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From Evidence to Adoption: How datosX Is Redefining Digital Health Validation · StartUp Health NOW Podcast

• 0:00
  [upbeat music] Welcome back to "Startup Health Now." My name's Unity Stoakes, co-founder of Startup Health.
• 0:13
  This is the podcast where we get to have deep and meaningful conversations so we can get to know the change makers, the entrepreneurs, and innovators who are transforming health. Stick around.
• 0:24
  It's gonna be another great show.
• 0:26
  [upbeat music] Hi, my name's Unity Stoakes, co-founder at Startup Health, and today is a good day because I am here with health transformer extraordinaire, Robin Roberts, the CEO and founder of DatosX. How are you?
• 0:45
  Been good. It's good to see you again, Unity. It's great to see you.
• 0:48
  I thought we would start today's conversation learning about you, and then of course, we're gonna jump into the genesis story of DatosX, and also really what you guys are doing is quite extraordinary, so I wanna dig into the meat and bones of it because I think the world needs to know about your solution going into 2026.
• 1:09
  I think it's gonna help a lot of digital health companies that are looking to validate. But before we get to all of that, let's start with you. Tell us a little bit about your background. Where are you from?
• 1:18
  Where are you based? I know the answer to all these things, but I want the world to know too, and tell us a little bit about how you started your career as well. Sure.
• 1:27
  So, uh, technically I'm based out of Oakland, California. I was born though in Dominican Republic, so quite a bit, a bit away out in the Caribbean, and I moved over to New Jersey, the New Jersey/New York area.
• 1:40
  You have to be specific about that for New Jersey people. You can't always be [laughs]... There's a big difference between South Jersey and North Jersey.
• 1:45
  I'm not gonna say which one's better, but I'm from North Jersey, and spent the majority of my time there. I actually started my career
• 1:53
  initially at a company called Schering-Plough, where I was, my first job was as a molecular biologist.
• 1:58
  That was my first job out of, out of graduate school, and I worked in the labs like every other good molecular biologist.
• 2:04
  And within about a year I learned pretty quickly that it wasn't necessarily what I wanted to do for the rest of my life, and it was an interesting,
• 2:14
  it was an interesting awakening simply because I had spent so long training to become a molecular biologist that when I actually started doing the job of a molecular biologist in a lab, I, I realized pretty quickly it wasn't necessarily for me, and I decided to move on to clinical research.
• 2:30
  And I went over to Merck, where I spent the better part of a decade, first learning to manage large phase three clinical trials across a couple of different therapeutic areas.
• 2:40
  And by the time I left, I was leading and helping to design some of those large phase three clinical trials.
• 2:46
  I then went over to Novartis, where I spent the majority of my career, 16 and a half years in total at Novartis at roles of differing levels of increasing responsibility.
• 2:58
  The latest role was as a chief operating officer and co-founder of the Novartis Biome.
• 3:03
  Just at the beginning of- And that's where we first, I guess we met maybe a little before that, but you as the founder, one of the founders of the Biome, really an extraordinary initiative that was very impactful to the industry and the ecosystem, but also you were a great partner to StartUp Health from the very beginning of that.
• 3:21
  Yeah, absolutely. I can recall those first days when we were putting the Biome in place, and you and I had just met.
• 3:26
  We were-- We actually had our first Biome going up at that point in time, which was the Biome San Francisco.
• 3:32
  A- and, and then we had the good fortune of meeting, and I'd learned about StartUp Health, and I immediately was like, "We've gotta be a part of this somehow."
• 3:38
  So we started working together, and by, i- fast forward a few years and we had 13 different Biomes in 13 different countries all over the world. So it, it grew like a weed.
• 3:47
  It's still in place today and still is Novartis' extension into the digital health ecosystem. And it was while you were at the Biome and Novartis that the genesis of DatosX really came to life. Is that right?
