From 30 Days to 3: How IgniteData Is Rewiring Clinical Trial Data with Zach Taft

0:00Good morning, Tim. Good morning. It's good to see you got a little scruff going today. I like it. It's good luck. Yeah, I'm definitely going to shave it before the day gets too far ahead. I'm working in my office this morning. I get up pretty early and I've started hearing some creature in the ceiling above me, but I'm in the basement. Something is living in our floorboards and it sounds pretty big. I'm kind of scared of what I'm going to find, but it's definitely nocturnal because I only hear it in the wee hours. Do you know what it is? It's got to be a mammal of some kind. It's big enough. It's definitely a multicellular organism.

0:35It's a complex species. That's the worst. I'll keep you posted. Yeah. I want to see, like, on video, the mouse that ends up in your trap. Yeah. This sounds bigger than a mouse. We'll find out. Oh, man. Stay tuned on the next episode. I'll talk about it. I'll plop in, man. All right. Let's do it.

0:58welcome to health tech remedy this is the show where physician leaders and health technology break down the stories behind today's most innovative health care companies and we speak with the leaders building the future of medicine i'm trevor royce i'm an oncologist i'm a researcher with experience in real world data informatics and artificial intelligence and i'm joined by my co-host tim shellalter a radiation oncologist entrepreneur and health tech focused physician currently focused on bringing AI technologies to workflow within healthcare. And today we're lucky to feature a very interesting company, Ignite Data, using technology to transform the clinical research process, which we know is quite a clunky one, Trevor.

1:38Yeah, this is always kind of a fun topic for us because we've sort of lived it and can see just the incredible bureaucracy and machinery involved in like a large sponsored clinical trial and inefficiencies in that whole process. It's painful. There's got to be a better way. Yeah, I mean, my experience of this would definitely echo what you're saying, that it's a very clunky process. I mean, just to set the context for folks. So we have electronic health records now, but very often the data formatting in the particular way that that data is tracked requires pulling PDFs out of the electronic health record, completing forms directly within a research database.

2:22And it's a very duplicative and redundant system. Of course, for clinical trial data, you need to make sure that it is clean. And so that does require some work. But it strikes me as something that's a little antiquated, that we're still maintaining study reminders and saving PDFs to track the source documents. It just seems so inefficient. Totally. And like, if you think about it, you're already doing all this painful documentation when you care for a patient. A lot of that data that you need for your clinical trials already being put into the system just in the form of your clinical encounter.

2:55Boy, it'd be nice if you could take that data without, to your point, duplicating it and putting it directly into the clinical trial data set from your electronic health record and cut out all the noise in between. Yeah, to me, it's just such a, I think in that step, in the transition step for information, it just seems like there's an opportunity to make that flow of information seamless. And if you do need something in terms of a particular data model, why not just stick it in the upstream in the EHR or some sort of step to make sure that you're collecting the right data at the right point? It just seems like it should all be connected to make sure that you've got, you know, high quality data and that it's consistent and just part of the process overall.

3:36Yeah, I think this is like a kind of core recurring theme that we've had on the show is clinical trials are expensive. And the initial reaction is, well, I don't understand why they're so expensive to run this trial. This is a good example of just how painful it can be to run a trial. You're duplicating the data, you're using manual input processes, you're using paper forms. It's just kind of antiquated and slow and expensive at the end of the day. And some of this, we can get into this, Maybe we can talk kind of more about the sponsor side and how they may or may not be incentivized to innovate in clinical trials.

4:08But a lot of this kind of is a bit of regulatory capture, where the regulatory burden for running a clinical trial in some ways appropriately put in for patient safety and to demonstrate the high quality science for your trial. But it does carry all this sort of baggage that makes it hard to run a trial and expensive to run a trial. That's so true. And I think the regulatory risk is a big concept for folks. And I think if you just step back and think about risk in general. So on the institutional side, I've been part of building budgets from the bottom up. And when you factor in like 30 minutes to consent to patients, you know, 30 minutes for each of the study forms and you figure out how much personnel time is required, you really do end up getting these pretty massive budgets.

