Karim Galil:
Welcome to the Patientless Podcast. We discuss the Good, the Bad, and the Ugly about Real World Data and AI in clinical research. This is your host, Karim Galil, co-founder and CEO of Mendel AI. I invite key thought leaders across the broad spectrum of believers and defenders of AI to share their experiences with actual AI and Real World Data initiatives.
Karim Galil:
Hi, everyone, and welcome to another episode of Patientless. Today's guest is Jason LaBonte from Datavant. Jason joined Datavant as part of Datavant's acquisition of UPK, where he led a product management there. Jason actually comes from a very scientific background. He did his PhD at Harvard in virology, which means he's really in high demand nowadays, given all that's happening with COVID. And, obviously, at Datavant, he kept innovating on the product side.
Karim Galil:
We're really honored to have you, Jason, on this call. Thank you for giving us the time for it.
Jason LaBonte:
No, thanks for having me.
Karim Galil:
Awesome. So why don't we start by telling our audience more about you guys at Datavant and also maybe about you. I just gave a very quick introduction, but the journey from virology and Harvard to product management of a Silicon Valley tech company is, obviously, an interesting journey that the audience will be interested to know more about that.
Jason LaBonte:
Sure. Yes, as you mentioned, I had my PhD in virology. It was a lot of fun to do the bench work, but I did not envision myself as being a bench scientist for the rest of my career. So I was looking around at roles that would allow me to still interact with the scientific literature and thinking about science and medicine, but not actually performing that research.
Jason LaBonte:
And so my first job out of my PhD was with Decision Resources Group where I was a market analyst, and that was a role that was primarily centered on doing deep research on specific disease states. We did a lot of interviews with thought leaders, with physicians, but we also played with a lot of the data that was available at the time to build market forecasts. And so that was my first entree into health data. I was using retail pharmacy counts from folks like Ikea.
Jason LaBonte:
As I progressed in my career, we started playing with claims datasets, looking at how we could use that data to understand lines of therapy and switching patterns within specific disease sets and across therapies using patient claims.
Jason LaBonte:
And so over my career, I've got progressively involved in how you can use that real world data to understand how a disease is manifesting itself within a patient population and how treatment paradigms can be monitored using that dataset.
Jason LaBonte:
And so that was my background, of going into Universal Patient Key before they were acquired by Datavant, where really you became ... Now, Datavant focused on, how do I not stay limited by having a single dataset to work with? So a single pharmacy claim set or a medical claim set. But how do I actually find all of the data that is relevant to the analytical problem I'm trying to solve?
Jason LaBonte:
And so, as a former analyst, I could really appreciate the benefits that would bring if I didn't have to choose between dataset A or dataset B, but I could actually get both together. That gives me the ability to fill geographic gaps, to fill demographic gaps in the types of patients I have coverage of. It allows me to find different variables that aren't in a single dataset, but might be found by combining the different fields of different datasets from different sources.
Jason LaBonte:
So, I really liked the mission of Datavant in that regard. Stepping back, Datavant's vision, big picture is to connect the world's health data. Our partners are using our privacy protecting linking technology to link together disparate data sorts, datasets from across the entire ecosystem of folks who are collecting that data from patients. We're allowing our customers to really link those patient datasets together to build a longitudinal history without knowing the identity of those patients. That's a really important piece of our model, is to preserve patient privacy.
Jason LaBonte:
Our big picture goal is to create an open data ecosystem, where organizations retain full control of their data, so they get to decide what data, under what terms and to whom they're going to share with. But because they have that control, it really increases the liquidity of data to move to the people who need it to do their analytics.
Jason LaBonte:
And so our role in that exchange is really to provide the technology infrastructure to enable a data source to safely share data with a data consumer. And we support all of our clients in linking that data together, wherever it's sourced, without compromising patient privacy.
Karim Galil:
So on that patient privacy point, you guys are linking one of the world's most sensitive data. Clinical data today is the most sensitive data and there's a lot of concerns about patient privacy. How can you link datasets without compromising patient privacy?
