AI Helping Solve Global Healthcare Challenges

Welcome to the unDavos Summit - A community-organized series of interactive panels, talks, and networking taking place in Davos, Switzerland - and online - in parallel to the World Economic Forum’s Annual Meeting 20-24 Jan 2025. Our mission is threefold: 
• Democratizing Davos: We open the doors to diverse voices and ideas, breaking down traditional barriers to participation. 
• Humanizing Davos: We foster genuine, relationship-driven connections that go beyond transactional networking. 
• Bringing Action to Davos: We turn meaningful discussions into tangible, real-world solutions.

Join us for the Health@Davos 2025 panel discussion themed "Advancing Global Health Policy & Innovation." This crucial dialogue will tackle pressing challenges in global health resilience, emphasizing how innovative technologies like AI, big data, and telemedicine can bridge health equity gaps. Focusing on ethical implementations and the potential for growth in low and middle-income countries, this session aims to generate actionable solutions for today's healthcare challenges.

Meet our distinguished speakers who will be sharing their insights:

- Dr. Timothy Ferris, President of Red Cell’s Healthcare Practice
- Dr. Beatrice Vetter, Director of NCDs, FIND
- Dr. Pascal M. Golec, Co-Founder, Heights Health

Moderated by Matthew R. Kittay, Co-Chair of Fox Rothschild’s M&A Practice Group, this panel is set to explore the intersection of AI technology and healthcare, ensuring patient privacy while promoting enhanced care.

We invite you to engage in these transformative discussions and explore how we can collectively revolutionize global health systems. 

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Transcript

(00:08) thank you very much for the introduction we're excited to be here today my name is Matthew Kay as I was introduced I'm the co-chair of the mergers and Acquisitions practice at Fox Rothchild which is a law firm with 30 offices in the United States one of the verticals that I have the um the opportunity to work on there is helping grow and expand our health care practice and in that role I get to work with uh leading thought leaders and innovators in the healthc care space in the US and all over the world uh when I first reached
(00:37) out to Mark Terrell the organizer of V Davos and asked him um you know help me find a panel help me find a discussion point that I can lead on he said well how do you feel about healthare and Ai and I told him um it's it's something that's really critical to kind of what we're doing from a legal perspective at my Law Firm um we're working with early stage companies that are raising capital in the space um we're working with funds that actually presented and launched here at andavo last year um multi-million multi hundred million
(01:06) dollar funds investing in the healthcare AI space uh working with Hospital systems that are integrating AI into their processes and basically seeing all of the evolution of the industry into the space and so I said I know a little bit about that Mark but who do you have for me and he said well I can put together an All-Star panel of people that will present to you all different perspectives on it and um as they started trickling in I was like wow I can't believe I'm going to have this opportunity so thank you very much for
(01:33) on and Davos for having us and thank you panelists for joining us today I'm going to um I think we've got 40 minutes right and we're going to reserve a few minutes at the end for questions and answers questions and answers I think are gonna or questions are going to come in over an electronic submission platform right no we changed that so now it's going to be direct in interactive which is great um I just want to remind people of a fewel rules were being filmed and taped so anything you say here on the panel
(01:59) anything you say out there in the audience could probably get picked up also on social media um in addition to that I know I invited some members of the press I'm not sure if they're here today or not but you should assume that there are so um so that being the case I'm going to first Let each of the panelists introduce themselves briefly and give a little bit about uh a little bit about their own background and yeah why don't we start at the at the end beatric yeah thank you yeah thank you so much thank you for having me on the
(02:27) panel so I'm Beatrice Veta I'm the director of the non-communicable diseases program or chronic disease program at F find is a global NGO working on improving access to Medical Diagnostics in loow Middle income countries we're headquartered in Geneva and operate in lmics have a few offices around the world here you go hand it over thank you Beatrice hello everybody first of all I want to say I'm really honored to be in this panel here with these distinguished people uh uh Partners directors presidents I myself
(02:59) uh I'm a uh entrepreneur background in AI actually so the the PHD there is in Ai and not in healthcare I'm learning more and more about Healthcare every day um I founded a company that does early stage brain tumor detection with AI and I'll tell you a bit more about it afterwards switch very protective for her mics uh want to thank Norwell health for having me here uh SMY kandan also call me Simi based in Los Angeles uh background Robotics and public health uh managing director enture responsible for uh some of the healthcare payers and
(03:45) providers in the United States and also healthtech which is hyperscalers Health uh truly an honor to be here excited as you can see my background touches anything everything in healthcare and Tech uh so it's going to be quite interesting hi my name is Tim Ferris um so I'm a primary care doctor um I'm a a professor of medicine at Harvard and um for the last four years I was the national director of transformation at the NHS and it was while I was there um that I started doing the math around the capacity crisis facing Healthcare and it
