>>Welcome to this exclusive discussion. IBM, the next three years of life sciences, innovation, precision medicine, advanced clinical data management and beyond. My name is Dave Volante from the Cuban today, we're going to take a deep dive into some of the most important trends impacting the life sciences industry in the next 60 minutes. Yeah, of course. We're going to hear how IBM is utilizing Watson and some really important in life impacting ways, but we'll also bring in real world perspectives from industry and the independent analyst view to better understand how technology and data are changing the nature of precision medicine. Now, the pandemic has created a new reality for everyone, but especially for life sciences companies, one where digital transformation is no longer an option, but a necessity. Now the upside is the events of the past 22 months have presented an accelerated opportunity for innovation technology and real world data are coming together and being applied to support life science, industry trends and improve drug discovery, clinical development, and treatment commercialization throughout the product life cycle cycle. Now I'd like to introduce our esteemed panel. Let me first introduce Lorraine Marshawn, who is general manager of life sciences at IBM Watson health. Lorraine leads the organization dedicated to improving clinical development research, showing greater treatment value in getting treatments to patients faster with differentiated solutions. Welcome Lorraine. Great to see you. >>Dr. Namita LeMay is the research vice-president of IDC, where she leads the life sciences R and D strategy and technology program, which provides research based advisory and consulting services as well as market analysis. The loan to meta thanks for joining us today. And our third panelist is Greg Cunningham. Who's the director of the RWE center of excellence at Eli Lilly and company. Welcome, Greg, you guys are doing some great work. Thanks for being here. Thanks >>Dave. >>Now today's panelists are very passionate about their work. If you'd like to ask them a question, please add it to the chat box located near the bottom of your screen, and we'll do our best to answer them all at the end of the panel. Let's get started. Okay, Greg, and then Lorraine and meta feel free to chime in after one of the game-changers that you're seeing, which are advancing precision medicine. And how do you see this evolving in 2022 and into the next decade? >>I'll give my answer from a life science research perspective. The game changer I see in advancing precision medicine is moving from doing research using kind of a single gene mutation or kind of a single to look at to doing this research using combinations of genes and the potential that this brings is to bring better drug targets forward, but also get the best product to a patient faster. Um, I can give, uh, an example how I see it playing out in the last decade. Non-oncology real-world evidence. We've seen an evolution in precision medicine as we've built out the patient record. Um, as we've done that, uh, the marketplace has evolved rapidly, uh, with, particularly for electronic medical record data and genomic data. And we were pretty happy to get our hands on electronic medical record data in the early days. And then later the genetic test results were combined with this data and we could do research looking at a single mutation leading to better patient outcomes. But I think where we're going to evolve in 2022 and beyond is with genetic testing, growing and oncology, providing us more data about that patient. More genes to look at, uh, researchers can look at groups of genes to analyze, to look at that complex combination of gene mutations. And I think it'll open the door for things like using artificial intelligence to help researchers plow through the complex number of permutations. When you think about all those genes you can look at in combination, right? Lorraine yes. Data and machine intelligence coming together, anything you would add. >>Yeah. Thank you very much. Well, I think that Greg's response really sets us up nicely, particularly when we think about the ability to utilize real-world data in the farm industry across a number of use cases from discovery to development to commercial, and, you know, in particular, I think with real world data and the comments that Greg just made about clinical EMR data linked with genetic or genomic data, a real area of interest in one that, uh, Watson health in particular is focused on the idea of being able to create a data exchange so that we can bring together claims clinical EMR data, genomics data, increasingly wearables and data directly from patients in order to create a digital health record that we like to call an intelligent patient health record that basically gives us the digital equivalent of a real life patient. And these can be used in use cases in randomized controlled clinical trials for synthetic control arms or natural history. They can be used in order to track patients' response to drugs and look at outcomes after they've been on various therapies as, as Greg is speaking to. And so I think that, you know, the promise of data and technology, the AI that we can apply on that is really helping us advance, getting therapies to market faster, with better information, lower sample sizes, and just a much more efficient way to do drug development and to track and monitor outcomes in patients. >>Great. Thank you for that now to meta, when I joined IDC many, many years ago, I really didn't know much about the industry that I was covering, but it's great to see you as a former practitioner now bringing in your views. What do you see as the big game-changers? >>So, um, I would, I would agree with what both Lorraine and Greg said. Um, but one thing that I'd just like to call out is that, you know, everyone's talking about big data, the volume of data is growing. It's growing exponentially actually about, I think 30% of data that exists today is healthcare data. And it's growing at a rate of 36%. That's huge, but then it's not just about the big, it's also about the broad, I think, um, you know, I think great points that, uh, Lorraine and Greg brought out that it's, it's not just specifically genomic data, it's multi omic data. And it's also about things like medical history, social determinants of health, behavioral data. Um, and why, because when you're talking about precision medicine and we know that we moved away from the, the terminology of personalized to position, because you want to talk about disease stratification and you can, it's really about convergence. >>Um, if you look at a recent JAMA paper in 2021, only 1% of EHS actually included genomic data. So you really need to have that ability to look at data holistically and IDC prediction is seeing that investments in AI to fuel in silico, silicone drug discovery will double by 20, 24, but how are you actually going to integrate all the different types of data? Just look at, for example, diabetes, you're on type two diabetes, 40 to 70% of it is genetically inherited and you have over 500 different, uh, genetic low side, which could be involved in playing into causing diabetes. So the earlier strategy, when you are looking at, you know, genetic risk scoring was really single trait. Now it's transitioning to multi rate. And when you say multi trade, you really need to get that integrated view that converging for you to, to be able to drive a precision medicine strategy. So to me, it's a very interesting contrast on one side, you're really trying to make it specific and focused towards an individual. And on the other side, you really have to go wider and bigger as well. >>Uh, great. I mean, the technology is enabling that convergence and the conditions are almost mandating it. Let's talk about some more about data that the data exchange and building an intelligent health record, as it relates to precision medicine, how will the interoperability of real-world data, you know, create that more cohesive picture for the, for the patient maybe Greg, you want to start, or anybody else wants to chime in? >>I think, um, the, the exciting thing from, from my perspective is the potential to gain access to data. You may be weren't aware of an exchange in implies that, uh, some kind of cataloging, so I can see, uh, maybe things that might, I just had no idea and, uh, bringing my own data and maybe linking data. These are concepts that I think are starting to take off in our field, but it, it really opens up those avenues to when you, you were talking about data, the robustness and richness volume isn't, uh, the only thing is Namita said, I think really getting to a rich high-quality data and, and an exchange offers a far bigger, uh, range for all of us to, to use, to get our work done. >>Yeah. And I think, um, just to chime, chime into that, uh, response from Greg, you know, what we hear increasingly, and it's pretty pervasive across the industry right now, because this ability to create an exchange or the intelligent, uh, patient health record, these are new ideas, you know, they're still rather nascent and it always is the operating model. Uh, that, that is the, uh, the difficult challenge here. And certainly that is the case. So we do have data in various silos. Uh, they're in patient claims, they're in electronic medical records, they might be in labs, images, genetic files on your smartphone. And so one of the challenges with this interoperability is being able to tap into these various sources of data, trying to identify quality data, as Greg has said, and the meta is underscoring as well. Uh, we've gotta be able to get to the depth of data that's really meaningful to us, but then we have to have technology that allows us to pull this data together. >>First of all, it has to be de-identified because of security and patient related needs. And then we've gotta be able to link it so that you can create that likeness in terms of the record, it has to be what we call cleaned or curated so that you get the noise and all the missing this out of it, that's a big step. And then it needs to be enriched, which means that the various components that are going to be meaningful, you know, again, are brought together so that you can create that cohort of patients, that individual patient record that now is useful in so many instances across farm, again, from development, all the way through commercial. So the idea of this exchange is to enable that exact process that I just described to have a, a place, a platform where various entities can bring their data in order to have it linked and integrated and cleaned and enriched so that they get something that is a package like a data package that they can actually use. >>And it's easy to plug into their, into their studies or into their use cases. And I think a really important component of this is that it's gotta be a place where various third parties can feel comfortable bringing their data together in order to match it with other third parties. That