Jason LaBonte:
Yeah, great question. So this is a technology that's been around for a little while in different forms, but I think we've refined it to what we believe is the most secure way to do that.
Jason LaBonte:
Essentially, in a nutshell, our software is an on-prem piece of software. So we ship our software to a data source that is holding PHI. That data source, therefore, does not have to share PHI with us or with anybody else, but they run our software on-prem, and what the software does is two things: It removes identifying information from that PHI dataset to turn into a de-identified dataset per HIPAA. As it removes a patient's name and address and other identifying info, it's adding back a unique encrypted identifier for that patient, which we call a data map patient key. It's also known as a token in the industry. And this key is unique to each patient. So as I create this de-identified output dataset, it's got unique patient keys for each of those individuals, and that key is interoperable with any other data sources also running the Datavant software.
Jason LaBonte:
So while I might have a record in three different datasets as Jason LaBonte, once those three datasets have de-identified their records, I now have a unique encrypted patient key in each one so that when they send their data off to another party to join my records together, they can use this linking key to know that all these records belong to the same person, even though they no longer know it's me. So that's really in a nutshell how the software works.
Karim Galil:
And how do you guys deal with things like nurses missing something in data input? So one of the problems of this industry is there is no really standard way of how you input data in an EMR system. And in many cases, Johnson can be sometimes John and sometimes can be J-O, and someone missed the rest of the name spelling and all those kinds of errors. So how can you guys ensure that you have fidelity of that integration piece of making sure that this patient is actually unique and identifiable across the whole system?
Jason LaBonte:
Yeah, great question. This is one of the longstanding difficulties with anybody, whether they're dealing with identified data at a hospital and trying just to link together all of your patient records, or, for us in the de-identified world, how do we do that matching correctly of a patient's records?
Jason LaBonte:
So, as you mentioned, there's a lot of ways that the data can be less than ideal when we work with it. It can be that different spellings of a name are used, people move and change addresses. And so the way we handle that is when we create these linking keys in a de-identified record set, we're actually creating different versions of those keys. So each record may actually have six to eight different keys appended to it based on different combinations of the underlying identifying information. So one of them may be built from your first name, your last name, your date of birth and your gender, but another one might be built off your social security number and your first name, if those happen to be present. We build them off of email addresses or phone numbers or street addresses or varying combinations of those elements.
Jason LaBonte:
So the idea is twofold. One, if there is a missing data element that restricts you from making one of our patient keys, hopefully you have the elements necessary to make some of the other ones, so you can link on those instead. But, secondly, and probably a little more importantly, is when I have multiple keys made, I can actually tease apart different matches to know which ones are correct and which ones are incorrect by making my matching algorithm a demand or stringency by saying, "I need, at least, five of the eight linking keys to be the same before I declare this to be a match."
Karim Galil:
That's really cool. And what about cases where data is unstructured? So, for example, in today's world, a lot of the physicians actually are dictating data to some sort of device and getting someone to input this data for them somewhere else. How can you guys deal with cases like that where actually the data doesn't exist in the form where you can see where's the first name and where's the last name is?
Jason LaBonte:
Yeah, that's a great question. So our system works really seamlessly with structured data because we know all the different elements that are supposed to be there and we can properly de-identify it and create these linking keys out of the structured fields. For unstructured, medical notes, imaging notes, and things like that, there's a really extreme wealth of information that's captured in that type of data. But to your point, there are two problems in working with that data. One is how do I properly de-identify it so that I remove everything that might be identifying in that note? And then how do I, perhaps also at the same time, extract those elements so I can create these linking keys out of it? Datavant has a solution for working with structured data to remove some of those elements. But, quite honestly, we have a lot of partners who are really excellent at that as well.
