About this Webinar:

What is possible using the power of complete patient data? The Life Science Leader Lab welcomes Dr. Aswin Chandrakantan to share ways that you can:

• explore the potential for marketing and clinical teams to leverage the power of complete healthcare data at the patient level

• learn how artificial intelligence is creating opportunities to reveal signals and connections not seen before

• connect with peers and talk about where the industry is heading

Webinar Transcript:

00:17 Frank Dolan: Well, hi! Welcome to the Life Science Leader Lab. I'm Frank Dolan, I'm your host, and it's great to have everyone here today. Very excited to introduce someone that I've gotten to know over the past couple years, Dr. Aswin Chandrakantan. How are you, Aswin?

00:34 Dr. Aswin Chandrakantan: Doing well, doing well. Thank you so much for having me today.

00:36 FD: It's great to have you. So today, we're gonna talk about some real advances in leveraging technologies to help us create more value for enterprises, and most importantly, our patients. Aswin works for a company called Komodo Health, an organization that I discovered some time ago during my biotech career, and I'm just amazed at, really, some of the leading-edge thinking and work that they are doing. With that being said, Aswin, why don't you introduce yourself to everybody, and then we're gonna take them through an incredible presentation.

01:09 AC: Yes, my friend. Well, my name is Aswin Chandrakantan, Chief Medical Officer at Komodo Health. Doctor by background, management consulting in McKinsey for four years, built and led their healthcare analytics practice, joined Google, did a lot of product-facing work there, joined Komodo Health about five years ago to lead product and platform development, and have now diversified onto the business partnership side. So super excited to chat with the entire audience today and to also to work with you.

01:39 FD: Well, we're gonna have a lot of fun. So we've been focusing in this Life Science Leader Lab series on topics that every ambitious life science executive really needs to know and understand, and it's been an incredible conversation when the community come together. And one topic that we really wanted to dive in deeper is: What are the opportunities in the immense amount of data that exists within the healthcare system and within life science companies? And is it possible to derive further insights? And the work that you're doing with your team, Aswin, is really, really cool, and I'm hoping that we can share that with everyone today so they could begin to think about how they may wanna leverage that in their own enterprise. So I'm gonna bring up some slides so we can walk people through because although the content is incredibly intuitive, I think for many of us, we wanna go ahead and bring it to life. So without further ado, Aswin, take it away.

02:35 AC: Sure, absolutely. So today, we're gonna chat a little bit around how we improve lives and the standard of care, given the current context of the world, by harnessing the power of complete patient data. A little bit around Komodo Health. We were founded about six years ago. We have 200 team members in both San Francisco and New York. We serve over 150 disease indications. Our claim to fame has been building a healthcare map, which to summarize very quickly, is the journey of 320 million US patients, of providers and institutions where those patients seek care, the therapies and interventions they use to treat those conditions, and the outcomes that relate to that. We use that healthcare map in order to drive our software, which is an intuitive analytics-based workflow designed for both medical affairs, commercial and clinical development audiences. And so what I wanted to chat through today is A, what is this healthcare map? Why is it different? How does it drive better outcomes for patients? And what are some of the live impact stories that we're seeing on both pre-COVID, but then also in the context of our current challenges in healthcare? How do we drive education of the standards of care in the market, given where we are today?

04:05 FD: Excellent. So let's hear a little bit more about you, Aswin. Let's brag a little bit.

04:11 AC: Sure. [chuckle] I think I've already introduced myself on a couple of these fronts, but originally spent a lot of time at McKinsey, thinking through and serving both payers and life sciences. So the very nascent analytics practice, built it, led it, developed it into a strong service line. On the Google side, served their ad tech division. And so, I'll tell you some interesting stories about Google and their healthcare map in just a bit. And joined Komodo Health about five years ago to lead first our product and then our platform. Then and now focused much more on the Chief Medical Officer, evangelizing our healthcare map, and driving strong business partnerships.

