AI for Rx | Dave Billiter at Deep Lens

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Dave Billiter 0:00
Too many patients Alex are being missed in the healthcare setting and not getting an opportunity to really potentially get on a life saving clinical trial.

Alexander Ferguson 0:19
Patient patients seeking new treatments for the conditions face a special problem, their need is immediate. But the process for being admitted to a clinical trial is slow and complicated. As the head of innovation at Nationwide Children’s Hospital, Dave Billiter set out to solve this problem with deep lense a company he hopes will quickly and efficiently match patients and researchers using artificial intelligence. In this episode of UpTech Report, Dave tells us his personal story that led to the creation of Deep Lense and the special challenges of implementing technology into the workflow of healthcare professionals. They’ve I’m excited to hear more about deep lense and the unique solution the technology that you’re bringing, if I were to ask you to describe your company in five seconds, in a very brief format, what what would you what would you say?

Dave Billiter 1:08
Yeah, we’re a digital company really helping care teams match the right patient? To the right clinical trial at the right time.

Alexander Ferguson 1:18
Love it. So simple. Right? Patient, right trial, right time. What what year, did this really begin this organization?

Dave Billiter 1:25
So it was late 2017. And that that was really more Alex on on paper and putting the company together. 2018 was really when we when we really kicked off with the company and really started to advance our technology. And really start putting the technology at work and the problems that we’re trying to solve.

Alexander Ferguson 1:50
This is the the first organization that that you’ve led, though you’ve probably managed and directed several, but it’s the first one correct? That’s correct. That is That is correct. You were in Cardinal Health before. And really you saw the problem firsthand that led to this type of solution. Yeah,

Dave Billiter 2:05
I think it wasn’t just my my time at Cardinal Health. I think it was a combination of, you know, problems that I was trying to solve when I was the director of Informatics at Nationwide Children’s Hospital. And really, and really then seen another angle of the problem when I was running data strategy for the specialty unit at Cardinal Health.

Alexander Ferguson 2:28
So let’s really dig into the problem. What is the problem that you saw that you ended up trying to solve?

Dave Billiter 2:34
Yeah, no, Alex, I, I appreciate this question. It’s a I would say it’s a complicated problem. And I’ll also say, that’s why we started the company. You know, we really wanted to dig in and try to solve this complicated problem. But the problem at its at its highest description, is too many patients, Alex are being missed in the healthcare setting, and not getting an opportunity to really potentially get on a life saving clinical trial, being supported by you know, the pharmaceutical and biotech companies that that are out there running these trials. It’s, it’s, it’s, it is a known problem. And again, it’s it’s it is a complicated aspect, when you really start digging into to why.

Alexander Ferguson 3:26
And this complexity is a problem, why it hasn’t been solved. So we’re looking to bring that solution. So give me a use case exam. Yeah, and how your technology can help solve

Dave Billiter 3:35
it. Yeah, no, I think I think when you look at clinical trials, whether they’re being run in a big comprehensive cancer center, or they’re being run in, you know, hospitals that are, are in an integrated delivery network, you have individuals, like clinical research coordinators, that that may be in charge of, you know, it’s just one clinical research coordinator, and they’re in charge of sometimes 1015 20 different clinical trials. And you know, their role is to really try to identify and produce a screening process to see if those patients are eligible for that specific trial. Well, when you really start breaking that down, you have one individual trying to manage, you know, multiple different trials in the complexities of those trials with the inclusion exclusion criteria, it’s absolutely overwhelming.

Alexander Ferguson 4:33
There there’s almost prohibits them from being able to see all the opportunities that could

Dave Billiter 4:39
be That’s it That’s exactly right. And these these individuals that are in that role are very talented individuals, and work extremely hard. And I think you know, when you look at Deep lense, and really what we’re trying to do is an essence Augment, right? Not replace the CRC, but it’s really augment Come back at them. Yeah, exactly all powering them to do more. That’s right, and help optimize that entire process. And then it really then enables them to communicate, you know, with a specialist like oncologists and pathologists to make sure that that specific patient, right, and that really small window, can can be matched in screen to get enrolled on that clinical trial.

Alexander Ferguson 5:26
We’re getting to a world where it’s individual individualized Health Options, correct? That’s exactly right. And this type of technology that you are bringing is trying to facilitate that.