is a, a real value, uh, that the industry is increasingly saying would be important to them is, is the ability to bring in those third-party data sets and be able to link them and create these, these various data products. So that's really the idea of the data exchange is that you can benefit from accessing data, as Greg mentioned in catalogs that maybe are across these various silos so that you can do the kind of work that you need. And that we take a lot of the hard work out of it. I like to give an example. >>We spoke with one of our clients at one of the large pharma companies. And, uh, I think he expressed it very well. He said, what I'd like to do is have like a complete dataset of lupus. Lupus is an autoimmune condition. And I've just like to have like the quintessential lupus dataset that I can use to run any number of use cases across it. You know, whether it's looking at my phase one trial, whether it's selecting patients and enriching for later stage trials, whether it's understanding patient responses to different therapies as I designed my studies. And so, you know, this idea of adding in therapeutic area indication, specific data sets and being able to create that for the industry in the meta mentioned, being able to do that, for example, in diabetes, that's how pharma clients need to have their needs met is through taking the hard workout, bringing the data together, having it very therapeutically enriched so that they can use it very easily. >>Thank you for that detail and the meta. I mean, you can't do this with humans at scale in technology of all the things that Lorraine was talking about, the enrichment, the provenance, the quality, and of course, it's got to be governed. You've got to protect the privacy privacy humans just can't do all that at massive scale. Can it really tech that's where technology comes in? Doesn't it and automation. >>Absolutely. >>I, couldn't more, I think the biggest, you know, whether you talk about precision medicine or you talk about decentralized trials, I think there's been a lot of hype around these terms, but what is really important to remember is technology is the game changer and bringing all that data together is really going to be the key enabler. So multimodal data integration, looking at things like security or federated learning, or also when you're talking about leveraging AI, you're not talking about things like bias or other aspects around that are, are critical components that need to be addressed. I think the industry is, uh, it's partly, still trying to figure out the right use cases. So it's one part is getting together the data, but also getting together the right data. Um, I think data interoperability is going to be the absolute game changer for enabling this. Uh, but yes, um, absolutely. I can, I can really couldn't agree more with what Lorraine just said, that it's bringing all those different aspects of data together to really drive that precision medicine strategy. >>Excellent. Hey Greg, let's talk about protocols decentralized clinical trials. You know, they're not new to life silences, but, but the adoption of DCTs is of course sped up due to the pandemic we've had to make trade-offs obviously, and the risk is clearly worth it, but you're going to continue to be a primary approach as we enter 2022. What are the opportunities that you see to improve? How DCTs are designed and executed? >>I see a couple opportunities to improve in this area. The first is, uh, back to technology. The infrastructure around clinical trials has, has evolved over the years. Uh, but now you're talking about moving away from kind of site focus to the patient focus. Uh, so with that, you have to build out a new set of tools that would help. So for example, one would be novel trial, recruitment, and screening, you know, how do you, how do you find patients and how do you screen them to see if are they, are they really a fit for, for this protocol? Another example, uh, very important documents that we have to get is, uh, you know, the e-consent that someone's says, yes, I'm, well, I understand this study and I'm willing to do it, have to do that in a more remote way than, than we've done in the past. >>Um, the exciting area, I think, is the use of, uh, eco, uh, E-Pro where we capture data from the patient using apps, devices, sensors. And I think all of these capabilities will bring a new way of, of getting data faster, uh, in, in this kind of model. But the exciting thing from, uh, our perspective at Lily is it's going to bring more data about the patient from the patient, not just from the healthcare provider side, it's going to bring real data from these apps, devices and sensors. The second thing I think is using real-world data to identify patients, to also improve protocols. We run scenarios today, looking at what's the impact. If you change a cut point on a, a lab or a biomarker to see how that would affect, uh, potential enrollment of patients. So it, it definitely the real-world data can be used to, to make decisions, you know, how you improve these protocols. >>But the thing that we've been at the challenge we've been after that this probably offers the biggest is using real-world data to identify patients as we move away from large academic centers that we've used for years as our sites. Um, you can maybe get more patients who are from the rural areas of our countries or not near these large, uh, uh, academic centers. And we think it'll bring a little more diversity to the population, uh, who who's, uh, eligible, but also we have their data, so we can see if they really fit the criteria and the probability they are a fit for the trial is much higher than >>Right. Lorraine. I mean, your clients must be really pushing you to help them improve DCTs what are you seeing in the field? >>Yes, in fact, we just attended the inaugural meeting of the de-central trials research Alliance in, uh, in Boston about two weeks ago where, uh, all of the industry came together, pharma companies, uh, consulting vendors, just everyone who's been in this industry working to help define de-central trials and, um, think through what its potential is. Think through various models in order to enable it, because again, a nascent concept that I think COVID has spurred into action. Um, but it is important to take a look at the definition of DCT. I think there are those entities that describe it as accessing data directly from the patient. I think that is a component of it, but I think it's much broader than that. To me, it's about really looking at workflows and processes of bringing data in from various remote locations and enabling the whole ecosystem to work much more effectively along the data continuum. >>So a DCT is all around being able to make a site more effective, whether it's being able to administer a tele visit or the way that they're getting data into the electronic data captures. So I think we have to take a look at the, the workflows and the operating models for enabling de-central trials and a lot of what we're doing with our own technology. Greg mentioned the idea of electronic consent of being able to do electronic patient reported outcomes, other collection of data directly from the patient wearables tele-health. So these are all data acquisition, methodologies, and technologies that, that we are enabling in order to get the best of the data into the electronic data capture system. So edit can be put together and processed and submitted to the FDA for regulatory use for clinical trial type submission. So we're working on that. I think the other thing that's happening is the ability to be much more flexible and be able to have more cloud-based storage allows you to be much more inter-operable to allow API APIs in order to bring in the various types of data. >>So we're really looking at technology that can make us much more fluid and flexible and accommodating to all the ways that people live and work and manage their health, because we have to reflect that in the way we collect those data types. So that's a lot of what we're, what we're focused on. And in talking with our clients, we spend also a lot of time trying to understand along the, let's say de-central clinical trials continuum, you know, w where are they? And I know Namita is going to talk a little bit about research that they've done in terms of that adoption curve, but because COVID sort of forced us into being able to collect data in more remote fashion in order to allow some of these clinical trials to continue during COVID when a lot of them had to stop. What we want to make sure is that we understand and can codify some of those best practices and that we can help our clients enable that because the worst thing that would happen would be to have made some of that progress in that direction. >>But then when COVID is over to go back to the old ways of doing things and not bring some of those best practices forward, and we actually hear from some of our clients in the pharma industry, that they worry about that as well, because we don't yet have a system for operationalizing a de-central trial. And so we really have to think about the protocol it's designed, the indication, the types of patients, what makes sense to decentralize, what makes sense to still continue to collect data in a more traditional fashion. So we're spending a lot of time advising and consulting with our patients, as well as, I mean, with our clients, as well as CRS, um, on what the best model is in terms of their, their portfolio of studies. And I think that's a really important aspect of trying to accelerate the adoption is making sure that what we're doing is fit for purpose, just because you can use technology doesn't mean you should, it really still does require human beings to think about the problem and solve them in a very practical way. >>Great, thank you for that. Lorraine. I want to pick up on some things that Lorraine was just saying. And then back to what Greg was saying about, uh, uh, DCTs becoming more patient centric, you had a prediction or IDC, did I presume your fingerprints were on it? Uh, that by 20 25, 70 5% of trials will be patient-centric decentralized clinical trials, 90% will be hybrid. So maybe you could help us understand that relationship and what types of innovations are going to be needed to support that evolution of DCT. >>Thanks, Dave. Yeah. Um, you know, sorry, I, I certainly believe that, uh, you know, uh, Lorraine was pointing out of bringing up a very important point. It's about being able to continue what you have learned in over the past two years, I feel this, you know, it was not really a digital revolution. It was an attitude. The revolution that this industry underwent, um, technology existed just as clinical trials exist as drugs exist, but there was a proof of concept that technology works that this model is working. So I think that what, for example, telehealth, um, did for, for healthcare, you know, transition from, from care, anywhere care, anytime, anywhere, and even becoming predictive. That's what the