Deborah Kilpatrick, PhD, CEO of Evidation Health, explains how the company is creating a way to validate the efficacy of digital tools in health care. Bonus Breaking Health Podcast Trivia: What famous Yankee game did Steve Krupa attend as a child?
Tom Salemi: Hey, everybody, welcome back to the Breaking Health Podcast. This is the intro guy, Tom Salemi, here with the actual host, Steve Krupa of the Psilos Group. Hey, Steve.
Steve Krupa: Hi, Tom, how are you?
TS: I’m doing great, doing great. It’s wicked hot here, and I know you’re here in Boston as well. You wicked hot down there, Steve?
SK: We are. We’re pretty hot. We’re getting into the 90’s. I’m only about – what am I, 30 minutes south of you?
TS: Yep, that’s right.
SK: Southwest of you. And one of my colleagues wore their Red Sox shirt into work today just to aggravate me.
TS: Well, you’re acting CEO. Can’t you do something about that?
SK: I can. I told him to go change his shirt.
TS: It’s an abuse of power, Steve Krupa.
SK: It is.
TS: You’re out of line.
SK: And it’s just wonderful. There is one Yankee fan here who had a Reggie Jackson jersey, which is kind of cool
TS: Oh, that’s cool. Like a legit – should he know who Reggie Jackson is, or is he just being ironic?
SK: Yeah, he’s older than me; he knows who he is.
SK: And I had the pleasure of telling him that I was at game 6 in 1977 when Reggie hit the three home run.
TS: You were there?
SK: I was there with my dad, yeah.
TS: Oh, that’s a great memory.
SK: It sure is.
TS: Gosh. And your dad became a Mets fan after that?
SK: He didn’t – Well, I begged him to take me. I was 13. He did.
TS: Good call by you. You’ve led a charmed life, Steve Krupa.
SK: Every day is wonderful.
TS: And it continues with – we have a great interview today with Evidation Health, Deborah Kilpatrick. And it’s nice to see someone sort of trying to bring a uniformity to digital health sort of therapeutics, to try to develop a system that would ideally indicate what works and how it works and what benefits it has for the healthcare system.
SK: Well, yeah. I think, look, I think the goal of any digital health company has got to be to solve a problem that nobody else has solved. And the only way that you can actually demonstrate that you’ve solved that problem is through experimental evaluation. And this is where Deb and her company come in: the idea that we can now connect to people in the field through mobile technology and sensors enables us to begin to measure more precise feedback as to how therapies are working, whether those therapies are drugs or overall care plans, or maybe even some of the digital therapeutics that we’ve been exploring on this program. So the idea is if you’ve actually solved that problem, let’s do some real world experimentation and prove it.
TS: As an investor in these companies, do you welcome that sort of outside validation? Do you worry about it? Because you know, if one of your companies gets that seal of approval, great.
TS: But if it doesn’t, then you’re like, Hey, what the heck?
SK: Well, I think it depends on the precision of the application. But what I would say is in a broad context, I want to find the companies that are solving problems that no one else is solving. And if this is one way for them to demonstrate to their investors that they are in fact doing that, then I think it’s a positive. And that’s always been the risk of what drug companies would call phase 4 clinical trials. They go through the premarket clinical trials, phase 2 and 3; they get their product approved for use and prescriptive use. And then the question is “Do they want to take it the step further and see what is actually going on?” And there’s a risk to that. There’s a risk to that. You may find side effect information, you may find efficacy information that you were not uncovered in the experimental trials, and then you have to deal with that as a marketer of these products. But as a citizen, as a person, as someone that is dependent on the healthcare system, I think that if you’re going to make a therapeutic claim, you should be able to back that up not only in a controlled trial, but in a real world evaluation of real use data. So in that way, I think it’s very good.
TS: Absolutely. And Deb’s a cool lady. I know she told me she bikes. I think one day a week she bikes her 35-mile commute or something. So something to be said for someone who takes that on. And she –
SK: Yeah. I’ve gotta get one thing in about her: she’s a mechanical engineer, and I’m a mechanical engineer as well, and so she’s awesome just because of that.
TS: I could hear the connection at the very start. You guys definitely got each other. So great. Well, this is a great conversation and let’s get it started.
SK: Welcome to the Breaking Health Podcast. I’m here with Deb Kilpatrick from Evidation. Welcome to the Podcast, Deb.
Deborah Kilpatrick: Hi, Steve.
SK: Glad you could make it. We got you early in the morning out there on the West Coast, but welcome. I’m very curious. The company is very interesting to me. I love the sort of approach of proving out the benefits of digital health because we have a lot of conversation on this Podcast about various ideas of digital health, and it sounds to me like the mission of your company is demonstrating the value of digital health. So before we get into that, I’d love to hear a little bit about how you found yourself in a position of starting a company and being an entrepreneur. What led you to this part in your life?
