This week Harry catches up with Christine Lemke from Evidation Health, a startup in San Mateo, CA, that helps drug developers and other organizations analyze the effectiveness of smart devices and wearables in new types of therapies.
This week Harry catches up with Christine Lemke from Evidation Health, a startup in San Mateo, CA, that helps drug developers and other organizations analyze the effectiveness of smart devices and wearables in new types of therapies. Lemke is Evidation's co-CEO.
Our Fitbits and Apple Watches are with us so much of the time that the data they collect can go way beyond telling us whether we’ve completed our 10,000 steps for the day. They can also help doctors diagnose cardiovascular problems, and even provide early signs of cognitive changes like the onset of dementia. But the data comes in so many forms from so many sources that it’s a real chore to set up population-wide studies and keep the incoming data organized and anonymized. That’s Evidation's specialty.
The company came together in its current form when a company Lemke helped to start, The Activity Exchange, merged with another company called Evidation. (Harry helped to incubate Evidation at GE Ventures with colleagues Rowan Chapman and Deborah Kilpatrick.) In its early years, Evidation focused simply on helping other companies prove that real-life data from consumer wearables was reliable enough to be useful in health decisions. But nowadays Evidation works mostly with Big Tech and Big Pharma companies like Eli Lilly, Johnson & Johnson, and Apple to test specific ideas, like whether data from people’s smart watches and smart phones can help predict cardiovascular disease or cognitive decline early enough to help slow or reverse the conditions with new drugs.
In July 2020 Evidation raised $45 million in Series D funding to expand its so-called Achievement platform, which includes a network of nearly 4 million people who’ve agreed to share at-home sensor data and other health records. In September Lemke became co-CEO alongside Deb Kilpatrick. Before joining Evidation, she was co-founder and COO of Sense Networks, a machine learning platform for mobile activity data. And before that she worked at Microsoft, helping to manage the Xbox hardware engineering group.
You can find more details about this episode, as well as the entire run of MoneyBall Medicine's 50+ episodes, at https://glorikian.com/moneyball-medicine-podcast/
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That's it! Thanks so much.
MoneyBall Medicine - Christine Lemke
Harry Glorikian: Christine, welcome to the show.
Christine Lemke: Thanks, Harry. Great to be here.
Harry Glorikian: Christine. I mean, I want to say I remember when this started, but then I was looking back at some of the data and then I was thinking, no, Christine was working on this long before we were. We talked about it. And so, give us you know, for the listeners who want to understand Evidation or maybe the predecessor to Evidation. Give us the thumbnail or the biography of the company.
Christine Lemke: It is true, we've been at this for a little longer than maybe folks understand or know. Evidation actually started with a company that myself and three other co-founders started called the Activity Exchange. It was a Rock Health company. And the premise of that company was, we had all come out of behavior-based advertising like that was our world prior to Evidation. So we were trying to understand how behaviors in the real world might influence people's purchasing behavior or more specifically, their clicking behavior on a mobile phone. You've got this mobile phone with all these sensors, specific location data. We ought to be able to tell you whether you're a frequent business traveler or not. We ought to be able to understand whether you're a weekend getaway person or not and be able to hit you with an ad kind of at the right time separate from demographics. We were doing behavior targeting and some of the brightest minds were doing that. My co-founders at my last company were the head of MIT Human Dynamics Research ahead of Columbia's Machine Learning Lab, etc. and some of the brightest minds were focused on this. And it works. If you look at a person's behaviors, you can definitely do a better job of influencing their future behavior. But what a shame that we were taking all of this brainpower, compute power at the time, and applying it to advertising. And when we exited that company, we looked to health care as a way to really put the technology forces to good. And this is the idealistic story that lots of tech founders come to health care with is hey, we're doing the stuff in tech and now we're going to save the world and go to health care. The reality of that idealistic view is, you know, it's really idealistic and you quickly realize that health care is actually very twisty and complex.