4:52It starts to make a lot of sense. And of course, when a site agrees to run a study, they want to make sure that they're compensated for that effort and that they're not agreeing to do something that they're not going to have the resources for. And then on the sponsor side, of course, there's lots of risks. So imagine if it's a registration study and you're a sponsor. One, you want to make sure that the studies run quickly. So you need to have insights about the sites or you need to bring one a lot of sites, which adds a lot of money. and then you want to make sure that the data is in the right format.

5:24And so that can lead you to go with data systems for the research study itself that are kind of clunky and adhere to what's worked in the past. And that kind of sets a tone of not wanting to innovate too much. Yeah, totally. I mean, if you are a sponsor and you're running this very expensive drug trial, a registration trial, you're going to take it to the FDA. the FDA has certain expectations on, you know, how that data was collected and protected and data provenance and change logs and all the rest. You know, why would you take on the risk of working with like a small innovative startup that all of a sudden has found a new or innovative way to do this data?

6:03And sure, it may be easier and cheaper, but in the big scale of things, that really risks that you want to take on for this trial that may sink or launch your company, the biotech side. and yet it is super expensive. So there is some reason for pharma to be interested and clinical trials take too long. And I think, you know, I've certainly on the multi-site clinical trial aspect seen situations where we're going with too much subjective information. So, you know, a well-regarded physician might say, you know, subjectively, oh, I see patients like that all the time. You know, I can enroll 20 patients on your trial.

6:44And so sponsors don't really have the objective data to question that or get real numbers from an institution. And so I do think if sponsors could have more objective data to help them plan sites to go after for a clinical trial and to project how many sites they need to bring on and which ones to make sure that they complete a study on the ideal timeline, it would be worth it to them. And I think that's where Ignite Data comes in as a company. And so obviously they're taking this on and really focusing on this problem to do it at sufficient scale. I think that it becomes potentially more trustworthy to sponsors and has more relevance to the sites as well because it can potentially streamline a lot of their research work.

7:32yeah yeah i mean so i think you articulated well you know fundamentally that's the value prop is clinical trials often fail they don't agree they seem like good ideas at the time they take a really long time you know can we improve all these efficiencies through these novel tools and we'll talk about the solution but you know it helps if you have great partnerships for this sort of thing like say memorial slum kettering cancer center who runs a lot of clinical trials and sort of has the scale and the capacity to say, okay, let's try this in a better way. That's spot on. Well, I'm excited to hear more about their approach.

8:06Their platform is called the Archer platform, which is described as a virtual research assistant. And we're fortunate to bring on their CEO, Zach Taft, to hear more about it. And I think fundamentally, the core product here is basically a data transfer system, taking that data from the EHR, the electronic health record, from the clinical encounter, and putting it directly into your clinical trial data set in a way that is accurate. You know the data is what it is. It's quick, efficient, and cuts out a lot of that layer. Hopefully it makes for a better clinical trial and more accurate data. Well said.

8:38Well, let's cut over to the interview. Let's do it.

8:48Zach Tapp, welcome to the show. We're excited to have you. Excited to be here. Thanks for making the time and inviting me to join. I'm really looking forward to our conversation today. I think it'll be a lot of fun. It's sort of set the stage. You know, we've got the CEO of Ignite Data joining us, Zach Taft. We're really excited for our conversation today. Ignite Data, you know, I think is most well-known for operating at the intersection of clinical research and health system infrastructure and advancing that and the synergy between them two. But historically, those areas haven't necessarily integrated well or communicated well with each other.

9:18And we'd kind of love to hear your origin story and tail that in with the origin story behind Ignite Data.

