Getting Value out of Electronic Health Records, with Verana Health
Healthcare is one of those areas where more data is almost always better. And I talk a lot on the show about how data is helping doctors and patients make smarter decisions. But a lot of the data we’d still like to have is stuck in those arcane Electronic Health Record systems or EHRs that medical practices or hospital systems use to track their patients. These systems tend to be closed, proprietary, user-unfriendly, and incompatible with one another. And we've repeatedly made the case here on the show that EHR technology is holding back innovation across the healthcare market.
That’s why we like to meet companies that are working to make EHR data more useful. And in this episode we welcome a pair of guests from a company called Verana Health that’s trying to do just that. The company recently brought in $150 million in new venture capital funding to help scale up its data services, which currently focus on the subspecialties of ophthalmology, neurology, and urology. Verana takes data on patients in these fields, cleans it up, analyzes it, and pulls out insights that could be useful—both for clinicians who want to increase the quality of the care they’re providing, and for pharmaceutical companies who need new ways to measure the effectiveness of their drugs and better ways to find patients for clinical trials. Here to explain more about all of that are Verana’s CEO, Sujay Jadhav, as well as its senior vice president of clinical and scientific solutions, Shrujal Baxi. (If you’re a longtime listener you might remember that we had Shrujal on the show once before, back in 2018, when she talked about her previous company Flatiron Health.)
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Harry Glorikian: Hello. I’m Harry Glorikian. Welcome to The Harry Glorikian Show, the interview podcast that explores how technology is changing everything we know about healthcare.
Artificial intelligence. Big data. Predictive analytics. In fields like these, breakthroughs are happening way faster than most people realize.
If you want to be proactive about your own health and the health of your loved ones, you’ll need to learn everything you can about how medicine is changing and how you can take advantage of all the new options.
Explaining this approaching world is the mission of my new book, The Future You. And it’s also our theme here on the show, where we bring you conversations with the innovators, caregivers, and patient advocates who are transforming the healthcare system and working to push it in positive directions.
Healthcare is one of those areas where more data is almost always better. And I talk a lot on the show about how data is helping doctors and patients make smarter decisions.
But a lot of the data we’d still like to have is stuck in those arcane Electronic Health Record systems or EHRs that medical practices or hospital systems use to track their patients.
These systems tend to be closed, proprietary, user-unfriendly, and incompatible with one another.
And I haven’t been shy here on the show about my opinion that the chaotic state of EHR technology is holding back innovation across the healthcare market.
That’s why I’m always interested in talking with companies that are working to make EHR data more useful.
And today I have a pair of guests from a company called Verana Health that’s trying to do just that.
The company recently brought in $150 million in new venture capital funding to help scale up its data services, which currently focus on the subspecialties of ophthalmology, neurology, and urology.
Verana takes data on patients in these fields, cleans it up, analyzes it, and pulls out insights that could be useful—
both for clinicians who want to increase the quality of the care they’re providing, and for pharmaceutical companies who need new ways to measure the effectiveness of their drugs and better ways to find patients for clinical trials.
Here to explain more about all of that are Verana’s CEO, Sujay Jadhav, as well as its senior vice president of clinical and scientific solutions, Shrujal Baxi.
If you’re a longtime listener you might remember that we had Shrujal on the show once before, back in 2018, when she talked about her previous company Flatiron Health.
We’re glad to welcome her back.
Now, on to the show.
Harry Glorikian: Sujay, welcome to the show, and Shrujal, welcome back to the show, now that you're at a different place. It's great to have you both here.
Sujay Jadhav: Thanks, Harry.
Shrujal Baxi: Happy to be here.
Sujay Jadhav: Happy to be here as well. Thanks.
Harry Glorikian: So. I want, you know, I want to ask you guys like if one or both of you can describe Verana's reason for existing, at least at a high level, and what is the unmet need in in the world of patient care or drug development that you are meeting?