Christine Lemke: And, you know, nothing. Literally. You might think is a tech person you know a lot. You know nothing. And so along the way, one of the first things that we did was we actually realized nobody was collecting a lot of this data and doing it in sort of a privacy-sensitive and focused way. And so we set up an app called Achievement, and we began collecting this data really for the purpose of experimentation, really for the purpose of collecting all of this, new-at-the-time... Fitbit was kind of brand new and getting started. There really wasn't an Apple Watch yet, etc. And so could we collect this data and understand anything at all? And it turns out you could, which is great. But then we had no idea what to do with that as a team. And so we met you, Harry, and Rowan Chapman over at GE Ventures, and we met Deb Kilpatrick, at GE Ventures as well. I think she was someone who had a long relationship with you and Rowan. And we joined forces with a company that you and Rowan were starting at GE Ventures to form Evidation. And it's been you know, it's been fantastic ever since because we came from this background of tech and behavior-based targeting and large scale compute and AI and you guys and Deb came, from "Guys, guys, guys, this health care industry, it works really going to play over here. You've no idea what you're doing. You actually have no idea the value of what you're building if you pointed in the right direction." And that those two viewpoints really combined to give Evidation, I think, its superpower, which is we have built an entire system in a population to measure health in everyday life. In the beginning of our journey, these measures are really aimed at understanding therapy effectiveness in the wild. But I think as we look to 2021, as our company looks to 2021 and forward, and we foreshadowed this a bit in our last financing, these measures, to really make an impact, need to be applied in a delivery or care delivery setting. And so that's what you'll see more of our company talking about next year and forward. How do we engage individuals and motivate action based off of a lot of our work that we've done to understand their effectiveness in the wild? But how do we do that in a delivery setting versus just a study setting or research setting in which we do it today?
Harry Glorikian: I mean, it's, if I think about like and I remember like the first few days where there was a lot of whiteboarding going on and it's super exciting. I mean, we were talking about all sorts of stuff. It was great. I sort of miss those days every once in a while.
Christine Lemke: I totally miss those days. Right. So much potential.
Harry Glorikian: There's so much potential and we didn't have to make it work. Right. Then we were coming up with ideas, which is always good. Ideas are wonderful. But I feel like also that the company has evolved. I mean, it's had to like, you know, we've learned a few things and and and so, you know, where do you think it's gone from where it started to where it is today? Where do you think those, you know, some of the learnings maybe?
Christine Lemke: You know, I look at the news. Clippings from that series we did with GE Ventures and the news clippings are all about, "We're going to validate digital health solutions," which was a great way to get started to build some of the infrastructures of the company. But that was clearly not the money shot. That was clearly not where we were going to have massive impact on the world. And one of the first we've involved that we did really was we were connected with the biopharma industry through our prior lives. And top global biopharma came to us and said, "Hey, you guys are collecting all of this information in a consented way, you're studying it. You're understanding like how people are are reacting to things in the wild, so to speak, or in the real world. We really need that. We really want to understand whether our therapies are solving a true patient need or not. We really want to understand whether people are on the right therapies in a faster cycle than we do today, because what we do today is all driven off of things like claims. Data is all driven off of super long delay, really messy data, not always accurate, you know, outcome data. And we want to put a finer point to understand what's happening with that patient in in real world settings, not in sort of clinical real world settings. And so our first weave and bob was to focus on biopharma. Now the entire, like everyone was trying to get us to go, employers, payers, and things like that. And we went completely the other direction and we said, no, no, biopharma is where it's at. And so we made a bet on that. The bet has turned out well for us. So that was our first weave and bob. And we started doing all these research studies with biopharma. And then, you know, our second bob, or second act, is really going to be how we apply those measures, provide some of those measures back to individuals and really motivate the next action, especially in conditions where it's not clear what to do next.
Harry Glorikian: All right. Well, I remember when I'd come to visit you in Santa Barbara, and we were talking about how the data showed different things. And I remember like, yeah, I was ecstatic. I was like, oh, we could do that. That's amazing. Now, how do we do prove it? Do a trial.
Christine Lemke: How do we prove it?