Dave Billiter 5:38
That’s right, Alex, I think and I appreciate how you frame that, because it really gets into you hear a lot on precision medicine, you hear a lot on personalized medicine, right. And that is true. So the the clinical trials that are being designed and developed by by your big pharma companies in your biotechs, that what’s exciting is they are getting more to that precision based right. What it also does, it also creates a challenge, because with being precise, you need a lot of of tools and support and resources to make sure that precision is realized within the healthcare setting. To actually get that patient in that window. Again, I talked about that window, because the precision based trials support, that precision based aspect. And that’s really where our technology is really enabling that care team to to make that

Alexander Ferguson 6:38
match. So the current situation is these pharma companies, they need patience for clinical trials. And it can take a long process because he said that CRC is just inundated and trying to get can make all the matches. So technology coming here to speed up the entire process. That’s what you’re trying to solve.

Dave Billiter 6:55
That’s exactly right, Alex, and I think what what gets us excited, and why and my partners and those that are a part of deep lense get out of bed is because our platform, our solution is actually solving two sides of the problem, right? What we see within within the hospital systems, and those providers these challenges to identify patients and screen and get them on trials. And on the other side, the sponsors who are sponsoring those clinical trials, they’re trying to do everything they can to get you know, the numbers of patients to enroll in their trials, right, so that they can actually get that drug to market. Right so that more patients have the opportunities to benefit from that drug. So that that’s really where the excitement comes from, from deep lense is seeing both sides of the problem. And our technology really enabling that on both sides. Because in the end, the patient benefits

Alexander Ferguson 7:51
digging into the technology. Yeah. Can you describe how does it work? How does it speed up the process and what information and data is being provided?

Dave Billiter 7:59
Yeah, so you know, when we break it down, Viper is the name of our platform. Viper integrates data from, I would say, three primary source systems. It’s the electronic medical record, the laboratory information system. And then there’s genomic results that come in those three data sources. We actually ingest all three of those data sources. And think of it as we harmonize and normalize that into the back end database that supports Viper. And then what what we’ve done is we’ve developed some very, very smart logic and AI techniques that allow us in real time to take the data that we’re receiving from those source systems to programmatically match that to inclusion exclusion criteria for the trials. And that can be a very challenging process for clinical research coordinators. So that that’s really where we talk about trying to provide, you know, being an assistant to them in those challenges to try to manage all those trials. So that it’s a it’s a combination, Alex, I think when you look at the technology, but it’s also the process, right, so we combine some really advanced AI techniques and logic engines, as well as a process that we implement with our with our collaborators, our partners at the hospital systems and cancer centers, to really embed that into a workflow that matches what they’re doing on a daily basis. So that that’s really where that combination of technology and in process come into play.

Alexander Ferguson 9:39
If you’re not getting the data in the the hospitals aren’t providing it, it doesn’t solve anything. So that’s right and the importance of the process. Now the hospitals are not paying for this. It’s the pharma, Big Pharma. That

Dave Billiter 9:53
is that’s correct, Alex and I think that this is another aspect of our company, and even our approach that that I get pretty excited about is, you know, when we started deep lense, you know, I say this We didn’t start the company didn’t really try to squeeze dollars out of the health systems. They’re they’re already burdened enough, right. So even though we’re solving significant problems and providing some really advanced technology and techniques, it was not our goal to go in and try to sell it and, and really gain and monetize from the provider side, what we really wanted to do, knowing that we are a business, we wanted to look at the other side of the challenge knowing that, you know, your big your pharma companies, your sponsors, as we as we allude to the pharma sponsors, and those that are supporting those clinical trials, we do look to collaborate and partner with them. And that’s really where the dollars come from is, is really from the sponsors to really help support them to try to move their trials faster to get that drug, the market. Got it.

Alexander Ferguson 10:58
Data being transferred here. We’re in an age of data privacy concerns. Absolutely. How do you address that? And to the effect of does the patient have in control? But who has control? And how is it being protected?

Dave Billiter 11:14
Yeah, so I mean, you know, the way I look at it, Alex, the, the patient is always in control. And I think that, you know, the patient really looks to, you know, the health systems and the providers, as the broker, the governor, the honest broker, that you know, that that partnership is really what enables it, but it’s still a patient centric model and approach you to really solve the problem. But it’s, it’s then, you know, the pharma sponsor side that that we really look to, from a, from a business perspective, those dollars really come from them to help support the entire the model.