DK: Well, it’s probably a good thing to start off by saying I’m doing the exact thing I said I would never do. I said I would never go to a startup, and I said I would never be a CEO of a startup. And lo and behold, I’m proving out that one should never say what they’re never going to do because it’s precisely what they will end up doing.
SK: God bless you.
DK: My background is in healthcare, for the last 20 years, exactly this year, actually, in Silicon Valley. My training – I did a PhD in mechanical engineering with a focus on bioengineering at Georgia Tech and in collaboration with the University of Chicago Medical School. And my background was always focused in cardiovascular disease, early on in research and development in a large company called Guidant Corporation. Guidant was acquired eventually by Abbott and Boston Scientific. My role at Guidant was very much on the early stage R&D side for the internal incubation side. And I was very fascinated with the planning process of what – to see the future, but more importantly, to create the future. And not only do that from the standpoint of a business, but look at it from the standpoint of what does, in this case, the US healthcare system need, and how could I or any company or team I was a part of be a part of creating that. And I was very focused on that and stayed at Guidant until it was acquired, and I then left and went to a genetic medicine company called CardioDx. And that was my first time going in to do what I said I would never do, which was go to a startup. And it had just raised its series B in Silicon Valley at the time I joined. I then immediately did another thing I said I would never do, which was run sales and marketing. And I did that because I wanted to understand fully what it took to develop new, innovative technologies and products in healthcare, and understand fully what it took to actually get them used. Because it’s not enough to get them through the clinical development plan or to get them approved, or to get them even in physicians’ hands. Frankly, it’s much more important to me to see them used more broadly, which includes reimbursement, includes how do you look at the incentive structures that exist around the entrenched technologies that you’re trying to displace or that you’re trying to improve. And I really wanted to understand on the ground. And so I did that for a number of years. And ended up leaving CardioDx after almost 9 years. And I did that because I felt like I had made the contribution that I committed to the board that I would make there. We had a Medicare decision at that point. We had a favorable decision from Aetna. We had used the product approximately 100,000 times in the US at that point, so we felt like we understood how it worked and all the levers and degrees of freedom of what was driving adoption. And I felt like it was time for my team at that point to sort of lead without me. And I was deciding what to do next, and digital health was a huge attraction to me because A, I didn’t understand it, and I’ve always find I’m attracted to jobs and things I didn’t understand. And B, I was very attracted to the team at GE Ventures that was discussing putting together what would become Evidation Health. And I was sort of in discussions with them about who they should get as a CEO for Evidation Health, and decided that perhaps I should not give them my Rolodex and should throw my hat into the ring, and that’s what happened. And that was in late 2014.
SK: Well, we’ll have to find out what else it is that you are promising yourself you’re not going to do, and I guess we’ll have a glimpse into your future by doing that. First of all, I would tell you, as a fellow mechanical engineer, that that’s always good to hear.
SK: I didn’t quite go to the PhD route, but I did spend a fair amount – I still love it, right, I still love the idea that I can figure out how things work, and a lot of that is certainly applicable to what we’re doing.
DK: I wonder if you get the question that I get sometimes, which is people say, Wow, do you miss engineering? Or do you miss being an engineer? And I look at them with a very puzzled face, and I say, What do you mean? Of course I don’t miss it. I am one. Like I’m an engineer every day and it affects the way that I look at complexity, and it affects the way that I problem solve and the way I run teams. And so of course I don’t miss it; I am it. So yeah, I’ve always been sort of fascinated by engineering as a way of thinking.
DK: And I’m biased, as are you probably, Steve, but I think we’re really good at thinking about things.
SK: It’s true. All of my friends that I studied engineering with, and you included now, we all believe that what engineering taught us was sure, it taught us physics, but it really taught us how to solve problems, I mean really become a problem solver.
SK: And that’s always valuable because I’m sure that the case is with you, every day I turn up and there is another problem to solve. So very cool.
SK: And I can’t really let you off the hook on this, unfortunately. I’ve got to probe living in Silicon Valley and vowing not to be in a startup. Was that because you sort of looked at startups from the outside and disliked what you saw? Or what were you thinking?
DK: Yeah, it wasn’t that. It’s more that I’ve never looked at – I tend to pride myself on being very aware of what I’m good at, and equally aware of what I’m not good at and need to rely on other people or other teammates to be able to bring to the table. And to be honest, I never looked at myself as the kind of engineer that like invents things in a garage. And that’s sort of the mythology of Silicon Valley. Certainly, 20 years ago when I came here, that was very much the mythology of Silicon Valley, even in the medtech sector, which is, you know, there’s a bunch of people, they go into a garage, they invent something, and boom, a company is born. And while that mythology has some basis in truth, the reality is many, many companies, especially in the digital era, don’t start that way.
