Decentralized Healthcare Solutions (Panel 1)

Transcript

Esther Health Data Foundation is doing exactly what this first panel will be about. That's why the description and the intro to them is made through the panel. And I will ask Mirela now from our sustainability working group as well to join here and introduce this fabulous panel. And I just wish you a an exciting next few minutes. Is this open? Can you hear me? Yes. So, welcome from my side as well. I'm uh very happy to be here today. And to be honest, I didn't really want to moderate this panel. The reason is simple. I have nothing to do with decentralized

healthcare. I come from the most boring industry in the world, insurance. Sorry, everyone does mistakes. However, um when I got in touch with the panelists, my wonderful guests you're going to meet soon, I realized these people have passion and they understand the topic way better than I do it and however I'm going to be affected because I'm a patient and I always been asked by my healthcare providers, what's your background just to know how much do I need to dumb down what I'm telling you and blood test results? No, sorry, you don't understand that anyway. So, please allow me to welcome our uh guests today, Teddy, Kate,

Kate, and uh Carolyn and um just to start properly, I would kindly ask you to introduce yourself briefly and tell us not only what is your background um but what are your hopes and concerns when it comes comes to decentralized healthcare. Terry, do you want to start? >> Yes. So, welcome everybody. My name is Terry Fumo. I'm a physician by training. Uh, I'm currently the the medical director of the His London Clinic in Geneva. And I've been uh for more than 15 years the head of an ICU and internal medicine in a in a regional hospital in Switzerland. And I'm not an expert in in

blockchain and and all these new technologies, but I'm quite interested in the way we can use that in the future. and I see that there is a paradigm change that is coming and so that's a good reason to be here. Thank you. >> Thank you very much. >> So start. Uh my name is Kat Nawak. I'm a professor of computer science. I lead a quality of life technologies lab at the University of Geneva where we leverage different data sources for quantifying daily life behaviors, health and quality of life in long term in healthy and uh uh diseased populations. And I'm also not an expert in blockchain. However, I have something to say about decentralized health and also specific

personalized preventive and uh participatory way of keeping us all healthy. Thank you. >> Thank you. My name is Caroline Perin. I'm directing the Geneva Digital Health Hub. The hub is based at the University of Geneva. It was co-founded with the Swiss Agency for Development and Cooperation. So, I'm going to bring a little bit the global health perspective. I think in the discussion um the hub was co-founded in 2021 but it's really based on 20 plus years of experience of implementing digital health in LMIC so the raft uh e-learning and tele medicine network but also through experience we had in the hospital here and in a more European context and what we learned is that you

have challenges that are very different in a different context but then there are challenges that are exactly the same. So um what we are focusing on is really how can we learn better from previous implementations, how can we connect different stakeholders better and especially how can we better connect policy and implementation layers. >> Perfect. Thank you for the intros. Now um let us structure discussion a bit. First part is going to be the first series of questions around data ownership. What does it mean? What can I expect as a patient when it comes to data ownership? Second part is going to be about data sharing. And last but not least, what are the benefits for me as a patient, if any? So, um, I'm going to start with you, Kate, because we had a

very passionate discussion when we got to know each other about patients that have to beg for their blood test results or detailed medical reports sometimes, and I know you have a very strong opinion here. So, what does true data ownership look like from a patient perspective? >> Uh, thank you very much for this question. And first of all we need to notice is that really an ownership or uh we do have a rights to access the data that's what's happening right the hospitals the clinics uh the health care system is owning the data and then the patient has some specific rights to access and ask for uh copies and what I think it's uh really import interesting

and important to see what we what we see also with research and development is the data I would like that the data more and or works towards our advantage, meaning keeps us healthy because right now we are as as um many of our colleagues mentioned sick care. We we are really practicing sick care. we're going to the doctor when sometimes it's too late and then specifically the the data being uh there uh is like there's treatments and why why we are not getting earlier this particular inputs from the data keeping us healthier being easier to share and uh not but I'm not really talking just about clinical data 60% of our future health state depends on dayto-day uh conditions behavior

sleep physical activity nutrition smoking alcohol We all know it and we all know we should behave better and why not all this data could then work towards our uh better future health and from this descriptive data to more prescriptive that uh will help us day by day change something uh that the future outcomes will be better. Thank you. >> So I guess you can see the passion here right? Um the next question is for you Teddy because healthc care IT systems are a bit more engineering driven like pretty much all the IT systems let's be honest and not that much patient centric. So how do you see a patiententric system being designed and are hospitals like yours for example

