better way to perhaps do it as you have your AI teammates, they're doing part of your workflow. And you know, from our perspective at until the, we have been through the research program, I myself have built a biotech company.

I've run clinical research programs as a sponsor. And so having that trust, I think, and building a team which deeply understands research, and then coming to you and saying, you know, we're not just talking technology. We understand the concept and the, and the importance around process for research itself.

And you know, like, you know, tying it all together from core competency and engineering and product building along with core competency in delivering research as a service right in, uh, from our team. I think those need to be there together. Um, our, our idea is that, you know, you can build trust if you are just one or the other.

I think especially in this domain, if you're talking about taking a sort of like a, a, a big bet, right? Where AI is going and you're building something really innovative, uh, you kind of have to have both perspectives, both from a clinician or clinical research perspective as well as from the technology perspective and, um.

Um, yeah, I'm, I'm really glad to hear like what you're talking about. I think that's exactly what we, we have seen multiple times, not just with your practice, but many other collaborations we've had or we're working with right now. Uh, they very quickly tell us, you know, this, this is great because I'm getting a breather for the first time.

I'm seeing my staff actually saying, okay, like, if things are gonna continue working in this direction, I think we'll be in a better place. At least I'll, like personally is great, right. I. That gives you a lot of other benefits. Your staff is happier, you have better retention, you have better trust. Like you, you start to build a much more productive team actually from, from that perspective.

Mm-hmm. And uh, I think one of the things I've realized building, uh, my previous biotech company was that I. Because research methods have been evolving as in like there's new discovery mechanisms, there's new products, there's new vectors in, in terms of what type of research you can perform. Mm-hmm. But the methods haven't changed.

Like you go to A CRO, it's the same exact methodology. You go to a site, same exact methodology. So you do end up running into these pretty big bottlenecks, essentially like large brick walls where innovators on the biotech side will come. And say, well, I have this amazing set of product innovation, which I'm bringing into the market, and then you hit this like brick wall because there is no, no way to, you know, accelerate past that, uh, bottleneck, right?

You have to go through what everybody else does. And I personally think that's, that's, you know, really not, not the right place for us to be as a society. I think, uh, you know, there's so much innovation waiting to happen in, in, uh, in medicine and uh, um. And, you know, there's only so many great individuals who are dedicated and would love to do great research, sort of like you and your team.

And, you know, we should empower them in whatever we way we can, right? So you kinda have to give them the right tooling and, and bring them there. Um, so yeah, it's definitely like AI plus a trusted team, delivering it all together. Working with companies or, or practices like yourself, um, you know, collaborating, fine, tuning it, and then bringing it into value.

So, uh, last, lemme, lemme say one quick thing on that, if you don't mind. Uh, so it's not like research has been run wrongly. We've found out how it can be run well and I think I. You when, I mean for me, I wasn't thinking about AI as being a solution to our challenge because I had the brick walls around us, you know, and it's not through lack of care or lack of trying.

I, I knew that the solution to the fact that the brick walls were there were to further reinforce those brick walls or to build on top of those brick walls. But when we have the opportunity, and this goes in medicine, this goes in anything, when we have the opportunity to move those brick walls. And I think the second we think, oh wow, we've, we've improved enough.

We don't have these brick walls anymore. We'll run into another set of brick walls. Uh, and it may be that you run into it for years and then, and then blow through those brick walls eventually. But I think right now we have an opportunity to. Move through some of the brick walls that we have rightfully stayed within, but may not be necessary to stay within and still produce quality research.

That's one thing. Another thing I wanted to say is that oftentimes people will call me Dr. Fox. Oh, expert. I'm the expert, I'm the whatever. And this is certainly the, or this is certainly the case, right? This is, uh, for sure the case. But I mean, our study coordinators are experts, man. I mean, they're experts in their domain in a way that.

I'm not even an expert in what they do to the level that they are. And quite frankly, if you have a great study coordinator, they better know more about, they better be getting to the place where they know more and then they need to excel in a way where they know more than you, they should. That's, that's what you should be, uh, developing and, and growing and, and in inspiring people to do and, and, and have people that are capable of that.