• 4:01
  Did it kinda, it started there and spun out, or what's the genesis story of DatosX? Yeah, that's pretty spot on.
• 4:08
  So in essence, the model that we use within DatosX, which we'll go in- I'm sure go into detail at some point in time, was started and, and really refined while we were in the Biome.
• 4:20
  And at the time it was called the Evidence Lab inside of the Biome, and we had, we ran that for several years and very successfully had several industry articles written on it.
• 4:29
  I think a piece in MM and some other folks wrote about it as well. And it, it really ended up being something that we had created out of a sheer necessity.
• 4:37
  What we found was that in the majority of scenarios where M- Novartis wanted to utilize a digital health technology, there was almost a innate requirement that we use that technology in a different use case than what it was intend- originally intended for.
• 4:54
  It just ended up being the way that it worked out for each one of the technologies we wanted to work with.
• 5:00
  And we had to figure out a way to be able to collect the data that we needed to understand how well these technologies worked or how well they didn't work in the, in the use case that we wanted to utilize them.
• 5:11
  And we of course, would go to the companies and we would ask them the direct question, "Do you have data that shows that this wearable works as well as in cardiovascular patients as, as we want it to work in oncology patients?"
• 5:24
  And they would almost always tell us the same answer, which was, "No, we can't run a study for every possible scenario for which our technology might be used," which is honestly a fair, a fair statement.
• 5:34
  So we need to figure out how are we gonna solve this problem.
• 5:36
  The companies, the digital health tech companies that have these technologies don't have the resources and ability and time to be able to run these studies themselves for every scenario where we might use them,
• 5:46
  and we as a large pharma company aren't built to run digital health technology focused trials. We're especially not built to run trials in a cost-effective and time-efficient way, which is really what we needed to do.
• 5:57
  And that was the birth of as I, what I called, what I mentioned earlier, of the Evidence Lab.
• 6:02
  We ran that for several years within the Biome and, and decided that this might actually be an even more impactful model if we were to take it out of the broader Novartis sphere, and we actually spun out into our own company as of October of 2023.And I'm so glad you did because it's something the whole ecosystem needs, which is if you're a digital health company, one of the first things you need to do is validate your solution, and we all know it could be very expensive, very time-consuming, and very complicated to go on that journey.
• 6:43
  So you spun out of Novartis, you launched Datosax, and tell us about the kernel of what Datosax is.
• 6:51
  I gave a leading kinda indicator there, but what's the problem you're solving today, and how do you describe your solution to the world?
• 7:00
  Yeah, so the problem we're solving today is very similar to the problem we designed the model to solve initially, which was how do we execute a digital health tech-focused trial in a cost-effective and time-efficient way?
• 7:12
  And w- the way that we solve this problem, keep in mind that at the time we were within Novartis, so we had a different environment that we were working within, obviously different perception from others as we were contacting them, especially healthcare systems.
• 7:24
  We had to design a model that made, uh, sense in terms of a value proposition for healthcare systems to wanna work with us and for digital health tech companies to wanna work with us.
• 7:34
  The digital health tech company side was not that difficult to be able to develop a value proposition for. It was more so the healthcare system side. And the reason we wanted to work with those healthcare systems
• 7:44
  is simply because they had access to what we didn't, which were the patients and healthcare providers that we needed to engage with to understand how well is this technology working, how well is it doing what we're expecting it to do for either our patients and/or our healthcare providers.
• 7:58
  And so the model that we put into place was the following.
• 8:02
  We went to healthcare systems and we said, "Look, we understand that innovation," and keep in mind, this was two thousand sixteen, 'seventeen, so times were a little bit different then than they are today in terms of how ubiquitous digital health technologies are in the healthcare industry.
• 8:17
  We knew at that time that healthcare systems were interested in incorporating innovation.
• 8:23
  They were interested in finding the innovations that were making their healthcare providers more efficient, that were making them better at their jobs, better at caring for patients, that were making the engagement with patients better, a number of different things.