05:02 FD: Yeah, well, one of the many things I admire about you, Aswin, is the fact that after spending so much time with you when we've been speaking at Harvard, Stanford, and Wharton at some of the summits, is your ability to understand, really, all sides of the healthcare continuum. Certainly, with your former academic training, but also the incredible business experience you have from what you've done within your career on the business side, but the understanding and frankly, the empathy to be relatable to what those of us in medtech, biotech and pharma struggle with in trying to create innovations for patients allows all the entire conversation to be connected. And I really do appreciate that about you, so I'm excited to learn a little bit more today.

05:51 AC: I think it's actually a little bit... It's built into the DNA of Komodo because we've taken, as you just described, folks who traditionally understand the use cases in life sciences and challenges in the market, combined them with people like myself who are much more analytically-focused but understand clinical populations, and the translation between data and those populations, and the last and perhaps most importantly, is engineers who can build scalable and generalizable infrastructure, technology and software so that folks who are not analytically-inclined can derive value from those software products and understand who to engage, how to engage them to drive adoption and the right standards of care on the market. So it's really the confluence of those three pillars that have made the DNA of Komodo successful.

06:39 FD: That's great. Alright, so shall we move on to the next slide?

06:46 AC: Absolutely.

06:47 FD: Alright, the virtual landscape, what's that?

06:51 AC: So I think this highlights some of the challenges that we're already seeing in the market and we did a little bit of a survey, which I'll cover off shortly. Right now, life sciences, field teams are grounded. There's a challenge in understanding who to engage, how to engage them. There's also a huge backlog of patient encounters, which I'll be very candid, worries me like crazy, which is right now, a lot of non-essential care has been deferred in the United States and internationally. And it's not that people are not getting diabetes, it's not that people are not getting cancer; it's that we're not testing for those diseases, we're not getting them the care that they need. And so right now, there's a both a huge challenge in terms of what we face as a country, but there's also, as I'll highlight, opportunities nested where you can actually drive better care standards and education in this narrow time. And so that you get better outcomes for patients at the end of the day and we sort of bend the curve. So we'll tap into those as well.

08:03 FD: That sounds great. So how are companies feeling today? This is the million dollar question.

08:11 AC: Yes, we ran a survey that was focused on both commercial and medical affairs teams. We serve 20 of the 20 largest life sciences companies, which I'll get into just a little bit. But nearly three quarters of the survey participants felt they're no longer confident in their team strategy in 2020. A lot of it is because it precludes provider engagement. And so they're really struggling for, "Okay, how do we get mindshare? Is this the most important thing?" So there's almost that existential crisis. Their timelines in funding for specific therapies are being pushed. And so 90% of them feel either unprepared or underprepared to meet their 2020 objectives. And what's actually really interesting about that is that we're finding a lot of companies have an opportunity to do that strategic self-assessment, which I think in the normal pace of a commercial team, in the normal pace of a medical-first team, it's quite challenging to do. But there's actually an opportunity in order to do that macro level and reassess what to do differently. And we'll talk through how some of our clients have capitalized on that.

09:22 FD: Yeah, well, that's fascinating because I can tell you that we talked to peers at emerging biotech, large pharma, you name it, and what I have found, as far as a summary's concerned, that folks are like, "Am I doing the right things when it comes to budget?" It's not so much, "Is the number correct?" but, "Are we investing in the correct areas?" Because as I've famously said in the past, winter is coming. I think winter is here, that's just a Boston point of view. So what's Komodo seen in the market?

10:00 AC: Yeah, so a couple of things that have emerged. And I wouldn't treat them as opportunities as much as optimizing a bad situation. So first of all, we're actually seeing from teams that are on the Komodo platform using Aperture, using Pulse, what we're finding is, is that when they're sending digital engagement to providers, there's about 4X open rate, which is... The numbers always tend to be around the half to 1%, but now, when you're seeing four times as much, and I can also relate to this because both of my brothers are doctors, and so what you're seeing is, is that there's teams in which each team is working half a day, and if any team, if any specific team member on the provider care team gets... Is tested positive, they pull out the entire care team. And so providers have a little bit more time on their hands. And so they're opening more emails, and that's an opportunity in order to reach them digitally.

