Founder Stories: Arif Nathoo, Co-Founder and CEO of Komodo Health

Hey everybody, it's Jessica DeMaso with What's the Future of Health. We are talking to the who's who of health technology and healthcare innovation. And today we are welcoming Arif Nathoo. He is the CEO and co-founder of Komodo Health. And Komodo, for those of you who have no idea what I'm talking about right now, This is a data and analytics company that connects the dots between individual patients and large-scale health outcomes. And so we are going to dive into exactly what that means and why they have just raised $220 million from Tiger, which is, you know, Tiger throwing money at great health tech companies along the way here. But you guys also have investment from others, Kasdan Capital, Iconiq, Andreessen Horowitz, SVB Bank. A really great pedigree of investors. So they must see something big in you guys, and we can't wait to find out what it is. So, Arif, welcome to the show. Thank you so much for joining us.

Hey, thanks so much for having me.

All right, so let's dive right into what it is you guys do, because like, I think a lot of times with data and analytics health tech companies, people, their eyes just kind of glaze over and they're like, yeah, yeah, machine learning algorithm, okay. So tell us what it is that you guys do and the value that you are delivering to the disruption of the healthcare system.

Fantastic. Well, thanks for the question. I mean, at the end of the day, um, data analytics platforms are so difficult to understand because they all solve like one little problem that someone has. And when we started this company, the thing that we were looking at is to say, well, every payer, they see their patients, but they don't see other patients. Every provider sees what happens when patients show up there, but they don't see what happens when they leave. And we said, well, if we could start assembling the story of how patients actually flow through the healthcare system, We can learn something from that, A. B, we can actually build great software that people will use to study populations at scale. And so that's what got us excited about it. So what we do is we build software that helps us derive insights from, you know, hundreds of millions of patients across the country that allow us to then help businesses actually go out there and address patient needs, whether you're a payer trying to figure out the risk or what care a patient needs, or you're a life sciences company that's figuring out how to launch a product. So that's in a nutshell, right? We sort of go—

all right, let's start at the basics here and just explain to me, like, so how does it work? Because this is like de-identified patient data. So how do you know that the data that you're getting from one person visiting their allergist is the same person who goes to the pharmacy and then picks up a prescription? Like, how does that part of it work?

So that part is actually very easy these days. And that's because we de-identify on the site with software that, you know, encodes that individual with a certain identifier, and then that identifier can match up with another completely de-identified record. We actually think that that problem is one that's allowed, like, interoperability of healthcare data to be used at a de-identified level. So that's the easy part. The harder part, though, is to the point you're getting to, which is like—

The $220 million part is?

Exactly. Well, Well, there could be two people with the same name born on the same day that both have the same token. And so how do I know they're the same person or different people? So there are those challenges. And then there are challenges, okay, once I've identified or linked those two people together to some anonymous identity, well, what do I do with it? And so there are all these problems that start the moment that you put two de-identified records together. And that's what makes this really hard. And that's what actually makes it a lot of fun for us who are like really big data geeks to go out and figure out these problems, do it in a way that protects privacy and then actually build software that people want to use. So we have to kind of reimagine the whole experience of using data to actually get to something that helps the healthcare system.

All right, so give us a sense of the kind of data that you're pulling in. And then I want to hear what some of that stuff— like, OK, great, now we've paired the record. And what are the insights that you were able to glean from that stuff?

Yeah, exactly. And so this is where I'm going to get to all that.

I mean, we're talking about claims data, right? We're talking about pharmacy data. But give me a little bit more. Tell me what else is in there that we might not expect.