Jason LaBonte:
And so, Datavant, we view ourselves as a piece of the larger ecosystem. So as I mentioned earlier, we're helping people who have data share it with people who want data, but there's a whole host of other enabling technologies that are involved in making that data usable. And so we have partnerships with folks like Mendel.ai, who are really exceptionally talented at certain problems like taking in an unstructured note, structuring it, extracting values that we need to make a linking key. And I think by using our technology, along with our partners technology, that's actually the optimal way to solve some of these more thorny problems.
Karim Galil:
We actually had a very interesting experience using Datavant's technology a couple of weeks ago. We were working with a customer of ours and, obviously, without mentioning names, and they had a really interesting dataset and we wanted to know do we have any kind of intersection? Is our data anywhere intersected with their data? And if there is any intersection, can we complement this data? Or we don't have to work together because we both have the same sets of data? And the experience of getting the intersection and getting all those analytics done within a few hours, I think, is a game changing experience because now two companies can really work together without exchanging any PHI, without even having to share data with each other and can decide whether it's a good fit or not early on before getting into a study or into a project.
Karim Galil:
But to the point of the structure, that unstructured data, we invite a lot of guests to this podcast who make the claim, "Listen, structured data is really good when it comes to certain therapeutic areas and we don't have to worry about unstructured data. The case of the unstructured data become more obvious in things like oncology, things like immunology, the more complicated diseases where there's a lot of co-morbidities and the patients go through this chronic journey." Do you agree with that? Or do you think that unstructured data is essential regardless of the therapeutic area?
Jason LaBonte:
I generally think that structured data is fine for a lot of the large chronic diseases that we have built a lot of the industry around over the last couple of decades: hypertension, diabetes, some of these disease states where the coding is really well understood, it's really well populated, probably structured data is pretty sufficient for a lot of those. But, as you point out, the nuances that we're starting to see, and I think we saw first in oncology where the coding architectures just aren't sufficient, is really where unstructured data starts to shine. So I think we saw that a lot with oncology when you're looking for tumor size and staging. When you start to look at biomarkers, a lot of those elements are just not well recorded in an EHR system and certainly not in a claims dataset. And so that's where unstructured data really starts to shine.
Jason LaBonte:
But I think we can extrapolate out and say as personalized medicine becomes more and more attainable, the same things we're seeing in terms of the value of unstructured data in oncology are going to start to actually pertain back to some of those larger indications where we start to look at more and more subsets of patients with these chronic disease and say, "You know what? These folks are actually different in this segment versus that segment and I can code them both with type two diabetes, but that's actually not that helpful anymore. I need to get into those physician notes to understand what their recent test levels are, what's going on with other co-morbidities, what their ability to maintain their healthy eating lifestyle is, all those other factors." But I don't think the value of unstructured data is completely diminished at all by that trend.
Jason LaBonte:
I think, especially, as we start to think about moving into rare disease spaces with more specialty products, those patients are often not diagnosed for eight to 10 years. So even if the structured side gets better and has a code for each disease state, a lot of folks are interested in how do I mine this data for the early signal before they're officially diagnosed? How do I look through the logs of symptoms and tests that are ordered and doctors they're referred to, to understand this might be a patient with the disease that I'm seeking to enroll in a trial, or that should be getting this lab test, because I think they have the genetic condition I'm treating? That information is often to be found in that unstructured note early on here. So I do think that, as an industry, we're going to find more and more uses for the unstructured side, even as we improve the structured piece.
Karim Galil:
One funny story that we have seen in some of the unstructured data is the patient decided to stop a line of chemotherapy and the doctor wrote, "Patient decided to move from chemotherapy to milkshake and marijuana." And that kind of nuance you cannot get from structured data no matter how good your coding is because the question comes in, why did this patient stop this chemotherapy? Is it because of side effect? Or is it because the patient passed away? Or is it because he jumps to other line? The last thing that can ever cross your mind is that this patient decided to jump on a milkshake line of therapy. And this kind of nuance, you just have to go through the unstructured data to understand the context of the events that happened.