ready for that? Yeah, it's a very interesting point. I think that as physician we we've been working in very fragmented uh systems where the data is as you mentioned owned by the hospital or the caregivers and not by by the the patient. So accessing to the to the data is is always difficult and even I would say transforming the data and information for the physician is very difficult. Um uh there is also the fact that culturally I think we are not ready. I mean most of the the hospital they they don't see um why they should uh transfer the data to the patient and give this this uh this data to the patient. So for me it's really something that we need a paradigm change and and

of course there is the technology behind but I think first of all it's a kind of human or cultural change that we need so that people change their mindset and realize that having the data owned by the patient where the patient has the control to share with the the physician or the other caregivers what he wants to share is something where we can all benefit the system the patient the citizens and and and we as as caregivers Yeah, that that's a very important point and um this leads me to another question because systems are not omniresent everywhere and I know that you Caroline has have um a very extensive experience in emerging markets. Uh you had projects

in Mali in Nepal. How does um data ownership for patients look like in these geographies? >> Thank you. Um yeah we currently run two large scale projects in Mali and Nepal uh where we're scaling to over 100 health posts in in both countries. Um and in many of these settings where we work discussions around data ownership looks very different than the discussions we have here. Um what we currently see in Malian Nepal is technically the patients are owning the data but on paper. So we are in settings where we still paperbased prescriptions and so on. So we implemented tele medicine um solutions where we are

connecting um rural care professionals to uh two medical higher level of care in in the central hospitals but um all the data the the discussions is still remaining on paper. So patients have ownership but no empowerment and ownership is not enough. I mean we need um the patients to really be able to understand, use and act um on on the data they have access to. Um there are a lot of barriers in in these emerging markets which are often similar as the ones we have in in higher income settings. Also there's fragmented health systems, interoperability is an issue.

Um you know limited digital um literacy and low trust sometimes also. Um so right now our focus in these settings is really also on on creating the kind of digital public infrastructure that is needed to have uh electronic medical records and we believe that blockchain could play an important role in that because it can help to manage consent. It can help to ensure traceability and and access. So for the moment we are not using it but I think in a global health context it's definitely a technology that's really interesting and important um particularly also when it comes to yeah electronic medical records and digital wallets that include a medical record.

>> We like this perspective and especially because you mentioned in one of our previous conversations that digitizing does not mean digitizing the existing analog systems. It's a whole revolution in terms of building something new. So if we speak about data sharing, how should decentralized data sharing work differently from the current hospital to hospital or healthcare provider healthare healthcare provider sharing? >> So I think before we speak about decentralization, it's important to um to remind that digital health is more than just uh you know data sharing. It's part of a broader ecosystem. So um we are in a process where we've digitalized

our entire society from you know computers to mobile phones to genomics where biology is uh available as data now um and this really creates an enormous amount of data um health information systems social media data WhatsApp I mean there are so many different um internet of things and so on and the real promise of digital health lies in really connecting in making sense of this information and being able and theoretically we should be able to be a little bit smarter about the way how we treat patients, how we prevent care and so on. Um but that only works if the underlying processes and

responsibilities are redesigned, not just the technology. And um I think it's a little bit like in the early days of electricity um people replaced steam engines with electricity and uh in the beginning the productivity increases that we saw were very little and it's only when they really started thinking about how to redesign the processes that uh electricity became the the change agent um that that we see it now. So um the goal isn't in my opinion to digitalize existing systems but really to take advantage of the technology in in rethinking processes. And um in that

case being in a um in a emerging market can sometimes be almost an advantage because you don't have existing legacy systems in place that you try to digitalize. you think a little bit different um about the processes when you do not have the legacy systems in place. >> Yeah. And sometimes emerging economies are doing a leap frog and adopt the newest and greatest technology. >> Oh yeah. We can actually learn also in a birectional way. >> Yeah. Um I like very much your electricity comparison because this is something which T said some time ago that AI and blockchain are it's it's a paradigm shift. is like bringing electricity to the village you mentioned

and I would like you to elaborate a bit more on that in in terms of sharing data with multiple specialists how would this work and is the health care system ready for it >> very interesting um first of all no I think the the the current health care system is not ready I think again that's a cultural change that we we have to to go through and many people see AI and all these new technologies just as as tools and they don't see that It's a it's a driver for a for a massive change in the way we we we we work. And I think one important point is that first of all um we should go uh from technologies that were developed by engineers for end users patient or uh physicians. And this