(04:20) is not a developing world or developed World capacity crisis it is a worldwide capacity crisis when I look at the numbers associated with the delivery of healthare everyone talks about the demographic well you have to multiply the demographic time times the number of treatable conditions that we accumulate as we get older and all estimates that I have seen significantly underpredict the the the um delivery crisis for healthcare throughout the globe 20 years from now because the the demographic wave doesn't start to level
(05:01) off until 2045 so it was because of that and it was because of everything I've been learning about um uh technology and the ability of technology to lower unit cost which that is the fundamental issue here we have to lower the unit cost of the delivery of a Health Care Service it's for that reason that I uh recently joined a um a tech incubator firm so I'm going the other way I'm going from Healthcare to become an entrepreneur I'm probably too old to do that but um uh but just uh joined uh an incubator and investment firm in uh Washington DC
(05:39) called Red Cell partners and I'm very excited to be here with this great group thank you so much and I think this might be your first public appearance since you joined red cell is that right and so we're very honored uh we're very honored to have you here um we're going to cover four topics today it's it's not so rigid and so well defined but it's going to highlight um not only the kind of the state of the the state of the um state of the environment in the AI Healthcare space but kind of highlight some of the
(06:05) different backgrounds and and um specific viewpoints that our panelists have and I think we're going to start today actually with um oh so the the topics are going to be on quality capacity cost and Innovation right and um you know Tim Tim fashioned it as a crisis in the healthc care space right but we're going to try to talk about it in terms of the challenges and the solutions hopefully uh and and and and look towards kind of you know opportunity for growth um that being said though let's start with quality
(06:33) beatric and um we'd like your perspective first how do you think that um AI can be leveraged in medical testing Technologies to improve the quality of care what do you see there yeah thanks and I really want to give a practical example where to you know use AI in terms of quality um and since my expertise is in The Chronic conditions uh and we're facing a global crisis on the number of chronic conditions particularly diabetes and cardiovascular disease um let look at self-management right self-management away from the
(07:03) healthcare provider because a person with a chronic condition spends 95% of their time without the healthcare provider self-managing that condition and particularly in diabetes this can be very challenging right because we have blood sugars that rise and fall all the time um and then we have the new technologies that can really help to the person to manage much better their condition there are the simple ones you know the digitally enable blood glucose meters with decision support on Range indicators you know do these necessarily
(07:28) qualify as using a lot of of AI probably a little bit in the background I think fascinating is the development in the continuous glucose monitoring space right um and these are little sensors of a couple of examples don't look at the uh manufacturer we're not necessarily endorsing them but these are lovely to see because many people are not familiar with them they sort of sit on your arm or your stomach and they measure glucose every five minutes and give the person living with diabetes an Insight of what their glucose is now but most
(07:52) importantly they have predictive algorithms to say where will it go your glucose because I might be in range just now but actually my glucose might be on on the rise or on the fall and going low and that's where then the quality goes low right because um this may then cause me to go into hypoglycemia need immediate action I may need to go to a healthcare provider at short notice you know and I may need to uh dose my insulin or you know take some immediate actions that I could have avoided had I known actually I'm going low now right
(08:20) so I need to take an action um and these are amazing technologies that can really support quality of care also for the healthcare provider because the health Prov can then really see what's happened outside of their practice um but I also want to take one moment to talk on health equality because earlier today there was this panel on health equality and there's been many things said these Technologies are an example of a massive Health inequality right because there are extremely Limited in Access outside the private healthcare sector in many
(08:50) low and midle income countries but I think we can come to that at later stage so here just one example and Pascal building a company in the space maybe you have a perspective as well in in terms of how you how how you envision your company kind of increasing or or you know increasing quality of care yeah thank you Matthew um so actually the the point I want to build upon is partially also Tim's about the per unit cost and I'd love for Tim also to expand that later but one other perspective I want to offer is that um I believe and that's
(09:23) eventually the company I want to build is to go more from kind of reactive treatment in healthcare to being more proactive so uh not just Levering technology to cure diseases and to solve problems but also anticipate them in advance and um have early warning detection signals um so that that that that is that is what I believe and I think uh uh that is also very quite C cost effective of course if you can prevent it in advance um and I think that is also future so uh how do you become more proactive with AI right I think that's
