 So with that said, let me now not take up any more time from you, Sastry. Let me quickly introduce you in the panel. This is a panel on data and digital. We've had this panel each of the past few years. It feels different this year. It feels as Lori Glimcher, for those of you who saw the oncology panel earlier in the day, mentioned, it's happened. We're now not looking when does this happen or what does it look like? We're living it. And for better or for worse, it took a forcing function, which was the COVID-19 pandemic. So Sastry, you have a great panel of experts here and I'll turn it over to you. Great. Thank you so much, Andy. It's been a great set of sessions and the last session was truly outstanding. With this panel, we really hope to dispel the myth that digital and data science is something new that we're trying to do and something novel. I always like to remind people that we moved out of the analog era many years ago. And throughout the day, we've heard these stories from the frontline, be it through the oncology panel or through the rare disease panel or around COVID around the role that digital and data science are playing. So we do have an all-star panel assembled here to talk about how they're meaningfully moving the needle across both their teams as well as the broader organization. So let me introduce them in alphabetical order. So first up is Ann Hetherington. Ann is the head of Data Science Institute within R&D at Takeda and she focuses on statistics, epidemiology, real-world digital and data. And she's most recently been leading the industry effort around data sharing for COVID as part of the COVID R&D annoyance. Next up is Arpaugare. She currently leads the global pharmaceutical business for Merck and she also leads data analytics, digital, and partnerships with a commercial organization where she's building the next generation commercial models and organizations. Third is Maya Said. She's the founder and CEO of Outcomes For Me, a Boston based digital health company that's focused on improving outcomes for oncology patients. And prior to that, she spent several years in senior executive roles at both Novartis and Sanofi. We also have Najat Khan. Najat is the chief data science officer and global head of R&D strategy at Janssen and she focuses on maximizing the impact of data science across the pipeline. And she's also a co-chair of the broader J&J Data Science Council. And then finally we have Nina Shelson. Nina is an early-stage investor at Canon, a U.S. venture capital focused both on health technology as well as healthcare. And she's been supporting biotech and digital health startups for more than two decades. We're really excited to have this panel and we'll talk about this more but for the first time in 14-year history of USAIC we have an all-star all-woman panel. So it's something all of us have been hugely excited about. So diving right in, let me get us to the first question and maybe we can go through in the order in which I introduced you. So there's a tremendous amount of hype in this space and there's a lot of investment money flowing in. Where do you see the greatest opportunities across the board? What are some of the successes that you've experienced but then more importantly what do you see as the tipping point so that when we come back here next year we can talk about here is what I called at last year's USAIC. And then where is COVID also serving as an accelerant because there are a lot of things happening be it around telemedicine or use of real-world data that that are dramatically accelerated because of the pandemic globally. So Anne why don't we start with you? Yes thank you and thank you to the USAIC for asking me to participate here today with all of my co-female leaders it's wonderful. And to the exact question like actually I think everybody around this virtual table as someone who's worked on data since my PhD days and continues to rely on data every single day on my job I actually don't really see this drive to data and digital as hype. I actually just see it as the rest of the world catching up with us and what we've been wanting to do and trying to do for many many years. But that's not to say that with advances in computing power and analytics we can't do more. As demonstrated by Wea Takira we've an alliance a collaboration with MIT at the intersection of AI and health so we can definitely do more. But I don't see it as hype and we should definitely not lose sight of the fact that actually data and algorithms and digital tools aren't the end game for us. A pharma getting medicines to patients faster is the end game and everything is an enabler for that. And with regard to the question about the current pandemic of course we have seen so many ways that flood gates have opened and for someone like me it has been magnificent. But the one example I'd like to focus on is actually what you alluded to in my introduction which is to do with the COVID R&D Alliance where we have had at least 20 pharma representatives around the table and talking about data sharing and what we're moving to is a world where we will rapidly share data both at a study level and at a patient level as trials read out. And at the same time we've had a group working on collaboratively working on real world data for COVID developing methods and protocols together. So the world has definitely changed with COVID and I for one am excited by what we can do with it moving forward. Harpa. Yeah so just to build on Anne and again thank you also for inviting me to this panel. To build on what Anne said around data and analytics being the core of R&D for you know decades in the industry I think from a commercial perspective we have been focused on how do we get our medicines and vaccines to