is a failure. We see that every day. I mean in Switzerland we we we used to have the bond that do electronic dupac and and this is exactly the the proof that it will not work and I don't know any physician in a hospital who is happy with the electronic health record is using so we should change it's uh we should develop tools that are made by engineers with end users and I think that's the that's the first thing and second uh the the the fact that um in the cultural change the fact that the The data is not owned by the the caregiver or the hospital but by the patient is something that is also very important because the the the patient must be empowered and know exactly what data he owns in his wallet and with whom

he can share this data for what purpose and what is the advantage that is going to take and I think that um to to to get back are we ready I don't see uh many people uh understanding that but I think that we we have to work on that empowering people and and there will be a lot of positive effect in the efficiency of the system in the health literacy of the of the population and also in in the participation of the population. But there's a downside. We must be very careful. Data does not mean information and there will be a lot of data that are not useful that are crap and and and only commercial data. So curation of the data will will be

another challenge. Thank you for that. Um Kate, I'm going to go to you now because when we discussed, you mentioned that your lab integrates several types of data sources. Is self-reported wellness data is sensors, healthcare data, environmental factors. And when we design sharing systems, we need to take into account these very complex correlations um in data and also allow patients to control who has access to what.

>> Thank you very much. Exactly. I will I will first echo with what Tiara said with respect to the digital literacy and also aspects of usability and usefulness and really co-designing with the users and meeting them whenever they are. There's too many systems being engineered by engineers who love their technologies and unfortunately we're really seeing uh the failure of some of them. So we need to meet the individuals uh then including the layered consent of respect to data sharing with whom and what but it needs to be designed such a way that it's not so-called black pattern like you click something and then somebody else on the other side of the globe has your data immediately because as we have uh if our credit card numbers leak out or something else we

may change them but our health data unfortunately it's not something we can change and and and the more uh the most uh sensitive uh and really private data the the genome or or personal predispositions or or current uh diseases or medications. this is really something which uh should be shared with very very informed content and should be designed this way and then uh this particular context awareness and that's where um in my lab when I set up a quality of life technologist lab and I was like oh boy what did I get into quality of life there exists 172 different models of quality of life from different perspective the medical uh policy makers uh developmental

psychological uh sociological point of view of quality of life is different and uh when I studied all of them then uh we decided to have a physical health follow the world organization quality of life model physical health psychological health social interactions environmental holistic point of view on behavior health and quality of life and that's what needs to be included and now we have multiple projects running in the lab and we'll be having a population of Geneva um following following and also consolidating different data sources together with environmental data sets and taggy transportation um uh specific API looking into who is using which particular line and and how

busy they are and how much noise and and what's the weather like and and the traffic and we're going to incorporate these different data sources in Geneva. There is so-called system information Geneva information system which we're going to integrate into following the individuals and daily life with their wearables. By the way, how many of you have a wearable at least one? Well, so next time when you go to the doctor and the doctor start looking the doctor, next time when you go to the doctor and you point out that you have a wearable and why this person is not yet looking into your data and where your disease happened and what happened and how how you should take your medication and what

needs to be done next and this person is look at you with big eyes and saying like can we just stick to clinical guidelines then please uh also blame um who who shall we blame the system the system the acceptance but also please point out like I want to point out we still work on the validity of this different data sources I have right now four three three sometimes four but today three different devices and at the end of the day I have different number of steps and the duration of sleep doesn't match right and and there is this different elements which we are not yet figuring out uh and uh we it will it will be consolidated and and the data

will be integrated just give us time but also help us in do our science. Thank you. >> I've been there. I went to my medical doctor with my warding my son in PIC with my fitness tracker. She looked at me and said uh no and actually I asked about that and uh he agreed somehow with my medical doctor. These are not precise enough. But now I have another question for you because you challenged me on this wearables thing. AI tools are trained with my data and clinical decisions are complex um processes. So how can I benefit from that?