9:24Zach Taft: Yeah, we'd love to do it. So I've been in healthcare for 20 years, started one of the very first medical scribing companies, and so helping ER physicians look at how they can spend more time with patients and less time doing data entry work. So this is going to sound like a theme throughout my career. I ended up foregoing medical school to integrate front-end voice recognition into the Epic workflow, and so looking at how to make those workflows more efficient, effective, capture high-quality data while allowing clinicians to practice at the top of their licensure. Ended up selling that company to Nuance and then started a division in a company starting one of the very first Ambient Solutions, again, looking at how do we capture data in the natural workflow of how a clinician practices to allow them to focus on patient care and the things why they went to medical school.

10:11Zach Taft: I, after this journey, went back and got my MBA at the University of Michigan and had the opportunity to join Memorial Sloan Kettering and leading its digital ventures program. So looking at how do we bring companies into the institution to allow us to continue to be one of the top cancer centers in the world, while also focusing on how do we help companies provide better services and better technology to cancer centers or academic medical centers around the world. It was while I was there that we got introduced to data interoperability for clinical research. How are we getting data out of a digitally captured system like an EHR and moving that data electronically into the electronic data capture systems and EDCs that are being hosted by big pharmaceuticals and sponsors.

10:57Zach Taft: And we as an institution, we're spending millions and millions of dollars on the duplication of data that are already been captured. And with manual data entry, you're going to have misses and errors because we're human. We make mistakes. You look at so many numbers throughout the day, you're going to miss them from time to time. Also, that data needs to be transformed and harmonized as it goes into the EDC. So incredibly complex. So we started looking at a bunch of companies out there that could do this. And at this time, there were no companies that we had found that were doing this at scale.

11:26Zach Taft: There were a few that were talking about doing it, maybe one or two studies that had used what's called EHR to EDC or eSource to sponsor, but none of them at really any scale. We did find a company out of the United Kingdom called Ignite Data, and we convinced them to flip into the United States and become a part of the MSK ecosystem and incubate and take the product to GA, which we did in 2024. And that's where I left MSK to become the CEO of Ignite Data, which has been incredibly exciting since I joined. Most of the NTI-designated and comprehensive cancer centers in the United States are utilizing our EHR to EDC platform called Archer.

12:04Zach Taft: We're working with many of the top pharmas, several of the top CROs, and we're international. One with our employees across four different continents. And then we're actually providing this technology across four continents as well. Great. And one question just sort of at the outset as approaching this problem, is this primarily like technical interoperability, workflow issues, like structural problems? Like what were the biggest challenges and how trials are run that you're solving for? Yeah, great question, Timothy. So there's a couple of different areas. This seems like a very easy thing to solve for.

12:43Zach Taft: You've got data that's already being captured electronically at sites, and pharma has been utilizing EDC systems for decades. And you would think that just having an API to an API would be, you'd allow this data to move. It's not that easy. Every healthcare system is capturing data uniquely, as both of you know as practicing clinicians. So take MSK captures white blood count in a different format than, well, Cornell right across the street captures a white blood count. But yet, when it streams into the EDC, that data has to be harmonized and has to be uniform. So one, the big challenge is data interoperability.

13:21Zach Taft: How do we get data out of source? Because of TEFCA and recent interoperability guidelines and laws and requirements, that's become easier over the last decade. Previous decades, it was almost impossible to do that. So that certainly has helped. Number two is the way that that data is being formatted at these sites, we have to create uniformity for that. So as the data is streaming out, we need to be able to create a homogenous data set. So again, incredibly difficult because every healthcare system does this differently. And as you know, a clinical trial is not run at one site. It's run at sometimes thousands of sites that are capturing data a little bit uniquely.

13:56Zach Taft: And then the last big one that we focus on is the actual workflow itself. So today, clinical research coordinators and the clinical staff are seeing patients in an environment where the data is being captured into Epic or Cerner or any one of the other numerous EHRs that are out there. They're not built for clinical research. They're built for patient care and revenue cycle management. So a clinical research coordinator has to go into these records today. They need to find the data, whether it's in a structured, discrete, captured format or in unstructured data like tumor progression, staging, ECOG, so on and so forth.