Yeah, absolutely. So claims data is exactly the record— like when you go to see a doctor, they basically bill the insurer for that experience. And then they kind of negotiate with the insurer back and forth. And we see that transaction that goes between the provider and the payer adjudicated through a lot of different intermediaries that touch it. That is just a proxy for your health, right? I mean, it's not your personal health record, which has all this richness and detail. A claim is literally an artifact of the billing system, which is this really archaic thing that's existed for decades. To that though, there's all this interesting data that gets generated every day. There's data on your watch, on your wearable. There's data in your apps for whatever, whether it's a digital therapeutic or it's some app that you're using for some behavior. There's data in your labs that come out of genomics data, as well as like just regular everyday tests that you run that get sent off to a big national reference lab. And all of that data is in this world, like just becoming more and more available. And so if you start thinking about this, well, you know, we focus on the billing transactions because A, they're like kind of the root and the base of the healthcare system. You wanna know how much something costs actually? Well, follow the money and like follow the actual transaction from a provider to a payer and see how it comes back and what it gets billed at. So that's just the foundation, but that's a really good place to get cost, to get outcomes, to kind of get like comorbidities of patients. But to that, we actually have to enrich it with all these other forms of data. And that's where we find a lot of opportunity these days to really enhance machine learning and AI in healthcare by using these other forms against kind of traditional claims.

Okay. So it's like, I mean, could you, can you share with us some of like the things that maybe we would never expect? Like, I mean, you mentioned wearables, digital therapeutic stuff. Are you guys using like maybe zip code data, voting data? Like, is there any other stuff that's out there that's publicly available that you guys are drawing in that we might not expect?

Well, like a lot of the data that you're describing, like, you know, kind of household information that's happening in different areas. Well, it allows us to see these incredible correlates. Now there's limits to what you can do at a de-identified patient level versus what you do at a population level. You can still draw these incredible trends by using publicly available information on like census and on just kind of looking at cohorts and populations in different ways. But I think what the magic happens is like when you think about like really rare diseases and you say, well, there was this like real big thought for a while that, hey, Well, I wanna know the browsing habits of individuals and maybe there's a trigger or a cue that would suggest that this individual has this ultra rare disease. That's a really good idea in theory. And we've seen so many examples where, you know, like companies are trying to stitch that together against kind of a claims backend to really figure out, are there behaviors that I can impute on a patient that will say, hey, you're likely to have this based on the things they're searching for? Well, we started with just even the, The basic view of that was just use the claim, like follow the claim's history on a patient as they get tested for different diseases. Know a population that's actually, you know, positive for a disease versus negative for a disease, and can you train just on that and like learn something about how patients come through the system? And there's a ton we can learn from that. So we started with the basics, which were just like, let's just build predictors off of like really boring claims data with lab outcomes. And the performance of that is incredible, and you can learn a lot that actually helps providers and payers intervene better with patients. Now the cool stuff happens. Now it's like, okay, well, tell me where care is very bad. Well, let's look at the dynamics of that area or that zip code. Tell me where, you know, there's some interesting correlations between, you know, races, race and ethnicity, and how a patient experiences the healthcare system. And you can find that across different conditions. And that's really where it gets very interesting for us as we start bringing some of these newer data forms That's very cool.

So can you share with us, like, okay, so because we've got an audience of like healthcare geeks and data geeks and healthcare data geeks, so like blow our minds with something that you were asked to figure out. Like, can you, can you share like a really, like in your mind, like you think back and you're like, whoa, I can't believe that we were able to figure out X. Like, do you have a good example?

Every day. It's like every day we're like grabbing something. So because every, every person that's solving a problem is facing this question of like, I don't understand what's happening here. Like, tell me what it is. Like, and so, you know, we had this, we did this very interesting study looking at the usage of CAR-T, which is a new therapy that allows patients to get a pretty curative treatment for diffuse large B-cell lymphoma, DLBCL. And what's really fascinating is that there are only certain centers that actually do CAR-T therapy. Well, Guess what? If you happen to see a certain oncologist, your probability of actually making it to that center is like 5%. And there are other oncologists you could see that like you have a like 80%, you know, probability of getting to that site. And it's solely based on who you're seeing, what kind of patient you are, and, you know, kind of even matching the characteristics of those patients. And so it teaches us that the system is full of all of these very big idiosyncrasies that are driven by like what kind of insurance coverage you have, what's the predilection of the provider you happen to see, who you happen to see based on which app you chose to pick the doctor you wanted to see. All of these things go into your likelihood of actually getting a therapy you may need. And so we think about these things all the time, but we were looking at patients who had to jump through 10 different providers to get referred to a center and others on their second. It had nothing to do with where you were, like which geography you're in, because you could always find a provider that would actually path you to the right place. And so it's, fascinating to us because that's, that, that just shows you where the healthcare system is in our country.