as many people as possible as quickly as possible and I think we have been slower to adopt data and technology in that in that sort of journey. So from a commercial perspective I think the opportunities are huge everything across the value chain from getting broader access across countries around the world based on the data that we have around the patients who could benefit and pricing and market access to patient identification better understanding of which patients could benefit from which therapies leveraging data that we have around clinical decision support in terms of when different therapies may be most beneficial you can see a huge value even in supply chain on how to deliver differently to different countries and different populations to reduce some of the disparities and care around the world and then last but not least improve the broader outcomes right help leverage technology and data to help supplement the medicines and the vaccines to make sure that patients are getting the best outcomes that they have been promised so the opportunities are significant across the value chain I'd say where we have focused in the past on a commercial side has been more around looking at it more from an efficiency play and I think what sort of the tipping point or the news sort of way to look at it is to think about what are all the opportunities to actually improve care and improve outcomes and the more payers and consumers around the world start demanding more of sort of this outcomes-based approach I think the more we will see better leveraging of analytics as well as technology including partnerships right I think that's the other sort of tipping point that I'm seeing is not everything has to be done by a single company it's really around working through the ecosystem to find the players that can partner with us to get to those best outcomes. From a COVID perspective I can give just one example so I'm sure there is many more I think COVID has really accelerated a lot of the predictive aspects of the work that we do so a specific example is you know when COVID the pandemic first hit we did a lot of predictive modeling to look at at a country level where do we expect increased utilization of some of our anesthesia products just given all the innovations around the world we were able to predict at a country level where we expect a demand to increase and at what time period we were able to increase our supply and manufacturing of those products as well as completely change our supply chain to make sure the countries that would need it at certain times would get it when they need it so I think you know that's just one example of how we are accelerating a lot of our predictive capabilities to get our medicines to the people who need them. So maybe I'll go next Sastri thank you for having me for having me here it's always a great pleasure to participate in this meeting so you know to build up on a few things I may be to take it more from the patient angle so the most exciting you know promise of digital health is really it has the potential to put back the patient at the center of the healthcare ecosystem and and just to provide a bit of context in terms of what we do and hence kind of you know the opportunities I see from that angle our mission at the outcomes for me is to empower patients and improve outcomes and we start with cancer so some of the examples I'll be giving are linked to cancer we do so by engaging cancer patients and enable them to take charge of their care and in doing so our goal is not only to improve patient outcomes even though that's ultimately the goal but also to accelerate research by collecting the data needed to drive innovations so this is where you know data science becomes a very important component but in the past year I think you know I'm most excited about you know five key opportunities COVID being the last one which is the accelerant but I'm getting some echo so the first being the consumerization of healthcare I mean consumerization of everything but we're starting to really see a real pull on the healthcare side from the consumer side patients demanding to be part of the decision part of the information we definitely see it with the people we interact with these are patients that have been diagnosed with cancer the second is really around access to information so as some of you or maybe many of you know the CMS and the ONC the office of the national coordinator issued their final interoperability rule on May 1st of this year this is going to fundamentally this is probably going to be a major tipping point because it will break down the barriers that impede patients ease of access to their information in a digital format and so it will accelerate the empowerment of patients and enabling to leverage that information to drive outcomes the third opportunity also happened this year or continuing to build this you know experiments it's along the lines of value-based cares and experiments that we see regulators starting to take so starting with you know the CMS innovation center has been doing in the past five years a number of experiments on new innovative payment models but the recent model so the oncology care first model which will launch next year which is in succession to the oncology care model which they had for the last five years has two major changes in it and in particular on the patient side so it actually provides the potential to reimburse and to create value based on quality of life impact on patients which I see as a as a great opportunity and potential accelerant for digital health and then the FDA has recently actually just last month launched a program to communicate patient reported outcomes from cancer clinical trials and so this project they call it