Sujay Jadhav: Yeah, yeah. Happy to jump in and Shrujal, you can sort of add in sort of the health care sort of goals that we have as well, but you know, in essence, what Verana is all about, we have an exclusive real-world data network focused on three therapeutic areas: ophthalmology and neurology and urology. And in essence, what we are doing is we're helping provide insights to providers in helping improve quality of care, helping improve their participation in clinical trials and also provide insights to life sciences companies across the drug lifecycle all the way from study design helping out in trial recruitment to helping them out in launching drugs, commercializing drugs so they can overall improve the quality of care in a more holistic fashion. You know, the crux of how we're going about doing it, in essence, is accessing HER data and eventually identifying it to provide these particular insights and high level there's data which is very, very structured and there's data which is unstructured and there's a sort of an increased focus on the unstructured data because I would say that's probably where there is the largest opportunity out there to provide insights across that overall value chain.
Harry Glorikian: Yeah, I know I know the area well, but I want to sort of spend a moment on the origin story of of Verana Health, and I'm assuming it has something to do with the relationship between Verana and the American Academy of Ophthalmology, since I think the Academy's CEO David Parke is also a co-founder and executive chairman on Verana. You also have partnerships with the American Academy of Neurology and the American Urological Society. So it seems like these. And it's funny because I think of these associations as publishing journals or, you know, organizing conferences or maybe, you know, having representation in Washington. But it seems like you guys were a spinoff or a piece that came out of at least the American Academy of Ophthalmology. Is that correct?
Sujay Jadhav: Yeah, you're absolutely correct. I mean, really, Verana was founded on, you know, sort of the ophthalmology registry, in essence. And, you know, the ophthalmology registry, is probably one of the leading registries in terms of the way that well, first of all, participation, you know, from the specialists, I think it's close to 70 percent of ophthalmologists are part of the registry, but they're one of the leaders in terms of taking the actual data from the ophthalmologists. And they were actually processing that particular data via third party out there to help provide insights, you know, to predominantly the ophthalmologists out there, but eventually to provide insights to help further research. And so Verana was really founded on sort of the ophthalmology registry. They decided to spin out that capability as an independent company, then bring in some external investors, sort of investors, which are very committed to digital health. Brooke Byers from Kleiner Perkins. Google Ventures. And they funded the separate entity. And then ultimately, the goal was to take that data capability that they have and then help normalize it and provide more insights around it to further the overall drug lifecycle. And then, you know, along the way, you know, other societies saw the progress that were making and decided to also partner with Verana, starting with the neurology society and then urology as well.
Harry Glorikian: Now, you know, just so like for the listeners, if and you guys can correct me if I'm wrong, but I think like and because I like to give credit where credit is due, right is a lot of these, you know, medical associations began to gather a lot more data and build some giant databases. But I think that was driven by the, you know, CMS or, you know, Centers for Medicare and Medicaid Services sort of setting up this merit based incentive payment system and sort of driving this. So it's sort of like I always like to give government credit when they actually do something right, but they actually put some money behind this to encourage this sort of activity, which has resulted in this sort of dataset that's now available for us to really glean some insights for patients.
Shrujal Baxi: I mean, I think I think when we when we look back sort of the development of the electronic health record is what set this off. And that was also a government initiative right to really move us all from paper charts and to electronic health records. And then sort of the potential that comes from that. If we think about the brilliance of these registries, they were smart enough to start collecting the data early on and sort of answering your first two questions from a medical perspective, like what is Verana here to do? We're here to help transform that data that's available in the electronic health record, sort of generated as part of regular care, and get all of the insights we can in health care, the way that data is generating insights and every other industry out there. But there is a particular sensitivity in health care to de-identification, making sure we're taking care and responsibility of that process, which makes sort of what Verana is doing a little bit different than what might be happening out there. But when you when you think about why there's so much excitement around what we're doing, it's that we're actually going to do it from a technological standpoint. So the scale at which we're hoping to do it should drive insights like we haven't been able to do with sort of the first pass at getting value from the EHR, if that makes sense.
Harry Glorikian: Oh yeah. I mean, if you've listened to many of my shows, I have a big pet peeve with the normal EHR system. I mean, I've gone so far to say, you know, if anything breaks health care because of its inability to change, it's this arcane accounting system that got morphed into, you know, quote, we're going to manage patients. But you know, you mentioned from a physician's perspective, what kind of data do these databases that you have access to contain that's important or valuable for, you know, assessing quality of care, let's say?