Harry Glorikian: Like, but it's always exciting to see that, you know, everything looks like it's going in a particular direction beyond anything that I think we imagined at the beginning. A great, great direction of a company. But, you know, let's step back a second, right? you've been at this since, like you said, early Fitbit, which I don't even know. Those are now maybe in a museum somewhere. But now we've got Apple Watch and that didn't come out till, what, 2014? And now it's got functions like ECG, which they're constantly updating, and VO2 Max, which, by the way, I've got to get back to, at some point because it says that I'm below average for the last three or four days. So it means I've got to get back to exercising. But you know, what have been the biggest kind of challenges or changes for you with all these digital technologies. Evaluating them, employing them, putting them in place? I mean, it's not one data stream. It's a cacophony of things that are coming into Evidation that you have to... Do I use it? Do I not use it? Does it make sense? Is it helpful? What works and what doesn't? How do you guys think about that?
Christine Lemke: I think early, early on, we encouraged, so I think the hardest thing has been about obviously as the industry grows, there's more and more noise in the industry about what's the right thing to do. What's the wrong thing to do. And people tend to hold on to these heuristics like, "Well, you can never use a consumer grade wearable to understand therapy effectiveness," or vice versa. "You can never use a clinical grade wearable to understand behaviors in real life." As the industry learns how to leverage these tools, one of the biggest challenges is getting everyone to understand, look, let's pull out the use cases, prioritize them specifically, and then figure out the right tools to meet that need versus sort of these broad heuristics that are good sound bites, but not actually very helpful. And so along the way, we've had to combat things like, "Well, you can't use an Apple Watch for understanding, you know, whether people might be at risk for heart failure at all." And it turns out like, you know, yes, you can. You just have to know the right way to combine the data, the noisy data you might be getting from a consumer grade wearable at the time with the clinical data and clinical history and with patient reported outcomes in the moment.
Christine Lemke: And when you combine all of these things, that's where you get power. That's where you get more sensitivity and specificity. But I think folks have been dogmatic in the early days about, no, we're just going to use the sensory data and what Evidation has had to learn ourselves but also tilt the industry towards is "No, no, no. It's the combination of all these contacts that give you the most power and that there's a real danger in just relying on one data stream in and of itself. And that implies a lot about the future, where this is going. It applies a lot about all the components you need to have in place to do this well, that I think everyone has put together really well. Of course, it's really combating these heuristics and opinions that get hardened, informed by the industry before the industry really understands or knows what's possible or not. So that's actually I found the biggest challenge.
Harry Glorikian: If I remember how the back end was designed, it was sort of designed to do that from the beginning, from the get go. That was not trivial to create.
Christine Lemke: No. It's not trivial to maintain it. Yeah, absolutely.
Harry Glorikian: And I mean, also, at the same time that you've been doing, Evidation has been around, I mean, the whole space of artificial intelligence and all the tools and the advancements in hardware and cloud and everything is also sort of erupted at the same time. How much is that? How much is that playing a role in sort of the advancement of the company?
Christine Lemke: I think early on in the company, we thought we had to build everything ourselves, we thought the value of the company, in fact, was tied to the technology of the company. And this is actually kind of profound. The difference between "Are we selling a technology product and platform or are we selling something else?" And so in the early days of the company, Harry, when you and I were talking, I think you were pushing us on, "Guys, guys, you guys got to focus on the data asset. You've got to focus on what you're learning from the data." And I think our team had this bias of "No, no. I think the technology platform is interesting." Now, it became really clear really fast a few years ago that all the technology was going to get hyper commoditized at a rate none of us could have predicted, that all of the ways to process streaming data, that all of the sort of backend data stores to chop through that data at a rapid pace and a fluid pace, we're going to get commoditized, that almost every component of the system of the technology platform was going to get commodities by various vendors in hyper competition with each other. Google, AWS, Azure, plus all the services that live on top of that. It's been astounding, the rate of commoditization that has happened on the back end. There's still a technology edge in being able to choose the right things, having the right requirements, designing the system, stitching all this stuff together in concert so it all works, especially when a big pharma company or the FDA comes to audit you. So all of that is still a big technology and process challenge. But we didn't have to be experts at designing like the code base that streams the data efficiently. And that's something that we wouldn't have predicted, I think, back when we knew you and we're forming this company versus today. It's very, very clear that's not the core of our value to the industry.
Harry Glorikian: And it's interesting because I'm not seeing that part of it slow down, depending on where you're talking about.