(10:07) where Predictive Analytics comes into play so to use algorithms to understand better our personal health and to know in advance hey there's some risk factor that is increasing for me or I might this might happen to me and to anticipate that um so in that regard an example that I'm working on is uh together a collaboration with the University of yumia in uh Sweden which is leading in the field of uh brain cancer research and we are leveraging uh a really interesting data set that um spends multiple uh years for many people
(10:46) so a big part of the population is in that data set it's they're being tracked 50 years of data we have all the uh medical diagnosis of these people and have uh a lot of every few years these people their blood is taken and we can save that and and get markers out and we use this data set to uh predict whether somebody uh will get a brain tumor a few years in advance so that also reinforced the point that for these things you need really high quality data um and that's what we also should be building towards
(11:19) in the future so let's let's talk about that and go a little bit deeper Tim um you know how do these Solutions these kind of individual Solutions because you've had a a macro perspective right putting putting together the programs you put down how do they really ensure capacity and and increase capacity in the space um so I I've heard so many times at this conference already the importance of data um H and Healthcare um the the data tends to be the the let's just say the Holy Grail in healthare is multi multimodal data that
(11:57) allows better decision- making that that actually um uh produces the um uh prompts clinical decision support is one of the um commonly used terms and the the reason why it is so difficult to produce is you need multimodal data you need it at scale and you need it in real time that is very hard to come by especially in a space where um it's private this is we're talking about at the individual level some of the most most personal and private information that you could possibly imagine so when I was at the NHS we spent a lot of time
(12:37) a lot of people think oh the National Health Service 70 million people that that would be a great data data resource to develop AI tools yes it it would be a great data resource to develop AI tools but the um the permissioning processes are all local um and uh a lot of people don't know that but what I'm excited about is the fact that there are now Technologies secure data environments and um and Federated data um mechanisms that allow you to leave the data in place and maintain local control um and still use
(13:18) vast amounts of data kept private for training AI algorithms and that is a that's a very exciting that's that's lower down in the technology stack um uh but it it produces um at the multimotor level I'll go pus to a a diet another diagnostic so the um Europe uh a company called Skin analytics just got the first C mark for a completely autonomous skin cancer detection from photographs okay um uh with accuracies well above published reports of what dermatologists can do that is um when I talk about unit cost that is going to
(14:00) lower the unit cost of detecting a a cancer a skin cancer by probably by 90% at least okay um and lower the the the environmental um uh issues associated with bringing people together to do this in person I mean that you can go on and on and on that's just one vertical like Dermatology sin cancer imagine that now compounded AC cross every vertical because that's what we're talking about here every vertical and then the cross cutting um um uh issues like ambient documentation and so forth we can get into later will also lower
(14:42) unit cost so um so I'm I'm very excited about the um the opportunities here but we haven't quite solved the data problems yet um we we still have um uh barriers the barriers are no longer technical they're political um and they're legal um but but there's a there is a way through this yeah so simy I I want your thought on that because I know I know a topic that you're focused on is kind of responsible use of data and and Leadership from the top down I mean can you tell us a little bit about your perspective this is going to take many
(15:19) years to cover but I I I love all the touch points on Equity data and responsibility right so I I align with uh leaders over here that data is key right with the right data the outcomes are not accurate predictions are not accurate is one uh the second problem is see is equity right uh 23% or maybe 30% of the united in the world don't have access to Internet so how are we truly making Healthcare accessible Equitable and you know truly Innovative is still a question mark so uh in terms of responsibility and AI I think uh you
(15:51) know at enture we we are Innovative and creating platforms where we can create our own agents to your point with different DC Stacks or different issues where we can create more agency we call it low cost low code right so that way there's not much resource required not much cost required but truly focusing on the accuracy and the Precision of care so that we able to deliver the Right Care at the right time at the right you know for the right people right cuz uh if I cover a little bit more issues of data issue is uh educating Physicians
(16:23) and the nurses to kind of uh get trained to use Ai and to truly understand computer science and there is a big adaption that's required for Physicians right on the other end for example United States we have um 650,000 Physicians for 300 million Americans truly not proportional right and then if we intersect that with the rise of Aging in which most of us will be about the age of 65 and to 2050 it's uh I know it sounds like Doomsday but that's where you know Solutions such as a data Ai and Equity you know truly have to come to
(16:57) play to make a difference thank you um so you hear all these these challenges Tim you hear all this forecasting I actually just did the math in my head this is the first time I've ever done it right here at Davos I'm going to be over 65 in in 2050 like all of this seems like this elder care seems very far out but you know I'm getting older um let's talk about like where the rubber meets the road because this is something you you played Major roles in efficiency to me you you know you are a physician and you are a leader in the