Shrujal Baxi: It really, it spans the gamut, right? Because data is just that. It's what we do is we provide, we transform it into a structured format that can be analyzed. But what you use it for is really it's limitless, right? Do we want to look at how to optimize how patients are seen? Who sees those patients? How do we get them into a clinical trial? How do we get a trial set up at a practicing site that happens to be seeing a lot of patients of a particular disease subtype? Are we starting to pick up patterns in how new medications are released into routine care that have been tried in clinical trials? Can we pick up safety signals in the real world that you can never capture when you only have a small clinical trial of 200 patients? But when you launch that drug at a larger cohort, what is actually happening in the real world? All of that is possible once you figure out how to transform all the information that's entered into the EHR into analyzing all formats. And so, you know, it's interesting, because Sujay gave all the real use cases, but in my mind, what we're doing is the technology, which is how are we going to do this in a sort of scalable way? So as the data is coming in, we can take it and output structured data that can then be used for analysis. And the better we get at that transformation step, the faster and the more reliable that is that really sort of unlocks what we can do with the data.
Harry Glorikian: Yeah, it's funny. Yeah, it's funny. You're covering, like, I don't know, half a dozen podcasts I think I've done with various companies that are doing different parts of this. But I mean, I've looked at the company's literature and you put a lot of emphasis on what's called real-world data. And this is a topic, you know, I've covered on the show many times. You know, last year, late last year, I did an interview with Jeff Elton from Concert AI, where they collect post-approval data and help improve decision-making inside drug companies. So I want to ask you first, what do the folks at Verana have in mind when they talk about real world data? Does it basically mean any data collected outside of the context of a clinical trial?
Sujay Jadhav: I know the real world data terminology has different types of descriptions, but fundamentally we look at it, generally any observational data, you know, is sort of what we categorize as real world data, and what we are focused on from a high level perspective. And you know what we see within the EHR, you know, there's a lot of that data available there. And in essence what we are doing is accessing it, extracting it, normalizing it, and then providing different levels of insights depending on the different types of use cases, which are important to improve the quality of care at the provider level there, and also help further research and within the life sciences arena as well. So, you know, that's high level the way we look at it. You know, one of the things, you know, in order to finish up sort of or complete the overall patient journey and have a holistic perspective, we need to also match that up with other types of data there. And so, you know, claims data, for example, at times there are longitudinal elements to it as well. So we spent a lot of effort and work doing matching there, you know, as well. You know, we're bringing in other types of data forms of imaging data and as well.
Sujay Jadhav: So while we are very focused on the data there, we are actively complementing it with other types of data sources to get a more holistic picture there. But you know, I would say that a lot of companies out there have been doing a really good job of accessing this data from a more structured format perspective, right? And one of the things that I've seen, and this is more of a high level comment, is when you look at some of the structured data that can be an element of sort of extra latency in terms of getting that information to make certain decisions or decisions such as hey, for a particular clinical trial, what are the right patients that you should target, et cetera. And so what we are really focused on is the unstructured data, you know, the physician notes, and then leveraging sort of AI techniques there to provide those signals. So that allows us to, on a close to real-time basis, target particularly particular patients, which could be a better fit for a particular trial versus historical means, which have been a little bit more sort of delayed in terms of getting those data inputs.
Harry Glorikian: So, you know, this begs the question before we jump into the product itself is. Do you guys have an opinion on why the medical establishment has not been so great at tracking or analyzing real world data up till now? I doubt it's lack of interest. It's probably more like technical limitations is my guess or maybe lack of interoperability, or all of the above.
Shrujal Baxi: Harry, since the last time I was here, a lot has happened in the real-world data space. To start with, and I think we talked about this last time, which is real-world data has been here forever, right? Clinicians have been doing chart reviews and publishing case series. That's all real-world data. It's taking a look at what happens in the routine care of patients, pulling it out and analyzing it in order to deliver insights. I think what the electronic health record did or what we believed it would do is allow us to do that type of work at a scale that we couldn't do it before. The second thing, I think, that real-world data is now considered potentially useful for that it wasn't previously, is causation and the analytic ability to actually make linkage between input and output in a way that isn't just hypothesis generating. And the regulators are really sort of driving the space with the guidances that are coming out and really framing for companies like ours, how we should be thinking about data and the data quality and sort of where this data could be used.