decentralized clinical trials model is doing for clinical trials today. Great points again, that you have to really look at where it's being applied. You just can't randomly apply it across clinical trials. >>And this is where the industry is maturing the complexity. Um, you know, some people think decentralized trials are very simple. You just go and implement these centralized clinical trials, but it's not that simple as it it's being able to define, which are the right technologies for that specific, um, therapeutic area for that specific phase of the study. It's being also a very important point is bringing in the patient's voice into the process. Hey, I had my first telehealth visit sometime last year and I was absolutely thrilled about it. I said, no time wasted. I mean, everything's done in half an hour, but not all patients want that. Some want to consider going back and you, again, need to customize your de-centralized trials model to, to the, to the type of patient population, the demographics that you're dealing with. So there are multiple factors. Um, also stepping back, you know, Lorraine mentioned they're consulting with, uh, with their clients, advising them. >>And I think a lot of, um, a lot of companies are still evolving in their maturity in DCTs though. There's a lot of boys about it. Not everyone is very mature in it. So it's, I think it, one thing everyone's kind of agreeing with is yes, we want to do it, but it's really about how do we go about it? How do we make this a flexible and scalable modern model? How do we integrate the patient's voice into the process? What are the KPIs that we define the key performance indicators that we define? Do we have a playbook to implement this model to make it a scalable model? And, you know, finally, I think what organizations really need to look at is kind of developing a de-centralized mature maturity scoring model, so that I assess where I am today and use that playbook to define, how am I going to move down the line to me reach the next level of maturity. Those were some of my thoughts. Right? >>Excellent. And now remember you, if you have any questions, use the chat box below to submit those questions. We have some questions coming in from the audience. >>At one point to that, I think one common thread between the earlier discussion around precision medicine and around decentralized trials really is data interoperability. It is going to be a big game changer to, to enable both of these pieces. Sorry. Thanks, Dave. >>Yeah. Thank you. Yeah. So again, put your questions in the chat box. I'm actually going to go to one of the questions from the audience. I get some other questions as well, but when you think about all the new data types that are coming in from social media, omics wearables. So the question is with greater access to these new types of data, what trends are you seeing from pharma device as far as developing capabilities to effectively manage and analyze these novel data types? Is there anything that you guys are seeing, um, that you can share in terms of best practice or advice >>I'll offer up? One thing, I think the interoperability isn't quite there today. So, so what's that mean you can take some of those data sources. You mentioned, uh, some Omix data with, uh, some health claims data and it's the, we spend too much time and in our space putting data to gather the behind the scenes, I think the stat is 80% of the time is assembling the data 20% analyzing. And we've had conversations here at Lilly about how do we get to 80% of the time is doing analysis. And it really requires us to think, take a step back and think about when you create a, uh, a health record, you really have to be, have the same plugins so that, you know, data can be put together very easily, like Lorraine mentioned earlier. And that comes back to investing in as an industry and standards so that, you know, you have some of data standard, we all can agree upon. And then those plugs get a lot easier and we can spend our time figuring out how to make, uh, people's lives better with healthcare analysis versus putting data together, which is not a lot of fun behind the scenes. >>Other thoughts on, um, on, on how to take advantage of sort of novel data coming from things like devices in the nose that you guys are seeing. >>I could jump in there on your end. Did you want to go ahead? Okay. So, uh, I mean, I think there's huge value that's being seen, uh, in leveraging those multiple data types. I think one area you're seeing is the growth of prescription digital therapeutics and, um, using those to support, uh, you know, things like behavioral health issues and a lot of other critical conditions it's really taking you again, it is interlinking real-world data cause it's really taking you to the patient's home. Um, and it's, it's, there's a lot of patients in the city out here cause you can really monitor the patient real-time um, without the patient having coming, you know, coming and doing a site visit once in say four weeks or six weeks. So, um, I, and, uh, for example, uh, suicidal behavior and just to take an example, if you can predict well in advance, based on those behavioral parameters, that this is likely to trigger that, uh, the value of it is enormous. Um, again, I think, uh, Greg made a valid point about the industry still trying to deal with resolving the data interoperability issue. And there are so many players that are coming in the industry right now. There are really few that have the maturity and the capability to address these challenges and provide intelligence solutions. >>Yeah. Maybe I'll just, uh, go ahead and, uh, and chime into Nikita's last comment there. I think that's what we're seeing as well. And it's very common, you know, from an innovation standpoint that you have, uh, a nascent industry or a nascent innovation sort of situation that we have right now where it's very fragmented. You have a lot of small players, you have some larger entrenched players that have the capability, um, to help to solve the interoperability challenge, the standards challenge. I mean, I think IBM Watson health is certainly one of the entities that has that ability and is taking a stand in the industry, uh, in order to, to help lead in that way. Others are too. And, uh, but with, with all of the small companies that are trying to find interesting and creative ways to gather that data, it does create a very fragmented, uh, type of environment and ecosystem that we're in. >>And I think as we mature, as we do come forward with the KPIs, the operating models, um, because you know, the devil's in the detail in terms of the operating models, it's really exciting to talk these trends and think about the future state. But as Greg pointed out, if you're spending 80% of your time just under the hood, you know, trying to get the engine, all the spark plugs to line up, um, that's, that's just hard grunt work that has to be done. So I think that's where we need to be focused. And I think bringing all the data in from these disparate tools, you know, that's fine, we need, uh, a platform or the API APIs that can enable that. But I think as we, as we progress, we'll see more consolidation, uh, more standards coming into play, solving the interoperability types of challenges. >>And, um, so I think that's where we should, we should focus on what it's going to take and in three years to really codify this and make it, so it's a, it's a well hum humming machine. And, you know, I do know having also been in pharma that, uh, there's a very pilot oriented approach to this thing, which I think is really healthy. I think large pharma companies tend to place a lot of bets with different programs on different tools and technologies, to some extent to see what's gonna stick and, you know, kind of with an innovation mindset. And I think that's good. I think that's kind of part of the process of figuring out what is going to work and, and helping us when we get to that point of consolidating our model and the technologies going forward. So I think all of the efforts today are definitely driving us to something that feels much more codified in the next three to five years. >>Excellent. We have another question from the audience it's sort of related to the theme of this discussion, given the FDA's recent guidance on using claims and electronic health records, data to support regulatory decision-making what advancements do you think we can expect with regards to regulatory use of real-world data in the coming years? It's kind of a two-parter so maybe you guys can collaborate on this one. What role that, and then what role do you think industry plays in influencing innovation within the regulatory space? >>All right. Well, it looks like you've stumped the panel there. Uh, Dave, >>It's okay to take some time to think about it, right? You want me to repeat it? You guys, >>I, you know, I I'm sure that the group is going to chime into this. I, so the FDA has issued a guidance. Um, it's just, it's, it's exactly that the FDA issues guidances and says that, you know, it's aware and supportive of the fact that we need to be using real-world data. We need to create the interoperability, the standards, the ways to make sure that we can include it in regulatory submissions and the like, um, and, and I sort of think about it akin to the critical path initiative, probably, I don't know, 10 or 12 years ago in pharma, uh, when the FDA also embrace this idea of the critical path and being able to allow more in silico modeling of clinical trial, design and development. And it really took the industry a good 10 years, um, you know, before they were able to actually adopt and apply and take that sort of guidance or openness from the FDA and actually apply it in a way that started to influence the way clinical trials were designed or the in silico modeling. >>So I think the second part of the question is really important because while I think the FDA is saying, yes, we recognize it's important. Uh, we want to be able to encourage and support it. You know, when you look for example, at synthetic control arms, right? The use of real-world data in regulatory submissions over the last five or six years, all of the use cases have been in oncology. I think there've been about maybe somewhere between eight to 10 submissions. And I think only one actually was a successful submission, uh, in all those situations, the real-world data arm of that oncology trial that synthetic control arm was actually rejected by the FDA because of lack of completeness or, you know, equalness in terms of the data. So the FDA is not going to tell us how to do this. So I think the second part of the question, which is what's the role of industry, it's absolutely on industry in order to figure out exactly what we're talking about, how do we figure out the interoperability, how do we apply the standards? >>How do we ensure good quality data? How do we enrich it and create the cohort that is going to be equivalent to the patient in the real world, uh, in the end that would