DK: They don’t start with tinkering the way that we thought about tinkering 20 years ago. And so I guess my vision of what was plausible for me to do, and what was interesting for me to do, began to evolve over time in part frankly because of the digital era, because it became possible to quote-unquote invent companies or start companies in a much more virtual way that I was more comfortable with. In my role as – on the technical side, and I did leave this part out as my background, before I did my PhD, I actually was in the aerospace industry with my undergraduate. And I worked on the F-22 Raptor. And what I was doing for the F-22 Raptor program was I had the great fortune of being one of the first people to program on one of the 5 Cray Supercomputers that were on the planet at the time. And I was charged with developing all of these different numerical techniques for looking at solid behavior, using finite element theory, which I’m sure you’re familiar with because you’re a mechanical engineer. And I’d always been very comfortable doing things numerically and virtually, and that was not how Silicon Valley started companies way back then. And so that led me to not think of myself as a startup person. It wasn’t at all that I wasn’t fascinated by how people started companies; it’s just that I didn’t come about in an era where I felt like that was something I would be good at, if that makes sense.
SK: Yeah. Yeah. So let’s move to this question of digital health. So I’m going to enter the category discussion with a statement, and then I would welcome disagreement or agreement with that statement. So I often think about categories of investment or categories of invention or improvement. And I think of them as big ideas, right? What’s the big idea that is going to sort of change things in a very material way? And then when I think about digital health, sometimes I feel like digital health is sort of too covered up by trying to make incremental changes to things. So tell me if I’m wrong or right about that, and why. And then give me what you think digital health really is and what its promise is.
DK: I think the problem that digital health, if we can call it a sector – is it mature enough yet to call it a sector? – the digital sector faces is one in some ways of how it is defined, which I think is in part what you’re sort of asking about. Which is it tends to be defined from the bottom up, which is Oh, there’s interoperability solutions and there’s devices or things you can use to track activity. And oh, over here, there are sensors and wearables. And so those tend to be technology solutions. And I never think of a sector as being effectively described by the solutions themselves. I think of the most effective way to describe any new sector, particularly brand new ones like this, is in what are they actually ultimately doing to the ecosystem that they’re trying to improve or disrupt or change. And to me, my definition of digital health is just this very sort of top down one is it’s the use of digital as a vehicle, digital tools as a vehicle to find or create completely new efficiencies in the healthcare system. Those efficiencies can be efficacy signals or concentrations of efficacy that weren’t possible to see before or possible to influence before. Those efficiencies can also be cost efficiencies. Those efficiencies can be work flow efficiencies. Those efficiencies could be simply getting data in a more efficient way to look at a clinical development plan or to look at the clinical endpoint in a trial. And all of those things can be technology mediated now in a way that simply wasn’t possible ten years ago, it wasn’t even possible 5 years ago. And so when I think about digital health, I think about it as using digital tools as they are solutions and vehicles to find these new kinds of efficiencies that simply weren’t seeable or doable or enabled before. And so I just look at it in a fundamentally different way. I don’t think about it as the sort of bottoms up technology description, if that makes sense.
SK: Yeah. It’s almost like I think of it as when we begin to aggregate all these companies that are making these efforts, the net sum of the sector in and of itself has the opportunity to eliminate a lot of the waste in healthcare, which I believe actually is necessary in order for healthcare to back and innovate again, if that makes any sense. So if I think about the medical device side of the business, I say, Well, medical devices are in a sort of tough spot because while they can innovate, the system is tired of new technologies being brought in and really increasing costs in some ways from that result. So if we can take a digital infrastructure, a digital ecosystem and make it work, and we’re nowhere near doing that, right? But if it does work in the way I think you’re envisioning it, and I am, then there’ll be room for new technologies to come in because there’ll be a system in place to monitor its use and make sure that it’s not being overused and overcharged. Does that argument ring a bell with you in any way?
DK: Absolutely. Absolutely. I could not have said it better myself. And I think I’ve been saying when I talk to investors in different discussions over the last couple of years I sort of believe that if you could magically stop all product development and just say OK, all of healthcare whether you’re pharma, biotech, medtech, diagnostics, therapeutics, combination products, all of us, we’re all going to stop working on new products for ten years. We’re just going to agree to do it and we’re going to magically be able to do that somehow financially. And instead, all we’re going to do for the ten years in that period is we’re going to work on making sure that things we already have approved today, or in use today are actually working best in the people that they could work best for. Like if you could do that, if you could concentrate all of that efficacy and concentrate all of that benefit and minimize wherever the risk is or wherever the losses in the system are, which is a very engineering way of thinking about it, right, then imagine what our healthcare system would be capable of doing. Just forget everywhere in the world world, just focus on the US. And then we could start up again magically ten years after that, and then we’ve magically got this new, more efficient infrastructure, or more – this new ball field on which teams can play. I think that would be an incredible thing. Impossible, I realize. It’s just the way that I think about where we are as a healthcare system right now. And I don’t know that we would have said that – I don’t think I would have said that 10 years ago. I don’t think about 10 years ago, I think it’s like right now. Just think about all the therapies and diagnostics we have right now. It’s incredible. We have incredible capabilities. My goodness, we can actually look at genomic profiles in people and target their immune system with different therapeutics. I mean that is astonishing. That wasn’t possible 10 years ago.