project patient voice which you start seeing regulators really moving to really put the patient back at the center of the healthcare system and then just on the science side so here maybe I'll share some of the data we've seen recently Laurie mentioned earlier that you know some of the greatest potential here is also around you know early screening inherited cancers but also genomic profiling and liquid biopsies so just to give a sense of what we've seen recently only 50% of patients are tested either for genetic profiling or inherited cancer among the ones that should be tested and so if we can really move that needle empower people to have a better conversation with their physician which we've shown to be able to do that could really drive more impact just to give one example in breast cancer which is a disease we're very present and there's been a recent approval for a PIC3CA mutation where 40% of patients with hormone positive herd to negative breast cancer advanced breast cancer have that mutation but our data shows that only a third of those people are even being screened for the mutation so this is where we can see opportunity to quick you know enabling outcomes so finally in terms of COVID-19 I mean a lot of it has been said but I'll just you know highlight a couple telehealth you know the movement to telehealth has an impact that not only providers are pretty much have really gotten into digital health but also now they're embracing tools that help empower patients and manage them at a distance and here I'm thinking about asynchronous patient reported outcomes we see that in the context of clinical trials also we're doing a clinical trial with MGH where the entire consenting system went online and remote a fundamental shift for the organization and then finally where Laurie mentioned that unfortunately there's been a huge backlog of screening now when it comes to cancer and potentially impacting the outcomes in the short term in terms of overall survival here digital health could play a huge role a huge role in terms of getting people the information they need access much quicker when they need it quicker so with that maybe just to finish on the tipping point you know if I were to pick one it would be patient access to their own data would be the real tipping point right Nina so you can tell from the introductions that I'm not the at the health IT genius on the panel and the venture capitalist who is a broker and a backer of genius so maybe I'll attempt to summarize a little bit of the great comments that have been been made which I think the reason that there is hype about that big data or digital and in the ecosystem is because there's a sense that data science has been slow to come to the biopharma industry and I think that's a real misnomerized as Anne had said we've been using data science and and computational tools in our industry for a long long time and it's just staggering the pace at which we are accelerating discovery of new chemical entities using transcriptional and translational omic data on the target side and then fantastic in silicon computational screening and structural information on on the drug discovery side on our portfolio at Canaan where we do a significant amount of biopharma investing you know we're going from new biology new pathway to a candidate going into iind enabling in faster pace than than we have seen in the 20 years that I've been in this business it's really exciting whether that's a company like vivace that's driving the hippo yeah pathway which has been previously undruggable and they've gone to a candidate going into iind enabling work in less than three years or tyra going after gatekeeper mutations in an entire shin kinase inhibitors and following that path and targeting oncology going into a candidate development within six months of of investment so data science science is data science I think in drug drug discovery I think sitting in a diversified fund at Canaan where I have the privilege of two-thirds of our investments being on the tech side I think they're they sort of scratch their head at why is there not more sort of consumer and mobile and empowerment and ready or access to that that data because they see that disrupt every other industry much more readily and that of course is because our industry is a heavily regulated one and we have to deal with that and that was our first points about the challenges on the commercial side and go to market and why it's more difficult because of liability reasons privacy and security reasons to access those data sets and then also our industry whether you're on the pair side or on the farmer side is also a paranoid data hoarding very protectionist at times paternalistic industry and so we haven't opened up and and created large data sets I think Hal baron has pointed out that what we do in the pharma industry is you know sophisticated multivariate analysis because we're dealing with data sets that are so small because they're not side likes to laugh at us and say you're not doing big data you're doing small data with some statistics so I think that's another reason why there's some thought about about hyping and so I think what we are facing little bit is an existential question as an industry you know are we really going to democratize data are we going to open up and how are we going to work in a regulated environment to really bring consumer insights to bring open source to really liberate the data for better for better insights are we going to publish and make access to failed studies are we going to open up all the data so that we can learn and accelerate for the interest of public health and and global human health I think that's something that that we're going to have to wrestle with because consumers