otherwise be in the clinical trial and how do we create something that the FDA can agree with? And we'll certainly we'll want to work with the FDA in order to figure out this model. And I think companies are already doing that, but I think that the onus is going to be on industry in order to figure out how you actually operationalize this and make it real. >>Excellent. Thank you. Um, question on what's the most common misconception that clinical research stakeholders with sites or participants, et cetera might have about DCTs? >>Um, I could jump in there. Right. So, sure. So, um, I think in terms of misconceptions, um, I think the communist misconceptions that sites are going away forever, which I do not think is really happening today. Then the second, second part of it is that, um, I think also the perspective that patients are potentially neglected because they're moving away. So we'll pay when I, when I, what I mean by that neglected, perhaps it was not the appropriate term, but the fact that, uh, will patients will, will, will patient engagement continue, will retention be strong since the patients are not interacting in person with the investigator quite as much. Um, so site retention and patient retention or engagement from both perspectives, I think remains a concern. Um, but actually if you look at, uh, look at, uh, assessments that have been done, I think patients are more than happy. >>Majority of the patients have been really happy about, about the new model. And in fact, sites are, seem to increase, have increased investments in technology by 50% to support this kind of a model. So, and the last thing is that, you know, decentralized trials is a great model and it can be applied to every possible clinical trial. And in another couple of weeks, the whole industry will be implementing only decentralized trials. I think we are far away from that. It's just not something that you would implement across every trial. And we discussed that already. So you have to find the right use cases for that. So I think those were some of the key misconceptions I'd say in the industry right now. Yeah. >>Yeah. And I would add that the misconception I hear the most about is, uh, the, the similar to what Namita said about the sites and healthcare professionals, not being involved to the level that they are today. Uh, when I mentioned earlier in our conversation about being excited about capturing more data, uh, from the patient that was always in context of, in addition to, you know, healthcare professional opinion, because I think both of them bring that enrichment and a broader perspective of that patient experience, whatever disease they're faced with. So I, I think some people think is just an all internet trial with just someone, uh, putting out there their own perspective. And, and it's, it's a combination of both to, to deliver a robust data set. >>Yeah. Maybe I'll just comment on, it reminds me of probably 10 or 15 years ago, maybe even more when, um, really remote monitoring was enabled, right? So you didn't have to have the study coordinator traveled to the investigative site in order to check the temperature of the freezer and make sure that patient records were being completed appropriately because they could have a remote visit and they could, they could send the data in a via electronic data and do the monitoring visit, you know, in real time, just the way we're having this kind of communication here. And there was just so much fear that you were going to replace or supplant the personal relationship between the sites between the study coordinators that you were going to, you know, have to supplant the role of the monitor, which was always a very important role in clinical trials. >>And I think people that really want to do embrace the technology and the advantages that it provided quickly saw that what it allowed was the monitor to do higher value work, you know, instead of going in and checking the temperature on a freezer, when they did have their visit, they were able to sit and have a quality discussion for example, about how patient recruitment was going or what was coming up in terms of the consent. And so it created a much more high touch, high quality type of interaction between the monitor and the investigative site. And I think we should be looking for the same advantages from DCT. We shouldn't fear it. We shouldn't think that it's going to supplant the site or the investigator or the relationship. It's our job to figure out where the technology fits and clinical sciences always got to be high touch combined with high-tech, but the high touch has to lead. And so getting that balance right? And so that's going to happen here as well. We will figure out other high value work, meaningful work for the site staff to do while they let the technology take care of the lower quality work, if you will, or the lower value work, >>That's not an, or it's an, and, and you're talking about the higher value work. And it, it leads me to something that Greg said earlier about the 80, 20, 80% is assembly. 20% is actually doing the analysis and that's not unique to, to, to life sciences, but, but sort of question is it's an organizational question in terms of how we think about data and how we approach data in the future. So Bamyan historically big data in life sciences in any industry really is required highly centralized and specialized teams to do things that the rain was talking about, the enrichment, the provenance, the data quality, the governance, the PR highly hyper specialized teams to do that. And they serve different constituencies. You know, not necessarily with that, with, with context, they're just kind of data people. Um, so they have responsibility for doing all those things. Greg, for instance, within literally, are you seeing a move to, to, to democratize data access? We've talked about data interoperability, part of that state of sharing, um, that kind of breaks that centralized hold, or is that just too far in the future? It's too risky in this industry? >>Uh, it's actually happening now. Uh, it's a great point. We, we try to classify what people can do. And, uh, the example would be you give someone who's less analytically qualified, uh, give them a dashboard, let them interact with the data, let them better understand, uh, what, what we're seeing out in the real world. Uh, there's a middle user, someone who you could give them, they can do some analysis with the tool. And the nice thing with that is you have some guardrails around that and you keep them in their lane, but it allows them to do some of their work without having to go ask those centralized experts that, that you mentioned their precious resources. And that's the third group is those, uh, highly analytical folks that can, can really deliver, uh, just value beyond. But when they're doing all those other things, uh, it really hinders them from doing what we've been talking about is the high value stuff. So we've, we've kind of split into those. We look at people using data in one of those three lanes and it, and it has helped I think, uh, us better not try to make a one fit solution for, for how we deliver data and analytic tools for people. Right. >>Okay. I mean, DCT hot topic with the, the, the audience here. Another question, um, what capabilities do sponsors and CRS need to develop in-house to pivot toward DCT? >>Should I jump in here? Yeah, I mean, um, I think, you know, when, when we speak about DCTs and when I speak with, uh, folks around in the industry, I, it takes me back to the days of risk-based monitoring. When it was first being implemented, it was a huge organizational change from the conventional monitoring models to centralize monitoring and risk-based monitoring, it needs a mental reset. It needs as Lorraine had pointed out a little while ago, restructuring workflows, re redefining processes. And I think that is one big piece. That is, I think the first piece, when, you know, when you're implementing a new model, I think organizational change management is a big piece of it because you are disturbing existing structures, existing methods. So getting that buy-in across the organization towards the new model, seeing what the value add in it. And where do you personally fit into that story? >>How do your workflows change, or how was your role impacted? I think without that this industry will struggle. So I see organizations, I think, first trying to work on that piece to build that in. And then of course, I also want to step back for the second to the, uh, to the point that you brought out about data democratization. And I think Greg Greg gave an excellent point, uh, input about how it's happening in the industry. But I would also say that the data democratization really empowerment of, of, of the stakeholders also includes the sites, the investigators. So what is the level of access to data that you know, that they have now, and is it, uh, as well as patients? So see increasingly more and more companies trying to provide access to patients finally, it's their data. So why shouldn't they have some insights to it, right. So access to patients and, uh, you know, the 80, 20 part of it. Uh, yes, he's absolutely right that, uh, we want to see that flip from, uh, 20%, um, you know, focusing on, on actually integrating the data 80% of analytics, but the real future will be coming in when actually the 20 and 18 has gone. And you actually have analysts the insights out on a silver platter. That's kind of wishful thinking, some of the industries is getting there in small pieces, but yeah, then that's just why I should, why we share >>Great points. >>And I think that we're, we're there in terms that like, I really appreciate the point around democratizing the data and giving the patient access ownership and control over their own data. I mean, you know, we see the health portals that are now available for patients to view their own records, images, and labs, and claims and EMR. We have blockchain technology, which is really critical here in terms of the patient, being able to pull all of their own data together, you know, in the blockchain and immutable record that they can own and control if they want to use that to transact clinical trial types of opportunities based on their data, they can, or other real world scenarios. But if they want to just manage their own data because they're traveling and if they're in a risky health situation, they've got their own record of their health, their health history, uh, which can avoid, you know, medical errors occurring. So, you know, even going beyond life sciences, I think this idea of democratizing data is just good for health. It's just good for people. And we definitely have the technology that can make it a reality. Now >>You're here. We have just about 10 minutes left and now of course, now all the questions are rolling in like crazy from the crowd. Would it be curious to know if there would be any comments from the panel on cost comparison analysis between traditional clinical trials in DCTs and how