• 8:35
  But they had a simple problem that was a common denominator across all healthcare systems, and that was that they had a limited amount of funds to be able to test any one or two technologies per year to know which ones are the ones that are working well within their system, that their healthcare providers like, or that their patients likes.
• 8:55
  So they had very few bullets to be able to fire in, uh, in any one year, depending on their overall resources. And we said, "Okay, look, how about we do the following?
• 9:07
  How about we work with you as a healthcare system," keeping in mind, again, we're part of Novartis at the time.
• 9:13
  "We work with you, healthcare system A, and we make it our job to understand what are the innovations, what are the priorities that are important to you, what are the technologies that you wanna test, what are the solutions you wanna solve with those technologies, or rather problems you wanna solve with those technologies, and we bring to you only the ones that fall within the scope of work or your priorities for your particular organization.
• 9:40
  And then we give you," the healthcare system A in this scenario, "the option to opt in to any of the projects we bring to you."
• 9:48
  What that did is it gave them, it, it gave them the ability to retain the control over whether they were running a study that was aligned with their priorities or objectives, and they were the ones that would make that decision.
• 10:01
  So whatever project we would bring to them, one, they knew that we would run it across a filter that they had provided for us that told us, "This is probably something that's of interest to healthcare system A," and then they had the opportunity to say, "Okay, yeah, I'm interested in this.
• 10:14
  This aligns with my objective. This is the kind of technology I would have wanted to test anyway." Then on top of that, and what made this a really sweet deal for them, was that we would say, "Look,
• 10:25
  we're going to reimburse you for the expenses that you incur in executing this study, but you get to see all of the data that the study creates since you're running it, and you get to make a unilateral decision around what you want to do as a result of that data, whether you want to do a commercial deal with this company, whether you want to co-develop something with this company, or whether you wanna say, 'Hey, it wa- we thought it might solve a problem for us.
• 10:50
  It didn't necessarily do that. We've decided to move on.'"
• 10:53
  So now suddenly what we've done is we've taken a problem that exists with the healthcare system, where they only have a certain amount of opportunities to be able to test certain technologies, and they have to hit right on each one of those in order to be able to solve the problems that they have, and we've created an unlimited opportunity for them to be able to run studies and run pilot studies and understand what's working and what's not working.
• 11:17
  And on top of that, we're providing them with a level of almost business development support and being able to go out, externally face, and find these technologies that are solving the problems that they're interested in solving.
• 11:28
  So suddenly the value proposition made sense for healthcare systems, and they stopped hanging up on me, which is great, when I would call them- Well, you have a big, massive network of health systems now because you've reduced all the friction.
• 11:39
  You've got this great value proposition for that side of this marketplace you're creating. Absolutely.
• 11:45
  Once we got one or two on board, the pitch was pretty easy, and that's simply because it doesn't cost the healthcare system anything to be a part of our network.
• 11:53
  It doesn't-- They can only win from being a part of our network.
• 11:56
  Remember, they get to opt in to any of the studies that we bring them, so they get to decide whether they wanna be involved or not, and then we reimburse them for the execution of that study.
• 12:04
  So it costs them nothing to execute that study.
• 12:07
  And on top of that, we provide all of the, the gap-filling of services that they may need in order to be able to execute on a trial that they may have otherwise said, "We don't have the source data verification capabilities to run this study," or, "We don't have the study coordinator that's needed in order to really provide the oversight here."
• 12:23
  We can fill gaps like that where they can run a study that even if they had an interest in before, they wouldn't have been able to run and really give them the opportunity to advance the technologies that are really impactful to patients and healthcare providers.Now, on the other side, let's move to the other side of what you've created here as part of your solution, because I think this is what a lot of listeners here are gonna really be interested in, which is the world, as you pointed out earlier, very fast-growing world of digital health solutions, digital health companies, wearables.