11:07 AC: The second piece that I'll highlight is that... And this is somewhat speculative, but I call this almost like an attention monopoly because people actually are looking for things to do with the time that they have. And as soon as the care centers reopen, I feel like there's gonna be a massive spike in demand. And Komodo, we're actually doing a lot of analytics around what that spike is going to be, and what the impact on different populations and at-risk pools are going to be, and how those might be best addressed. And so we put in for some grant funding and so on and so forth to government in order to help understand that problem. And what I would imagine in that spike is that life sciences engagement is going to be even more difficult. And so I think that right now, there's an opportunity, as I call it, for a strategic self-assessment in order to say, "Okay, well, COVID-19 has been bad for patients, bad for populations at large, proven to us that there's areas of unpreparedness. But is there actually an opportunity for the folks who are still tasked with driving better standards of care across different therapeutic areas to target educational engagement opportunities with providers who essentially have more time on their hands?" And so we'll get into a little bit around our approach in terms of our healthcare map and then also, how our products are helping to drive to that end point.

12:35 FD: Yeah, I think that's a really important point that folks need to absorb, which is there is no going back to normal; it's gonna be the new abnormal. And from a sales and marketing perspective, I would just render my opinion, which is if our teams, if our programs, if our marketing assets, for example, don't provide incredible insight, value, and help providers who are constrained for time actually be able to solve problems within their practice in a meaningful way, it's not going to help get over that hump of low access and low engagement by those providers to industry. So if we deliver... We show up, we've gotta deliver, I guess, is my point.

13:16 AC: Yup, absolutely.

13:19 FD: So tell us more.

13:20 AC: Yeah, so a little bit around Komodo, and we're gonna double-click into some of these, we'll race through other portions just out of respect for everyone's time. So first of all, Komodo brings the healthcare map, which I described, which is the journey of 320 million patients and the providers and institutions where they seek care. We have census-level representation of patients across all settings of care for any therapy area in any geography. The second piece is that our granularity of understanding those patient populations is par none, given our unique approach of partnering with payers. And so we have contractual relationships with over 150 payers, and that gives us an eligibility lock population. So imagine a world 20 years ago in which you're thinking about, "Oh, I need to serve the breast cancer population because that's all these platinum therapies were just have massive hammers that could hit almost any nail." And in a world in which now, which you have targeted therapeutics, now, you have to think about, "Well, I'm looking for a metastatic breast cancer patient that is triple receptor negative that has failed two lines of therapy." And then the amount of granularity you need to know about the patient journey has exponentially increased.

14:35 AC: And so Komodo's payer partnerships really help to underpin our offering and provide the best intelligence in the market, and aggregating those patient journeys down to the providers and the institutions that represent the education vectors for a life sciences company. And lastly, and perhaps most importantly in a world of COVID, we have demographic and contact information for over 90% of those relevant providers, which means email addresses, phone numbers so that you can do your outreach and education, and at a time in which you actually have a little bit of more of an attention monopoly on the provider side.

15:18 FD: That's unbelievable. So Aswin, I have been a part of over 15 product launches, and when you say things like data on over 320 million people, relationships with 150 payers, I'm just thinking about every launch I've been a part of has been using these single-digit percentages of data that we're extrapolating out to the market and basically, relying on anecdotal evidence from qualitative market research to make these big billion dollar, hopefully, blockbuster product decisions to... There's hoping and then there's knowing. So what you folks have done is just incredible, I love it.