could the outcome effect the implementation of DCTs any sort of high-level framework you can share? >>I would say these are still early days to, to drive that analysis because I think many companies are, um, are still in the early stages of implementation. They've done a couple of trials. The other part of it that's important to keep in mind is, um, is for organizations it's, they're at a stage of, uh, of being on the learning curve. So when you're, you're calculating the cost efficiencies, if ideally you should have had two stakeholders involved, you could have potentially 20 stakeholders involved because everyone's trying to learn the process and see how it's going to be implemented. So, um, I don't think, and the third part of it, I think is organizations are still defining their KPIs. How do you measure it? What do you measure? So, um, and even still plugging in the pieces of technology that they need to fit in, who are they partnering with? >>What are the pieces of technology they're implementing? So I don't think there is a clear cut as answered at this stage. I think as you scale this model, the efficiencies will be seen. It's like any new technology or any new solution that's implemented in the first stages. It's always a little more complex and in fact sometimes costs extra. But as, as you start scaling it, as you establish your workflows, as you streamline it, the cost efficiencies will start becoming evident. That's why the industry is moving there. And I think that's how it turned out on the long run. >>Yeah. Just make it maybe out a comment. If you don't mind, the clinical trials are, have traditionally been costed are budgeted is on a per patient basis. And so, you know, based on the difficulty of the therapeutic area to recruit a rare oncology or neuromuscular disease, there's an average that it costs in order to find that patient and then execute the various procedures throughout the clinical trial on that patient. And so the difficulty of reaching the patient and then the complexity of the trial has led to what we might call a per patient stipend, which is just the metric that we use to sort of figure out what the average cost of a trial will be. So I think to point, we're going to have to see where the ability to adjust workflows, get to patients faster, collect data more easily in order to make the burden on the site, less onerous. I think once we start to see that work eases up because of technology, then I think we'll start to see those cost equations change. But I think right now the system isn't designed in order to really measure the economic benefit of de-central models. And I think we're going to have to sort of figure out what that looks like as we go along and since it's patient oriented right now, we'll have to say, well, you know, how does that work, ease up? And to those costs actually come down and then >>Just scale, it's going to be more, more clear as the media was saying, next question from the audiences, it's kind of a best fit question. You all have touched on this, but let me just ask it is what examples in which, in which phases suit DCT in its current form, be it fully DCT or hybrid models, none of our horses for courses question. >>Well, I think it's kind of, uh, it's, it's it's has its efficiencies, obviously on the later phases, then the absolute early phase trials, those are not the ideal models for DCTs I would say so. And again, the logic is also the fact that, you know, when you're, you're going into the later phase trials, the volume of number of patients is increasing considerably to the point that Lorraine brought up about access to the patients about patient selection. The fact, I think what one should look at is really the advantages that it brings in, in terms of, you know, patient access in terms of patient diversity, which is a big piece that, um, the cities are enabling. So, um, if you, if, if you, if you look at the spectrum of, of these advantages and, and just to step back for a moment, if you, if you're looking at costs, like you're looking at things like remote site monitoring, um, is, is a big, big plus, right? >>I mean, uh, site monitoring alone accounts for around a third of the trial costs. So there are so many pieces that fall in together. The challenge actually that comes when you're in defining DCTs and there are, as Rick pointed out multiple definitions of DCTs that are existing, uh, you know, in the industry right now, whether you're talking of what Detroit is doing, or you're talking about acro or Citi or others. But the point is it's a continuum, it's a continuum of different pieces that have been woven together. And so how do you decide which pieces you're plugging in and how does that impact the total cost or the solution that you're implementing? >>Great, thank you. Last question we have in the audience, excuse me. What changes have you seen? Are there others that you can share from the FDA EU APAC, regulators and supporting DCTs precision medicine for approval processes, anything you guys would highlight that we should be aware of? >>Um, I could quickly just add that. I think, um, I'm just publishing a report on de-centralized clinical trials should be published shortly, uh, perspective on that. But I would say that right now, um, there, there was a, in the FDA agenda, there was a plan for a decentralized clinical trials guidance, as far as I'm aware, one has not yet been published. There have been significant guidances that have been published both by email and by, uh, the FDA that, um, you know, around the implementation of clinical trials during the COVID pandemic, which incorporate various technology pieces, which support the DCD model. Um, but I, and again, I think one of the reasons why it's not easy to publish a well-defined guidance on that is because there are so many moving pieces in it. I think it's the Danish, uh, regulatory agency, which has per se published a guidance and revised it as well on decentralized clinical trials. >>Right. Okay. Uh, we're pretty much out of time, but I, I wonder Lorraine, if you could give us some, some final thoughts and bring us home things that we should be watching or how you see the future. >>Well, I think first of all, let me, let me thank the panel. Uh, we really appreciate Greg from Lily and the meta from IDC bringing their perspectives to this conversation. And, uh, I hope that the audience has enjoyed the, uh, the discussion that we've had around the future state of real world data as, as well as DCT. And I think, you know, some of the themes that we've talked about, number one, I think we have a vision and I think we have the right strategies in terms of the future promise of real-world data in any number of different applications. We certainly have talked about the promise of DCT to be more efficient, to get us closer to the patient. I think that what we have to focus on is how we come together as an industry to really work through these very vexing operational issues, because those are always the things that hang us up and whether it's clinical research or whether it's later stage, uh, applications of data. >>We, the healthcare system is still very fragmented, particularly in the us. Um, it's still very, state-based, uh, you know, different states can have different kinds of, uh, of, of cultures and geographic, uh, delineations. And so I think that, you know, figuring out a way that we can sort of harmonize and bring all of the data together, bring some of the models together. I think that's what you need to look to us to do both industry consulting organizations, such as IBM Watson health. And we are, you know, through DTRA and, and other, uh, consortia and different bodies. I think we're all identifying what the challenges are in terms of making this a reality and working systematically on those. >>It's always a pleasure to work with such great panelists. Thank you, Lorraine Marshawn, Dr. Namita LeMay, and Greg Cunningham really appreciate your participation today and your insights. The next three years of life sciences, innovation, precision medicine, advanced clinical data management and beyond has been brought to you by IBM in the cube. You're a global leader in high tech coverage. And while this discussion has concluded, the conversation continues. So please take a moment to answer a few questions about today's panel on behalf of the entire IBM life sciences team and the cube decks for your time and your feedback. And we'll see you next time.

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. Yeah, hello, everyone, and welcome back to the Cubes Walter Wall coverage of AWS reinvent 2020. We've gone virtual along with reinvent and we heard in Andy Jassy is hours long. Keynote a number of new innovations in the area of storage. And with me to talk about that is Milan Thompson Bukovec. She's the vice president of Block and Object Storage and AWS. That's everything. Elastic block storage s three Glacier, the whole portfolio Milon. Thanks for coming on. >>Great to see you. >>Great to see you too. So you heard Andy. We all heard Andy talk a lot about reinventing different parts of the platform, reinventing industries and a really kind of exciting and visionary put talk that he put forth. Let's >>talk >>about storage, though. How is storage reinventing itself? >>Well, as you know, cloud storage was essentially invented by a W s a number of years ago. And whether that's in 2000 and six, when US three was launched, or 2000 and eight when CBS was launched and we first came up with this model of pay as you go for durable, attached storage. Too easy to instances. And so we haven't stopped and we haven't slowed down. If anything, we've picked up the rate of reinvention that we've done across the portfolio for storage. I think, as Andy called out, speed matters. And it matters for how customers air thinking about how do they pivot and move to the cloud as quickly as they can, particularly this year. And it matters a lot in storage as well, because the changing access patterns of what customers air doing with their new cloud applications, you know they're they're transforming their businesses and their applications, and they need a modern storage platform underneath it. And that's what you have with AWS Storage. And he talked about some of the key releases, particularly in block storage. It's actually kind of amazing. What's what's been done with CBS is here. We launched GP three GP two was the previous generation general purpose volume type. We launched that in 2000 and 14 again thief, first type of general purpose volume that had this great combination of simplicity and price, and just about everybody uses it for a boot or often a data volume. And with GP three, which was available yesterday with Andy's announcement, we added four times peak throughput on top of GP two, and it's a 20% lower storage price per gigabyte per month. And we took the feedback. The number one feedback we got on GP to which was how can I separate buying throughput and I ops from storage capacity? And that is really important. That goes