Shrujal Baxi: So there's sort of two parts, right? One is how do we generate it in real time at scale so that we can understand important questions? And then the other is the part that I think really sort of leaned in on, which is the entirety of a patient's journey. And this is a very patient-centric problem. It isn't captured in a single EHR. So how do we bring together all the different components of a patient's journey such that we get the complete picture, the genetics, the imaging, the multiple different providers, the claims for what was paid for, right? And so it's kind of an exciting time in the sense that we've sort of gotten to second base. Maybe we figured out how to get all the data. Now we're figuring out how to transform the data. Now we're going to figure out how to link the data. In the meantime, in parallel, we're figuring out how to analyze observational data. And a company like Verana is really well poised to do those things because of all the different components and the partners that we have to do that, I think.
Sujay Jadhav: Yeah. And I'll just add to what Shrujal said there. And I think it's sort of inherent in your question around the technology was, has the technology been there before, et cetera? I think to some extent it has, but it has been evolving and obviously in the last decade with sort of AI techniques, natural language processing techniques, they've started to mature and kind of scale. But one of the key things around our industry is patient privacy, right? And so we have the technology and it's been leveraging a lot of other different industries. But the stakes are very, very high here because of protecting patient data overall. And so, you know, working through how can you access this data at scale and ensure and make sure that you're adhering to the patient’s privacy? There has taken a little bit longer to do, but now we have it. Currently, right now, we have a lot of techniques on the de-identified data identified realm where we can now leverage that to address that particular point. And I think, you know, it's an exciting time right now. You know, which is we now have the tools to do this at scale, but ensure that we're keeping patient privacy intact as well.
Shrujal Baxi: But we also have a responsibility on the end of that spectrum, which is we have to have high quality data. So we need to protect the patient's privacy. We need to be very responsible with the data, but we also have to be very responsible for how the data is generated, such that we don't end up with conclusions that are harmful. The integrity of the data throughout that process needs to be maintained because people are going to act on the output of our analysis using the data that we're generating. And so that's an incredible responsibility that I think we take on and sort of critical to how we think about what we do. It's not just data, it's data that's generated to make decisions in health care that impacts patients very directly.
Harry Glorikian: Yeah, absolutely. So, you know, I think this is a maybe a good segue or opportunity. What have you guys actually built? Because we've been talking around it. What is, I think it's called the VeraQ health data engine, if I got it right? Tell me a little bit about the product. And you mentioned natural language processing and machine learning, and so how does that fit, at what point and where does it fit? And I'm sure there's a few people who are going to like listen closely at this part because they're interested in this stuff. Some others may not listen as closely. But if you could tell me a little bit about the product, that would be great for everybody listening.
Sujay Jadhav: Sure. Sure, absolutely. So, you know, in essence, what we have is we do have a real-world data network where we access 20,000 providers. We have 90 million de-identified patients currently, and it's growing at a good clip right now. And what we do is we take that data, we ingest the data, we normalize it and curate it to provide insights to providers to improve quality and participate in trials and then also to the life sciences community as well to help further research. Broadly speaking, there, you know, sort of our technology platform is called VeraQ. We released it last year, in essence, it's sort of the secret sauce around ingesting it and normalizing and curating the data. And then once we do that, then what we do is we deliver it in what we call de-identified data modules called Qdata modules, which are aligned according to the therapeutic areas in certain disease states. And so what we do is within each of the three therapeutic areas we release on a quarterly basis different disease modules, Qdata modules there. And then that helps serve out, you know, a lot of insights that life sciences companies can use across different areas. So anything from a helping out in trial design work with a lot of large pharma companies around helping improve sort of how they target particular patients out there by leveraging these de-identified data modules to helping out on recruitment as well. In terms of working through what are the right providers that you're targeting to have that patient population trial to eventually see when you actually launch the drug, you know how the use is occurring out there in the marketplace, how they can better target it to improve the value of the particular drug they have there as well. And, you know, we eventually set that up instead of application modules across that overall drug lifecycle. So, you know, to summarize, our platform is VeraQ. We then serve it up in these Qdata modules and then we deliver it in these solution sets, which are provider facing and also life sciences space.
Shrujal Baxi: I was going to say something that's really unique about Verana and shouldn't be glossed over is the fact that so many different EHRs are out there and they're they're created differently and they are so specialized to the provider with bells and whistles that each different practice pays for. And to take all of that disparate information and ingest it and harmonize it such that the output or variables that can be generated at a scalable fashion across millions of charts and then use that for analysis. I mean, GE made it sound really good and clean, but that's actually a that's a lot of work for anyone who's ever touched an EHR and try to get value out of the data that's entered. It's a feat. And I think that that that engine now that it's built, is sort of poised to take in and give out, right? That was the infrastructure build that was 2021. And 2022 is the data that's going to come out as he was describing. But I just want to, I'm particularly passionate about this because I've worked now at different companies that think about this and that particular part of harmonization from the starting point that are so many different places is really, I think, a technological advancement.