>> I think this is a very important point because it's a it it will probably drive the acceptance of of people and the adurance of people to to that kind of new technologies. But again, I I'm saying what I said before. The problem is that data doesn't mean information and and I think we are overflown by a lot of of data that are useless. I'm an intensivist and you know in the intensive care when you have a patient you have hundreds and hundreds of data um alarms and whatever and only a few of them are really very important and one of the problems that I see currently is that we are overwhelmed with data that uh bring no no value but it's very important. let's work together to see what kind of data are in the end bringing added value um to to to the

patient and I think that um uh for the patient of course if uh many um uh data or information that the the the physician do not have access yet can be brought by this kind of technology not only wearables but just you know what we call patient reported outcome or patient reported experience for me it's more important that someone tells me you I I do not sleep well instead of showing me that the device is telling him or her that she or he is not well uh good sleep. I mean that that's something that is very important. So I think the value that in the end the patient will get is that he can share many many information many data with the with the patient and

AI will help us to to draw information valuable information for the physician and the patient to then uh act um to add value. I think it's a challenge because I can tell you in modern medicine we have a lot of toys but we don't have many tools. So differentiating the toys from the tools is something I'm fighting every day with my colleagues and people love people love toys and physicians particularly. We love them too because um at one point I realized I I rely much more on the uraring telling me uh how well I slept and uh then I heard a study from

Stamford speaking about the placebo effect when you look at your sleep score in the morning. Although you slept well, if the bloody tool is saying you had a poor quality sleep, oh my god, I'm going to stay home. I'm not working. I'm not doing anything today because I'm a wreck. Um and wearables it's um it's one of the things where we discuss quite a lot Kate we had a very passionate discussion why because we buy the device and then we send the data and as a reward for us we have to pay a subscription to understand something about our data is this fair I know we agreed it's not but let's elaborate on that >> it's not fair because it's not working towards advantage actually the current

services for those of you have this devices. It's a lot of time series. It's a time series with some annotation not uh binding from perspective of implying some specific behaviors because that's where the manufacturer will get into trouble right have you heard about recently what was what was the device which got into trouble the blood pressure one I'm forgetting I'm wearing a blood pressure continuous blood pressure but it's another one I'm I'm wearing hilo the other one got into trouble anybody knows which I'm talking about I forgot never mind it's a it's it's like we have tons of time series and then it's up to us to make up the the information and the and the knowledge and get the wisdom of what

needs to be changed and and really do this end of one kind of trials where we try different things and see what works what doesn't and also I would um make you aware of this advanced metrics sleep metrics even sleep um stages please do not pay attention to those this I promise my colleagues at Stanford Medical Center when we work together. Sleep happens in the brain and the hands and and everything else is too far. It's too far. Please, it's too far. It's it's some kind of estimate and it's too far.

And please do not believe in sleep stages. Do it for science. And but the sleep duration is more or less okay. And then the physical activity about like overall volume of physical activity. But do not again look at the steps. So let's get back to science. What what's missing? I I I I chopped some uh notes. What's missing? We have this individual level models and tons of minute-by-minute data sets and we are missing the really accurate models and validated models to map it on population level models, epidemological level models. What happens if I keep continuing doing my 10,000 steps, which is again we all heard 10,000 steps. It's it's a myth. What happens if I really

sleep well and and everything and I eat? How does it really change my future behavior uh of health and quality of life state? We do not have yet validated models. There are different underlying uh um distributions and really assumptions and really different groups are working on this and we're all busy working on our things. We do not have yet time to uh to to really validate these different models. Then yeah then also relates to timelines like the short term like this 10 minutes this one day this 10 days how does it relate to the fact that somewhere in the future I'm already building this damage in my body and and something will happen and the context we're missing context

unfortunately as I said we're fortunately at least my lab I was like well we need to get the uh environmental aspects and and the weather and and and really the the noise and the temperature when you're sleeping and all of this needs to be collected. However, this is mostly missing and I'm sorry to say as much as POS the patient reported outcomes the self-reports are there but they are totally missing the context. In many cases it's in last two weeks how have you been feeling and how much you've been walking and how many days of uh you've been active and how many days you've eaten vegetables and fruits in type two diabetes. So that's um that's where we're getting adding the context which is more important it's

more difficult because it adds additional variables right and then uh really we're getting also in my lab we're getting beyond the correlations because there have a lot of correlations or the moment when we look at the UK bio bank or this long studies when we have some data no not much wearables then we get into correlations we write our papers but we need to get into causality what really causes what at which level right and and this and then it goes back to timeliness different elements of I feel bad today I feel bad tomorrow I feel good I feel I do this I do that I eat that I don't eat that it's all correlated but also there's coella loops and we're still discovering them so I think I'll stop there but there will be benefit from data keep if you want to