Alexander Ferguson 11:56
Yeah, the whole, the whole, the whole ecosystem? Yeah, I had another conversation with another tech company, that same focus that the future is, we all have important data that other companies people want. And so we shouldn’t be the ones having to pay to provide our data rather, rather, paying to get x right.

Dave Billiter 12:14
And I think, even to the data aspect, and to, you know, HIPAA and privacy concerns, right. So this is something that we spent a lot of time energy and dollars at the very beginning to make sure that we were building, you know, part of our technology is making sure that we have all the right techniques, and security and regulatory components. We spent a lot of time upfront to do that. Because because we’re managing data. And the data is the triggers, right? So we’ve spent a lot of time and energy up front to make sure we’re HIPAA GDPR compliant on the way our structure works. But it also Alex helps facilitate, really that collaboration and partnership that we have with with our provider partners in solving that big problem, even with with the sponsors.

Alexander Ferguson 13:09
Got it. So I see the power of this system coming into play for patients and for them, pharma companies needing those clinical trials. Yeah, how far along are you see? You said only about 2017? About 233 years ago that on paper? How many hospitals and patients are you able to be seeing now? And what’s the projection?

Dave Billiter 13:29
Yeah, so I mean, I think, I think right now, we’re still at the beginning stages. And, you know, we’re, it’s really what we refer to as our our lighthouse initiatives. So we’re being very, you know, we want everyone to take advantage of this. And when we say everyone, we’re talking about all the providers and sponsors that, you know, that are running trials. But you know, what we’re doing right now is those individual groups, providers, and sponsors that are coming to the table, that we can really enable them at the very beginning to realize the technology, and then we’re going to continue to look to expand within even your integrated delivery networks, and with your big, comprehensive cancer centers. So just you know, numbers change every day, because we’re bringing on, you know, new institutions. With its within each institution, we’re continuing to add trials that are configured in the platform. So it’s, it’s really not static, it’s continuing to grow, which, which we’re excited about,

Alexander Ferguson 14:31
right? Can you share roughly how many patients or stuff that is data, just any kind of number that shows the progress?

Dave Billiter 14:38
Yeah, so we have, I’m just trying to think we have over 35 different trials running right now. Um, and you know, patients are being identified in the hundreds, just even from an identification and screening perspective. And those can be broken down even at a per trial basis. And we’re looking to fix And even from those numbers at institutions, and you know, even on a ratio of upwards to three to four different institutions across multiple months,

Alexander Ferguson 15:11
that’s fantastic. Looking forward from here, yeah, where? Where do you see the company? And in five years from now,

Dave Billiter 15:17
yeah, no, I get excited. Alex about answering that question. You know, and I really, I really see deep lense, and the Viper platform, becoming that de facto system. And, you know, really enabling care teams, and the communication between the providers in the form of sponsors, really being that enabler. And when when folks really turn to a system, or a technology or a process that wants to facilitate, you know, optimizing how they’re, you know, patients are getting on clinical trials, and then even on the sponsor side, being able to, you know, really increase their drug to market deep lenses who they turn to. And that’s really where I see us and five years, depending on who you’re talking to, whether you’re on the provider side, or the sponsor side, deep lens is the platform and the company that you turn to, to really help facilitate those activities.

Alexander Ferguson 16:17
I’m excited to see that vision. Yeah, absolutely. where can folks go to to learn more? And what’s a good first step for them to take?

Dave Billiter 16:25
Yeah, so uh, you know, the, the very first step is, you know, we’re, we’re very heavy in the social world. So you know, even through LinkedIn, you can search up deep lense, Facebook, you name it, we have multiple outlets, because we really want to inform that, that we’re out there and, and really want to make sure that community, you know, whether whether you’re a patient, whether you’re an oncologist, a pathologist, you name it a sponsor that’s running trials. We just want to right now it’s awareness and letting folks know we exist and we’re solving a big problem. So those social outlets, you can use search of deep lense and find us as well as our website. So you can you go to any of those are all those and then that all that are really helped facilitate connecting to our team members to have those conversations.

Alexander Ferguson 17:20
Be sure to check out part two of our conversation with Dave filter, in which he discusses the particular difficulty of rolling out a new technology in a sector where people’s very lives are at stake.