That's crazy. I mean, and this is like where it's like, I, you know, I asked you to blow our minds. Like, that's a great example. Good example. Good job. So I want to know, so you guys, you guys are working hand in hand with like healthcare incumbents to try to provide this type of like analysis and insight so that they can kind of, they can work on improving the way, you know, care is either delivered or how patients are routed or like how clinical trials are recruited for or whatever it is. So you guys work with pharma companies, you work with health plans, you work with health systems. So can you tell me what they come to you looking for? So like, if I'm a pharma company, it's like, I feel like that's the easy example is the let's recruit for a clinical trial in a more, you know, in a quick, more comprehensive way. But like, what else does a pharma company use Komodo Health for?

Yeah, so great, great question. So if you think about like the lifecycle of a drug from like I've got to, you know, I'm in discovery and then I'm in my preclinical and then I'm going through my clinical trials. The moment you start hitting stuff in people is really when Komodo gets involved, which is to say that, you know, we help companies really identify where that unmet need is, where those patients are and kind of work them through the clinical trial lifecycle. But then if you think about the real challenge of a pharma company, once the therapy gets approved, how you commercialize it is really what we're out to revolutionize because so much of the way it's done today is through like legacy data aggregators where they just kind of count pills that doctors prescribe and say, ah, you're the top high-prescribing doctors, go talk to them. And that's the way that pharma companies go market. Well, look at what COVID did. It's decimated the entire sales model for them. They can't sit around the doctor's offices with folks who are just gonna sell them on the merits of a product. We now need to start looking at the patient in a totally different way. We need to start treating the patient as a human being. We need to start engaging them in a very real way. We need to think about the provider, not as like kind of a pill, pusher, but as a, like, you know, kind of an overseer of patients at various stages of care in their lives. And like, if we start thinking about it that way, then the entire pharma commercial model has to change. And so a lot of actually where we focus in life sciences is helping them rethink the way they're doing business, which became much more patient-centric, helping link between those patients and the providers that take care of them, helping understand the payer environments that those patients are in, and using that to make better decisions on, on what they do. So that's really where we see that huge opportunity. So not just the trial side, but really pushing that, that, that, that whole life cycle and helping that patient get the therapy they need. And then if you—

yeah, go ahead. I was gonna say, talk to me about health systems next. Cool. So health systems, I like hearing these specific examples and I hope for the audience it's helpful.

Yeah, it totally makes sense. Right.

Brings the concept to life rather than, oh yeah, it's an algorithm.

Right. And I mean, that's, that's exactly most healthcare data analytics is just doing something to your point, incredibly arcane. So now I put myself into, in the like, the position of a, of a provider, it's like, okay, great. I see the patient when they're here. And the moment they leave, I don't know if like they're seeing 10 other providers, like outside of my system, or they're actually not. And they're only using me. And that visibility or the lack of visibility is really what limits healthcare systems' ability to actually provide a more comprehensive kind of experience for that patient. They all are focused on how do we create a better experience? And if you think about the rise of telehealth, well, now all of a sudden patients are like, They're pulling up an app. They're finding their own doctor. That doctor may not be tied to the institution that they're getting their primary care at. And so all of these different ways that patients are sourcing providers is just completely changing the nature of what it means to be a healthcare system. And we think in the next 5 years, you're going to see this incredible balance between a digital native experience for patients who want digital only and thinking about how does the institution, which has traditionally had both inpatient and outpatient services, live in that, in that world. And so we're seeing a lot of our healthcare systems really come to the table with trying to understand quality, cost, and care within their institution, but then looking at what happens to that patient outside of it. So very much where our bread and butter is, which is providing visibility when that patient exits the system to say, here's what's happening, here's what you can do better to kind of get the right kind of quality and care for that patient. So that's on the provider side.