DK: And so yet we don’t have a way of even getting new electronic health records systems implemented in the most sophisticated of health records think that it’s almost like we need to stop – we need a break from putting new stuff through the system because I agree with you, it’s tired of it. The incentive structures are tired, they’re entrenched. It’s just getting more entrenched the more you try to shove in there. And you need to let the engine breathe a little bit so that we can actually retool it and tighten up the chain.
SK: Very cool. Listen, that thought experiment is often the way I described why I’m turned on about the digital health opportunity. And in fact, the FDA and in some cases CMS and in some cases the insurance market broadly is placing enough constraints in technological innovation to where that thought experiment will never be perfect, but it is sort of occurring in a subtle way today. So let’s get into your company and talk about the commitment to digital health. And you got GE in there. Tell me the story that you guys put together that led to the development of the company and the types of services and products that you guys are offering today.
DK: Well, there is this strong interest – to give you a little bit of the internal GE Ventures lore behind this, although by every measure that I have, it’s true. There was a very serious interest inside the walls of GE from Jeff Immelt on down to Sue Siegel and GE Ventures and the team that was there at the time to do something about this issue that they saw as impeding adoption of digital health or of digital health solutions on a broad level. And they were thinking about it in a way that only a company like GE can do, right? They’re thinking about it from an infrastructural way, they’re thinking about it from an ecosystem way, and they’re thinking about it through the winds of a corporate venture group that can actually do something about it and bring the resources to bear of a large corporation to do something about it. And they were like, you know, what’s the fundamental problem? Like we have a fundamental problem around adoption in digital health, and that if you look at all the point solutions that are propagating, there’s no basis for understanding what works, A, in any population, or B, in sub-populations where they actually may have better efficacy than others, right? So there’s a targeting problem. And we don’t see – this is GE talking – we don’t see anyone doing anything about it. We don’t see the digital health companies themselves rising up and deciding to go out and develop evidence to back up their claims or develop evidence based on health outcomes. And this is a fundamental problem if you’re a healthcare company, which GE has a huge healthcare business, because as you know, in certainly the US healthcare system, we’ve now got a good 50 years of evidence based medicine under our belt where the criteria for making decisions about new technologies and products is very much driven by the evidence generation around the clinical efficacy signal. Now in more recent times, that’s also being driven by economic signals and economic efficiencies. And so now we look at those things. But in the early days of evidence based medicine, it was really all about clinical outcome. And so now we think about health outcomes as clinical and economic, and if you believe that in the transition from volume to value that those criteria are only going to get more seriously needed, right, to look at new technologies, then you’ve got a fundamental problem. You’ve got like enormous potential of digital health technologies and solutions, and you’ve got none of them actually generating evidence, and a system that wants to use them but doesn’t have any criteria being met for how they should use them. So GE sees this. They were also in a discussion with the head of Stanford Healthcare at that time, Amir Rubin, who’s now at United, and they got together and he said, Well, why don’t we do something about this? Is there a business idea here? Is there a business idea around the definition of outcomes in the digital era of medicine? And they felt there was. And that was the sort of genesis of the conversation or the origin story of how Evidation Health as a business concept came about. And we began in fall of 2014 to fit together a team that would be able to do this. And there’s obviously a strong technology part of this, and there’s a strong healthcare part of this. And so one of the first things we did is we quickly found an incredible company with incredible technology called The Activity Exchange that was in the Rock Health portfolio at that time. And Christine Lemke, who is the CEO and is now the President and cofounder of Evidation Health – Christine and I got in a room at GE Ventures one afternoon in September, October of 2014 and we decided that there was an incredible opportunity to direct the technology platform that they had developed, which was essentially a connectivity platform towards wellness applications. We said that’s an incredible idea, but let’s aim it directly at healthcare. And let’s go after opportunities in digital health to define outcomes in a completely different way. And you can do that for digital health companies themselves, but you can also do that for pharma and biotech in the digital era of medicine. Imagine what’s possible now to define a new basis of competition for traditional therapeutics or new therapeutics using digital technology and digital data. And so sort of at the highest level, what we were really doing is creating a company that would be able to quantify health outcomes in the digital era of medicine, using the best of consumer technology solutions and technology platform solutions to link populations in provider systems in health plans, in the real world, people walking around, and analytics and machine learning. And that’s what we did, and that’s what we’re doing. And it’s been a great ride. We actually raised our series A in late 2014 and we actually just did the first close in a series B in May of this year.
SK: I love it when money gets raised. That’s cool.
DK: We do, too.