14:31Zach Taft: And then they have to open up the EDC in another window and then they have to do the swivel-trail movement of finding the data, making sure it's in the right format, and then entering it into the EDC. So there's a lot of different areas that Ignite Data is tackling from workflow to interoperability to data harmonization, etc. And so the goal here is a streamlined way workflow that the CRCs are using it. So it's in their environment. Think of Apple Pay, where it automatically drops your credit card and your home address and all that. And you're just confirming that it's accurate and then hitting send.

15:06Zach Taft: That's essentially what we're doing here. And for pharma, they're getting data that is linked back to source. They're getting it faster. So on average, it takes 30 days for data. Once it's been captured clinically to enter into the EDC, we're doing that in less than three days. And then we're meeting the data requirements as it's being moved into the EDC. So certainly tackling a lot of different challenges. And so what seems really easy as a one-to-one, it's actually a mini to a mini. Yeah, that's so interesting. And there's a lot, it's a great response. And I think there's a lot in there. And, you know, a question that always comes up is, well, why wouldn't the main EHR system do this?

15:44Or why wouldn't a CRO do it? But I think in your answer is the crux of this, that this is that filtering point between the EHR, as you described, designed for like filling and coding and the research systems, which are designed for research purposes. There's got to be a connection between those two. And it's ultimately a challenge.

16:03Zach Taft: Yes. And I think the other component of that as well is every hospital system has its own unique EHR. So even on your Epic, a version of MSK's Epic is going to be different than that of Weill Cornell's. And so thinking of, you know, how do we capture that data uniformly? But only about 70 % of data that's used for clinical research is captured in the EHR. The other 30 % is captured in other areas. So even if the EHR is helping harmonize this data or make it interoperable for clinical research, you're still missing those other areas. So labs, if you're doing central labs, it's coming directly from LabCorp.

16:37Zach Taft: So where Ignite Data sits here is we are vendor agnostic. So we're pulling from all these various different source systems. We're creating a harmonized data set, and then we're allowing it to go into the EDC. One of the big challenges that we had when I was at MSK and I was researching clinical research technology was, as a site, you're managing multiple different technology platforms and multiple different EDCs. That's a huge burden for the sites. It's expensive. It's time-consuming. Clinical research IT is typically underfunded at these AMCs, just as a byproduct of clinical research IT. And so having to manage all those solutions is fraught with, you know, cost overspend, etc.

17:16Zach Taft: But now we're seeing the same thing on pharma. If you're having to manage multiple different instances of how data is coming into your EDC, that's no longer scalable. So if you're using an e-source platform that's doing EHR to EDC or e-source to sponsor, you've got to manage that template. And typically you're using multiple different e-source platforms and then you're using multiple different EHRs and then you're grabbing data from multiple different labs. where Ignite Data sits in this is we are that focal point. All those data points are coming in and then they're stringed through Ignite Data into a single EEC template at the pharma.

17:50Zach Taft: So it's creating economy scales, not just on the hospital side and the clinic side, but it's also creating economies of scale on the pharma side. It's a good call, Timothy. Yeah. So, I mean, you've articulated very nicely the incredible scale of the technical problem and would love to hear a little bit. I don't know how you guys were able to sort of solve that and crack that nut. But I think before we get into that, just to take a step back and reflect, I mean, hopefully the listeners can begin to appreciate just number one, how expensive and costly and complicated running a clinical trial is.

18:19And this is like a direct reflection of that. And the overarching problem, which is what I love that you guys are solving is clinical trials are so hard to execute. They're so expensive. It's hard to accrue patients. You put a lot of resources into, you know, starting your trial, opening it, writing the protocol, and then you can't get patients on the trial because it's complicated for sites or there, you know, there's just so many ways clinical trials fail. And what you guys are solving for is such an important piece of that to make this easier and advance care and develop new therapies and support the evidence behind them and ultimately make outcomes better for patients.

18:49So this is like an amazing way to do that. And so I guess to go back to that initial point, you know, how did you guys solve this technical issue amongst all these, as you put it, very different EHR interfaces?