16:01 AC: Yeah, I think primary market research, I think it's increasingly going to seed its ground to analytics-based software approaches. And so even in our helping in the COVID crisis in terms of modeling populations that are at risk and making sure they get the interventions they need, accelerating clinical trials, what you'll find is, is that there's speculation and then there's truth. And there's a big gap in between both of those. And so I think that there's, oftentimes, a representation of more science on the primary market research side that involves extrapolating data, but then, there's the actual ground truth of what's happening and overcoming any sampling biases that occur. And I think that Komodo's approach is unique in its ability to A, both impact the global burden of disease and B, do it through a data-driven healthcare map-based approach. And so that ties directly to our mission here around reducing the global burden of disease.

17:03 FD: Excellent.

17:05 AC: So I think all of us have worked with, at some sort of data or data product in our lives. Just very simply, most of the data out there with legacy data aggregators, I call them glorified pill trackers. That's the way that I think about them because they're deeply embedded in the pharmaceutical supply chain, they understand how to track pills through the supply system, and then the patient is the end receptacle for a pill. But well, whether or not the patient has prostate cancer or colorectal cancer or esophageal cancer is not of that much importance because they're just the end receptacle for a pill in the old legacy data aggregator world. And this is why at Komodo, we've really focused on understanding patient populations and overcoming some of the fundamental biases in this open data, which means sampled data sets, which both miss visits, they miss patients in terms of their representation. They miss the longitudinality of, "Okay, well, this particular patient got this intervention. Are they really second line or are they first line, but I missed the first line intervention?" You're making a lot of speculative hypotheses around that. And then you're missing linkages, which is a lot of these are worked through you either go through switches and clearing houses. And so as a consequence, there are certain data elements that are suppressed.

18:28 AC: So it's funny, you'll see patients that are discharged with as congestive heart failure, and you'll see an ER doc, either at the admitting or the discharging provider that's on the claim, and you'll know nothing else about the fact that they were in the ICU, and the step-down unit, and then on the floors, and all the providers that had provided care for them. So I think really that epitomizes for us this notion around glorified pill tracking and really poor sampling is it chronicles the challenges that we have as an industry to provide the best care for all patients, the right setting of care with the right provider at the right time. And I think that's the thesis that we've been building towards at Komodo Health.

19:15 FD: That's great.

19:18 AC: So, I actually use this example and I'm just gonna go through it. At the top with the sampled claims and open claims set, you might see a patient that has blood in their urine, and then, all of a sudden skip forward, four months later, they show up and they're RC, they're a renal cell carcinoma patient, and they're on second line therapy. Now it's like, well, did they skip the first line therapy? Who got the labs? Who was their urologist? You know nothing about that. And this is why when you have the canonical truth about what's happened in the past around the stages and the settings of care and providers and the interventions that are rendered, you can actually see the full patient journey. And when you're able, and I've said this too many times Frank and we've laughed a lot about this, when you know what happened yesterday, you can then better understand what to do differently tomorrow. And this is the value of a patient journey centered approach.

20:17 FD: It's fascinating when you think about, God forbid any of us are patients. But when we are a patient and we are treated, we know that a world-class provider who had all the time in the world that only had us as a patient would learn everything about our past to help them make a good decision today. But those gaps exist, those pressures are real, and to leverage your data source to fill in these gaps is just... This is important. This is important stuff for our industry, but also thinking about how this could affect us, any of us as future patients.

20:51 AC: Yeah, maybe just to go back for just a moment, and give you even another case example, we're working with a company right now that's focused on prostate cancer, and one of the biggest challenges that they have is the patient would have had to get a castration. So there are no soft tissue samples left, and they need to actually understand the genomics of the disease. And so, there's this massive search effort that goes out to understand where – was this biopsy done, which provider did it – so that I can actually access those records, and understand if my patient population is screening eligible. That can delay bringing the patient out to the trial by four months. And when the clinical trials, the standard of care, you're losing lives every single day as a result. So, this is where you have this canonical truth around the patients turning to the healthcare system, it's not only around educating, around providing standards of care, but sometimes a clinical trial or a specific intervention, this delays in understanding where the patient has been massively impacts outcomes.