• 12:56
  Lots of new technologies have entered the market space, and they all need to validate. They need to have that credibility. They need to really prove their efficacy. So describe your...
• 13:10
  I know I've seen some of your materials. You've got this four-part value proposition to how you help them, the digital health solution, more effectively validate their solution. What's that look like? Sure.
• 13:26
  And you're describing it perfectly. It's really a two-sided network, and I've described one side of it, which is the healthcare systems and the value proposition for them.
• 13:33
  But to your point and your question, on the other side lie the digital health tech companies, what we call our sponsors, the folks that pay for the studies that we wanna run.
• 13:41
  So the digital health tech- You were des- you were describing, I think, your, a very important part of the DataSec solution, which is the other side of the marketplace you're creating here, which is for the digital health solution.
• 13:56
  Talk about how that process works and really what the value proposition that you're delivering, 'cause you've created this amazing network of health systems to pair these digital health companies with and match them with so they can validate their study.
• 14:12
  Now what are you doing on, for the digital health companies out there that are getting ready to go to market or going to market? Sure.
• 14:20
  So you can imagine that when we were building this within Novartis, we had to have some serious focus on the value proposition for healthcare systems because it was difficult to even get healthcare systems to pick up the phone for me when I was talking to them about wanting to work with their patients and healthcare providers.
• 14:37
  But w- the beauty of the model that we put into place, which is a two-sided network as you have described, and I've described only one side of it so far, the healthcare system side, is that on the other side, there's an incredible value proposition for our digital health tech companies, what we call our sponsors, right?
• 14:52
  So there are two main classes of sponsors, really three, but I'll describe two.
• 14:55
  One are digital health tech companies that pay us to execute trials for a number of different reasons, and we'll get into those, and the other are pharma companies that will work with us to, to execute trials to solve the very problem that we were designed to solve within Novartis.
• 15:09
  Now, for digital health tech companies in particular,
• 15:12
  we were able to provide a value proposition that not only included the important data that they needed, which you're absolutely right, the validation data, the data that they need for FDA submission, the data that they need to make new claims in the market, whatever it might be.
• 15:25
  This is the lifeblood of any digital health tech company.
• 15:29
  So we're not only able to provide that data, but we're able to provide that, that data in partnership with a leading healthcare system like a Kaiser or Banner or many others that we're associated with, which creates a distinct advantage.
• 15:42
  The advantage it creates is that you're now not just collecting that important data that you might need to submit to the FDA or that you might need to run a new partnership, but you're also w- now also piloting your technology with a healthcare system that, for the most part, probably wouldn't have picked up the phone for you not too long ago, but now are looking at your technology as a potential fix for a problem that they have within their organization.
• 16:06
  Now, keep in mind our model as I described it earlier. The sole reason that the healthcare systems are working with us is because they have an interest in the data itself.
• 16:16
  They wanna know whether this thing works or doesn't work, how well it works or doesn't work with their patients and healthcare providers, because they're trying to see if this technology solves a problem that they wanna solve within their organization.
• 16:27
  So they're, in essence, looking at this as a pilot opportunity to test the technology to see if it's going to do what it is they need it to do.
• 16:36
  And this is the golden ticket for a digital health tech company that is, is looking to not only collect the data that they need, but also create real commercial traction.
• 16:45
  And when we bring these opportunities to digital health tech companies and talk to them about the value proposition of working directly with these healthcare systems as opposed to working with, let's say for a CRO, for example, that is collecting this data through a paid site of some sort, the advantages are clear.
• 17:01
  And the last advantage on top of that would be the fact that if they're submitting this to the FDA, for example, for some level of, of approval, that data coming from a healthcare system is much more valued than data coming from a paid site at a CRO.
• 17:17
  So there are some distinct advantages to our model and the collection of that data in the way that we collect it and the value proposition that we bring to bear.