19:00Zach Taft: Yeah, great question. So I'll give the one-minute version of this. So at MSK, we'd been looking at EHR to EDC for a long time. Joe Langfelder, who was the head of clinical research IT or MSK, had actually run a pilot with Yale in 2016 where they had built a prototype of this. And when it's one study at one site, it's actually fairly easy to move that data because you create the form, you're capturing it in a homogenous format and how you're capturing that data and you're streaming it into it data lake. In that format, this was really simple. It's when we added the second, third site that there's no economies to scale there and it broke.

19:38Zach Taft: Ignite Data took a really unique approach. It started off as a patient recruitment company, manually calling patients to recruit them into clinical trials. We don't do that today. We don't do any sort of patient recruitment. So we've totally divorced ourselves from that part of the industry. When 2019, the founders started recognizing that interoperability was going to be allowed. But there are so many challenges in how to access that data, how to format that data, and then how to stream that data into either a data lake or into an EDC. And so they took a small portion of a clinical trial and looked at how do we at least capture the structured data?

20:14Zach Taft: First, how do we capture that data and pull it out of Epic? So they partnered with University College London Hospital, and they partnered with AstraZeneca. And so we took very small bites of the apple. If we can do one thing really well and it solves for 20 % of data capture for a clinical trial, that's meaningful. Is it a true ROI that sponsors could have paid for long term? No, absolutely not. But you become really good at the little bites. So then we started expanding our data domains and we started expanding to other hospitals like Berkshire, also in the UK. And then we came into Duke and then ultimately came across to MSK.

20:49Zach Taft: At MSK, when we took a look at it, it was promising. but it was very much an alpha product at that point. It was not set up for scale. There was a lot of human data points that had to be adjudicated just to even get the thing installed. That was number one. And then number two is the business wasn't really set up to scale. So what if you sold a thousand studies for Archer to use? How are you gonna deploy it at all these hospital systems? So part of our agreement with Ignite was to also bring in operators, people who have scaled businesses from not just zero to one because the founders had done that, but from one to a million.

21:26Zach Taft: And I have a deep background of that and so do a lot of members of our team, which has been incredible. So we took that within MSK, we ran IITs. We were very data-driven of what are we solving for? What's the impact it's gonna have? What's the ROI to the hospital system first? And then we looked at the sponsor. Most technologies in this industry, because they're sponsored by the big pharmas, are focused on what is the ROI for the pharma, not for the ROI for the academic medical center. So we did take a little bit of a different tilt there because if it's not solving the problem at the academic medical center for the clinical research coordinator, there's a very high likelihood it's not gonna be adopted.

22:05Zach Taft: And so we did take that site-centric approach first and then looked at solving it for the sponsor. And so in 2014, when the product became GA, it was in a position where we can scale it operationally. It was driving enough of an ROI and the data was capturing and streaming into the EDC that pharma would pay for it. and then it was actually improving the way that clinical trials would be conducted. So reducing queries by nearly 90 percent, time to data went from 30 days to less than three days, and CRC satisfaction went through the roof. We see about 4.5 to 4.7 out of 5 in terms of user satisfaction of Archer at this point.

22:41Zach Taft: So all those different things allowed to take us from something that's been tried multiple times and for decades in the industry to something that now has enterprise agreements and is BAU with some of the top pharmaceuticals in the world. And again, it's being used across border continents. That's fantastic. And when you're talking to the IT systems of hospitals and the decision makers there, what does that deployment model look like? What's the playbook for putting resources together and managing that change at a health system? Yeah. So background of installing voice recognition technologies into Epic and other RCM platforms into the EHRs and working with IT teams at hospital systems.

23:24Zach Taft: So when installing digital healthcare technology, there are massive teams at these large healthcare systems that are involved with Epic and the RCM platforms. And you know how to go through the procurement lifestyle and how to work with the VPs of IT, the CMIOs, the CNIOs, the chief digital officers, et cetera. A lot of that is very similar for the clinical research IT side of the house, but on a much smaller scale. And so when we interact with them and we were designing the product, it had to be something that was easier to deploy. If you were to go to the chief digital officer and say, hey, I need five resources for a year to install the Ignite Data Archer platform, that chief digital officer is probably gonna laugh you right out the door.