Christine Lemke: Yeah, it's just it's honestly, it's accelerating. It's alarming. When I meet a company and they're like, "Yeah, we custom-built like everything." And I'm just like, "Uh oh, I don't know, unless that custom-built thing is actually your business, if you're going to go compete with Snowflake," then I don't know. That's a little scary to me.
Harry Glorikian: So so now it sounds like, especially based on a lot of the studies, it's predicting health outcomes is the direction that the company is taking. Is that a fair assessment?
Christine Lemke: We want to do more than predict, Harry. We want to make. We want to, we want to try and influence the outcome. But I think I've always thought and kind of said that in order to in order to help make the outcome, you have to be able to predict the outcome. So we have to, we've had to start with predicting the outcome and not to split hairs. And what prediction means that sometimes in our world prediction is just describing what's going on. You don't even have to be that far ahead of the curve, even if you can even guess what's current for that individual, like where they are in their diagnostic journey at that moment of time, you're already like 10 leaps ahead of the game so.
Harry Glorikian: Well, and if you look at where CMS is going, right, I mean, if we're fee for service, you really do want sick people, because that's how you make your money. But value based payment, you want healthy people. You want to keep them healthy and you want them paying their insurance premiums. And so it looks like where you're going with it is exactly where the puck is going from a financial perspective.
Christine Lemke: That's right. That's exactly right. And I know that sometimes in health tech, we think, you go through enough logical steps, everything asymptote towards "Oh now you're a payer." I don't think that's where it is going, to be clear. I don't think Evidation's ever going to be a payer or necessarily take risk on patients in a care setting. But I think we can enable a whole host of things on our platform to help those physicians or those companies who are going to take risk, take better risk and influence that risk.
Harry Glorikian: Well, I mean, you know, I think we had at some point talked about like once, you know, that a drug works, right? It's there's an adjudication process that can take effect. Right. Which is sort of.
Christine Lemke: That's right.
Harry Glorikian: Instead of it being a guess, which drives me nuts, right. "Try this. If it doesn't work, let's come back and we'll change your prescription." Right. Having hard data, you can nip it in the bud much sooner. I always think to myself, people actually or most people, maybe not everybody want to be healthy. They don't enjoy being sick, so if you can nudge them or at least make them aware of it, they may change their behavior. Not everybody is, but I think a lot of people will. I do miss being able to look at the data with you guys every once in a while.
Christine Lemke: Oh, totally.
Harry Glorikian: It's just it's fascinating. But so OK, let's talk about some examples like you guys are doing this multi-year collaboration with Eli Lilly, studying the data from glucose monitors and insulin pumps and looking for digital biomarkers of how diabetes patients are doing. And so. So where are you in that whole process? I mean, if that's a good example to use, yeah.
Christine Lemke: Maybe I'll talk more broadly about a couple other examples, too. So one of the one of the actual first collaborations that we did with Eli Lilly was with Eli Lilly and Apple on a study in Alzheimer's disease, really trying to understand could we use this massive digital data to understand the early signals of cognitive decline. Which, given all the recent news about Alzheimer's, it's no wonder that companies are chasing a way to identify these earliest stages of cognitive decline in a very sensitive way, because all the therapy is pretty much only work if they get there before the plaque gets there versus after the plaque gets there and builds up. So it's incredibly important to find these sensitive, these early signals. It's also incredibly, it's incredibly important to get to highly sensitive measures versus what I'll characterize as the crude measures for measuring cognitive decline today. So if you look at, like, every study of cognitive decline, there are a few biomarkers. There is tau PET scans, there's cognition tests that are administered by a clinician. And if you look at some of these cognition tests, they're actually pretty crude instruments versus. If I had all of your digital interaction data for years, I could start to see in a very sensitive way where your cognition was slowing. I could get incredibly sensitive. If I have your voice recordings over longitude of time, I could get incredibly sensitive, if I had, you know, the number of times that you had to ask for your password again, et cetera, like just way more sensitive than what a cognition test is going to be as a slightly more crude measure.