back to the promise of the cloud. And it goes back to being able to pick what aspect do you want to scale your storage on? And so, with GP three, you could buy a certain amount of capacity. And if you're good with that capacity, but you need more throughput, more eye ops, you can buy those independently. And that is that fine grained customization for those changing data patterns that I just talked about. And it's available for GP three today. >>Yeah, that was I looked at that, like my life is a knob that you could turn Okay, juice my eye ops. And don't touch my capacity. I'm happy there. I don't wanna pay for more of it. >>And thio add to that it's a knob you could turn if you need it. We have more throughput, more eye ops as a baseline capacity for your storage capacity than we did for GP to. But then you can tune it based on whatever you need, not just now, but in the future. >>So so given the pandemic, I mean, how has that affected E? Everybody is talking about going to the cloud, because where else you gonna go? But But how has that affected what customers are doing this year, and does it change your roadmap at all? Does it change your thinking? >>Well, I have to say, there's two main things that we've seen. One is it's really accelerated customers thinking about getting off of on premises and into the club. It's done that because nobody really wants to manage the data center. And if there's ever a year you don't want to manage the data center, it's 2020 and it's because, particularly with storage appliances, it takes a long time to acquire. Let's just take storage area networks or sense super expensive. You get a fixed amount of capacity you have to acquire. It takes months to come in you gotta rack and stack. Then you gotta change all your networking and maintain it. Ah, lot of customers don't want to do that. And so what it's done for us is it's really, uh, you know, accelerated our thinking and you saw yesterday and Andy's keynote as well. Of how do we build the first san in the cloud? And we launched Io two. In August of this year, we introduced the first nines of durability, again reinventing how people think about durability and their block storage. But just this week we now have a Iot to block Express with 2 56 K ai ops, four K megabytes of throughput in 64 terabytes of capacity, that sand level performance. And it's available for preview because I 02 is going to be your son in the cloud. And that is a direct correlation to what we hear from customers, which is how can I get away from these expensive on premises purchases like Sands and combine the performance with the elasticity that I need? So that's the first thing. How can we accelerate getting off of these very rigid procurement cycles that we have and having to manage a data center. It's not just for EBS, its for S. Trias. Well, the second thing we're hearing from customers is how can I have the agility? So you talk to customers as well. He talked to CEOs and C. T. O s. It's been a crazy year in 2020. It was one thing that a company has to do its pivot. It's really figure out. How are you going to adjust and adjust quickly? And so we have customers like Ontario Telehealth Network up in Canada, where they went from 8000 to 30,000 users because they're doing virtual health for Ontario. And we have other customers who, you know, that's a pivot. That's an increase. And we have other customers, like APS Flyer, where their goal is to just save money without changing their application. And they also did a pivot. They used the intelligence hearing storage class, which is the most popular storage class, as three offers for data lakes, and they were able to make that change save 18% on their storage cost, no change of their application, just using the capabilities of AWS. And so his ability to pivot helped you know really make us think and accelerate what we're building as well. And so one of the things that we launched just recently for intelligent hearing is we added two new archival tears to intelligent hearing. And those are archival tears, you know, just like intelligence hearing automatically watches every object industry storage and your data lake and gives you dynamic pricing based on if it's frequently accessed in a month or inflict infrequently accessed, you can turn on archival tear. And if your object your pork a file, for example, isn't access or your backup isn't access for 90 days, intelligence hearing will automatically move it to glacier characteristics of archival or too deep archive and give you the same price. A dollar, a terabyte per month. If your data is an access to 180 days, it's done automatically, and it means you save up to 90% 95% and cost on that storage. And so, if you if you think about those two trends, how can I get away from getting locked into those on premises Hardware cycles? How can I get away from it faster for sands and other hardware appliances and then the other trend is how can I pivot and use the innovation and the reinvention in our storage services to just save money and be more agile in these changing conditions? >>So I gotta ask you follow up question on staying in the cloud, because when you think of sand, you think of switches. You think of complexity, but I get that you're connecting to the performance of a sand. But you guys are all about simplicity. So how did you What's behind there? Can you take us under the covers? Just you guys build your own little storage network because it's cloud. It's gotta be fast and simple. >>That's right. When we're thinking about performance and cost, we go down to the metal for this stuff. We think about Unicosta a very fine grained level, and when we're building new technology that we know is gonna be the foundation for everything we're doing for that high performance, we went down to the protocol level. We're using something called Us RD. It's all rolled up under the hood for Block Express, and it's the foundation of that super super high performance. As you know, there's a lot of engineering behind the scenes in the cloud and for for what we've done this year, as part of that reinvention we've reinvented all the way down to the protocol way. >>Let me ask you that the two things that come up in our survey when you talk to CEOs, they say two priorities. Security is actually second cloud migration actually popped up to the top. So where does storage fit in that whole notion about cloud migration, >>Storage eyes, usually where a lot of people start, you know, Luckily, with a W s, you don't have to choose between security or cloud of migration. Security is job one for every AWS service. And so when customers air thinking about how do I move an application, they gotta move the data first. And so they start from the from the data. What storage do I use? What is the best fit for the storage and how do I best secure that's storage? And so the innovation that we dio on storage always comes with that. That combination of, you know, migration, the set of tools that we provide for getting data from on premises into the cloud. We have tools like aws data sync which do a great job of this on. Then we also look at things like how do we continue to take the profile of security forward? And one example of that is something we launched just this week called Bucket keys s three bucket keys. And it drops the cost of using kms for service side encryption with us three by over 90%. And the way it does it is that we've integrated those two services super closely together so that you can minimize the amount of costs that you make for very, very frequent request. Because in data lakes you have millions and billions of objects and our goal is to make security so cost effective people don't even think about it. That also goes for other parts of the platform. We have guard duty for us three now, and what that does is security anomaly detection automatically to track your access patterns across as three and flag when something is not quite what it should be. And so this idea of like how do I not only get my data into the cloud? But then how do I take advantage of the breath of the storage portfolio, but also the breath of the AWS services to really maximize that security profile as well as the access patterns that I want from my application. >>Well, my way hit the major announcements and unfortunately, out of time. But I really would love to have you back and go deeper and have you share your vision of what the cloud storage piece looks like going forward. Thanks so much for coming in. The Cube is great to have you. >>Great to be here. Thanks, Dave. CIA. >>See you later and keep it right, everybody. You're watching the cubes. Coverage of aws reinvent 2020 right back.

>>Hi, everyone. Good afternoon. Thank you for joining this session. I feel honored to be invited to speak here today. And I also appreciate entity research Summit members for organ organizing and giving this great opportunity. Please let me give a quick introduction. First, I'm a Takashi from Marvin American population, and I'm leading technology scouting and global ation with digital health companies such as Business Alliance and Strategically Investment in North America. And since we started to focus on this space in 2016 our team is growing. And in order to bring more new technologies and services to Japan market Thesis year, we founded the new service theories for digital health business, especially, uh, in medical diagnosis space in Japan. And today I would like to talk how health care has been transformed for my micro perspective, and I hope you enjoy reasoning it. So what's happened since the US identify the first case in the middle of January, As everyone knows, unfortunately, is the damaged by this pandemic was unequal amongst the people in us. It had more determined tal impact on those who are socially and economically vulnerable because of the long, long lasting structural program off the U. S. Society and the Light Charity about daily case rating elevator country shows. Even in the community, the infection rate off the low income were 4.5 times higher than, uh, those of the high income and due to czar straight off the Corvette, about 14 million people are unemployed. The unique point off the U. S. Is that more than 60% of insurance is tied with employment, so losing a job can mean losing access to health care. And the point point here is that the Corvette did not create healthcare disparity but, uh nearly highlighted the underlying program and necessity off affordable care for all. And when the country had a need to increase the testing capacity and geographic out, treat the pharmacies and retails joined forces with existing stakeholders more than 90% off the U. S Corporation live within five miles off a community pharmacy such as CVS and Walgreen, so they can technically provide the test to everyone in all the community. And they also have a huge workforce memory pharmacist who are eligible to perform the testing scale, and this very made their potential in community based health care. Stand out and about your health has provided on