(17:28) healthcare space but you almost kind of operated as a CFO right in your role in terms of reducing cost and increasing um increasing efficiency so what are the challenges to paying for this Innovation so um it it it is there are the the the challenges associated with paying for this um come in in several different categories as as is always true right um the first is um that actually it's so great to hear about the low cost of the development of of these things and that that has been my experience too because the primary
(18:03) reason why Pharmaceuticals are so expensive is because it takes more than a billion dollars to to create a pharmaceutical um agent boy if if that's what it's going to take to to create a you know a solution in a particular vertical that's a Information Technology solution we're in big trouble but but we're not because it it isn't going to take that much um but I let me just say um we do have to um there is one uh uh Finance issue that is top of mine for me and um and that is that it has become really common for um uh business models
(18:48) with annual recurring Revenue so ARR and a SAS model to have 80 90% margins and I'm just thinking well if you can get 80 and 90% margins and lower unit cost wonderful but boy I really hope that when you're talking about health care and we're talking about health care bankrupting most countries if you look at the the debt burden in most countries I really hope that we will get health information technology that is um that is actually cash relasing and addressing the national debt rather than the other way around
(19:29) and honestly that's going to be a matter of where they set the price point and I've had negotiations with big tech companies where they were setting the price point at precisely what they thought the efficiency benefit we would get and I don't and and you mean you're not going to leave any for us um so so that's um that's thing one on the financing thing two is it is going to take longer to adopt technology in the clinical space it should not take time at all to adopt AI technology for solving administrative problems and the
(20:05) administrative overhead in healthcare is really really high so in the US and I'm sorry I don't have other examples a banking transaction costs about less than 0.1% of a of a dollar okay so less than a penny a a single economic transition transaction in health here in the United States costs over $3.
(20:36) 75 so you do the math that's um a, uh time order of magnitude difference and if we can just if we can reduce that by 50% we will release sufficient cash to pay for the ongoing benefits that will acrew from the clinical um side of this so that's that's my hope it's both easier um and potentially cash releasing if we do it right to get do the back office even as we're working on the clinical stuff and there's there's evidence of that in the market I mean um one of the funds that I work with in the space is is looking to invest in Solutions like that right not
(21:15) Therapeutics or even devices but um administrative Solutions right dictation software um a company that I sold recently to a private Equity Fund was trying to close the uh AI trying to close the information gap between Emergency Medical Services and the ER right and so it's just collapsing that you know trying to shorten the time period in the flow of information between what happens in the ambulance and what happens in the ER right simple stuff like that I mean I know it's not simple but but you don't have to be kind
(21:45) of curing cancer although that's an important objective as well to have meaningful impact in the space I want to talk to the people who are out there actually putting products into the space and here are your perspectives though on on um you know what it's like pricing and kind of negotiating pricing for an AI enabled solution right again how much time do we have like it's certainly for the Technologies I addressed earlier it's tough right because of course particularly in sort of Emerging Market opportunities manufacturers are very
(22:17) protective of their prices you know so what we're trying to do is actually talking about financing we're trying to generate data for a health investment case that's not so much for the manufacturers but initially it's for the government to basically would generate in data to show different use cases right do you need to wear the CGM all the time or is it okay in the interim if you wear it once every 3 months and will this have a positive impact on your measurable short-term outcomes and then you can do some modeling to say well in
(22:41) the long term you know it's all about uh avoiding complications right living longer healthier more productive lives with so we can go to the governments and basically demonstrate to them there is an opportunity for the governments to invest and then the volumes will come and then the manufacturers become more interested right because the private sector you know has its limitations it's really the government sector that is is a bit of a nugget for many uh suppliers you know once they get interested and want to put some money behind it so
(23:08) that's sort of what where we stand at the moment Sim's gonna Sim's gonna come in on you guys and give her perspective but how's it working in your space Pascal well from my perspective it's basically are you guys covered by Insurance yeah okay well well it's basically creating a business plan where we need to know how much is it going to cost to do the uh blood tests and that's something like in our space like $15 so it's that's not too much it's also about actually how good the algorithm is right if if there's how many lives we can save
(23:40) how accurate it is so that all factors into the business plan and then of course eventually how well we can negotiate with it providers who build it into their system as as uh as Tim mentioned and then what scale can we achieve and to what extent can we kind of generalize so those are all kind of factors in this planet there's some uncertainties and those we are kind of figuring out along the way I just want to say one more thing the example I was talking about is in South Africa right this is in the low and middle- inome countries because you