Christine Lemke: In addition, now I can get to the holy grail of medicine, which is n of one. Now I can get if I have several years of your interaction data like this, I can literally compare you against yourself versus again, the crude way we do it today, which is you get thrown in a bucket of people. Everybody gets a score out of this cognition test. And you're just compared to the mean, which, gosh, if you're, I don't know, rocket scientist, you shouldn't be compared to the mean. You should be probably compared to other rocket scientists. Or even better, you should just be compared to yourself over time. So that's what we're trying to get to in cognition. And so the work was sort of kickstarted with Eli Lilly and Apple.
Christine Lemke: And there's a bunch of other work with a bunch of other players that are going on. Evidation's really grateful to be involved in a bunch of other things to further that score. But you can see where the promise of something like this unfolds very quickly. A, we identify people more passively, which means we can do it over many years because it takes hardly any effort from you. It just takes like your everyday behavior. B, if we can catch that signal early, then we can actually potentially do something about it. We'll see what happens with the Biogen drug. We'll see what happens to other drugs like it, and see for those drugs that might be stuck in pipeline or stuck in development because there aren't sensitive enough measures to understand their improvement. We can get those past, too.
Harry Glorikian: Yeah, I've had this conversation with some of the pharma companies who don't... Everybody's responsible for a different data stream and it's the coming together of the data streams. The second thing is, now using AI and ML to actually provide automated measurement of some of these brain scans and so forth, also like will help drive this. I interviewed Rhoda Au from Mass General on voice. And, you know, it's funny when you talk to the some of my neuroscience doctor friends. They're not buying it as much right now. They're always hopeful, but they're not really buying where it can go. And you can see the power of this. And then I keep thinking to myself, based on a lot of the stuff I'm reading about other parts of machine learning and AI is, do you need as much data as time goes on? Do we need do we need smaller data sets where we can sort of see changes or do we need continuous monitoring to see those changes?
Christine Lemke: Yeah, I hadn't thought of it along those two vectors, small versus continuous. So I'm a big proponent of, like, you don't need every day, like especially for voice. You don't need every day. By the way, there's a bunch of signal to you in video, so it's not just voice, but it's also video. You don't need high resolution continuity for all use cases. And again, this is back to what's the problem we're trying to solve and then work backwards from that to see what you actually need in things like voice and speech recognition. I totally understand some of the skepticism around it. I'm a true believer, obviously, because I've seen some of the early data out of it. So I understand some of the, maybe the opportunity there a little bit better. But I don't need your voice every day. I don't need your voice, every conversation. I just need some samples over time. Maybe one way to think of it is yes, at some point predictive analytics will get great enough where you need fewer samples of things to draw a conclusion. That's true for some use cases. You really want high resolution, high frequency, continuous data, for example, in heart failure patients to predict a heart attack. You want high resolution, high frequency every day for a period of time, probably weeks, not years. But in something like cognition, you want years and you want samples, if you could. It just mostly depends on the use case.
Harry Glorikian: I almost feel like during covid, my cognitive functions have slowed down.
Christine Lemke: My my two year old is in a sleep regression. I can tell you, I'm bombing all of my cognition scores right now.
Harry Glorikian: So so this evolution of Evidation. So what does success look like for evidence?
Christine Lemke: Yeah, that's a great question. That's maybe where the most weaving bobbing has occurred at our company. I can tell you when we started the company, Harry, as like a bunch of tech idealists, success for us was being on the back end of millions of people to change their behaviors. Like I think if you're an expert Googler, you could find some presentation of me talking about, look, success for Evidation is being able to change the behaviors of millions of people and figure out a way to quantify and improve an outcome. So it was very mission driven and mission oriented. And still is. That actually could still hold true for us. I think for success for us is if we are a core part of the system to help people understand their behaviors or individuals understand their behaviors and their care teams understand their behaviors in order to give them a more personalized care experience and improve an outcome. We would be super happy, we would be super happy if we were embedded in just a few systems to do that. We'd be even more happy if we were sort of the global system of record for it.
Harry Glorikian: So right now, you've got various customers, and I remember like when the first press release came out, I remember a deluge of people knocking on the door. So I don't think that's changed. But right now, for your typical customer collaborator, however you frame it at the company, is how do you work with them? How do you make money by what you're doing? Right. Because we're talking about the big picture. And so what are the brass tacks if someone was interested?