But you do not wanna bog down experts with things that, uh, are not necessary for their level of expertise. And so we would bring in temps. Uh, we had brought in temps to do some of this, these lower level tasks as it were. But then that trust factor attempt doesn't necessarily know what they're putting in, or doesn't, uh uh, uh, you know, may not recognize that, ooh, that doesn't sit quite right, or that doesn't make much sense.

Um, and so. To add skill to that level, that lower level of things. And by the way, uh, that skill has not always been uniform in our experience just yet. And we're three months, three and a half months in. So, but we've seen that skill increase over time and we know that, that as we partner together, that AI model is gonna train better and better and better.

And. And we are going to get the point where we have high quality, you know, temps working for us, quite frankly, uh, and allowing our experts to be experts. But yeah, absolutely. I, I, I, I personally think, at least in the domain of ai, there's only one way to go. You know, it's gonna be more and more intelligent, right?

That, that is just the trend. And, uh, we have not hit a brick wall just yet in terms of where that, that ceiling is. So it's exciting. I think things will get materially better. Um. In the near future. And, uh, and, you know, this is all going in just one direction, in my opinion. Um, so I think my, one of the last, uh, areas I wanna discuss is, let's just say, you know, everything you and I are envisioning comes together or comes true, right?

Like, in terms of where AI can go. We have AI teammates, which are, uh, not only doing, uh, you know, in, in your case we're doing data regulatory finance. Mm-hmm. Maybe they do more, maybe they do more workflows within each of those categories. Where do you see things going? I mean, not maybe start with your practice, but I also would love to understand what what do you think will happen to the space?

Because you've been in research in ophthalmology specifically for the past 12 years. Yeah. And you've seen where it goes. You've seen the pace and you've seen how innovation happens in the industry. But how, how do you envision things will change? Like is the next 10 years gonna be about the same, will be different?

Like, and and what, what's your thoughts on that? Paint a very broad picture. Boy, very broad picture. I mean, I'm not an expert at AI like you are. You know, I don't, no, no. I'm, I'm asking you. No, I know. If AI were there, what does that mean? From a clinician's perspective? Yeah. From a clinician standpoint, I, like I said, I don't have the experience you have.

I've just, I'll tell you, I've used chatt PT since rather early, and it's a hell of a lot better at. Helping revise some emails of mine that might come across a little aggressive. So I'm making fun, kind of mocking myself in this and, and, uh, and, and, and so now, uh, it can be a, it's become a real help. It's not a cheat code.

It, you still have to be yourself, but. I think that where AI has the potential to go is, is kind of absurd to what Scap, I think to to, I mean it's fair, I hear you rom to ask me what I think is gonna happen over 10 years of ai, but I think I. AI six years from now will be able to tell you where it's gonna go over the next four years better than any human being will be able to.

So that is su that is super, super, uh, very interesting comment you made. Yeah. Yeah. I mean that sentence right there, insight level, right? That, that sentence right there. Yeah, exactly. Like, uh, where does a caveman think that it's go, that we're gonna be in society in tens of thousands of years? I mean that in 10 years, things could be a whole lot different.

But I think my goal biting off bite-sized chunks, I, I mean, quite frankly in our entire thing, I mean, I think in our whole industry, in in what devices are available, in what, um. Basically, I mean, I don't know. I can't even, it's, it's gonna expand far more than research, of course, but to take up a bite-sized chunk of this for what I see it doing over the next six months, nine months, one year for us, because I think it's hard to project out where AI is gonna be, uh uh, with how.

Nearly limitless things appear. It could be at this time. Uh, I think that where I see it is functioning on the level of, uh, one and a half to two study coordinators. And it will function as more study coordinators. The more busy we are and it will function as less study coordinators, the less busy we are.

So it will help us with the scalability. Uh, I'll be able to keep my. Group tight and small and nimble in learning how to do things better on the areas that we're experts in, and I think it's going to first do all our menial work and then it's going to continue to grow. I don't think it's gonna replace my team, and I think there's fear.

I think part of why AI is not looked into is the fear of what might come. When will I, Dr. James Fox be replaced by ai? And, uh, it's coming whether we like it or not, or our fears are there or not. And so figuring out a way where it can work in conjunction with the, the human beings that are the experts in your program.