24:04Zach Taft: And I've witnessed that with other technologies throughout the years. So we intentionally designed Archer to be very straightforward. We do most of the work on the Ignite Data side, But we do need a fractional Epic resource when we're installing an Epic or a Discerner to help match the codes up and to make sure that we're doing that initial install, meeting the coding requirements. So it's a very light lift, takes only a matter of minutes to pull it off the marketplace and get it installed. And in that first mapping, we're aligning the various different codes so that we're stringing the data accurately and able to transform that data in flight as it goes into the EC.

24:40Zach Taft: So incredibly, incredibly lightweight for initial install, incredibly lightweight for the first study. And then for the clinical research coordinators, they go through very light training. It takes less than five minutes. It's right in their workflow, so it's easy to use. And now we're starting to see where CRCs are prioritizing studies that are on Archer over those that are being manual. Ah, that's a nice value add. I'm curious, in getting the final approval for a yes to implementation, who are typically your main customers for moving things forward? Yeah, so customers, I'm assuming internal to the hospital system, it's usually the clinical research operations team at these healthcare systems.

25:24Zach Taft: So it can be a director of clinical research. It can be a VP. it typically is not getting into the VP of IT or to the chief digital officer, chief medical informatics officer. Typically, that's not where it's going. Most of these clinical research teams at the large AMCs have a team that's at least partly dedicated, if not fully dedicated, to these systems. So typically where we go through is, does the technology actually work? It's always the first question because they've been talked to about this technology for over a decade. So does it work? Always the first question. And then number two is what's it going to cost in terms of resources to get it installed?

26:03Zach Taft: And when we show it's a very minimal cost, and sometimes we can help offset those costs with our own resources or for a grant. And then through that, we go through the vendor assessment. Vendor assessment can take anywhere from two weeks to six months, depending on the institution. And then once we've gone through vendor assessment, it usually is then a matter of days or a matter of weeks to get the final contract in place. Once the final contract's in place, it takes maybe about a week to get it installed, and then about a month post that at the first trials, the patient is being put on the Archer platform.

26:35Zach Taft: So typically it's procurement, InfoSec, that is the longest part of the journey. Fantastic. Yeah, I mean, we've really covered a lot of ground in this conversation, which has been great. You know, the problem, the nuance of the technical issues, the solution, the impact. and one thing that we haven't covered is kind of the modern era of AI and how that has either made your job easier or harder. I would love to hear you reflect a little bit on how this modern era of AI has impacted, you know, your business and your solutions. Yeah, so you all appreciate this and I'm sure you get this from your family as well.

27:09Zach Taft: So I have an eight-year-old, we were watching, I think it was Sunday morning, a little bit earlier this year and my son looks at me and he goes, hey, daddy, do you ever work with AI? And I look at him like, that's my entire job. But thanks, buddy. I appreciate it. When his friends ask, what do I do for a living? He said, daddy just works. Cool. Oh, and he talks on the phone for 90 % of the time, which is not inaccurate. So I'm sure you with your kids probably get very similar feedback. So it's been interesting with AI in this industry. And the industry wants to move incredibly fast as an industry, but individually as pharma and as hospital systems, it's very reluctant to change.

27:49Zach Taft: So the desire to change versus the actual willingness to change are two dramatically different things. So for our industry, if you think of structured data that's being captured in the EHR, we can move all that data today. That's very easy. And we can stream it into the forums. But a lot of the really big nugget data, disease progression, TNM staging, so on and so forth, that is in the unstructured and often often not called out specifically. So in theory there, large language models can find that data and service it to the clinical research coordinator. That poses a couple of different challenges.