22:00 FD: Yeah. That's brilliant.

22:03 AC: Maybe we can just skip over the next slide here. And we'll also just skip over this slide as well in the interest of time. So, a little bit around our healthcare math, I've already talked about the 320 million patients, 120 million of them come from pair complete data sources. The gap between the 320 million and 120 million, those 200 million, we see three to five times as many encounters as any of the legacy data aggregators out there. We serve all of top 20 and over 50 Bio TEKS. We have thousands of users of our product, and so this forms a really strong bedrock for us, to then empower our products and offerings and... We're not gonna get into the offerings as much on this slide but we'll talk to you about the stories that our clients are helping to think through as they soar up some of their market challenges.

22:56 FD: That's great.

23:00 AC: So we're to go through three practical examples. One, around shortening the path to life-saving treatment. Secondly, around under-diagnosis in a rare disease and how finding those patients can be accelerated through artificial intelligence. And then third, around clinical trial recruitment.

23:19 FD: Sounds great.

23:24 AC: So, the background is is that when CAR-T therapies first launched there was a limited set of care centers that could absorb all the delivery and logistics challenges of both bringing in the patients, getting them all the sampling that they need, sending it back to the lab, developing the therapeutic, and then administering it back to the patient. And so, when we actually looked at here is as a single care center, which was Moffitt. And so if you could go ahead two slides. So, in the middle here, you see Moffitt Care Center. And you see... This is essentially the centers that are clustered around Moffit and in the purple are centers in which are really, really clean. And so, you're actually seeing patients directly coming to the Moffitt Care Center. The centers are highlighted in the ring, on the green rings on the outer edge are places where patients are taking multiple jumps in order to make it to the Moffitt Care Center.

24:30 AC: Of unfortunate repute is on the left side. I did my medical residency at the University Hospital in Newark, and we were partnered with Hackensack University Medical Center. Patients on average were taking about four to five hops in order to make it to the Moffitt Care Center, and that for us was extremely concerning because; A, that was a standard of care, so why is it taking so many hops in order to get to Moffitt Care Center? B, this is in Bergen County, Jersey, it's one of the richest zip codes in the entire US. So, why are people who have premiere access to institutions not able to get the care that they need? And, third is, about 40% of patients never make it to the Moffitt Care Center because they got lost somewhere in these hops. And so, our provider, and I'm gonna skip over the results of this, our life sciences partner, focused on educating this outer ring and quickly constricted that concentric circle so that it became a tighter circle. And then they brought in Hackensack and like San Antonio, Florida Care Cancer Specialists towards the middle, and then they repeated the exercise of, "Okay, let's educate all the other facilities and providers. And that helped to get the right care to the right patient at the right time.

25:48 FD: Wow! That's great.

25:53 AC: I'm gonna talk a little bit around artificial intelligence. I think it's been a very overblown term. And the only thing I would say about AI is that you should not be in the business of using AI, if you don't know what happened yesterday, you should not be in the business of predicting tomorrow. And so when you try to build artificial intelligence models on top of sample data, you're oftentimes training the artificial intelligence models on the gaps in the data itself inadvertently and it's very hard to correct for that. And this is why we believe in building payer-complete AI models, based on patient-journey-complete-data creates, A, a canonical few of potential disease burden that's unaddressed, and then, B, also provides really impactful work for life sciences companies where they can engage providers that might benefit from education around a specific therapeutic area that they were unaware of or a patient population they were unaware of.

26:52 FD: Yeah. I totally agree. And for so many folks that we've been talking to and interacting with for the last couple of years and getting... Everyone's excited about machine learning and AI, and I feel like every executive needs to appreciate that. If they were trying to find an analogy for this, if someone taught them how to bake a cake, and they practiced and they practiced and they practiced, but they would go back and realize they were given the wrong recipe, they may get really good at practicing but they're making the wrong cake. They started with the wrong formula, and that background data is just so critical to getting AI right in our businesses. So to your point...