All right. And then you, you, you said the magic words, healthcare costs. So we're going to ask about the payers too. So, I mean, as far as they're concerned, concerned, because this is, I think, where things like, for some people, it's like, I, you know, it's like, oh sure, the healthcare providers, I'm glad they have my data because my care is going to get better. But as soon as you start talking about the plan and the dollars get involved here, and it's like, are my premiums going to go up? What's going to happen if they know what's going on? It's all of a sudden people become a little suspect. So what are you guys doing with health plans?

It's a really big challenge. And what's also happening is that patients are moving from one health plan to another. Quickly these days that they lose the continuity. And, you know, there's this big kind of shift in risk, like, and what level and what time horizon does a payer actually care about a patient's risk? Well, it depends on the dynamics of that population, depends on their contracts. And so a lot of what we see in the behaviors you would expect— someone who thinks very long-term has a long-term risk view on their population, someone who thinks very short-term tends to keep patients just for a year— that plays out in the decisions that they make all the time.. And so one of the things that we've really been able to do is like look at risk in a very different way. Look with a broader lens, look over a longer horizon so that people can start thinking about care management in a totally different way. Right? So if I'm acquiring a population of patients and they, there's a lot of arrhythmia in that population, and it's not something that I'm going to see right away, guess what? Knowing that, you know, I may offer them programs that may help them with, yes, it manages my costs, but B, it prevents them from having a bad event. And that's good for both of us. The payer and the patient.

And so you're able— if somebody just lands in a health plan, you're able to kind of figure out from the past if you've got that person in your system, and even though they're de-identified using their token, figure out, okay, this is what has just arrived on your doorstep, health plan.

Correct. And what for us with health plans, even just population-level insights help them radically, you know, the way that they're behaving. And so that's where we focus is to say, hey, look, this is, you know, where you're going to have a lot more issues. And that kind of visibility allows them to then ask 10 other questions that helps them get that So these are— and it's a big investment for them. A lot of them are investing in data and analytics and trying to figure out how to make better decisions that go beyond essentially just underwriting and very simple kind of interventions to a fully digital experience for patients. I think everyone's trying to wrestle with what does that mean for the kind of data we need in order to make better calls.

All right, I've got to ask a question on behalf of my OG health tech friends, old man health tech, let's call him, Matthew Holt, who had pointed out to me as we were planning for this interview that there have been companies that have done this been doing this for years. Like, I mean, there is a company called IMS Health that's been doing this for 30 years where they've been taking prescription data and, and matching that to figure out how drug reps get paid. They're part of a huge company that makes $40 billion, is publicly traded. Like, so how are you guys different than that? And it's like, you're not the only newcomer in this space. I mean, there are others like Clarify Health. I mean, so how is what you're doing different from what they've been doing for all this period of time? And how are we going to benefit from it in a new and different way?