• 17:26
  I wanna come back to that differentiators to the CRO here in a, in a second. But so for the digital health company, if I've just synthesized this down, you're designing the study with them, your trial pairing with
• 17:43
  amazing health systems, potential partners, and potential future customers, which is absolutely essential. You're helping with the trial execution, and that's the validation part, and then
• 17:57
  you also do something I think really important, which is you help ensure that there's quality data coming out of this study and help with the actual final kind of report. Did I get that right? Where...
• 18:10
  Or maybe correct me where I'm wrong there in terms of what you do for the digital health company along that journey.Sure. A-absolutely right.
• 18:17
  So what we found was that, look, providing just the opportunity to match with the right healthcare system isn't enough. The digital health tech companies, when we're working with them, need a lot more than that.
• 18:26
  And so we provide a full suite of services that really provide end-to-end capability when it comes to the collection of the data, but also where these digital health tech companies wanna end up.
• 18:36
  So that starts with, for example, the development of the protocol.
• 18:39
  We have biostatisticians and, and medical writers on staff that will help us to actually write a, a protocol that is of the same rigor that you would find in a clinical study.
• 18:49
  All of our backgrounds within the company are on the clinical study side, so we, we write protocols with that level of rigor.
• 18:56
  Whether you're submitting it to the FDA, whether you are doing it just for make a claim in the market, whether you're doing it for a validation study, we have that capability to execute.
• 19:05
  Then we match that study with the right healthcare system.
• 19:09
  Again, a healthcare system that wants to opt in to running the study because they have an interest in the technology itself, not solely because they're interested in running a study.
• 19:17
  So once we do that match, now we provide all of the oversight, all of the support that's needed for that healthcare system to be able to execute that study effectively.
• 19:26
  So we provide all of that oversight through the execution of the study.
• 19:30
  Then once that study is completed, we also have, as I mentioned, the biostatisticians, the medical writers on board to be able to execute the writing of the clinical study reports, or, and/or the journal articles that might come from the study itself, both of which we can provide.
• 19:45
  We can even provide support in the development of the FDA submission package that might be needed for that particular technology. So we can provide a suite of services.
• 19:56
  On top of that we can also provide a plan for commercial success for the organization if needed, given the experience that we have within the organization.
• 20:04
  But we provide an entire suite of services that really is an end-to-end solution for these digital health tech companies that are looking to go from a technology that needs validation, maybe even needs FDA submission support, a- all the way through to the end deliverable being that submission to the FDA and/or, uh, the writing of that clinical study report and/or that journal article.
• 20:28
  What's the sweet spot of when a digital health company should be reaching out to you? What's the profile of a company look like?
• 20:37
  I know, for example, there's a lot of companies in Europe, in Israel, throughout the Middle East, that are all looking to commercialize and go to market in the United States.
• 20:48
  I imagine that's a very important growing market opportunity for you because you have the expertise, you've got the process down, and there's a pathway that can help all those companies go to market.
• 21:01
  Expand on that in terms of what the profiles, who should be reaching out to you. What's the customer really look like? Is it the startup, two people in a garage, or is it a little later stage, or everything in between?
• 21:14
  Yeah, it's a great question.
• 21:15
  It, you know, in practice over the last couple of years, what we've found is that our value proposition speaks really well, particularly to ex-US companies that are looking to, to come into the US, whether it's needing to gain FDA submission, FDA approval, and/or to gain commercial traction.
• 21:30
  Our model is in essence built to be able to get you data and also get you commercial traction all in one, so it becomes a really valuable tool for them.
• 21:39
  For y- We also work with many US companies that are looking to do much of the same thing.
• 21:44
  If I had to point to a sweet spot in terms of the type of company that, that, that tends to get the most benefit out of working with us, it's a company that's ready to run a study both, both operationally and resource-wise, and is, is, it has an idea of what that study might look like.
• 22:03
  They don't have to have a study protocol in place. They don't have to even have a defined summary for that study protocol in place.