Karim Galil:
But that being said, you guys at Datavant are dealing with insane amount of data every day. Do you have access or visibility to what are the end use cases your customers share with you, what they have done with those projects, or your job starts and ends at tokenization and linkage of the data?
Jason LaBonte:
Yeah, great question. So primarily we are the infrastructure piece for moving data from party A to party B. They don't even have to move that data through us. We are just an enabling technology so that when they share directly with each other, that data is coming through de-identified and linkable. However, because we do sit between these two sides of the equation, the folks that have data and the folks that need to use data, we are actually often involved in those discussions of, "Here's what I'm trying to do. This is the problem I'm trying to solve. What types of data are out there that I could use to feed this analytic? And can you introduce me to the right folks?"
Jason LaBonte:
So we do actually have pretty good visibility into the various use cases that our clients are trying to perform. We don't do analytics ourselves, so we're not inventing a lot of these, but there are a lot of smart people doing a lot of smart things out there. And so it's a great part of the job actually to see what folks are doing, see the innovations they're leading with and to be involved in a small way with helping them out with that.
Karim Galil:
Can you share with us some of those interesting projects, obviously, things that are not confidential, but one of the most interesting projects or use cases that you have seen in the last, say, year or something?
Jason LaBonte:
I'll give you two. I think we talked a little bit about rare disease patients. One of the really interesting use cases is a bio pharma company had a new therapy for a genetic disorder. It was a rare disorder. And the concern was that physicians may have patients that could benefit from this therapy but had no idea that this patient actually had that disease, didn't know to order the confirmatory diagnostic test to say, "Hey, this is an eligible candidate." And so what that company did is they, working with a very smart vendor, basically, aggregated a number of different real world datasets into a large linked dataset, and then they built an AI model on top that said, "I have a training dataset of people I know have this disease and what they look like in real world data. And then I have all these other patients who I don't know if they had the disease or not, but I have all their data."
Jason LaBonte:
And they basically built an AI model that was able to predict which patients in the larger real world data setting were likely candidates to have this rare condition. And so, using that, they built this predictive model, and now they're at the stage of seeing how they can use that model to identify physicians who might have patients with that disease, and then educate those physicians. You know, if you have a disease, a patient with any of these symptoms, we suggest that they may be a candidate for this diagnostic test to confirm that they actually have this rare condition. And if they do, now you know how to treat them. And so that was really exciting because they were trying to pick up signals that, in isolation, in a single dataset, didn't really mean much. One test, one referral, one set of symptoms.
Jason LaBonte:
But, in aggregate, when you start to link across all these different datasets, you can now start to see a pattern and identify these folks before the physician could do so. And I think what we're going to see a lot of. Any single physician who's seeing the patient for the first time because they were referred, because the last physician didn't know what they were doing in terms of trying to identify this, that each new physician has to start from scratch. But we, sitting with real world data on top of this as an industry, can start to say, "Well, while each person's starting from scratch, I can actually see the whole picture. I can help guide physicians towards the correct treatment path here because I'm seeing more than they can see themselves." Ideally, long-term, we give the physician that tool to be able to see all of that data in one spot and they can do that themselves. But I think, right now, this is a really elegant approach to solving that problem.
Jason LaBonte:
The second really interesting use case that we're seeing a lot of interest in is how do I use real-world data to accelerate or enhance my clinical trial? Clinical trials are the gold standard for how we evaluate therapies. But in our view at Datavant, clinical trials are really just another silo of data. It's a really expensive silo, a really rich silo of data, but these are patients where we're collecting a lot of data, but we have put a subject ID on them so that we can't unblind the study that has the unfortunate side effect that we also can't bring in any more data about that patient. And so if I have a clinical trial that I've run and I have outliers that the dataset does not explain, I'm stuck as a sponsor. I have to try to guess at what might've gone wrong. Hopefully I design my next trial better.