Oh, this is a great question. So, um, because like I think it's like we all were in a place where we've used IMS Health or IQVIA's data for many years. In fact, a lot of the inspiration for Komodo came out of a world where, you know, I was doing analytics on de-identified data 10 years ago and being constantly frustrated with the quality of it. When you get the data, you have no idea whether a record is partial or complete. You have no idea what happens when you don't see an event. Does that mean nothing happened or does that mean that they're truly was nothing. And the difference between the two is like a massive difference in how you would train any model. And so the quality of the data that's existed in the market to date is massively inferior to solve problems that require, or that are ones of machine learning and where we kind of want to take the world. The problem is that the data has so many holes in it that makes it virtually impossible to train rigorous models against it and then end up with really good performance outcomes. And so when, what we realized was that that data, which a lot of the legacy aggregators, like you mentioned, sell, is derived from provider experiences. If I see a provider, I get their data, great. But then when they leave that provider that I have, they go see another provider, I have no idea what happened. And so we've been able to kind of work with both payers and providers to stitch that truth. And that's what we mean by a ground truth, because a payer sees one thing, a provider sees another thing. But the truth is somewhere in between the two, right? Neither of them have the complete story. The payer only sees the patient when they're enrolled. A provider only sees the patient when they show up within their four walls. And therein lies the problem with all of the data that you're using to train models. So there is this huge issue with, like, all the data that's being kind of traded and sold is just of one flavor, and that's derived from providers. The second problem that we ended up solving is that, well, all these new forms of data require our ability to link and to analyze populations. As new— newer and newer datasets become available, it becomes very hard to do that at scale. It becomes hard to do that compliantly. And so a lot of times what these companies have done is control the entire process to say, well, you have to give us the data, we do the certification, and then we will perform an analysis and share it with you. And that's like very antithetical to this sort of democratization of data. And there are a lot of companies out there that are now making datasets available. And we sort of eventually see what we are doing with the healthcare map as being the ultimate public benefit, because I want everyone to link their data into it and perform analyses. I don't have to control the analysis. You can perform the analysis, and the opportunity for you to grow and get better and learn something about your population grows. And so that's the— really, the opportunity is a totally different business model that is changing the way people think about and use data. And my job is to provide a really, really high-quality data map as well as a really high-quality software experience that allows you to get the value from it. So we're taking a totally different approach to doing that too.

All right. So say a little bit more about that data map. And then I'm also curious, you made me think of this when you started talking about the way that you are stitching things together to build truth here. How do those new data sharing rules impact your business? I mean, is this a great thing for you? Or like, have you been like, you know, like you're—

We've been like waiting for this for years, right? So because you could look at the world, it's like, oh, I had— I, you know, I'm allowed to, you know, give population-level data, de-identified, so that you can work with it across other data sources. Great. Once patients start to take charge of the system, and we're working with a bunch of patient-centered apps that are doing this, we can, through consent, start to bridge that in. And imagine now you can get personalized recommendations. Now you can actually control how your data is getting used. And we think that is truly like where the industry is all going. And we think the data sharing rules are just part of that progression, which makes a lot more patient-level data available. So we think it's awesome.

And are you ever going to cut the patient in? There's like a whole group of patients out there who demand being paid for their data. Um, I always think of Mighty Casey Quinlan, but like, you know, there's a group of them that are very strong advocates. They've got a point. You're using my data, you're benefiting from it, the health system is benefiting from it. When am I going to get a kickback on this? What are your thoughts on that?

Well, I actually think patients are entitled to make these calls, right? So for data that is really theirs, and I would agree that like your EHR, it's about you, like that's your data, you know? And so your ability to control that, to tell like the big EHRs, whether you're Epic or Cerner or someone else that, you know, I control this record, I should have the ability to get paid for it. I should be able to opt out of you sharing my EHR with someone else. Like all of these things, I believe like That's great. That's absolutely the way and direction we should take. And if you think about it from our perspective, unlike advertising, right? If I was going to serve up a, you know, user data to kind of shoot you a better advertisement and make money off of that, the benefit you have by sharing your data is that you actually create public health benefit. And there's this really great opportunity if you say, look, I have this really rare disease and I want to share it because I want others to learn from it. And there is like an altruism in that. And then there is, well, an expectation of fair compensation for that. If those two things exist, you know, where I do want to share because it's good for science and I do want some piece of it, whether that's, hey, I can pay this forward, or hey, it actually benefits me financially, or it gives me access to therapies or whatever that form of compensation is, that's good for the population. So I think this is like the beginning of a conversation that the country is going to be having over the next few years. But I'm incredibly supportive of it because I actually think that contributes to the democratization of analyses that benefit the public. And that's really what I— yeah, what I'm a big fan of.