27:35 AC: And this actually takes me back to the Google story that I promised to share, which was, Everybody thinks about Google as being the best AI company in the world, and they have some of the best data scientists and artificial intelligence models out there. But all of that data science intelligence is predicated on one thing, which is they have built a map of the internet. So they trace the behaviors of billions of users and their journeys through the internet, and then they target interventions to them along the way, namely ads in order to drive their business model. And so everybody thinks about it is, "Oh my God, they have the best AI in the world." But really have the best data in the world.

28:15 AC: And so best AI needs to start with the best data. And so when I joined Komodo Health, I thought, "Oh great, we're gonna be building those artificial intelligence models," considering that I came from an ad-tech business at Google. Then I realized the state of data in healthcare was abysmal, and that's what then led to a $50 billion five-year investment in building this healthcare map. Let me talk to you about an application on hereditary amyloidosis. So this is a disease that has 10,000 patients worldwide. It has diffused symptomology across multiple organs like the liver, kidney, heart, and some peripheral neuropathic syndrome symptoms. And so because of the diversity of symptoms and the very long duration of disease course, it's oftentimes very hard in order to make a diagnosis. Let's go ahead to the next slide.

29:13 FD: Sure.

29:17 AC: So the truth is, the clinician may never see a hereditary amyloidosis patient in their 40 years of practice. And so you going and educating a provider around that would be like, "Okay, why is this important? Or why this is interesting to me." If you move forward to the next slide, you see that AI can help distinguish the patterns and proactively identify these patients.

29:37 FD: Wow...

29:39 AC: So in this... And I'm only gonna show you this single slide in the interest and respect for everyone's time, you could see that at month-zero is when patients are typically getting a hereditary amyloidosis test. And so in this rainbow chart, you see the cardiomyopathy, the kidney disease, the liver dysfunction, the peripheral neuropathy, and you could have predicted this, that this patient could have benefited from a test years before they actually got it. But look at this. All of these different conditions cut across different providers, different specialties, different care settings. And so assembling the full picture can be incredibly hard.

30:18 AC: And so this is the value of bringing in patient-journey-complete-data, because then you could predictively identify patients years before a traditional clinical practice would identify them. And so in this particular case with this client, they were able to identify hundreds of patients that would have benefited from testing. And those patients were being identified rather than 12-15 years post-symptom onset, were being identified three to five years post-symptom onset. And that's incredible because, A, you elongate their lives, B, it's reduced cost to the system, and C, you're able to get them the treatment that they need and lead to a better outcome.

31:00 FD: That's incredible. And just the value to a patient... Again, we never want anyone to suffer from any malady, that's for sure. But if this is a loved one and it's for 30, 35 months, they're suffering with something and their thing doesn't have a name, they can't figure out. That's just unbelievable what that insight should bring. So should we go to the next slide?

31:28 AC: Yeah. We can skip a little bit beyond this.

31:29 FD: Okay. And we can get a copy of some of this material in follow-up. Correct?

31:36 AC: Absolutely. This is actually, this particular case example was predicated on our Pulse offering. And so what I mean by that is that you're essentially sending alerts to a field team in which that are flagging, "Hey, this is Sunday night." Sorry. Give me just one moment.

31:57 FD: Yep. So what's really interesting, everybody, around this is just leveraging the power of this data. And when you think about the healthcare system and the entirety of the care that any of us have consumed, I just think about my last 10 years. I've had probably four different insurance companies. I've had three different primary care doctors. I can't even count the number of lab tests that I've had like a couple of times a year. I've had different pharmacies. And what would happen to my care as a patient if that was centralized, it was fluid, and you could leverage whether it was a person or technology to really go through, examine, and truly understand what is happening with me, and if there's disease process in me, that's incredible. And that's one of the many opportunities that we have leveraging AI as long as we've got the ground truth of data. Aswin, back to you.