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And now, back to the show.
Harry Glorikian: We talk about major EHR systems sold to hospitals and, you know, on this show a lot and the interoperability and it's just, oh my God, it's a mess, right? And I hear it from patients too, right? I had someone call me the other day and they were like, I don't want to say they were chewing my ear off, but they were unhappy and they were like, and it was a friend of mine who had gone into a hospital. And he goes, I'm sure you know all this. I'm like, Yes, I know all this. But hearing it from somebody is interesting. It sounds like, I mean, you guys are harmonizing, or correct me if I'm wrong, at least from what I’ve heard, ophthalmology and neurology and and urology. If you were able to give that back to the physician, I think that would be hugely of value as a physician, assuming that it's simple for them to interact with and, you know, they don't need a degree in computer science, if you know what I mean.
Sujay Jadhav: Yeah, yeah, that's exactly what we are also focused on. I mean, you know, sort of part of sort of the way we are approaching this opportunity is sort of twofold. Number one, working with the providers, helping improve that, they can provide care. And we're very committed to that in a way we're committed to. That is. You brought up sort of MIPS quality reporting, which is important for CMS submissions. And, you know, we actually have a particular solution that is provider facing, which allows them to work through sort of their quality scores, understand how they're doing overall in terms of overall quality of care from from a high-level perspective. You know, in addition to that, we're also allowing them to access sort of our patients, you know, data to help improve how they can participate in clinical trials. And, you know, we understand that their bandwidth is very, very constrained. They need to focus on care. And, you know, folks like myself and sure, you know, we've been doing this for a couple of decades as well and spent a lot of time, you know, working with physicians out there. And so we understand that, you know, making this user friendly, allowing them to come in and out as quickly as possible to get the insight they do is extremely important.
Sujay Jadhav: And that's sort of a key part of what we are focused on as a company. And we're committed to helping improve quality of care by taking on sort of this MIPS reporting obligation as part of sort of our overall business model as well. But, you know, through that particular process, you know, we obviously have access to the data. We then de-identify it and then we can provide the next generation of insights to the life sciences industry, which is a very, very compelling across the board. And it's a really, really interesting that I've been doing this for, you know, 20 years plus, and I still feel we're just barely scratching the surface in how we leverage this particular data. And so, you know, there's definitely a lot of work that we are doing, leveraging natural language processing techniques to allow us to do this at a particular scale. And that's sort of core for us helping to deliver on this sort of next level of opportunity that we see to help improve care across the overall value chain.
Harry Glorikian: Do one of you or both of you have your favorite case study that highlights the different strengths of the system that you can sort of, you know, put it into context for someone.
Shrujal Baxi: Sujay, do you want to go first?
Sujay Jadhav: Sure, yeah, absolutely, absolutely. So, you know, you know, from my perspective, where we're providing a lot of insights, as I mentioned, across sort of the overall drug life cycle, the area which got me the most excited around Verana is really on the trial side of the house, in essence. And so, you know, we do a lot of work around helping out in trial design, right? But you know, the areas that we're starting to see sort of the biggest next level of value that we're providing is really on the recruitment side of the house. And you know, as you know, recruitment is being a big pain point in the industry at large. I think a lot of companies are out there which are helping work through and target the right sites, help target the right PCs, providers, which you know, have the actual patients there. But that final mile of helping out do the actual recruitment is something which is very difficult to do. You know, the biggest influence in recruitment is a physician and, you know, via sort of the solutions that we're providing around our quality of reporting side as well, how safe we feel. At least we have some bandwidth there with the physician and we want to leverage that to improve recruitment. So, you know, we've done a number of projects in the recruitment side, particularly in the rare disease area, is an area that we've done a lot of work there because historically the way it is, the process has been, which is, hey, you know, these are the particular, you know, you know, physicians out there which have participated in historical trials, et cetera. Let's just target them as well. And it's more around historically which providers participate in trials. But what we're doing is we're doing it from a data level up there. And so what we did with a large pharma company out there in a particular rare disease area there is we actually identified a number of patients with actual providers which have never participated in trials before. And so we yielded a set of patients, which probably they never would have gotten via the normal mechanisms out there. You know, and I would say the types of improvements we're seeing starting to see in the trial side is north of 30 percent sort of efficiency improvements in the trial process overall. And if you extrapolate that to how much they spend in clinical trials, that's tens of millions of dollars of cost savings that you can take out of change. So, you know, that's probably the area where I've seen a lot of value that we're provided with this particular data. Shrujal, do you have any other examples?