Jason LaBonte:
What we are now doing with some of our clients is embedding at Datavant a linking key for that patient inside the clinical trial dataset. So as I said earlier, our patient keys are anonymized and encrypted. So they don't unblind the study in any way, but they do give the optionality to the sponsor to now bring in real world data about that same patient cohort. And now let's say a trial does not go well and they can't explain the results, and then, unfortunately, many of the trials end up in that situation, they can bring in real world data to say, "What is different about this outlier group that can help me understand how I can improve the trial design, the inclusion/exclusion criteria, or the other factors that are underlying why this patient responded or didn't, why they had a certain safety event when other folks didn't?"
Jason LaBonte:
That should really help us design better trials. It should help us select the right patients for the right treatments. And it doesn't even stop there. I think that by doing that tokenization in the patient cohort, once the trial ends, we can actually use real world data to follow that patient over time without them ever having to come back to a site visit. We can monitor them even as they move locations to see is there any long-term event that we need to understand around safety or efficacy? And so that's really exciting because I think it's no longer choosing between real world data and a clinical trial, it's actually saying, "Let's take the best of both worlds and let's make them combinable in a way that still protects the trial design, still protects patient privacy, but now we haven't even richer dataset for us to use for analysis on whether this intervention works. Is it safe and who is it best suited for?"
Karim Galil:
Yeah. A symbiotic relationship between both datasets, I think, is the future. The answer is not either/or. The answer is going to be the combination of both. But to the first use case that you talked about, I believe this is super interesting. A lot of patients go undiagnosed with rare diseases, and an AI model that can help detect that is, obviously, a super useful piece of technology.
Karim Galil:
The question is how are you seeing the providers? So you talked about pharma embedding tokens in clinical trials, and they have the incentive to do that, the financial and the scientific incentive to do that. But what about the providers' side? Do you see physicians using those kinds of AI algorithms, which require access to data? So, as a physician, you have to make the choice between, "Yeah, that's a great AI model, but I also don't want to get sued by the patients next day because their data were exposed." So you have this tough choice between a really useful tool but also big liability on accessing this tool or allowing this tool to access data of a patient. How are you seeing the adoption curve on the providers' side? Forget about the formula, forget about the CROs. On the providers' side? What's the adoption there looking like?
Jason LaBonte:
Yeah, I think that's going to take a while. Personally, I'm a strong believer that there always will be a physician in between data and a treatment decision. I don't believe in an AI generated treatment output where the doctor just blindly follows whatever the engine said to do. And I don't think any providers are thinking that's the right way to go. I think AI is going to be a great tool to sift through a large set of data about that patient and patients who look like them, and suggest potential areas of investigation for the physician to follow up on, potential lab tests that should be run, potential symptoms to ask about to clarify the diagnostic. But I don't think that we're going to get adoption of AI as the deciding element anytime soon.
Jason LaBonte:
So I think if we can be careful about building robust AI algorithms that are built on solid data and have been well vetted in the scientific side, I can see physicians being willing to apply those algorithms against their EHR data, to highlight patients who are at risk who needed a different type of follow-up, an earlier follow-up, than might otherwise be apparent.
Jason LaBonte:
And I think that we're seeing a lot of that happening from a population health angle from payers and providers who are looking at their overall patient population and trying to identify folks who may need an intervention that's different than what you might normally think. But I think in terms of being willing to access data and being willing to use these algorithms, there's a number of other problems that have to be solved. And, not to belabor it, but I think the data fragmentation is still the underlying issue.
Jason LaBonte:
Patients are seeing a large number of different physicians. Their data is being captured in different EHR environments, often with different data models and different data normalization rules, the ability to collapse all that data together quickly and make it available to the AI algorithm to even run on, those are big infrastructure problems for providers to solve before they could actually use an AI model at scale and in real time, anyway. So there's a lot of other problems that have to get solved, I think, before that can actually come to fruition.