32:55 AC: And yeah. So just to pull this through. Sunday night, your field team or your medical affairs team is thinking about who to engage and how to engage them. They get an alert that tells them, "Hey, this is a recently diagnosed, in this the case example, Parkinson's disease patient." They see the seven providers that have seen a patient that meets at very specific criteria. They select who they are. They see all the demographic information that relate to them. That's incredibly powerful to be able to deliver the right message, to be able to then talk to a provider around why their population might benefit from a particular intervention and therapy.

33:34 FD: And us, when I think probably one of the biggest questions that would jump out for many folks is that... So this is helping a representative go in with a meaningful message that's of high value and relevant to a provider, but it is patient de-identified, correct?

33:52 AC: Yes. All of our data is patient de-identified aggregated at the provider, and institution level. And all of our contact information is CAN-SPAM certified, as well as provider opted in.

34:07 FD: Okay. That's great. Should we go to the next slide? Let's talk about clinical trials a little bit. That's a competitive area.

34:18 AC: Absolutely. So just to talk through a little bit around our work with our clinical trial recruitment, we oftentimes in clinical trials, you're looking for a very, very specific patient cohort, and just the ability in order to carve out the specific population that you're looking for, and tie it back to a provider institution. As we know, more than 60% of trials fail because they fail to recruit patients, not because their drug is not efficacious, not because of anything else other than the fact that you can't find patients. And so I think this is incredibly important for the industry to move forward. So just to talk through a single slide in the interest of time, we were looking for a post-transplant lymphoproliferative disorder population that was anywhere from one to seven years post-solid organ transplant, that would receive the one round of Rituximab and was... This was an incredibly complex patient cohort that we were looking for.

35:22 AC: And so there were over a year delayed in recruiting the 66 patients phase two for trial. They had recruited less than 10 patients and by activating Pulse, we found 77 screening eligible patients. We recruited a good percentage of them. For contractual reasons, I can't state exactly the number. But the clinical trial is now on track and actually on course to close three months early. And that's the power of data. That's the thesis that all of us have been driving forward as an industry because you're getting therapies to patients that need them the most faster using data and workflow in order to do it. And so this is just an incredibly powerful, but not necessarily a typical example of using our healthcare map to track these types of results.

36:10 FD: That's incredible. And I think about the implications of this from three points of view. The first is from an investor's point of view, we often have to think about extending budget timelines, raising more money. Because so many of these things, the trials are often delayed. There's something that gets in the way. That's number one. Number two, the marketplace, there is war on patients, especially in certain therapeutic categories. So how do we bring those people forward faster versus traditional means. I've seen a thousand different applications and products and vendors who've come forward with like they're really good on Twitter. They're finding ways to bring patients in, and this is just a much more sophisticated way of doing it, and this is a sophisticated industry.

36:57 FD: And I think the third most important point around the opportunity, that the data and the technology brings forward is that this represents the opportunity for us to deliver on that shared value, which is to improve and extend patients' lives. And if we can bring an innovation to market sooner for patients that we know and love and are in our community, to me, this is just like... This hits everything, financial, logical and emotional, heartfelt stuff and why we're in this business to begin with. I love it. Thank you.

37:30 AC: Thank you. So just thinking about the power of our ground truth, we've talked about our coverage of 320 million patients, our representation across all settings of care, the fact that we are working contractually with payers and providers and that we're seeing every single encounter and sub-encounter within a particular episode or event, and that really builds a canonical view of our patient's events that you can then stage interventions and therapies and educate around the right standards of care for that patient moving forward.

38:03 FD: That sounds great.

38:04 AC: And if you just suppose that... I'm sorry, the slide repeated. Sorry.

38:08 FD: No problem.