TS: Hi, everybody, Tom here. I’m going to talk real fast to remind you to go to the Digital Healthcare Innovation Summit on November second in Boston. We sold out last year. We absolutely sold out, had to close the door a couple of weeks before the conference. So don’t get shut out this year. Go to Healthegy.com. That’s the word health followed by the letters EGY.com, and register now. So we’ll see you on November second in Boston. Now back to this conversation with Deb Kilpatrick.
SK: Let me go back then and ask you a couple questions, and maybe we can get into like a use case or two. So when you think about studying the digital health technologies, my buddy, Lonnie Riesman and I often talk about sort of the paradox between efficiency and efficacy. And so is it your thought that you will measure one or both of those endpoints? Or will you look even further into other aspects of the products to measure what they’re accomplishing?
DK: So sometimes companies come to us and they really only want to look at sort of truly clinical efficacy based endpoint. Like who is my solution working on, and how well is it working, versus some baseline standard of care, if there is one in those cases. In those cases, we won’t of course do what those partners are asking. But we decided as a company that we will always look at economic endpoints, whether the company is asking us to or not. We think it’s important to do for our own learning and our own insights because in much of what we’re doing, we’re developing very new methodologies for how do you measure clinical efficacy signals, how do you measure economic efficiency signals in the digital era? And there’s no recipe here. Right? We’re developing new ways of doing these things. And we decided that it’s incredibly important for our company to do that every chance we get in every use case we can, even if it means we invest in that effort ourselves. So typically speaking, we always look at clinical and economic stuff together. But companies will care differently about those, right? We’ve also had companies come to us and only want to look at economics because they’re so confident in what they’re doing clinically, or maybe they already have done some studies or have some proof points of their clinical efficacy signals and their clinical power. But they really have no idea if they’re bringing economic benefit. And that, I mean I do think we’re in the era ever again in the US healthcare system where that is not important. And so I always think that in some ways those are the most innovative digital health companies that are thinking that way, because they’re thinking beyond just getting something to market. They’re thinking about how you operate inside the incentive structure of healthcare, which I think is incredibly important because those things do not go away easily. And they’re still very much in place today, even on the heels of multiple years after ACA, and we’re still evolving as a value based healthcare economy. And that evolution is going to take a while, but it is definitely actively changing now. And so I think some of the most innovative digital companies are those that are looking at those economic efficiencies from the outset.
SK: Yeah. So I’ll make a confession to you. The first question that I often ask a company when I meet with them is I am going to take for granted that everything you say about your technology is true. So tell me what the economics are. And what I would tell you is that unfortunately that often leads to some very short conversations. Right? Because it’s sort of like if it’s not an I don’t know, it’s often an I don’t know with a longer explanation as to why or how come.
SK: And I’m not saying that that’s the be all, end all of the question, right, because sometimes a clinical endpoint is very valuable. But clearly, if you’re going to come up with a digital health product that is going to improve a clinical endpoint, it ought to have a demonstrated economic outcome down the road. So I’m psyched to learn that you’re evaluating that as part of your process.
DK: Yeah. And I want to add something on that point which is I think we have a very good surrogate or precedent, rather, is a better word, to look at with regard to the influx of new information and new technology into the existing healthcare system, where everybody agreed that the clinical benefit was clear, but without economics, no one would adopt. And that is molecular diagnostics. And there’s no question in many of these cases for genomic medicine, genomic based diagnostic in oncology, in cardiovascular disease, in autoimmune diseases, like the clinical benefit was very clear. The patient benefit’s very clear in many of these cases, right? There’s a convenience aspect, there’s a safety aspect. And you’re talking about using relatively benign procedures, versus some procedures compared to, for example, imaging, that are irradiating. So patient benefit clear, clinical benefit becoming vastly clear. But without the strong economic benefit argument. And I don’t just mean to the system, right, I mean to the payer, to the actual health plan. You have to understand the economic benefit to their budget in a year. And if you’re not doing that, it doesn’t matter if you get medical policy going in your favor. It doesn’t matter if you get a coverage decision working in your favor because your payment decision will be so weak you won’t have any margin. And so I think that as soon as companies are starting to understand and that that is something that, you know, you may be able to disrupt things over places, but your disruption on the economic side better be around how you prove it, not that you don’t need it. Because you absolutely need it.
SK: Right. That’s good. That’s good to hear. So tell me, so let’s get into some of the use cases of your products. Does an example come to mind in terms of some work that you’ve done around these areas?