Shrujal Baxi: Mine is not nearly as grandiose, but it's really sort of brings home why data is important. So recently we ran an analysis with the American Urologic Association just as a sort of look at how the data can show us what's happening, and it's going to come out in their spring newsletter that they send to all their members. And we partnered with one of their academic collaborators, and we just asked a question about uptake of routine bone density scans for patients with prostate cancer who are going to go on to hormonal therapy for about six months to a year. And that has been a, that's been a quality measure that they've been tracking as an organization because it's a place for improvement for urology overall. And we were just curious sort of in our data, what does that look like, right? And so perfect use, create data, analyze data. What we found is that the uptake of this particular recommendation over time has steadily increased. But lo and behold, COVID hit people didn't stop getting prostate cancer, but they did stop doing screening for bone density. And we know that if you don't look for bone density, you're not going to treat low bone density. And therefore these patients are going to be at risk for fractures, which are, you know, in a certain population, just devastating. And so the I sort of am stealing the thunder of the snapshot. So please forgive me, AUA, but the takeaway here is that there is something we can now do. Let's go back to those patients that we diagnosed in 2020, and let's make sure that all those patients get bone density scans. And if we can prevent even one fracture, then this data has served its purpose directly to the patient, right? And so that's just a glimpse of what we can do with the data. And there are so many opportunities like that to directly impact patient outcomes if we can just figure out what questions to ask and then how to disseminate that information. So not quite as big and grandiose, but really tactical and tangible, I think.
Harry Glorikian: Yeah, no, no, I mean, I, you know, once you have the data, my brain goes in, you know, eight, 10 different directions of what can I do with it? Which is why I like investing in the space because it's, you know, if you've really got access to the right quality data and you can actually interrogate it, you're not just a one trick pony. And one of the things that I was thinking of is with all the data you've got, you know, couldn't you create like really optimized digital twins that might be able to also be used in a trial? I mean, that's one of the first things that popped into my head. But Shrujal, last time we talked, you were head of clinical science at Flatiron. And I think if I got it correctly, your title now is senior vice president of clinical and scientific solutions. So what does that mean?
Shrujal Baxi: Good question. I think the fact that clinical and science are in both the titles sort of tells you that in many ways my role at a company like Flatiron or my role at a company like Verana is not all that different, right? It's to make sure that we are bringing through the perspective of the clinician who is fundamentally at the heart of the documentation that's happening and that we're translating that when we partner with our technology colleagues, to translate how that data is going to be transformed so that we don't lose the meaning of the information. As a scientist or an outcomes researcher, I was a consumer. I would interrogate databases that were generated like this and so I can put my outcomes or my health services research hat on, my clinician hat on. What questions do I need answered and what is the data need to look like? So I sort of sit in many ways at the start and at the finish and help partner along the way with our cross-functional colleagues who do really the bulk of the work. Like I think it's such a, the strength of these companies is how collaborative they are. The challenge of these companies is how many people have to work together and communicate and say the right words and the same words to mean the same things. And so the title sounds a little different, but in many ways I feel like my role is to preserve the voice of the provider and therefore indirectly the patient in everything that we're doing.
Shrujal Baxi: The other piece that I think I've the title seems bigger at Verana, but what it's actually, I think, expanded my my scope into is to understand where engineering and data science and that AI/ML component of the transformation can really take us. I think technology enabled abstraction is one thing, but I think actually applying technology to extract the data is a whole 'nother level of complexity and scale. But once built, it's sort of a receptor just waiting for new data sources to come through because you can take 10 hours, 12 hours, 100 hours. If you built the pipeline and you've built that ML/AI to put on top of it, the output should come sort of instantaneously, so I say that with a wink almost too, because I know it's a lot harder than that, I've learned. But ultimately, that's what Verana is building towards. And so the scope of my work and how I think has changed just slightly.