join my lab and and come and collaborate on different data sets and really working if you're a statistician or data science or something please come in and let's do it together thank Thank you. Thank you very much. Um, Caroline, the next question goes to you because we know sometimes patients pay out of pocket and we still need their data. So, how do we implement a system where patients can benefit from the data they share without creating inequalities. >> Okay. So, when um let me go a little bit back. So when we when we speak about um data sharing and health and benefits, we often hear the phrase data is the new oil. Okay. But I in at least from a

public health perspective, I don't agree with it. I'd say more it's like clean water. It only creates value if it um flows safely and benefits everyone. And I think um that's the the approach we see. Um the real value of uh of data in public health lies in you know better public health decisions being able to aggregate a data um identify gaps in access uh guide better their investment decisions. Um so I believe data dividends should not be at the individual level but at the system level and when we have data there we can also correlate health data with

non-traditional data. So we do some work around space and global health and often when people hear space and global health they think about you know going to space. Um yes there are some applications but in in general we do a lot of work around uh earth using satellite and earth observation data and how we can use it for health and there are so many use cases you can correlate the data to NCDs in cities to noise to heat and so on but also in a global health context a very concrete example is using um satellite images to analyze rainfall and then if you're in a malaria endemic region you can predict that there's going to outbreak so you can bring uh the medication that's needed.

You can uh better plan uh the response to this outbreak. And I believe that when people see that their data and and the the other data that is available in a system leads to better outcomes, that's when they start uh to trust uh in the system and are willing to share um more openly their data. This is a very interesting perspective because as a patient I only thought in my selfish way about my own dividends but your perspective is totally new to me at least. However, we need to educate people in in this respect and explain that this is possible. It's the first time I hear about that myself.

Fascinating. Um let's close this session before we go into Q&A with one question and I kindly ask each one of you to answer maybe I starting with you Caroline. What is one single most crucial factor for having success from patient perspective in implementing decentralized data sharing? I think uh you know from the patient perspective we can have the best algorithms, the best tools uh and so on um and the most advanced system but without one thing it's going to lead us nowhere and that's trust. So we really need to make sure that you know the way we design the systems integrates the patient perspective and um creates the

trust and the added value for the patient. Well, uh that was challenging one thing human- centric design and from the beginning and not as a patchwork where at some point in the process the engineers ask the users just because they have to about their opinion on this new product from the beginning when we design when we even think about any solution we have to understand if our if the pain point if the need is real and working with the users but actually listening what's the pain point? What's the what's the need for for for which particular uh need we design? And so human centric design as a basis and not just a um a factor which is a patch

after and in this spirit I'm I'm educating my students with qualitative methods talking to individuals patients non-patients collecting data through interviews surveys observations focus groups thank you >> short version nothing to add no um I think it's very important trust that you really own your data and also So that in the end you will get something back individually and collectively. And uh individually it means that uh remember that data are no action. They don't cure. It's just a way to understand how to cure and how to act preventively or when you're sick. And second in the uh in in the community and everything that

was said is very important. We can add a lot of uh new information uh in the moment. we we we don't know exactly you you mentioned climate or environment and all these things. So I think the the gain that we will have at the individual level and at the community level if we work correctly I think it can be huge but we must trust and be sure that uh the patient owns uh relevant data. >> Wonderful. And I thought only insurance is the boring part that has no user centricity, human centric design. But it seems it's the same in healthcare. I would love to open the floor for questions. So please don't be shy. I'll come with the microphone so everyone can

hear you. >> I would have a million of questions but then we will have the entire evening. So thanks to the panelists because it was extremely interesting for all the data discussion the wearables and I love the theory quote. We have a lot of toys but not that many tools these days is really and I will I will start from that point. We have a lot of toys not that many tools and also for Kina I think is the the question. Uh do we have a way to make sure that the data that we are acquiring also thanks to all the population the wearables for instance have the right quality

the certification of those wearables is it a way to go will it influence or give a step change to the data we are acquiring so I would like to to see what is your opinion the opinion of the panelists and the second question is this data, this production of data, these wearables are accessible to a certain type of population. Don't we risk to skew the data and generate models that helps us to predict based only on a part of a population?