(24:11) know those diabetes Technologies in the high inome countries are largely covered by insurance I just want to clarify because not everybody always thinks about the parts of the world that we work towards my dermatologist recommended me to a uh skin scanning program actually that was AI enhanced that my insurance did not cover unfortunately but um but it's getting there um simy how's Accenture looking at the cost at at the cost structure top down um I'm just going to say there two signs for a coin right one is obviously uh the cost right
(24:41) globally we have a financial crisis in healthcare each Health Care system is What minus 3 to- 7% operating cost in United States more than 51% hospitals are bleeding operating cost right uh with physician burnout everything piling up I think we don't have a choice we have to reduce cost right and administrative cost is one of them uh but we look at the lens from opportunity and hope right how do we repurpose the cost saving and move it towards Innovation right for example one of our large clients they saved around $ 30 to
(25:11) $40 million annually just by using Ai and claims management right now that was reused to create a pre like a Precision medicine program so it's really uh you know important for healthcare leaders to think about is not just how do you cut corners and reduce costs but how do you reduce the cost to repurpose and re-budget that for Innovation and to move that for better and higher reasons and AI is there to augment given that all the issues we have so we look at it from a transformative lens and an opportunity to really move the cost
(25:43) around right and also an opportunity to remove uh Legacy systems and to modernize because Healthcare truly is behind every other industry in the world and unfortunately United States Healthcare is also behind every other Healthcare in the world so uh I think it's a great opportunity to look at it in a different way highly fragmented very inefficient um lots of opportunities right um we've got a few minutes left we want to ask if there are any questions in the audience we want to give people the chance we have a
(26:14) microphone for that I'm gonna use your well I'm gonna lose my microphone that's fine um that's fine and I just ask if you are going to ask a question if you could uh introduce yourself by your name and the company that you're with and then um you know keep them to tight questions that are answerable by the panel please thank you sure any questions St three davo so you got to kind of lay down The rules hi it's um I'm Lena Neer I work at empirical jundan and the University of PID fiser lab and we are interest we do
(26:46) research in Ai and Healthcare and I'm very curious how what you're talking about relates to me so my question is what's the RO of Science in what you do and how that translate to let's say Innovation because everything if you talk to doctors everything they do always evidence-based research to advance something so I'm just wondering how um yeah my research relates to your work so maybe just a comment on that what are you researching specifically oh um if it's not yeah I made it now personally but it's also more a general
(27:23) question like how research in AI relates to what you do but um yeah that's the question how signs in know yeah well I can think of a few ways um so uh there are lots of companies actually here presenting at Davos who are using AI to lower the cost of research like literally the research drug development now there's seven or eight very large companies that are in that space um and so research itself the costs are coming down that should help on the overall costs and then um one of the big advantages and this is one of my pet um
(28:04) things is you know there's been a concept for probably 40 Years of the um learning health system that a learn that a health system operates and then in real time learns from that experience and gets better as it goes well the concept has been around for decades but it actually hasn't there there are no really great examples of it being implemented in part because it was too difficult to implement it well I'll tell you what AI allows us now to to both do the data collection the all the data privacy issues and the analysis of that data to
(28:44) make real world improvements from data that is occurring in real time and that's not fantasy that actually exists in lots of um micro uh health systems that are advancing that process that is that is here and that is going to change significantly change the the life of anyone in that's in healthcare whether it's on the research side or the delivery side and maybe one comment to add to that when you're developing AI algorithms you always always need the insights from the researchers who do the fundamental research to guide the AI there is no AI
(29:24) built in the world without the experts uh bringing in their opinion and helping you to iterate on that s did you have something to add um just slight deflection but not too much of a deflection from Ai and other the experts have covered here but what's truly exciting to me and thanks for your question is the concept of digital twin right I find that concept to be accelerating research right keeping the human in the loop what takes an FDA regulation for example Pharmaceuticals to approve a drug could take a few years
(29:54) could get declined but using digital twin where you can virtually Implement these data points to see how your drug is doing and the implications of that is a true format to accelerate what you're doing and reduce cost and truly create impact even before you hit the Finish Line uh but couple that with AI I think it's just a powerful du so we did get the sign that said wrap up please so one last question and then I'll get the mic back to you hi uh garv at dbug advisers um I'm curious about this uh idea of governance