That's probably what I'm most looking forward to is we've been three months. I'm looking forward to seeing. How our human to human collaboration will create a stronger AI partner that will allow our, uh, employees who are research coordinators to be more self-actualized in their ability to do things that they quite frankly have not been had time to do, uh, because they've been bogged down by other things.

So many different places. I mean, one thing about our, there are some study centers that do mi data mining and look at, uh, look for patients. We don't, we literally look for patients in the room that we are doctor to patient. I'm looking at the criteria of a research study and knowing that and then, um, and then having a conversation with the patient in that moment that we just don't have time to assign to our study coordinators, even though it's an effective tool to.

To do that. We got work to do. We got, we have things that we need to do. And so I, I wanna ask, it's just gonna, if it's just gonna, I think our functionality and our, our scope of what we're gonna be able to do as a research program is going to extend far more than what it has already. And, and I do think we're a fairly successful research site and for us to see, see that we could grow ideologically.

As quickly as we potentially could over the next six to nine months. I mean, that's, that's exciting. So, 10 years, I have no clue, dude. Uh, but six to nine months I think I have a pretty good idea as to where I want to see things going. Yeah. Yeah. And, and something you pointed out is interesting, right? Which is from your perspective, your team can grow, you're taking on more work.

Um, but at the same time, I think. If there were more programs like yours enabled, I also think it'll have a, a net, a really big net positive effect on medicine. At least that's my goal with building. Hundred percent. And, um, you know, another way to put it is when we look at your, uh, program metrics, how efficient things are, how you are, you know, you're consistently top one of the top performers on, on many of these studies.

And we're talking about from a quality perspective, consistency perspective, um, patient satisfaction, like various metrics, right? Mm-hmm. I would love to have like the world have, you know, 500,000, 10,000 of such like research programs because I do believe there will be that amount of volume of medicine or innovation coming down the pipe.

Uh, it's only, to me, it's only a matter of time if we are, you know, if we are building foundation models, which can answer core biology, basic science questions, and so on. Mm-hmm. You know, you are expanding the scope of how many innovations are gonna come down through the pipeline. And ultimately that means having more prolific programs like yourself, being replicated, being assisted by teammates like ours, like AI teammates will be a huge net positive for the industry, right?

For and, and for, for frankly everybody. For all of us. Um, so I, I think it's, uh, uh, I'm, I'm really excited. I think that, you know, what we're showing in, in metrics at the moment, uh, it's early, but like I said. It's second inning. It's not, we are not not playing the game. We are, we are in the game for sure. And, uh, um, yeah, I'm, I'm super excited to see where things go and uh, uh, how we can basically bring what you're talking about.

Yeah. Innovation is a passion of mine, and I look to this as a, I looked first, we, our first contact was due to a need that we had. And it is mo it morph very quickly into seeing how this could be beneficial to more than just Icon Eyecare, Dr. James Fox and the research coordinators within, and the, and other investigators within and so forth.

I mean, quite frankly, honestly, I do consider this a privilege to work at this level of where things are. I, I really, truly do. I, I think that. I, I don't particularly care that I'm remembered for anything. I don't really care, but I do do think it's a privilege to be in things so soon and to help be part of a solution that could make such a big.

Difference over time. And, and if it's just you and Rom uh, you and I rom over the course of our lives, uh, that's a shame, uh, because, uh, exactly that we need to make this be, have a larger scope of impact, you know, but it is a, it is a privilege to be a part of it, the early start on things. And I look forward to more and more people adopting.

More and more people adopting, quite frankly, will train the AI models better so that my AI model works better. So I, I do look forward to adoption of this, um, on a altruistic, like, I'm excited for what it can do and on a selfish level as well, I suppose, uh, in, in the fact that that will train AI models even faster.

So, yeah. Yeah, no, absolutely. That's like, uh, like I said, it takes a village. So we we're certainly, uh, getting there. Um, great. Well, thanks for your time. Uh, I think we're good. Um, so yeah, thanks so much. I really appreciate the opportunity to chat and have a good time here. Yeah,