Christine Lemke: Easy. Today, most people engage with us on studies so they have a question or hypothesis about behaviors in real world settings and the effectiveness of their "thing," I'll call it, whether that's a diagnostic test, whether that's a therapy, whether that's a digital therapy of some sort. So they have a thing and they're trying to understand whether their thing makes a difference in a person's real life, not in their sort of clinical claims life. And so the easiest way to engage with Evidation when you're trying to solve that problem or answer that question is a study. And so we in many ways are almost like a digital site or CRO where a lot of our studies happen on our own population and we can do them rapidly, we can do them efficiently. And we have a claim to fame of being able to generate some of the highest quality data out of the cohort of folks and being pretty full service about it. At the end of the day, like you don't have to go out and contract with 50 different people to run your study. And a lot of cases we were already partnered with folks. We already have all the pipes for all the data that you need to collect, et cetera. And so the vast majority of people who come knocking on our door have a hypothesis and have a study to run on our population.
Christine Lemke: And with us, there's another part of our business where we're licensing our technology platform out and we're partnered with folks on the operating model for larger types of products. Examples of those services and products are things like the Heartline study with Apple and J&J, things like the Lumi Health Program in the Singapore government with Apple and the Singapore government. And there's a couple of others that we're confidential about, but that'll be launched pretty soon and sort of talked about pretty soon.
Harry Glorikian: Excellent. You mention Apple a lot, so they seem to be spending quite a bit of money trying to understand their products as well as the impact that they have on patients.
Christine Lemke: Yeah, we talk about Apple partially because they are public with a lot of the work there. We do work with other technology providers, too, or technology companies, too. But they're pretty public, oddly, because they're not usually. But we're definitely announced as public partners of theirs and a couple different of their initiatives. The other thing that we love about Apple is there's this unified vision or this aligned vision on privacy related to all these things. You can imagine. Harry, if I can start telling you whether you're cognitively declined before you know it, before your loved ones know it, before anyone else knows it, that's pretty sensitive information. That's information you don't want your employer to know this information. You don't want your payer to know. Apple, I think more so than any other tech company is very aligned with us about the risk and value of that and how to treat people in that interaction in a sensitive way and how to push the envelope on making sure that that data is extremely private to you and controlled by you.
Harry Glorikian: No, I'm a firm believer, I mean, it's funny because I know a lot of people will argue with me about Apple. It's just a privacy policy. I'm like, you know, it's not. It's more than that right. People don't understand. I remember when you and I were talking about core iOS and, you know, even my my patents in location based services. I mean, the amount of detail that you understand about a person is insane. If people actually understood what we understood, that's why most of my stuff is off
Christine Lemke: We know at that level you could know more about a person than they know about themselves.
Harry Glorikian: Absolutely.
Christine Lemke: And yeah, so that's both exciting. But also there's a risk to that. And I know tech is famous for, tech people are famous for "move fast and break things." We are not. We're move fast and be extremely careful about not breaking things, especially as it relates to privacy.
Harry Glorikian: Well, I think we've gotten to a point now, I'm hoping that that mindset changes. I mean, some of the tools that have emerged of how you can look at data, how you can see patterns in data, how you can you break something today, it has a much more profound effect than, say...the thing you broke 10 years ago, at least from what I can see, from what you can do with it.
Harry Glorikian: So I don't know of any. But do you do you have any competitors in the space?
Christine Lemke: So if I think about it from a vision, perspective, our ideal feel and look is we're kind of a learning system, where people can put a lot of their tools and services to help individuals navigate the system. They can put those in, test and learn, and apply the ones that work, especially in a personalized way. And the folks who have the closest to that vision and a lot more money than us, we are definitely the underdogs in the stor, is Verily. They've got that vision. They're executing it in a different way. They're probably executing at a different scale in terms of billions of dollars and Evidation, I would say we're the scrappy underdog, but have done and accomplished a lot given relative resources. And I think we potentially push each other to realize this vision much faster.
Christine Lemke: So in terms of longer term vision, I would say, look, we want them to win. We also want to win. And I think we'll both end up winning in this if everything goes well. But we we think about them a lot.