38:10 AC: So thinking a little bit around how we help you bring you through the road to the last mile. A, it's the aggregation of disease burden to the providers and institutions, so CAN-SPAM certified opted-in, a lot of digital outreach. I think the fourth bullet here is incredibly important, which is to talk about the fact that we have demographic opted-in contact information for 90% of relevant providers. Bringing this even back to the COVID world, these providers are engaging with email and digital outreach four times as much as they normally do, and so there's a little bit of an attention monopoly that you have right now in order to talk to them about a specific average population that you didn't have before. And I think that there's going to be a massive backlog of patient encounters... To flip to the next slide... Sorry, there's gonna be a massive backlog of patient encounters that is going to sort of take away all of the opportunity there is to talk about these better standards of care with providers.

39:16 AC: And the last piece that I'll leave with you is a little bit like almost like pseudo spiritual, but I like to talk about it and think about it, because if you imagine the fact that there's deviations in the standards of care every single day in the US. There's no questions about that. And so you think about them almost as like micro-experiments, and that 85% of them negatively impact patients because it's the patients that are not getting the right standard of care. But then there's the 15% in which there's these micro-experiments that are happening in which the provider is giving them a therapy, and the therapy's actually working, but there's nobody there to actually chronicle the fact that, "Hey, these set of behaviors are negatively impacting the standard of care, but these other 15% are actually telling us about what the future might be or what it should be."

40:08 AC: And so that's the power of the ground truth. When you know, just to have this canonical view of all of US healthcare and these patient journeys, to stitch it back, not only to correct against the bad, but then to move to a better global standard of care. And that for me, represents one of the most un-manifest opportunities in all of healthcare to use data analysis, our healthcare map, to define what should things look like tomorrow.

40:41 AC: And so with that, I know that I've taken us through quite a lot, but think closely about the opportunities that we have with this current crisis, think closely about what you can do and your teams can do in order to serve providers and educate them in this time period. And the last piece is, think differently about what you can do and the investments that you're making in your companies, for the one, five and 10 year horizon. And they all relate back to data analysis in order for us to get the right therapies to the right patients at the right time in the right setting of care. Thank you so much, Frank.

41:25 FD: Aswin, I really appreciate you and the team and what you folks are doing. I just can't emphasize enough that in the life science industry, we have hundreds of thousands of people globally that walk into a research lab every day trying to create an innovation to help patients tomorrow, and as executives in Medical Affairs Commercial and in the C-Suite, we need to walk into our offices every day and think about how we're gonna deliver on the future, but a huge part of it is taking advantage of sophisticated data repositories and technology that will help us understand the past to better predict a future. We talk about being innovative companies, and that's great. I think technologies like these represent that.

42:14 FD: And one of the things that really drew me to Komodo Health in talking with you folks early on was what you're doing isn't necessarily an offering of our most common large trusted advisors in the consulting firms and what have you, and we appreciate and we respect them, but some of us wanna be innovators and figure out what's next, and I believe that you all are on top of that.

42:38 FD: And the final thing I would say from a branding perspective is that how in the world do we leverage the situations and opportunities and minimize risks with what's going on in today's market outside this data? But then getting back to business, whatever that looks like, where can we find competitive advantage? And we've gotta look for really, really trusted partners that can help us grab that competitive advantage because the data exists, it's just a matter of how we can grab it and do something incredible with it, and I think you guys are incredible partners, so, Aswin, just for you and your team, thank you, guys, for what you're doing. You're innovators in life science. I love it.

43:05 AC: Thank you, and thank you for giving us a voice and this opportunity...

43:19 FD: My pleasure. So, Aswin, I'm not sure that everyone is gonna be able to exactly spell your last name, but if we wanted to connect with you on Linkedin or what have you, how do we get in touch with you?

43:29 AC: Aswin, A-S-W-I-N, dot c@komodohealth.com, my email address and then if you type in Komodo Health, Aswin, A-S-W-I-N. You will also be able to find me, so would love to hear from the team.

43:36 FD: That's great, that's great. Well, Aswin, again, thank you for spending time.

43:40 AC: Take care.

43:42 FD: So on behalf of Aswin and me, I'm Frank Dolan, thanks for joining us in the Life Science Leader Lab today.