No, I love that. I think that's great. And I'm curious, too, about the way that you guys are even executing within yourselves. Is it possible for a patient— let's say I'm in there. And it's like, rather than— going back to your example with the health plan that might get a population of people that have an arrhythmia who are new enrollees. It's like, if I know that, rather than just like sit there and wait for the health plan to send me some push notification, can I also opt in so that I know how I'm being flagged? Is that something that could happen in the future here? Like, where it's like, I want to know, like, what— I mean, because I'll tell you why. I'm like, I feel like, you know, I don't even look at my own data to be able to draw any type of logical conclusions from it. But I would love to know the way that this is showing up. And if I could be actually I'm taking better care of myself as a result of some of the things that are learning that I'm even missing. So, I mean, what are your thoughts on that? Is it possible for a patient to like opt in and say, I do not want to be de-identified. I would like to be identified and I would like to know what's happening.

Oh yeah. We are going to see that re-identification pathway like be everywhere. And I totally agree with you. I feel like there are a ton of really new and exciting companies that are all nibbling on the edges of that consent. Give us regular But a lot of their business models are predicated that I'm going to resell this to pharma. So, you know, there's a whole, like, there's a very just fascinating business that's kind of coming up around, you know, come and get your records from us and, you know, we'll then use it for something else. What I love by what you're saying is like, hey, I just want to, I want to be able to opt in so I can contribute this. And, you know, if it gets me personalized recommendations, like, that would be awesome.

And yeah, years ago I interviewed the people of Oscar Health and I was like, I had an Oscar plan and I was like, I just did this DNA test, like, why can't I upload this? Like, help me help you.

It's great to hear you say that.

I think what stops that in mass is what you said is like the business model that usually ends with, I'm just going to sell this to pharma. I feel like that does put a lot of time.

Consent right here. And you too can be part of this great community of people that we're selling your data to. I mean, it's like an amazing amazing catch-22 with those. But on the other side of it, and this is what I love, Google and others are starting this world and saying, look, you can opt in to share your data. We can collect it. We can share it. But it's your choice. And if you do that, you have to go the other way too, which is say, we will also remove you fully if you want to be removed. And I think a lot of people don't— they want the benefit, but they don't want to take the risk. And so I think we're going to see some really interesting business models coming out of that. But more importantly, I think all providers and all payers are going to get much more conscious about this. And we're going to let this play out at a policy level too. Because I do think that people fall on both sides of the camp, and they should have the freedom to do that. And we're going to play a role in that too. There's no question that we're really advocating for people to share what they want to share and not more than that. And that allows all of us to perform better science. And I think that's really the best part of this.

All right, so tell me what's next for the business. Because we've been talking about big theory here for a few minutes. And I appreciate you letting us hear what you think is going to happen in the industry. I think that's so fascinating. But I want to hear about the business. I want to hear about Komodo and like what's next. You guys have raised like $314 million total, freshly funded with this $220, which is like, I mean, Tiger's been going in for some big rounds lately, but they led this one and it's like, oh my God, this was like double the fun of their usual investments, at least these last couple of weeks. So tell me what you guys are going to use the funding for. I mean, what's next? Give us a sense too, I mean, if you can, when you start answering this, you know, talk about where you guys are at right now. How many people work for you? Where are you guys based?

Like, let us get to know the business a little bit and tell us what's next. Fantastic. So we're about 500 people. We're based in San Francisco, Chicago, New York, the UK. We have a growing presence in Europe. And for us, you know, this is an opportunity to really drive our thesis in the market. So, you know, what we—

Come on, what does that mean? Specific.

So our platform is really allowing folks to access the healthcare map for the first time, and we've run a whole bunch of tests with different types of clients, from insurers to pharma companies, to see how they would use our platform if they were able to actually build their own models right into it and plug them right in, run predictions off of our data, and then put them into production on their side. And it feels like, as we've been scaling up these experiments, that there's a massive appetite to do that. Every one of these companies are investing in their own analytics teams, their own data science teams, and we believe in the democratization of like machine learning and like opportunity for anyone to build. Like, and I think again, this is a very different business model than a lot of SaaS companies that I have to control that. IBM Watson tried that. We saw what happened to that.