Karim Galil:
A tokenization engine like yours can even go beyond the healthcare system. If you're able to marry a patient EMR record to what kind of food or groceries or what kind of shopping they are doing, now you have access to their diet. And if you can marry this to what kind of behavior they have through their credit card purchases, is this someone who is always home? Do they have some psychological disorder? Or is it someone who is very outgoing? You can marry all those data sets, which not necessarily exist, even within the ecosystem of healthcare. It exists outside healthcare. And marry this to a patient's record. Now you have a 360 view of what actually happened. I don't even think that the medical records has the full story. The story goes beyond the medical record. What kind of diet? Where do you live? Which state you're in? What kind of neighborhood you're even living in? And all this kind of data.
Jason LaBonte:
Yeah, I completely agree with that. I think the medical record is only a record of your interactions with the healthcare system. There are a host of other factors that determine whether you get a disease in the first place and what your outcome is likely to be after the fact. And we've seen that right now with COVID. The more vulnerable populations are getting hit especially hard. Just the segmentation you'd want to do to understand COVID is not available in the healthcare record. So socio-economic status, access to care, race, ethnicity, all of those variables, are poorly captured in a traditional healthcare dataset. And so I completely agree. I think we're seeing a lot of interest in ... Datasets are traditionally used in the marketing world. So datasets about consumers and their buying patterns, their housing status, their family size, those are all things that are traditionally used when folks decide how to place an ad in front of you on the Web or on your phone.
Jason LaBonte:
But that data is there and that data is available to be linked if we can run our software on it. And we're doing that in certain cases. And that now allows you to really address the social determinants of health. So what is your access to food, to housing? What's your educational status? What is your access to care and your consumer buying behavior? All of that gets really interesting.
Jason LaBonte:
I know there is a group that is looking at credit scores because they're interested in understanding, "Can I look at missed bill payments as an early indicator of onset of Alzheimer's?" So I can see patterns happening in these other datasets that give me early clues to somebody who might be having something that's going on medically.
Karim Galil:
So rather than serving you a better ad, we're going to be serving you better healthcare,
Jason LaBonte:
Exactly.
Karim Galil:
... which is, obviously, extremely more useful than trying to harass you on Instagram. We're going to, basically, be able to better understand disease. And, as you said, it all starts by how can you link the data together in a way that does not step on the patient's privacy and their concerns on data identification?
Karim Galil:
Now, you guys did an awesome job with COVID. Datavant just really was one of those companies that quickly responded to COVID and were able to put their technology in good use. Can we spend some time talking about your COVID initiative and how you guys built some sort of very collaborative approach of different companies to help them to address COVID? You want to talk a little bit about that?
Jason LaBonte:
Yeah, thanks for asking me about that, because we have worked hard and I think that's a great example of our larger ecosystem coming to bear on critical problems. So, as background, I think the COVID-19 pandemic has really revealed that our existing healthcare infrastructure, especially here in America, is insufficient to answer a lot of the key questions that would almost seem pretty basic. Just trying to understand how many people are getting infected with the disease. What is their outcome? That data, because it's siloed and fragmented across the United States, has been really hard for us to gather.
Jason LaBonte:
And so Datavant was proud to, basically, pull together with a bunch of our ecosystem partners, the COVID-19 research database, which is a collection of de-identified, linkable real world data that has been generously made available pro bono to academic and public health researchers.
Jason LaBonte:
So this collective is not just Datavant. It is Datavant with our linking technology, but the data sources that are part of our ecosystem have made their data available for free. We have technology partners who are hosting that data and building an access controlled research environment for researchers to come into, and they're doing that for free. We have data hosting that's provided for free. We have services like expert certifiers making sure that it's all de-identified per HIPAA that are providing those services for free. So it's been a tremendously joint effort to stand this up. And we stood up this research database inside of a month and really put a bunch of data out to researchers quickly.
Jason LaBonte:
To date we have over 1600 researchers who've registered to get access into this research environment and we have over a hundred projects that are live and active in a research database.