alternative way for people to access to health care. At affordable applies under the unusual setting where social distancing, which required required mhm and people have a fear of infection. So they are afraid to take a public transportacion and visit >>the doctor the same thing supplied to doctor and the chart. Here is a number of total visit cranes by service type after stay at home order was issued across the U. S. By Ali April patient physical visits to doctor's offices or clinics declined by ALAN 70%. On the other hand, that share, or telehealth, accounted for 25% of the total total. Doctor's visit in April, while many states studied to re opening face to face visit is gradually recovering. And overall Tele Health Service did not offset the crime. Physician Physical doctor's visit and telehealth John never fully replace in person care. However, Telehealth has established a new way to provide affordable care, especially to vulnerable people, and I don't explain each player's today. But as an example, the chart shows the significant growth of the tell a dog who is one of the largest badger care and tell his provider, I believe there are three factors off paradox. Success under the pandemic. First, obviously tell Doc could reach >>the job between those patients and doctors. Majority of the patients who needed to see doctors who are those who have underlying health conditions and are high risk for Kelowna, Bilis and Secondary. They showed their business model is highly scalable. In the first quarter of this year, they moved quickly to expand their physical physicians network to increase their capacity and catch up growing demand. To some extent, they also contributed to create flexible job for the doctors who suffered from Lydia's appointment and surgery. They utilized. There are legalism to maximize the efficiency for doctors and doing so, uh, they have university maintained high quality care at affordable applies Yeah, and at the same time, the government recognize the body of about your care and de regulated traditional rules to sum up she m s temporary automated to pay a wide range of tell Her services, including hospital visit and HHS temporarily waived hip hop minorities for telehealth cases and they're changed allowed provider to use communication tools such as facetime and the messenger. During their appointment on August start, the government issued a new executive order to expand tell his services beyond the pandemic. So the government is also moving to support about your health care. So it was a quick review of the health care challenges and somewhat advancement in the pandemic. But as you understand, since those challenges are not caused by the pandemic, problems will stay remain and events off this year will continuously catalyze the transformation. So how was his cherished reshaped and where will we go? The topic from here can be also applied to Japan market. Okay, I believe democratization and decentralization healthcare more important than ever. So what does A. The traditional healthcare was defined in a framework over patient and a doctor. But in the new normal, the range of beneficiaries will be expanded from patient to all citizens, including the country uninsured people. Thanks to the technology evolution, as you can download health management off for free on iTunes stores while the range of the digital health services unable everyone to participate in new health system system. And in this slide, I put three essential element to fully realize democratization and decentralization off health care, health, literacy, data sharing and security, privacy and safety in addition, taken. In addition, technology is put at the bottom as a foundation off three point first. Health stimulus is obviously important because if people don't understand how the system works, what options are available to them or what are the pros and cons of each options? They can not navigate themselves and utilize the service. It can even cause a different disparity. Issue and secondary data must be technically flee to transfer. While it keeps interoperability ease. More options are becoming available to patient. But if data cannot be shared among stakeholders, including patient hospitals in strollers and budget your providers, patient data will be fragmented and people cannot yet continue to care which they benefited under current centralized care system. And this is most challenging part. But the last one is that the security aspect more players will involving decentralized health care outside of conventional healthcare system. So obviously, both the number of healthcare channels and our frequency of data sharing will increase more. It's create ah, higher data about no beauty, and so, under the new health care framework, we needed to ensure patient privacy and safety and also re examine a Scott write lines for sharing patient data and off course. Corbett Wasa Stone Catalyst off this you saved. But what folly. Our drivers in Macro and Micro Perspective from Mark Lowe. The challenges in healthcare system have been widely recognized for decades, and now he's a big pain. The pandemic reminded us all the key values. Misha, our current pain point as I left the church shores. Those are increasing the population, health sustainability for doctors and other social system and value based care for better and more affordable care. And all the elements are co dependent on each other. The light chart explained that providing preventive care and Alan Dimension is the best way threes to meet the key values here. Similarly, the direction of community based care and about your care is in line with thes three values, and they are acting to maximize the number of beneficiaries form. A micro uh, initiative by nonconventional players is a big driver, and both CBS and Walmart are being actively engaged in healthcare healthcare businesses for many years. And CBS has the largest walking clinic called MinuteClinic, Ottawa 1100 locations, and Walmart also has 20 primary clinics. I didn't talk to them. But the most interesting things off their recent innovation, I believe, is that they are adjusted and expanded their focus, from primary care to community health Center to out less to every every customer's needs. And CBS Front to provide affordable preventive health and chronic health monitoring services at 1500 CBS Health have, which they are now setting up and along a similar line would Mark is deploying Walmart Health Center, where, utilizing tech driven solutions, they provide affordable one stop service for core healthcare. They got less, uh, insurance status. For example, more than 40% of the people in U. S visit will not every big, so liberating the huge customer base and physical locations. Both companies being reading decentralization off health care and consumer device company such as Apple and Fitbit also have helped in transform forming healthcare in two ways. First, they are growing the boundaries between traditional healthcare and consumer product after their long development airport available, getting healthcare device and secondary. They acted as the best healthcare educators to consumers and increase people's healthcare awareness because they're taking an important role in the enhancement, health, literacy and healthcare democratization. And based on the story so far, I'd like to touch to business concept which can be applied to both Japan and the US and one expected change. It will be the emergence of data integration plot home while the telehealth. While the healthcare data data volume has increased 15 times for the last seven years and will continuously increase, we have a chance to improve the health care by harnessing the data. So meaning the new system, which unify the each patient data from multiple data sources and create 360 degrees longitudinal view each individual and then it sensitized the unified data to gain additional insights seen from structure data and unable to provide personal lives care. Finally, it's aggregate each individual data and reanalyzed to provide inside for population health. This is one specific model I envision. And, uh, health care will be provided slew online or offline and at the hospital or detail store. In order to amplify the impact of health care. The law off the mediator between health care between hospital and citizen will become more important. They can be a pharmacy toe health stand out about your care providers. They provide wide range of fundamental care and medication instruction and management. They also help individuals to manage their health care data. I will not explain the details today, but Japan has similar challenges in health care, such as increasing healthcare expenditure and lack of doctors and care givers. For example, they people in Japan have physical physician visit more than 20 times a year on average, while those in the U. S. On >>the do full times it sounds a joke, but people say because the artery are healthy, say visit hospitals to see friends. So we need to utilize thes mediators to reduce cost while they maintained social place for citizens in Japan, the government has promoted, uh, usual family, pharmacist and primary doctors and views the community based medical system as a policy. There was division of dispensing fees in Japan this year to ship the core load or pharmacist to the new role as a health management service providers. And so >>I believe we will see the change in those spaces not only in the U. S, but also in Japan, and we went through so unprecedented times. But I believe it's been resulting accelerating our healthcare transformation and creating a new business innovation. And this brings me to the end of my presentation. Thank you for your attention and hope you could find something somehow useful for your business. And if you have any questions >>or comments, please for you feel free to contact me.