want better public health needs better and we as a society can't get to 50% of GDP going to going to healthcare and the industry I think can really solve solve for that aside from incredible acceleration of drug discovery and that being a huge benefit of data science and computational power the other places of course that we're seeing incredible opportunity generally is through large data allowing us to make more consumer insight and customization personalization of products and services in healthcare that will come increasingly I think to Biopharma as well and the wraparounds around products for adherence and for a preferred side of care and for better better sort of precision prescribing for the right drive to the right patients precision medicine we have a cellular therapy we're doing you know really deeply personalized neoantigen T cells for solid tumors we think that has a real opportunity to eradicate solid solid tumors and then of course in development really adapting clinical trials for a better diversity of enrollment diversity of site and inclusion virtual clinical trials have synthetic control arms and the like using all sorts of data riches and alternate site and remote and mobility to to better better better execute the development of drugs and generating real-world evidence as well. I'll go next you know I think there's a lot of good ideas that have already been shared just to go back to the hype piece I mean I think I take a slightly different perspective on that in the sense that the last six months both the internal and the external ecosystem I think we've started to see convergence of real problems and when I think about real problems like just to echo what Anne said it's how do we build and make better medicines for patients really start to happen you know and as Roy Vagelis said I think there has been for some time a lot of chatter so it's exciting to see that the chatter is coming down with a lot of these data sets with exactly like specific problems that we're solving for so I think that's that's at least my perspective in terms of the opportunities I won't recap everything that's said but I would say that there is a opportunity for connectivity right so if you think about a lot of these real-world data sets across broader populations but also more niche data sets that are coming through I think about how do we use that and it's happening actively not just for approval purposes but also for access that connects to what ARPO is saying as well but doing it much much earlier in the in the development time frame right because a lot of the data sets the models even the partnerships that we do there is commonality across the board so if we really think about scale at an enterprise at a cross-value chain perspective that is something that's is going to be more more and more important and I think it's going to be critical for how we create scale so if I had to think about the tipping point as you mentioned I think it's really going to be how do we have endpoints that are more based on or at least reflective in some way of the real world data that we see and you know that will help us not just with our regulatory approval but also access I think that integration is going to become more and more tighter especially when we think about oncology or also rare diseases with that integration already happens more today and also this ties to other things like external control arms and you know Shastri you're very close to this work how do we see that go beyond oncology and rare disease to other areas where the standard of care is something that becomes important also for access perspective and then I would say just going downstream how we recruit patients how do we match patients the whole feasibility process and monitoring I think that there has been flickers of how we can do it differently but I think more and more the right data sets are being pulled together I would say more in the US X us is still going to be not at the tipping point right now but it's getting there how do we do that in a completely different way and we have had some success in that and then the last thing I'll say which is a challenge I'd love to see more progress in that is when we build all these models and we have great data a lot of it is around validation making it explainable and then the validation this is again something that I think Barbara was saying which is make ensuring you have consistent high performance not just a one-off because that's really going to drive traction with the clinicians with commercial leaders across the world right if you think about the change management perspective as critical here and there I think not just validation in our programs but the deployment of some of these algorithms through health systems that we partner with so this goes beyond just by a pharma through other organizations that we partner with that is going to be a really big tipping point if we can make a difference I think we've talked about it but if we have specific areas we can focus on that can have a clear shift in how we do our work but also get it embedded in the broader ecosystem to have impact last thing you know you mentioned in terms of COVID-19 completely agree on telehealth one thing I would say you know J&J we've been working really hard on our COVID-19 vaccine as many of others in the in the space have in the biopharma space and it really has catalyzed how we not just embed the use of real-world data and I don't mean just claims or lab results but social media mobility data many many different types of data sets that I would detect companies and consumer companies are much more active at using but then actually do that early on in a program right today a lot of the times it's an add-on is to check the