(30:24) and Market structure around the world that you're describing so I don't think any one here is going to argue that AI is not transformative for healthcare I think there's something that we need to contend with which is uh there's been a winner takes all whenever things have moved more and more to Tech and do you see the same Dynamic potentially playing out especially if you look at AIS I mean AIS themselves are already highly concentrated and all AI companies are talking about that they are I mean their whole funding cycle is based on a win a
(30:55) takes all strategy and so I'm curious what that will looks like when we're sharing our data with a market structure that's monopolistic uh we have seen data being used against people so as you guys advocate for the thrilling world that's out there when all of our data is then shared so you can build these products what would you be saying about governance of the market structure that emerges um great question thank you uh you know with great Innovation and great power comes great responsibility right uh and so so the same goes with AI and
(31:30) any technology we create I think it's truly important that when we creating a product or a technology we think about it backwards we think about think about empathy we think about the responsibility we think about you know the safety the implications uh you know we hear quite a bit about technology including social media AI having mental health concerns creating dple effect for young kids and adults right so I think the core of innovation has to be coming from responsibility so at least at a centur we have have some guard rates
(31:59) right there is transparency required there is data protection which hits the Forefront especially in healthcare given the rights and cyber security threats that's another issue we have so without governance policy rules and transparency on what we're creating and how are we creating that I truly think it just needs to follow a protocol and for that we need all stakeholders at the table can I just um I I'll go one step further Maybe and Just say um that uh ownership of the data m is the key issue who actually owns the data and the so
(32:35) the critical governance question if you give up ownership of the data you have you you you have no leverage um so that that the most critical governance question is who owns the data and I think it also needs a lot of government policy if I think about insurance right because if AI can predict things or can you get to show all the data and some insurers sometimes require the data to make sure that you actually continue to you know get the benefit access so I think to me is really government regulation also around for example
(33:09) insurance and so and then benefits you know that are really required and also source and quality of the output right and so understanding when you get a statement from AI exactly where did that statement come from right and how verified it is um we've got just time for a quick quick response from each of the panelists we could ask kind of uh a big question or something like that but I'll ask you something a little bit different kind of based on something Tim said so um what do you think is the hardest problem in in 50 words or less
(33:40) we'll start with beist and then come down what do you think is the hardest problem that AI could efficiently solve that you see right now in the healthcare space for the low and middle income countries it plays a great role in healthcare capacity and task shifting we heard the TB example earlier with the chest x-rays you gave the skin example so I think to me that is critical that you can really you don't need the highly specialized radiologist or dermatologist you can use a community health worker with a bit of training and task shift
(34:06) and you know really be more efficient in healthcare delivery in resource constraint settings Pascal the hardest problem it can't be brain tumors um I think the the the the hardest problem to solve is um the one building on Tim getting the data together getting a right governance model with the right ownership ship and allowing other actors to develop at scale because we are going to also need scale to get get to where we want to be I was trying to think of a Innovative answer but I was uh uh I think it's going to be Precision medicine right I
(34:45) think end of it all uh the third biggest cause of death in the United States is because of medical errors right so I would hope the hope is precision and for that all of these elements are required that's a great question and I um want to align myself fully with the answers before me so I I'm GNA go a little off-script and say there are two one is um in the financing and payment of healthcare that is opportunity number one it is completely broken and there is a huge opportunity there and the big opportunity that cross Cuts cuts across
(35:20) all Health and Care social care is clinical decision support so if there is an assistant by my side that is combing through all the data and and offering me up just the information that I need but all the information that I need that is precisely what AI does and so I can't wait for the day when I'm practicing where where I have an assistant that is a computer that will make me much more efficient and I will be a higher quality uh care provider so insurance payment and clinical decision support excellent um look you guys were all amazing thank
(36:03) you so much for your help and your prep and all of your insights today I'd like to thank thank Mark and his team at onavo did a phenomenal job and um with that I think we're going to wrap this one up thank you yeah big hand for our panel and for all of you many have been here for four hours you have earned that drink downstairs so when you get to the bar tell them you're coming from Health at Davos uh and we'll make sure you are rewarded thank you all for being our our closing panel tonight um and look forward to seeing you all downstairs
(36:30) thank you