That's my questions. >> Absolutely. Absolutely. Excellent questions. Um in 2018 in my lab we have looked into 438 different devices wearables on the body which you could put 12 on their feet and I guess we will not be able to walk but all of them they claim to u monitor activity and uh there was so there was so much how do I say toys right we've been looking into the validation level and there's there's analytical validity the sensor validity and and then really the algorithmic validity and then there's a clinical validity and many of them were really at the level of toys and actually one clinician many years ago told me in the

face you you computer scientist you play with your toys so you're right yes it's it's it's something which is because of the miniaturaturization because of accessibility of this different tools for designing the new hardware and really the battery and the connectivity and everything it's so easy and and easy meaning accessible And then you have all this really tsunami of these devices which are a really really bad quality and unfortunately then I'm still not telling you that based on validation you will really assure bulletproof end point you know in clinical terms we talk about end points if you put something trials and say I really make sure that somebody

has a better quality of life at the end right now this end points are exploratory secondary exploratory And also they are very much related to what you pointed out self-reported patient reported outcomes with specific so-called recall period. In the last two weeks have you been doing this or that or in the last week. So then we get into wearables. Everybody's wearing wearables. Why don't you have wearables in all these clinical trials? Right? Because they are not validated. But also there's so many human factors. So many human factors. If I don't put my device accurately then the data is going to be garbage. And then you as a as a a pharma you invested I don't know how many millions and you give me a um there was a drug right there was a clinical trial

and then half of the patients because the they don't like the I had patients in the face telling me that they don't want to wear our um research uh device because they have a Rolex right so it's like you know I mean this is extreme but you know they may not like the color and the shape and it's stigmatizing and so it's it's really something which we cannot assure there's so many human factors. But I have a good news. Good news is that some aspects has been mastered. How many of you know continuous glucose monitor? Any anybody was trying right the little patch? There is there is a lot of digital endpoints in type two diabetes and obesity trials right now where the FDA EMA is requiring to get continuous glucose monitor. We

kind of measure we kind of master it. You patch it for two weeks is there and yes it can fell off because you use uh oils or you you put it on the muscle and yeah I mean we've been testing a lot of things but somehow it becomes this divi this you master the body right and also another it's a polyonography at night some set of devices I'm not talking about the whole setup but there's also homemade homemade homebased polyonography somehow we are starting to master the body at night how to measure and how to collect this physiological data, biohysiological data and functioning data at night which then could be really taken seriously and the day is a mess. So not not yet but we are

working on this. Join John join us. Thank you. >> Maybe I can doesn't work. uh I can add that uh you know nothing new in science you know uh the accuracy of of data and the reliability of data is is a has been an issue for for for centuries and garbage in garbage out and that's one of the problem and you know uh in in in the intensive care which was my world before the the number of toys that we had that has that that were developed based on not accurate or uh I mean bad data is is just huge and I don't think it's different in s in in all these sensors

and all the things that we are inventing. I mean again uh data is not information and data is not curing anyone. It's just that we have to see what is really uh brought as an information and is this information actionable in something that will change the outcome. And the example of of glucose monitoring is very interesting because it has changed really the life of many diabetes patient type one or type two because they they are now they can live completely normally without having uh issues with uh their blood glucose level. So that that's a very interesting point. Again, curation of data is is probably one of the the the most difficult challenge that we will

have in front of us, >> right? Saleim here from WHO. I want just to show that like actually because Mela complains about like that insurance is not very important. Um like because we talked about blockchain before and uh like what was in here? We hear we heard about data and everything here, but like for example like concepts like smart contracts allowing like you know uh payment to happen automatically after conditions are are kind of put in place like uh so actually like in this kind of like you know I don't know if you heard about smart contracts in blockchain and so on. So that's can be you know applied easily to to execute and to like make

things happen faster. I want just to ask the panelists is like if there is because the fact that you decentralize the data then you bring in an ecosystem for example blockchain or or this kind of ecosystems uh that allows you to actually use this kind of like concepts. I don't know if you heard of any of this kind of concepts being used uh you know in real life uh smart contract for for for like you know execution of like certain aspects once or algorithm after after the data have been you know kind of fulfilled like certain conditions >> well actually that's an excellent question but Kman just says that's going to be covered in the second panel and I can assure you in in insurance we don't speak about these sophisticated things

yet anything the panelists want to add >> was if I understood your question correctly are you looking forward to understand what's the value of the phone as a sensor because I can comment on this no it's more >> okay okay sorry >> so for the most tech more technical panel Carmen will uh will answer in the and her guest will answer in the second part um I would have loved to have more questions from the audience you can still ask them because we're going to go in a break if I understand correctly and they are here. So, uh, thank you very much. It's been really a pleasure discussing with you today and thank you for the questions. Let's continue in the

break. Right, Steffy? Are we allowed to go in a break? Not without you. Thank you. >> Thank you very much to the panel.