• 22:09
  All you really need is an idea of what you want the end game to be in terms of what you're looking for, whether from that study or beyond that study, and we can certainly help you build all of the steps that are needed and put them in place and execute them to get you to that point.
• 22:26
  So now companies, startups, digital health companies, later stage companies, they either slog it out on their own, they take the hard way. It can be very expensive. It can be very time-consuming.
• 22:39
  You gotta go find the right partners or systems to do that. Describe, you mentioned CROs, clinical research, research organizations before. How are you different?
• 22:51
  What's your USP compared to maybe the way organizations have to do it themselves or what they do in- instead of the solution you provide? Like, why are you guys better? Sure. So we differ from CROs i- in two major ways.
• 23:07
  One is our model, right? CROs, generally speaking, will have a paid site or paid sites that they go out to.
• 23:13
  They provide the protocol, the sites execute the studies, they collect the data, and they provide that data, which is well and good and has been used for many years to get, obtain FDA approval and do all kinds of data collection.
• 23:24
  Our model incorporates the end user, and in, and in the end, it incorporates a potential client for that digital health tech company in the execution of the trial.
• 23:34
  So not only are we able to collect that important data, but we're also able to pilot this, this technology inside of an organization that is looking at it as a potential opportunity for them to work with them on either on a commercial basis or in a, on a partnership basis.
• 23:52
  The other way that we differ is that we don't just run prospective studies.
• 23:56
  We actually are quite successful at and are running a number of retrospective studies where we are leveraging many of the partnerships that we have with what we call data partners all across the world, not just in the US, but outside of the US as well, giving us access to hundreds of millions of patient lives in terms of EMR, EHR data.
• 24:18
  We're able to utilize that access to really find the best fit for our clients when they're looking for retrospective data, whether it be to test a particular algorithm, whether it be to prove out a, a particular app or whatever they might need to do.
• 24:33
  We're able to not only find the right fit, the right folks that might have the access to that right data, but also at the right price given the, the partnerships that we already have in place with these organizations.
• 24:44
  I like to describe that portion of the business almost like a Trivago type of, of, of offering, where-You could go to individual data partners one by one and try to figure out which ones have the right data that you need, and which ones are gonna have the best quality, and which ones are gonna give you the best price, and, and try to figure out all that out, and that might take you a few months, just like you might be able to go to every airline and try to figure out every ticket and every, every place that they go to, et cetera, et cetera.
• 25:10
  Instead, you could go to a site like Trivago, or you can go- come to us, where we already have existing relationships with the vast majority of these data partners.
• 25:20
  We already have existing relationships that you can leverage to be able to get better pricing from these data partners, and we can help you in understanding which ones have the best data that you might need for that particular project.
• 25:32
  So that speeds up the, the validation process right out of the gate. How do you make it significantly more cost-effective or efficient? I, I believe you're cheaper than a lot of the...
• 25:46
  or less expensive because it's more efficient. W- describe that part of your solution. Sure.
• 25:52
  We tend to be, on average, about five to eight times less expensive than what you would get from a regular CRO, uh, particularly when it comes to prospective studies, but especially when it comes to retrospective studies.
• 26:02
  So on the retrospective study side, it's fairly simple. We have existing relationships that allows us to pass on those savings to our clients. On the prospective side, it's actually a bit deeper than that.
• 26:13
  You have to keep in mind the model that we've put into place with these healthcare systems. They're not looking at these as revenue opportunities in running trials, right?
• 26:21
  This, this is not part of the way that they are accounting for their revenue for the year. They're instead looking at these as opportunities to test technologies that they may want to partner with.
• 26:31
  So we are leveraging considerable savings by being able to just reimburse them for their expenses, and then we can pass along those savings to our clients.
• 26:41
  So it allows us to be able to execute these trials, like I said, for five, sometimes eight times less expensive than what a CRO would do because our margins are far lower than that of a CRO.