box but you know we should do something with data science versus actually using it in a meaningful way to understand the disease itself given there's so many unknowns we would love to see traction of that even going forward post in the post-COVID era and we've used that and I think Ann mentioned a little bit as well to understand the severity distribution it's a really hard problem right and really important for her for our vaccine program and our case endpoints to say who's going to get more steak and how do we detect who are we going to detect more in the clinical trials why is that important if you get to events faster as a faster trial it's deployed quickly then the other thing I'd also say in terms of which countries to go to setting up are you are mentioning understanding the utilization and certain products in the space in in anesthetics and others in in the commercial space but also from our vaccine trial perspective where do we expect the incidence to be high where should we target in terms of our trials and we've taken a very global perspective of that but I can imagine that such predictions would also be helpful and we're not to go for other programs and then also how to use it for other infectious diseases other different modeling that they'll use so those are just some of some ideas where I think we can really leverage what we've already done the good work already done today great so I'm looking at the clock here and I think we have about eight minutes left so maybe I'll call on you for the next few questions so Arpa if you compare the tech leaders like amazon and facebook to a typical biopharma company the e-bit per fde is something like 5x higher for a tech company I know the business models are different but for me it's more a measure of agility and speed the organizations move faster they kill they make decisions faster and this came up a little bit in the investment panel as well so as you think of these large big pharma companies what do you see as the biggest barriers or challenges to the broad-based adoption of digital and data science yeah so I think it's a multi-factorial sort of set of issues I'd say I think the easy answer that a lot of people lean back on is around regulation privacy sort of where we're constrained and how we can operate directly with consumers or with payers or with customers based on world-wide restrictions that's certainly one issue but from my perspective I think the bigger issue is a culture a cultural bias almost at least that I see sometimes in terms of a risk aversion when it comes to healthcare to experiment in a way that maybe a tech company will experiment on at risk aversion to trying new technologies or new you know adopting new methodologies that are directly impacting a population or a patient's life right so I think there's a cultural sort of bias towards sticking with what we have done partly because what what we've done over the last couple of decades has been successful for the industry right so I think there's a little bit of we have been successful until now and I think I think you know there's also been as I mentioned the regulation there's also been in different countries around the world just significant fragmentation of data where we haven't been able to actually get the full value of what's possible so I think it's multiple different things that's where I see you know this tipping point I think Maya and others mentioned the consumers really getting empowered and taking charge will be a tipping point I think the other tipping point is clearly going to be sort of governments and payers mandating more of a value-based or an outcome-based approach to healthcare and as soon as we get there we're not going to be able to get there without fully leveraging the power of data analytics and digital technologies so I do see this changing and I see it changing quickly but it's going to require a lot of shifts in terms of culture in terms of talent and in terms of our business model so I mentioned at the beginning of this meeting that it took us 14 years to have an all-women panel and it would be a missed opportunity if we don't talk about the broader opportunities for women and STEM as well as in the biopharm industry so maybe we go in the reverse order and I don't start with you on what advice do you have for women who want to enter and more importantly thrive and grow and lead in this space Yeah I know I'm so glad we're having this conversation you know we have this I'm sure all of us want this panel on a one-on-one basis you know when I stopped the question I was reflecting a little bit on what was important for me and I said two things one now I always say if you want to go into a space especially when I think about all different fields not just data science it is becoming more and more interdisciplinary right and you have a team of epidemiologists stats data scientists data engineers I mean a lot of us do it's really important to know a lot of different areas and skills as well as the science so what I always say is start with yourself to really be honest is this an area I'm interested in and if so you will always be leaning in one field more from a trained perspective and then another field more from a self-trained perspective right if I look at my own background it's the same thing so how be proactive first to really make sure you sweat the details to be the best you can be in the space you want to thrive in so that's number one I always say focus on yourself and for that it actually it's super helpful to have a community of folks you can reach out to you know not all great ideas are in one's head or in a book and the space is constantly evolving so I don't like to use the word network because it sounds a little I don't know it's overused