Harry Glorikian: Well, it sounds like a critical piece of the puzzle to make sure that, you know, everything is translated correctly and everything is understood correctly, et cetera. So it's I think it's a valuable position. They might need to clone you, though, because I feel like there's a lot going on there.
Shrujal Baxi: I feel like there's a lot going on.
Sujay Jadhav: There definitely is. I mean, you know, and we have we have sort of a network of medical professionals that we leverage, you know, across all three therapeutic areas, you know, and that's really, you know, part of sort of our overall process, right? But you know, I think you describe it very, very clearly. But ultimately, you know what, we're trying to get out of Shrujal and the group is sort of how to medically inform the overall process that we're doing right now, and make it relevant and practical to truly provide insights to the clinician right at the end of the day there. And so, you know, there's a pragmatic element to sort of her involvement in the overall process because technology can only take you so far. But to get that sort of final, pragmatic element to that particular therapeutic area, you know, requires a medical professional.
Harry Glorikian: Yeah. And I think, you know, one of the most challenging things is how to present it. And like you said, I mean, real time is a that's a whole other, you know, dynamic to tackle that people don't understand. But. You guys just had some fantastic news. I, you know, a I believe recently a series of venture round brought in, I think it was $150 million, if I remember the number correctly, from J&J Innovation, as well as existing investors like Google Ventures. I mean, first of all, congratulations, that's a pretty good sized round. Can you fill us in on like, okay, somebody just handed you a $150 million check. What are you going to do?
Sujay Jadhav: Yeah, no, it's a good question. Firstly, the $150 million raise is a significant raise, and we're very fortunate that it's come from comes from a diversified set of, you know, digital health investors, broadly speaking in combination of growth investors, innovation funds from life sciences companies as well as academia as well. And so I think it's a good cross-section mix that we have fundamentally a number of investors which are very committed to digital health overall and will allow us to sort of accelerate the business as we take it to sort of the next level. You know, in a lot of ways I think it's sort of recognition of where Verana is, you know, and you know, we've done a really good job of building out our digital technology platform. We are now commercializing the business very, very well, you know, in terms of what we're going to do with a capital, in essence, it's fuel for growth. We've anchored on a really good business model right now. And what we are going to leverage the money for is to help execute on our existing sustainable product strategy, which is coupled by premium services on a solid data foundation and the sort of sort of three areas that we are going to focus on. The first one is on the provider side of the house that we already have an existing set of solutions in the, you know, the quality area and the clinical trial area. And we're going to further those particular solutions taken to the next level to make it easier for physicians and providers to do that job. The second area that we're going to be investing in even more is on the life sciences solutions side there as well.
Sujay Jadhav: And so, you know, both we have a set of trial solutions there. We have what we call data-as-a-service solution set, which allows these life sciences companies to access the curated data in a very easy fashion, so allows them to provide different levels of insights that they feel are important as well. And then, you know, the third area is just furthering sort of expanding sort of the data that we have currently right now. I think we've got a really good critical mass right now with 90 million de-identified patients, you know, 20,000 plus providers there. We're going to continue to increase that across our three therapeutic areas. But, you know, moving to other types of data sources, I think imaging is one we're going to invest in a big way. I think that can really, truly help complete the picture. Genetic information also is something that we're inserting in into the mix as well. And, you know, bringing in each data, bringing in claims data, bringing in imaging and genetic data, you know, is a complex equation, so to speak there, just to say the least. Yeah, it is. And you thought of doing that in a thoughtful way, doing it in a way which is scalable. It takes a lot of effort. And that's where we're going to be investing a lot of these funds to make that happen. And you know, we're well on the way to actually doing this. And so, you know, a lot of the money is in essence, just executing on the strategy.
Harry Glorikian: Well, you know, it's been great having you both here. I love talking about this stuff, as you can tell. And you know, I wish you guys incredible luck because, you know, I keep getting older and I think I'm going to be, you know, at some point you become more of a patient. So the more that this advances, the better my health and wellness will become. And I look forward to, you know, maybe having you guys in the future and seeing the evolution of where this goes.
Sujay Jadhav: Absolutely. Thanks a lot, Harry. Enjoyed talking.
Harry Glorikian: Thank you.
Harry Glorikian: That’s it for this week’s episode.
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