• 26:53
  You hear that, digital health folks? Trying to speed up your trial, save five to eight times, you gotta give Robin a call here. You're a technology person. You're a data person. We're living in an AI-first world.
• 27:08
  We're entering 2026 and a brave new world here. How do you think about the world of AI and how you're building for this future?
• 27:18
  I know this has really been part of your blueprint from day one, but share with the world kinda your lens on the future and where this all goes, and the impact- Sure... or how you're designing this into your solution.
• 27:31
  Sure.
• 27:31
  So w- the way we think of our company, DatosEx, we think of it, initially it was a service company, and we've transitioned from a service company to a tech-enabled service company, and we'll soon transition into a tech company, and here's what I mean by that.
• 27:45
  Our ability to deliver on our studies has now been made scalable by the development of a platform that we call Nova, an internal platform that we developed, that allows us to deliver on trials with far more efficiency and far more speed than any other platform that could be utilized off the shelf.
• 28:03
  So we've developed that platform ourselves. The AI portion of this platform incorporates a, rather is called Evidea. And Evidea is our- Evidea... Evidea. Evidea. Evidea. So our evidence and validation AI database.
• 28:19
  So think of Evidea like ChatGPT specific to digital health technology trials and trends.
• 28:26
  So if you're run-- let's say you have a digital health technology company and, and you wanna utilize ChatGPT, as we all utilize ChatGPT very often.
• 28:36
  Maybe you're asking it questions about trends i-in a, in the technology space that your offering is in. Maybe it's in a oncology wearable.
• 28:46
  Maybe you're asking it how have other wearables in the oncology space that do what we do obtained FDA approval? What have others done that have led to a, a decline in FDA approval?
• 29:00
  How have others obtained traction with either healthcare systems or payers or whoever their, their model might be pointing to?
• 29:08
  Sure, you could use ChatGPT to get generic answers on that today, but Evidea is desi- not only designed to specifically answer those questions, but it does it by utilizing something that ChatGPT doesn't have access to, which is non-public data.
• 29:25
  So ChatGPT only has access to publicly available data.
• 29:28
  We have access to all of that same publicly available data, but also have specific partnerships that we've put into place that give us access to thousands of journal articles and more of, of data that we, that ChatGPT simply doesn't have access to and can't draw from.
• 29:44
  On top of that, our system is designed so that as if you're that digital health tech company, you can incorporate all of the information you have for that digital health tech company, let's say from your data room.
• 29:55
  Let's say things like your commercial strategy, things like your studies that you've run in the past, things that have worked, have not worked, et cetera. All of this obviously very well secured.
• 30:05
  But you can incorporate all of that so that then the feedback that you're getting from the, uh, the platform itself incorporates all of that into what it's providing to you.
• 30:14
  So you don't have to worry or put in information each and every time as if you're asking it questions. It knows all of this background for your company.
• 30:23
  So it's almost like having a CIO in a platform where you could ask questions about trends in the market. You could ask questions about how-- that are specific to your particular technology.
• 30:34
  You can ask questions about how others have e-either won or lost in your particular space.
• 30:39
  You can really dive very deep into the ecosystem in a way that would've been very difficult to do by just utilizing ChatGPT.Love it.
• 30:50
  Love how you are going AI first, the progression from services to tech enabled to tech. Every company should be really doing this, I think, in the post-GPT world we now live in, so kudos to you.
• 31:06
  And I should mention availability on that. That platform we're actually in the process of building today, and we expect it to be available in the third or fourth quarter of next year. So we're not too far off.
• 31:16
  We're quite a few- Of 2026, that'll be- Of 2026, yes. Yep. Third or fourth quarter of 2026. So we're not too far off from that first version being available.
• 31:25
  It's expected to be a subscription model, but for those that, that, that come on board early, we'll likely have a beta version that will be available, and we'll be able to leverage some considerable savings for those folks if they're able to co- become the primary or initial testers for us.