28:27Zach Taft: One is whenever you're using a black box LLM, determining how that data was rendered out, where it was rendered out is almost impossible. And as you know, for clinical research, data has to link back to source and the provenance for where it was captured specifically. So utilizing AI and just simply moving that data without linking to provenance actually isn't of any value to anybody and actually creates more queries and more source data verification requirements. So you're not actually helping anybody. So that's the first challenge. The second challenge is you can spin up a real quick chat GPT open AI wrapper on being able to pull data out of a source system and be able to transfer that into an EDC or into a data lake.

29:12Zach Taft: And so it creates this false hope that we can go after this data faster and more reliably. When you actually get it into the hands of running research-grade studies that are going to be going to the FDA, it breaks down really quickly. So the perception where the industry is versus the reality of what we can do are two dramatically different things. So in terms of helping my life, AI will have a huge impact on the way that we're going after data. in terms of hindering our life, it is the perception of what is actually possible to what you really want to have happen are two dramatically different things.

29:46Zach Taft: One of the quotes that I actually love in the industry, one of our competitors used to say this, was, I can give you 100 % of your data, which I would always laugh at that and say, you don't want 100 % of the data. We turned on concomitant medications for one of our partners because they said they wanted it. So it streamed the entire medication list into their data lake. They wanted like 5 % of that data instead of the entire thing. So they very quickly turned it off and said, yeah, we'll use your algorithm for how we want that data. It's the same thing here. You don't want 100 % of the data. You want the data that is required for the clinical trial, and you want it in a format that allows it to easily be used.

30:24Zach Taft: And then most importantly, you want it linked back to source. So you're decreasing queries, you're decreasing source data verification requirements, and you're actually able to use that data to progress the clinical trial along. And unless you're doing that in a way that is consistent, is accurate, and is traceable, you're really not providing any value. So Trevor, I hope that answers your question. Yeah, yeah, really, really good answer. I mean, a very thoughtful answer and just such a reflection of, you know, you are in the trenches building this, seeing this happen in real time. And it's, yeah, I hear you.

30:58It really resonates. Zach, I'm curious this. So let's say that this continues to scale, right? So we're like, we're five to 10 years from now, what does the future look like? Like how is the clinical trial ecosystem different in five to 10 years with scaling this sort of technology?

31:13Zach Taft: So this is the thing that I love. So I got into healthcare originally because I lost my mom to cancer when I was three years old. So it's always been a huge motivation for me. I've had a lot of professional success throughout my career. I think arguably I could probably be retired and sitting on a beach somewhere. But I'm still angry at cancer and I still want to help cure cancer. My goal has always been I never want to see a three-year-old or a six-year-old crying at the bus stop because they're having to walk back to their house alone because their mom's not able to pick them up. So that has always been my motivation.

31:45Zach Taft: And one of the best areas to help in this space is in the clinical research space. I'm not a scientist. If I'm a bench side, we're in big trouble as humanity. But I do think I'm pretty good at being able to scale businesses and look at how to bring various different parties together, like the researchers at academic and medical centers with employees at large pharmaceuticals. So where I see this going with data interoperability and being able to not have data sources siloed is the ability to see data in near real time. So we heard about the FDA in near real time with SAEs. It'll be interesting to see where that goes.

32:21Zach Taft: And it's a first good step. But I would say in a decade, you are as a researcher and as a sponsor getting data accurately, uniformly, and more available for you, not only for the clinical trial itself, but also for study startup, digital protocol, being able to recruit patients to where they are, being able to bring the trial to where the patient is instead of having the patient uproot their life to move to New York or LA or to Rochester, Minnesota, or even down to Texas and Houston. So the ability for interoperability and what it creates and what it opens up for us is going to be huge. And it is quite literally just breaking down those barriers of unsiloing the data at the sources, but also being able to use that data more uniformly at the sponsors.