DK: Sure. So I’ll keep company names out of it, but just talk about use/ So we tend to talk about our solution offering in sort of 3 buckets. And on the front end are something called understand and insight. On the middle it’s intervention testing, and at the end, it’s full-blown evidence generation of the intervention impact. So on the front end, it’s sort of the simplest way of how can you look at patient insight in a whole new way? Patient profiling in a whole new way? And we do this with two different forms of information. We use medical information, and that’s sort of exactly how you would imagine it. It’s EHR data, it’s clinical trial data, it’s lab testing data, it’s claims data. It’s all kinds of forms of adjudicated and patient reported medical data. Then we look at behavior data, which is the new data to the party in the digital era. And behavior data for us can come from any number of places. It can come from connected devices, it can come from our social media activity, it can come from clinical grade wearables and sensors. It can come from any place where we’re looking at a digital data feed or an API that is reflecting what that patient is doing in the world in their real life at that moment in time. And so when we use medical and behavioral data as data feeds to profile a population, the first type of solutions we offer, like I said, is just insights and understanding. Like for example, if you’ve got a therapeutic for rheumatoid arthritis, and you know what the label population is, right, because you know what your approval is for; you know who’s taking it if you have the script data. But do you really know anything about those people? Do you really know anything about what’s going on when they’re taking your drug? Where are they taking it? Are they taking it at the right time of day? Are they dosing it up and down appropriately? This sort of all these questions that related in some way or another to adherence to a therapeutic regime. And we can actually look at that now in ways that simply wasn’t possible before. So a lot of companies come to us just trying to understand what are their label populations doing in the real world with regard to behavior and patient profiling. That’s one type of use case. On the intervention testing side, imagine that the simplest digital intervention that exists is a text message, right? Text message or a phone call. But let’s take the person out of it and just say it’s purely a text message. And there are times of day or times of the week where I think we can all agree we are all more receptive or less receptive to a text message, right? Said another way, there are times where we are going to be super responders to texting, and times when we are just not going to respond at all. Much in the same way that you can biologically respond to a drug or you cannot biologically respond to one. Imagine that in testing the efficacy of different messaging programs, if there are a coaching program, that there’s an element to this that is matching not only the profile of the person behaviorally, but matching to their situational context, much in the way that ad targeting and Internet based ad targeting has been doing for the last ten years. So intervention testing for us would be deploying intervention into a population digitally, and looking to see who responds, and then iterating on that over and over and over, toward some outcome. That outcome could be responsiveness or engagement metrics, or that outcome could be something much more sophisticated like taking your actual pill or taking your therapeutic, or going outside for your walk if you’re in cardiac rehab. And so we can deploy interventions, test them and optimize them very rapidly. That sort of leads to this third type of use case, which is really about OK, now that you’ve got this thing, you’ve got this intervention, this digital tool that you’ve optimized and that you’ve targeted, now let’s get the evidence. Now let’s essentially do a prospective, randomized pivotal, right? Let’s do what is equivalent to a phase 3 or a phase 4 study virtually. You can also do it in provider systems, and we do, do that. But some of the stuff we do is completely virtual. And here we’re trying to actually quantify the healthcare economic or healthcare – or clinical benefit, rather, of these new solutions, these new tools, of a therapeutic. But you’re quantifying it in a whole new way. And we can do it by getting data feeds that are passive and continuous. Everything is patient consented, by the way. Everything is patient mediated. But instead of having to show up to the hospital, or if you’re primary care visit to deliver data in a clinical trial, or to get measurements physiologically in a clinical trial, or to be weighed, or to have your blood pressure taken, etc., etc., imagine we can get data like this passively and continuously as you’re in your life. It’s a much more efficient way to get actual reflections of what’s going on with you physiologically, and what’s going on with you clinically. And so in our evidence generation use cases, that’s the primary benefit that we can offer is that we can actually bring all new types of continuous data to the definition of an outcome. Which I think is the most exciting part of the digital era of medicine.
SK: Yeah. So if I’m developing a digital product or I have a product developed, is it the intent that I would come to you and partner with you to prove out these points? Is that the idea behind the business model?
DK: Well, it certainly indicates that our customers who are digital health companies themselves, absolutely. That’s why digital health companies come to us. In the case of pharma and biotech, it can vary. Perhaps they’re coming to us just to understand, for example, if their autoimmune patients on a given drug are more or less active on their drug versus another brand of drug, right?
DK: So it can vary quite a bit. But the whole theme here is you’re using new types of information to quantify an outcome of interest to a healthcare business. And I think that is – when I go back to my original definition of thinking about digital health as ultimately bringing new efficiencies to an ecosystem, ultimately bringing new efficiencies into the healthcare, writ large with a capital H, that’s the most exciting part of it to me is that in order to bring new efficiencies, we need to see the outcomes we’re generating now. And that’s part of the thing we’re blind to outside of the clinic setting right now. We’re blind to what’s going on in the real world with regard to outcomes. I think that’s in some ways what Evidation can be a first order of benefit to the system to just simply quantify what’s going on now with outcomes potential in given label populations.
SK: So would you expect in the example of the therapeutic side that ultimately the work that you’re doing would result in enhanced labeling of the product?
DK: Oh, I think that’s quite possible.