• 31:41
  Okay, so calling all beta users here, get in touch with Robin Roberts at DatosX. I, I believe the URL is DatosX- D-H-L... D-H-L.com, and if you just Google DatosX Digital Health Labs, you'll get right there.
• 31:58
  What's your biggest surprise as an entrepreneur shifting from the lab to the big, massive global corporate to now building as a, as an entrepreneur, innovator yourself?
• 32:18
  What's been your biggest, most surprising lessons learned? That's a great question.
• 32:24
  So I look at everything, particularly the events that have occurred throughout my life, as kind of stages and things that needed to happen in order to get me to where I am today and hopefully get me to where I wanna be tomorrow.
• 32:35
  I think when I shifted over to big pharma, and I worked across several big pharma organizations, what I learned was that there were a lot of processes in place to make things work, and that processes are actually important in order to be able to replicate something in a somewhat efficient manner.
• 32:53
  But what I also learned is that when you have these large companies that have these very specific SOPs and processes that need to be followed, it makes it far less adjustable, particularly to changes in the broader environment.
• 33:09
  And AI is a fantastic example of that.
• 33:11
  AI has, over the past five years, has completely changed the way that not just we work today, but the way that, that, that we, healthcare is delivered, and likely will change the way healthcare is delivered and the way we work even more as we move forward.
• 33:25
  And being a part of a smaller company, of a startup in particular, you can see the level of agility that you really have to make these changes as quickly as you need to ta- change them to adjust to an ever-evolving environment.
• 33:41
  And there's good things and bad things to that.
• 33:43
  Obviously, I very much loved getting paid on a regular basis when I [laughs] was, when I was in, in big pharma, and, uh, that's a little bit harder to do, especially at least in the beginning of a startup.
• 33:54
  But the, that flexibility in your ability to, to be creative, to solve these problems, and then see that creativity become reality and actually impact people's lives is something that, that no bonus can, can make up for.
• 34:13
  So I've enjoyed every part of it. That's awesome, and the work you guys do is an important part of the process of getting more innovation to market and validating that, make sure the best solutions- Yeah...
• 34:24
  are out there more quickly. Every day counts when we're talking about people's lives here.
• 34:29
  Um- And I think there was, when we first put into place the Evidence Lab, my goal in creating that was thinking, how can I ensure that the voice of the end user, the healthcare provider, but especially the patient, is incorporated into the development of the new technologies that are coming out?
• 34:48
  Because what I saw often was, were that there were great te- pieces of technology that were made available to pr- to healthcare providers and to patients, but sometimes they were missing the mark on solving the problems that these folks actually had.
• 35:01
  And it made me wonder, why didn't you just ask them? Why did you develop this kind of in a silo and then hope to hit the mark for them as opposed to just involving them in the process of this development?
• 35:12
  And that really led to the creation of the first model of the Evidence Lab, and that was the reason why I thought to myself, working with healthcare systems who have access to these folks, who are the, in essence, the end users of these technologies that we're bringing to bear, that's gonna be the way to incorporate the voice of these individuals so that we can make sure that the technologies that get advanced are the technologies that are actually solving the problems for healthcare providers and for patients.
• 35:40
  Fantastic. Thanks for the work you're doing.
• 35:42
  We, we want the innovators out there building that need to validate, the big health systems that wanna get in on this, reach out to Robin Roberts at DatosXdlh.com, and let's speed up some of these innovation cycles.
• 35:55
  Thank you, Robin. Thanks so much, Unity. Appreciate it. [upbeat music] And finally, an invitation to all of the extraordinary health transformers.
• 36:08
  Whether you are a startup, someone building the future of health, or whether you are an investor, funder, or buyer of the solutions out there in the marketplace, this is an invitation to you to join the Startup Health member community.
• 36:23
  Learn more at startuphealth.com. Look forward to hopefully seeing you soon. [upbeat music]