33:10Zach Taft: So that's a high-level soapbox. Where do I see it going? So with data interoperability, to be able to get access to that data for patient recruitment becomes easier. You're running the data that's needed through algorithms to be able to find who is the right patient at the point of care and accruing them immediately when it becomes available. As both of you know, likelihood of being recruited to a clinical trial goes down by 50 % at each subsequent visit after diagnosis. So if you're not essentially doing a diagnosis, you're probably not going to accrue that patient to a clinical trial. That's number one.

33:40Zach Taft: Number two is when the patient's running through the clinical trial, to be able to see risk signals as it's happening and getting it back to the research team as it's happening is going to be huge for patient care, but also for accelerating drug development. That's where EHR to EDC or eSource to sponsor comes into play. And then the last piece I'll mention here is, you know, EDCs are entrenched in our ecosystem today and they're not going anywhere for decades. I think anybody who tells you the EDC is going to go away within the next few years is just wishful thinking or they're just trying to sell you on something.

34:12Zach Taft: I don't think that's going to happen. But what I do think is going to happen and what we're already starting to see is data being streamed into a data lake where the sponsor can be able to manipulate that data in real time. And whether or not they continue to use the EDC over time will be subject to that pharma. Where Ignite Data is uniquely positioned is because we're linking all the data that we're capturing back to provenance and back to source, the ability for pharma to be able to use that data and drop that data wherever they want becomes a reality. So continue to utilize the EDC, absolutely.

34:44Zach Taft: But now starting to use a data lake to allow you to more easily and effectively manage your data is becoming a reality. And that's what one has me really excited. But number two is the industry has us really excited. So as we head towards closing here, our time comes up. Was there any topics that you wanted to cover that we didn't cover in our time together here? Yeah, I think there is a couple of big topics. But I think the one for me is we as an industry, we're at this focal point right now with data interoperability where we can go into PASS. PASS to number one is we create a unified vendor agnostic system for how we are linking source systems, how we're linking where clinical trials are being conducted with where who is actually paying for the clinical trial and ultimately doing the FDA submission.

35:34Zach Taft: We're at a place right now where that can happen, where it's like electricity, just flip light, you get the electricity. And our obviously vision as Ignite Data is we want to see that vendor agnostic solution so we can create economies to scale uniformity and data integrity across the various different value chains of this ecosystem. The other route that we can go down to is essentially the EDC route that we've already seen, and that is disparate systems, disparate technologies, disparate ways of doing things. That will create a diseconomies of scale as hospital systems and community organizations have to continue to manage multiple different platforms.

36:11Zach Taft: And pharma is getting disparate data dropped into their data lakes or EDCs that then require them to have to go in and spend a lot of man hours and a lot of time and a lot of expense and slow down clinical development timelines. lines. That's where this industry is at that focal point of going. And I would say to academic medical centers and those conducting clinical research in the community, really focus on a vendor agnostic solution. Whether it's Ignite Data or another solution, really focus on that solution. That will bring scale to your organization and make it cheaper for you to conduct clinical trials, which is the thing that we hear from almost every site.

36:48Zach Taft: It's becoming more expensive and more laborious. It's an opportunity for you all to demand uniformity and demand economies of scale. And then I'd say from the pharma perspective, you have a lot of money and you like creating your own IP. Do that from the perspective of how to create uniformity and scale for sites in a vendor agnostic ecosystem, not trying to pit all these vendors in the industry against each other to create a fractured ecosystem. So I'm really excited that we're there. We as Ignite Data, we as MSK, we as an industry are championing that kind of economies of scale and uniformity in clinical trial technology.

37:25Fantastic. Thanks for sharing those insights. I am very excited about what you guys are working on. And it feels like this is finally a turning point in the clinical research infrastructure where this vision that you just laid out is starting to become a reality. We appreciate you taking the time to chat with us today.

37:40Zach Taft: Absolutely. Thanks for having me. It's an opportunity to really create change that we'll see amongst our own family and friends as we continue to progress in this industry. So thanks for having me. It's an absolute pleasure. Great. Thanks so much, Zach. Thanks so much for joining us and we'll catch you next time on Hill Tech Remedy. Thanks, Zach.