DK: Absolutely. And I think to get there, you know, we’re not talking in the case of label expansions, those are not things that happen every night, right? But imagine that just like in the more traditional biopharma world, label expansions come from the generation, typically, of new data, right? And often that data is in the phase 4 setting, and it’s specifically geared toward new label expansion. Imagine that you could get that information A, more quickly, certainly than has been possible in the past, so you can speed up that process. But I think more importantly, or certainly as importantly, the ability to use new types of data to get label expansions that reflect your drug benefits that you didn’t even know were there. I think that is also quite important. And the reason I think it’s so important, not from a patient perspective, although you could argue the patients are getting these benefits regardless of whether the company sees it or not, I think that you are benefitting more than you realize from your product, you deserve part of that value. And in the transition from volume to value as a healthcare system, you should be able to price against that and you should be able to get some of that value back. And so the vision of what we’re doing at Evidation ultimately is creating a bit of an operating platform that allows new types of data to define new types of outcomes that can be used to define that value.
SK: So how do you go about doing that? Are you creating a communication platform with each of the individual patients that say, are using the therapeutic so that you can capture their behavioral data, you can capture their time data, you can capture sort of their activities, and then be able to pull that together? Is that part and parcel to your product?
DK: Yes. We have that today. So imagine that our platform essentially has sort of 3 components to it. There is the standard data, what I call the standard data model component, which is essentially the API connectivities into different connected devices or different data streams or different EHR systems or different medical data sources. And so those are sort of the nuts and bolts, the pipes, if you will, in the tech world. The second portion is really the analytics and machine learning platform that makes sense of all of this incoming data. It is in fact a bit of the in situ lab in which we run a clinical trial, is a way to think about that, using the best of state of the art in analyst and machine learning capability. And the third part of the platform is in fact the populations that we’re connected to. And the portal that we use to do that is a consumer facing web and mobile product called Achievemint. And that’s Achieve like the world achieve, and then MINT on the end of it. And Achievemint is used to do exactly what you’re suggesting, Steve, which is it is the patient facing or consumer facing application that allows you to consent to participate in providing this data for studies, and it allows you to also connect directly, you know, your personal things, whether it’s your RunKeeper app or whether it’s your Fitbit or whether it’s your smart watch or your other connected thing or your other mobile app, connects into Achievemint. And that becomes the portal that gathers in all of the data into our platform. And you may ask why would a patient do this. Like what’s in it for the consumer? And this is an important part that we haven’t touched on yet that I think is now the right time to bring this up because it’s an incredibly important part of our business model. So when we think about the consumerization of healthcare, we do not believe that that means consumers will pay in the US. That’s not what we believe. We believe that consumers would be paid and should be paid if they have an asset to bring to that party. And the thing that the patients have that they’ve never had before is data. And in our view, they have this medical data which of course they own their electronic health record, which they are the owners of that, right? And they also have behavior data. And that behavior data has been generated constantly through their smart phone, through their smart devices, through their web and mobile app. And we are simply saying you have a right to be monetized for that, or you have the right to monetize that, rather, and achievement is both a data portal and a way to create pipes or allow them to put data into the pipes of our platform. But it is also a rewards engine that pays them back for that participation.
SK: That’s pretty cool. It’s funny, when you asked the question, I almost blurted out, Well, of course you’re going to pay.
SK: And even then they may not do it, but more of them will do it if you pay them than if you don’t.
DK: That’s right, absolutely. And if you think about it, patients have been paid for clinical trials for, you know, I mean years, right?
DK: Not always, but in many, many cases, they’re paid. So why would they not be paid in this case, too? Only now, they can be paid for data they’re providing pretty much all the time once they consent to it. And so we just strongly believe that the empowerment of the patient here is the key to creating this outcomes definition of the future. Like it has to come from – it is patient mediated in every way. And it means it’s got to be patient rewarded, too.
SK: That’s cool. All right. So I’m going to go back to the beginning as sort of the closing comments here. And by the way, thank you for your time, Deb Kilpatrick from Evidation. This is a longer conversation, but this is a nice start to it, anyway, right? You mentioned early in our conversation that one of the things that you like to do is to think about seeing the future or creating the future. At least I have that down in my notes. And I think what you’ve essentially done here is you’ve created a business that allows you to sort of sit in that seat where digital health and the interaction of behavioral data is enabling you to see what’s going on now, but is also giving you sort of a window into the future. So from that seat, I’d ask you what do you think are the interesting developments in digital health that you’re seeing in sort of all the areas, payer, provider, medical technology, pharmaceuticals? Where do you think that we are and where are we going?