but to actually have folks in your community that you just know you can reach out to so that's one how do you make the best of what you do and then then I think there's another aspect that gets less talked about it's talked about more in the headlines that we should have diversity inclusion we should have more women we should but really think about investing your time in a company biotech startup wherever with a group of people that share that same vision share the vision in terms of what's the overall mission to help patients better medicines but then also really just not just talk the talk or walk the talk in terms of will support and ensure that there's diversity on those teams that there's a plan in terms of how do you actually promote women and I don't mean promote just from a job perspective but promote in terms of ensuring that's the right opportunities just right and you know that's something that requires a lot of conversations and it's the worst thing to be in the wrong place where there's a lot of hype in terms of we do this and then get to the job and it doesn't really happen it isn't that day-to-day support that's needed so I think both those aspects are so critical to be successful once inherent and intrinsic to yourself and the other is I always say being a good detective and really keeping your ears open to understand how is that place where you're going to invest the time going to support you back so it's a cool investment Nina I would echo that I would say to the girls or the women go for it that advice is really simple you go for it and hang in there you deserve to thrive in an amazing field biopharma and digital health health sciences health IT is a fantastic career path it is the most rewarding to be making decisions for patient and public good and excel at it and enjoy go for it and the advice is really to the organizations and to the managers out there it is more than time for diversity equity and inclusion it is more than time for that girl and that woman not to have to do the work and do the heavy lifting look at your policies look at your organization look at your culture and have zero excuses for why a woman an lgbtq a black or brown person would not want to or be able to go all the way and thrive at your place of work this is fantastic that it's an old woman panel it's fantastic that there were women and people of color all over this agenda so hats off to andy and perun for that it's time it's more than time maya yeah so maybe i mean i agree with everything that has been said maybe just so a lot of two things well i'll add one thing and i'll reiterate another one so to the to the person kind of looking to enter the space i would or kind of you know like um early in her career my biggest advice is don't undersell yourself and the reason i really say that is as a as an early stage company we've been recruiting a lot and most recently since covet and now i see it systematically where women tend to undersell themselves and not even apply for a role because they don't have all the parts of that role so that would be my advice and then up to the organization i mean i just i agree i agree with what nina said but i will emphasize something that nina said which is culture culture gets said very early on and especially in the pure tech or digital health health tech you know the engineering culture and how you want that to be pay attention to it because that's essentially going to be how diverse the company is going to end up once as it's growing all right arpa and then so i i completely agree with everything that's been said i might just take a slightly different uh twist on the question that's i think about um i'm a parent i have three children one of them is a daughter and um the other sort of call to action i would throw out there as we think about the longer term is um there's a tremendous amount of evidence that early exposure to stem and to these types of careers is really um the tipping point right so um i think i would encourage all the viewers to also think about the younger generation and making sure that we're supporting them and exposing them to stem as early as possible um because there's a lot of evidence to show by the age of 10 or 12 if you haven't seen or been exposed to or um had interest in or been inspired by others um in the field it's very very difficult to change that going forward so um from a long-term perspective i think there's a lot we can do whether it's as mentors sponsors uh or as companies um to help from a long-term perspective as well and i have four very very simple things to add one is get educated get out there get your education and frankly phd is international it goes everywhere with you number two say yes opportunities are exactly that not extra work number three and i think it goes back to the community solid foundation family friends and people that challenge you and make you push yourself and lastly actually particularly in this data and digital space don't be put off by all these differing terminologies of data architects and data engineer and exposure of data it's all just data and mathematics and if you know that you're fine fantastic i think we're out of time here so my apologies to Anil Sawant and Peter Muller who had amazing questions around the role of regulators in terms of uh software as a medical device as well as uh thinking around uh how do you start to break the silos between the various uh parts of the organization within pharma companies and i'm sure we can keep this conversation going on for a much longer time than what we have here so i'd like to thank the panel a lot for giving us their time and their advice and insights it has been truly exciting and we look forward to getting together again next year hopefully in person