>>Hi, everyone. Good afternoon. Thank you for joining this session. I feel honored to be invited to speak here today. And I also appreciate entity research Summit members for organ organizing and giving this great opportunity. Please let me give a quick introduction. First, I'm a Takashi from Marvin American population, and I'm leading technology scouting and global ation with digital health companies such as Business Alliance and Strategically Investment in North America. And since we started to focus on this space in 2016 our team is growing. And in order to bring more new technologies and services to Japan market Thesis year, we founded the new service theories for digital health business, especially, uh, in medical diagnosis space in Japan. And today I would like to talk how health care has been transformed for my micro perspective, and I hope you enjoy reasoning it. So what's happened since the US identify the first case in the middle of January, As everyone knows, unfortunately, is the damaged by this pandemic was unequal amongst the people in us. It had more determined tal impact on those who are socially and economically vulnerable because of the long, long lasting structural program off the U. S. Society and the Light Charity about daily case rating elevator country shows. Even in the community, the infection rate off the low income were 4.5 times higher than, uh, those of the high income and due to czar straight off the Corvette, about 14 million people are unemployed. The unique point off the U. S. Is that more than 60% of insurance is tied with employment, so losing a job can mean losing access to health care. And the point point here is that the Corvette did not create healthcare disparity but, uh nearly highlighted the underlying program and necessity off affordable care for all. And when the country had a need to increase the testing capacity and geographic out, treat the pharmacies and retails joined forces with existing stakeholders more than 90% off the U. S Corporation live within five miles off a community pharmacy such as CVS and Walgreen, so they can technically provide the test to everyone in all the community. And they also have a huge workforce memory pharmacist who are eligible to perform the testing scale, and this very made their potential in community based health care. Stand out and about your health has provided on alternative way for people to access to health care. At affordable applies under the unusual setting where social distancing, which required required mhm and people have a fear of infection. So they are afraid to take a public transportacion and visit >>the doctor the same thing supplied to doctor and the chart. Here is a number of total visit cranes by service type after stay at home order was issued across the U. S. By Ali April patient physical visits to doctor's offices or clinics declined by ALAN 70%. On the other hand, that share, or telehealth, accounted for 25% of the total total. Doctor's >>visit in April, while many states studied to re opening face to face visit is gradually recovering. And overall Tele Health Service did not offset the crime. Physician Physical doctor's visit and telehealth John never fully replace in person care. However, Telehealth has established a new way to provide affordable care, especially to vulnerable people, and I don't explain each player's today. But as an example, the chart shows the significant growth of >>the tell a dog who is one of the largest badger care and tell his provider, I believe there are three factors off paradox. Success under the pandemic. First, obviously tell Doc could reach >>the job between those patients and doctors. Majority of the patients who needed to see doctors who are those who have underlying health conditions and are high risk for Kelowna, Bilis and Secondary. They showed their business model is highly scalable. In the first quarter of this year, they moved quickly to expand their physical physicians network to increase their capacity and catch up growing demand. To some extent, they also contributed to create flexible job for the doctors who suffered from Lydia's appointment and surgery. They utilized. There are legalism to maximize the efficiency for doctors and doing so, uh, they have university maintained high quality care at affordable applies Yeah, and at the same time, the government recognize the body of about your care and de regulated traditional rules to sum up she m s temporary automated to pay a wide range of tell Her services, including hospital visit and HHS temporarily waived hip hop minorities for telehealth cases and they're changed allowed provider to use communication tools such as facetime and the messenger. During their appointment on August start, the government issued a new executive order to expand tell his services beyond the pandemic. So the government is also moving to support about your health care. So it was a quick review of the health care challenges and somewhat advancement in the pandemic. But as you understand, since those challenges are not caused by the pandemic, problems will stay remain and events off this year will continuously catalyze the transformation. So how was his cherished reshaped and where will we go? The topic from here can be also applied to Japan market. Okay, I believe democratization and decentralization healthcare more important than ever. So what does A. The traditional healthcare was defined in a framework over patient and a doctor. But in the new normal, the range of beneficiaries will be expanded from patient to all citizens, including the country uninsured people. Thanks to the technology evolution, as you can download health management off for free on iTunes stores while the range of the digital health services unable everyone to participate in new health system system. And in this slide, I put three essential element to fully realize democratization and decentralization off health care, health, literacy, data sharing and security, privacy and safety in addition, taken. In addition, technology is put at the bottom as a foundation off three point first. Health stimulus is obviously important because if people don't understand how the system works, what options are available to them or what are the pros and cons of each options? They can not navigate themselves and utilize the service. It can even cause a different disparity. Issue and secondary data must be technically flee to transfer. While it keeps interoperability ease. More options are becoming available to patient. But if data cannot be shared among stakeholders, including patient hospitals in strollers and budget your providers, patient data will be fragmented and people cannot yet continue to care which they benefited under current centralized care system. And this is most challenging part. But the last one is that the security aspect more players will involving decentralized health care outside of conventional healthcare system. So obviously, both the number of healthcare channels and our frequency of data sharing will increase more. It's create ah, higher data about no beauty, and so, under the new health care framework, we needed to ensure patient privacy and safety and also re examine a Scott write lines for sharing patient data and off course. Corbett Wasa Stone Catalyst off this you saved. But what folly. Our drivers in Macro and Micro Perspective from Mark Lowe. The challenges in healthcare system have been widely recognized for decades, and now he's a big pain. The pandemic reminded us all the key values. Misha, our current pain point as I left the church shores. Those are increasing the population, health sustainability for doctors and other social system and value based care for better and more affordable care. And all the elements are co dependent on each other. The light chart explained that providing preventive care and Alan Dimension is the best way threes to meet the key values here. Similarly, the direction of community based care and about your care is in line with thes three values, and they are acting to maximize the number of beneficiaries form. A micro uh, initiative by nonconventional players is a big driver, and both CBS and Walmart are being actively engaged in healthcare healthcare businesses for many years. And CBS has the largest walking clinic called MinuteClinic, Ottawa 1100 locations, and Walmart also has 20 primary clinics. I didn't talk to them. But the most interesting things off their recent innovation, I believe, is that they are adjusted and expanded their focus, from primary care to community health Center to out less to every every customer's needs. And CBS Front to provide affordable preventive health and chronic health monitoring services at 1500 CBS Health have, which they are now setting up and along a similar line would Mark is deploying Walmart Health Center, where, utilizing tech driven solutions, they provide affordable one stop service for core healthcare. They got less, uh, insurance status. For example, more than 40% of the people in U. S visit will not every big, so liberating the huge customer base and physical locations. Both companies being reading decentralization off health care and consumer device company such as Apple and Fitbit also have helped in transform forming healthcare in two ways. First, they are growing the boundaries between traditional healthcare and consumer product after their long development airport available, getting healthcare device and secondary. They acted as the best healthcare educators to consumers and increase people's healthcare awareness because they're taking an important role in the enhancement, health, literacy and healthcare democratization. And based on the story so far, I'd like to touch to business concept which can be applied to both Japan and the US and one expected change. It will be the emergence of data integration plot home while the telehealth. While the healthcare data data volume has increased 15 times for the last seven years and will continuously increase, we have a chance to improve the health care by harnessing the data. So meaning the new system, which unify the each patient data from multiple data sources and create 360 degrees longitudinal view each individual and then it sensitized the unified data to gain additional insights seen from structure data and unable to provide personal lives care. Finally, it's aggregate each individual data and reanalyzed to provide inside for population health. This is one specific model I envision. And, uh, health care will be provided slew online or offline and at the hospital or detail store. In order to amplify the impact of health care. The law off the mediator between health care between hospital and citizen will become more important. They can be a pharmacy toe health stand out about your care providers. They provide wide range of fundamental care and medication instruction and management. They also help individuals to manage their health care data. I will not explain the details today, but Japan has similar challenges in health care, such as increasing healthcare expenditure and lack of doctors and care givers. For example, they people in Japan have physical physician visit more than 20 times a year on average, while those in the U. S. On the do full times it sounds a joke, but people say because the artery are healthy, say visit hospitals to see friends. So we need to utilize thes mediators to reduce cost while they maintained social place for citizens in Japan, the government has promoted, uh, usual family, pharmacist and primary doctors and views the community based medical system as a policy. There was division of dispensing fees in Japan this year to ship the core load or pharmacist to the new role as a health management service providers. And so I believe we will see the change in those spaces not only in the U. S, but also in Japan, and we went through so unprecedented times. But I believe it's been resulting accelerating our healthcare transformation and creating a new business innovation. And this brings me to the end of my presentation. Thank you for your attention and hope you could find something somehow useful for your business. And if you have any questions >>or comments, please for you feel free to contact me. Thank you.