DK: So let me start with the patient, since I made a case in the last couple minutes for all of this needs to be patient mediated and patient-centric in a very real way for it all to come together. I think from a patient perspective, the patient cohort that is going to be the most broadly benefitted by the digital era of medicine are the folks like my parents, who are, we hope, aging in place at home. And I think that we could all imagine ourselves as we get older as hopefully aging in place, right? It sounds like what we all want to do. We all want to be at home. And I think that the problem with this in the past has been that aging in place meant that you became invisible to the system. You became invisible to your family, you became invisible to your friends in some ways, as you were mobility-limited. And you became invisible, certainly, to your physician, your providers. And that creates problems. And I think it arguably certainly affects quality of life for that population and probably has an effect on quantity of life in an unproductive way. And I think that the ability of digital technology to come into that home and to allow a window into that population, I think is phenomenal. And the simplest example of this is me being able to communicate with my parents from FaceTime. Like that’s huge. Because I can physically see them in their home, and so it’s not just them telling me that they feel good. I can see if they feel good. And that’s the simplest example. There are many, many others and many, many companies, I think, are doing incredibly innovative things to allow people to age in place at home in a really health way, that gives them better quality of life, which I think we can all agree we’re hoping for as we age. And so I think from a patient perspective, I personally think that’s some of the most exciting and beneficial stuff.
SK: Very interesting.
DK: On the provider side, the ability to generate – people seem to worry a lot about the patient-doctor relationship, and is it being corrupted by digital technology. I actually think the opposite. I actually think there is now a digital way to enable this incredibly new kind of patient-doctor relationship. And so I think to see where that goes, and telemedicine is just the beginning. It’s at the tip of that iceberg. And of course those incentive structures and reimbursement, that has to be worked out. That also takes cost. Those things will come. That will happen, right? I mean to me, that’s an inevitability. And I think that the development in newer ways, in our sort of everyday lives, those patient-doctor relationships are something I think’s going to be really cool. And I think it’s not going to be a bad thing at all. I think it’s going to be a wonderful thing for patients and providers. On the payer side, I think – or the health plan side, this ability to be more willing to go at risk for new technology success. And I think the reason the reason they’ve been less willing to do that is because they really had no idea of how to measure it, right? They had no idea to quantify what their risk was. And so I like to think that technologies like ours, platforms like ours that allow you to see essentially outcomes based on risk and benefit potential in populations will allow – will be very pro-innovation in health plans. They will allow health plans to see precisely where certain technologies have higher likelihood of working and lower risk of failure, and based on behavioral phenotyping, much like we do now with biological targeting and genomic information. And so I think that’s a really exciting thing. That’s pro innovation, and that’s pro small company, that’s pro-business, that’s pro innovation. That’s pro all of the things, right? And ultimately pro patient, too, because it means that patients who need things and will possibly benefit will always get state of the art quickly, which is, I think, also as a patient, what I’d like to see happen.
SK: Very cool. So we are out of time. So I’ve really enjoyed the discussion. I could keep going, but I want to give you the opportunity. There’s a lot of interesting stuff on your website about what you’re doing. Where can listeners find you? Where can they find some of your published materials about the companies? And if they wanted to reach you or communicate somehow with you, how would they go about doing that?
DK: Absolutely. Thanks for the opportunity, Steve. So folks can go directly to www.evidation.com. And if you go to the news and public relations area, there’s a lot of PR and publications and interviews that are posted on our website, so you can read more about what we do and who we do it with and different partnerships. There’s a very easy way to reach out to us. If you reach out to me specifically, I guarantee you it will get to me, and that’s directly through our website. It does not go into a black hole. There is definitely a way to reach us through the Contact Us link on our website. You can also follow us on Twitter at @Evidation and see what’s going on with different interviews or different activities that we’re doing. We’re hiring, so if you are a software engineer in the Bay Area and you’re looking for a job and want to work in healthcare at one of the coolest new digital health companies on the planet, please come to our website or reach out to us through our website. We’d love to talk to you about joining the team.
SK: That’s great. And I would say to all the software engineers out there listening we’re seeing a lot of interest in the computer scientists of the world into coming to healthcare, and we’re psyched to have you. This is a big mission. This is a big job. We’re making significant changes to the world through these programs, and it’s an opportunity for computer and IT people to make a big difference. So Deb, thank you very much for joining me. I really enjoyed learning about you and the company. And we’ll be watching you as you go forward.
DK: Steve, thanks so much for the opportunity. This was super fun for me, and I look forward to meeting you soon.
SK: Yeah, me too.
TS: Deb Kilpatrick, thanks for joining us on the Breaking Health Podcast. It’s great to hear that Evidation Health is on the case of trying to find some evidence to support the effectiveness of the many, many digital healthcare applications that are out there. Steve Krupa, thanks again for being on the job and for leading a great conversation for the Breaking Health Podcast. Happy to have you and the Psilos Group as our partners on this effort. And thanks of course to our Breaking Health Podcast listeners. So glad you could join us today. Again, don’t get shut out. The Digital Healthcare Innovation Summit is happening on November second. Go to healthegy.com – that’s the word health followed by the letters EGY.com – to register. So we’ll see you on November second in Boston. Take care, everybody.