We saw what happened.

You know, and then we look at the legacy aggregators, like, you know, the ones we talked about earlier, and they're saying, hey, look, we'll sell you lots and lots of data, but we don't tell you anything about it, good luck. And so, you know, if we sort of look at the world and say, okay, if we're going to scale this thesis and market, we got to get really good at taking our platform, making it usable by different teams, building in great workflows, building in great analytic modules, making it easy for them to pull out the insights they need. So, so a lot of the money is actually being used to drive more engineering resources to actually build the platform to its fullest potential. This is one area we've been kind of an engineering product product-first culture from the get-go of Komodo, and we're going to continue to invest there. And then as well, as we've been starting to take our story to market, the demand for bringing us in is really high, and we are such a small and lean team that we're actually starting to really expand our commercial organization and our, our CSMs that service our, our software and really investing in and creating a great customer experience. So you'll see basically the investment going to driving the core business. We're doubling down on our platform investment We made one acquisition earlier this year, a great company. Yeah, Maven, right? Yeah, absolutely. And that's been an incredible opportunity to work with CIOs at large pharmaceutical companies and others to bring in their teams to start working with our platform. And we're going to continue to invest in that over the coming year.

So. All right, last thing for you before you go, tell us how COVID impacted you guys. Because you had alluded to it earlier when you were talking about just like the way that pharma does their business and like, okay, well now, so it's like, how did this impact you guys?

Well, as a, as someone who sits on essentially visibility into the system, when folks are walking the halls of these hospitals and they're seeing what's happening, you think like, oh, well, we don't really need data to tell us much because we can, with our eyes and ears, see what's happening. And the reality is that once that has gone away, and then now that so many, so much engagement is happening virtually, our ability to understand the system is totally different now. So for us, we've been seeing a big transformation in life sciences, a huge demand on their side to just to just get into understanding like what's happening, what's happening, what, you know, what are we not seeing? Are cancer patients actually getting treated? Are they getting screened? Are they not? Where's that happening? Where is it not happening? How is screening changing? So a lot of really interesting questions. We've done a lot of research on that, published some of that publicly. So you can start to see like the questions are just there and the ability to use data to answer those questions quickly has really been impacted in a positive way because of COVID That's showing no signs of abating. I think it's one of the big, big changes that we're seeing in the healthcare system. Alright.

Well, Arif Nathoo, thank you so much for stopping by. It is exciting to hear you talk about this, which is like saying a lot because like I said, you know, usually when it's like, let's talk about the healthcare data and analytics, it's like, oh, like, okay, let me get my interoperability hat on and just—

Yeah, exactly. Exactly.

How far Yeah, not me. It's a very exciting conversation. I think that it's really interesting what you guys are doing, and like, I love some of these big questions that you're trying to answer, and it's just, it's interesting to hear how, how the models are being created and how they're being deployed and what kinds of questions they're being sent out there to answer. So I appreciate you taking the time to walk through those examples in painstaking detail for us while we all learned exactly how it all works and then what it's being used for. So thank you so much for that.

Thanks, Jessica, for having me.

It's been a pleasure. And congratulations on your, on your fundraising. And the company is Komodo Health. They raised a total of $314 million. Their latest round was $220 million, just closed earlier this month. And yeah, thank you so much for stopping by. We'll have to check in with you as time goes on so we can find out how things are progressing. So you'll definitely have to come back. But in the meantime, thank you so much for joining us. I'm Jessica DeMaza. This is What's the Future of Health. You can find more interviews with those who are changing the way we do healthcare over on my YouTube channel. That is youtube.com/WTFhealth. Thanks so much for joining us, everybody. We'll see you real soon.

Bye. Thank you.