Christine Lemke: I think on a micro scale, you know, there are a bunch of folks who are sort of doing parts of the system, I'll call it. Like there are folks who have sort of study software where they have a cool whizzamajig, where you can just start to throw up like a study process and have an app in short order. And that's great. But like I said, the technology side of this is not the thing for us. That's not what we're focused on. We're focused on the population. We're focused on the methods for getting the data. We're focused on the data methodologies, et cetera, and consumer engagement. So that's great. Maybe someday we'll use one of those. Know people often conflate some of what folks are doing on that side of the house with whatever that is. But that's just a component of what we do and not even the focus of what we do.
Harry Glorikian: Yeah, I do think, however, after meeting the guys in Santa Barbara and you, I think there's a magic of a of the two coming together that really makes everything, makes the flywheel turn.
Christine Lemke: 100 percent.
Harry Glorikian: You're now the co-CEO. What are the challenges? What keeps you up at night, other than the your children?
Christine Lemke: My two year old who does that a lot lately. What keeps me up at night. One of the tough things when you're growing a company like this is I see some of my friends running sort of Silicon Valley breakout companies where they're scaling from zero to like thousands of employees. It feels like within a year they're just on this sort of exponential growth trajectory. And that's, I'm sure, hard in its own way. But they're skipping the messy middle and Evidation has grown in a way where we haven't been able to skip the messy middle. In a way, we felt the messy middle. And this is emblematic of, I think, lots of our peer set in the health tech space. You look at some of the best health tech companies out there, and we've all grown this way where we're not allowed to skip the messy middle. We feel it. And so what keeps me up at night is constantly reevaluating my own skill set to see if I still fit, to see if I'm still the right person to lead the next phase of the company, because you actually have to be very good at the next phase of the company in order to lead it. Because we spend time in that next phase, we don't get to skip it. We don't just get to hire a thousand people and go, OK, we went from a small company to a gigantic company. So that's one thing that keeps me up at night is what I enjoyed personally enjoy doing, making sure that the company stays agile and innovative, but then also knowing that there has to be a lot of organizational structure in place because the next phase of growth requires it. I think the other thing that keeps me up at night is just, look, I'm someone who lives in the future and wants to make the future, wants to pull the future to the present at a lot faster pace than I think most people are comfortable with and I constantly worry about that, like someone's going to beat us to the future. And so I constantly worry about that.
Harry Glorikian: So, well, I wasn't going to ask this, but now that you open that door, it's like I, I truly believe that covid has pulled forward the future in a way that if you and I were talking about it, I would on some things I'd be like, "That's another five years. That's another 10 years." But some of these items all of a sudden have become an imperative for providers and so forth. And they're they're pulling the technology forward. And the fact that we've got another, I dare say, five or six months in this covid dynamic. Some of this, I think, is going to be permanent and it's going to move Evidation or Evidation or companies like you forward faster. Do you see it the same way, or am I just spending too much time in this room by myself?
Christine Lemke: No, I see exactly the same way. There's this big wave that happened this year that caused a lot of obvious destruction in society. But is forcing the adoption, the rapid adoption of a lot of what we've been talking about for years, and it happened all of a sudden. So it's like being a surfer on this big wave and it's coming—-and all the other surfers are rushing out to the waves, by the way--and you're not just crushed by the wave and you're also hoping not to collide with all the thousands of other surfers who have now joined you on this wave you thought you had to yourself. And you want to you want to survive that. You want to excel at it, survive it, get ride that wave all the way to the end of it. And and so that's that's the trick now is, this acceleration has happened. You're right. This massive change in the industry's happened not by what anyone could have predicted, but consequently there's a lot of tailwind. But there's also a lot of new competition and people sort of pivoting into the space and so trying to distinguish yourself from that noise, being confident that you have the background, the experience to do it well, to ride that wave well, and trying to dodge all the other surfers who are probably going to wipe out is a trick. It's a trick.
Harry Glorikian: I have faith. I know some of the team members and I have I have lots of faith. So thanks for taking the time. It was great to catch up. I can only wish Evidation the greatest success. Right. It's been fun to watch the evolution.
Christine Lemke: Well, I appreciate the time this morning, Harry, and it's always so good to be with you.
Harry Glorikian: Thanks.