Karim Galil:
Wow.
Jason LaBonte:
Over a hundred projects. And we're starting to see those start to pop out now on the other side. So we've got some really interesting projects that started to come out, which is tremendous. I think that there are early projects looking at how mortality data, which is in the dataset, has been shouldered disproportionately by vulnerable populations and minorities. We've seen really interesting projects looking at building reopening models based on these data and what the likely effect of reopening under different circumstances would have on disease burden.
Jason LaBonte:
And so I think those are really interesting, basic science questions around the disease, who it affects and how we deal with it, that this large linked real world dataset is now answering for folks. And so it's really been a tremendous effort. We continue to get generous donations of data from more and more players. And so we definitely encourage folks to make use of that asset. It's really, I think, a great example of how this industry can come together and solve big problems pretty quickly with some pulling together of the best in class folks across all these different parts of the solution.
Jason LaBonte:
So, again, Datavant, we view ourselves as part of this larger ecosystem. We are not the best at everything that is needed, but we have lots of partners who are. And so I think we can replicate this for a lot of different needs in healthcare by now having the ability to pull together best in class folks from data and from technology, from services, and pull them all together quickly for serving some of these needs.
Karim Galil:
That's actually super quickly. You guys started that early Summer. So to see hundreds of research is being done now, that's really awesome. That actually brings me to this question: are you guys a technology company or an ecosystem company?
Jason LaBonte:
Great question. Datavant itself is a technology company. That is what we make, it's what we offer out to our clients, but like any good middleware, the ecosystem is really where the value is. I get asked all the time, "What is unique and special about our software?" There are some really nice things that we do that I think that make it really strong, but we're only as good as the people who are using our software. And so the ecosystem is really where, I would say, a lot of the value is. And so we take a lot of pride in the folks that are using our software because I think that those are the folks that are actually generating value for patients and for doctors. So I think that the ecosystem is really, I think, where we spend a lot of time making sure that we're really connecting folks together who can take advantage of each other's strengths.
Karim Galil:
Now, we come to my favorite question. The last question, my favorite question. If you can Zoom call any living person today, who would it be and why?
Jason LaBonte:
Yeah, that's tricky. I think I spend enough time on Zoom. I'm not so sure I want to do more Zoom calls. That is a complicated one. Any living person? And I'm not going to get into politics or any of those scary areas. Well, I don't know. You've stumped me on that one. Let's see. Maybe I'll come back to you on that one.
Karim Galil:
All right. Let me make it easier for you. Alive or dead?
Jason LaBonte:
Alive or dead? You know, I would-
Karim Galil:
When you actually said "Politics," I thought you were going to say Obama or something.
Jason LaBonte:
Yeah. He's still alive. I think it'd be interesting to go back to some of the civil rights leaders of the '60s. What would Martin Luther King Jr. think of where we are today? Would his approach to solving some of the issues we're still having be the same or different as the approach he took then? I think that'd be a really interesting discussion to have. I would hope he'd have insight that we would be able to apply to today's ongoing issues and, hopefully, not be sad that more progress has not been made. That would certainly be an interesting phone call to have.
Karim Galil:
That's actually a really good choice, especially given what's happening right now. And I think this applies in so many different ways, even in healthcare. I think in 50 years from now, if I ask this question, they're going to ask us, "Do you, guys, if you look at what we did today, looking back, would you have done healthcare the same way that you're doing healthcare today?" So it's always really a good retrospective question to see how your approach really worked.
Karim Galil:
Hey, Jason, thank you so much. You shared with us today really great stories and it's impressive work, especially with the COVID. So we'll just put that in congrats. Also, having someone coming from super a scientific background, like you, to lead a product in healthcare, we can see more of that happening, I think we're going to have a better healthcare system.
Karim Galil:
So, again, thank you so much and all the best of luck for you and for Datavant. Stay safe.
Jason LaBonte:
Absolutely. Appreciate it. Thank you for having me.
