- il y a 4 semaines
Tailored Care Accelerating Personalized Medicine
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00:00Hello and welcome. Thank you for joining us for this day three of Viva Tech Europe's biggest startup and tech
00:08event.
00:08My name is Ellie. I'm going to be your emcee this afternoon.
00:11We're going to spend the next two hours focusing on healthcare.
00:15We'll be exploring the major trends that are shaping healthcare today in diverse topics,
00:21including personalized medicine, digital twins for patients, AI diagnostics,
00:27and how e-health is transforming the entire health sector.
00:31So we're going to divide the conversation in four different sessions.
00:35So please stay with us for the full two hours and don't hesitate to send us your questions through the
00:41app
00:41and we'll address them to our different speakers on stage.
00:44So let's dive right in. Discussion number one.
00:48It's called Tailored Care, Accelerating Personalized Medicine.
00:58And for this first discussion, as you can see, I'm joined by Alizé Blanchin,
01:05who is head of strategy and innovation and expert at Hello Tomorrow.
01:10Alizé will be moderating this discussion with her guests.
01:13Welcome to you all. Alizé, the floor is yours.
01:17Thank you so much.
01:19So, wow, I mean, quite a lot of people are here.
01:23I thought health was for nerds, but I'm super happy to see that it's actually the mainstream.
01:28And we're actually going to talk about something that you are seeing in other industries today,
01:34which is personalization.
01:35So, I mean, a lot of our colleagues out there in VivaTech are displaying personalization in cosmetics, in food.
01:42But what is more personal than health, right?
01:45So, if you're expecting more and more personalization in consumer products,
01:50aren't you expecting that as well for something that's touching your personal life?
01:55Today, we are starting to see some solutions.
02:00And I'm really happy to be with Kale and Abbasi here
02:04because they are pioneering some very interesting things.
02:07If you're thinking about health, we have two really ways of personalizing the experience that we are having as a
02:16patient.
02:17There's your journey, right?
02:19The healthcare ecosystem is a bit complex to navigate.
02:23You have doctors, hospitals, you get your medication at the pharmacy, and it can be confusing.
02:29So, if we are to help patients to navigate from the surgery to the treatment
02:35in a more professional and helped way, that could be a very helpful thing.
02:42There's another aspect that's really interesting, which is treatment.
02:47Today, obviously, you have a disease, you're going to be given a medication.
02:52But each one of you is different.
02:54Even if you have the same disease, you might react differently to what you're being given as a medicine.
03:00And so, how can we really trigger personalization also in your specific genes
03:06so that we can make sure that you are having the proper reaction to the treatment you're being given?
03:13I think that's part of the discussion we're going to have today.
03:16And I want to open with your views about today's challenges in the healthcare world.
03:23and how the lack of personalization on the treatment, on the journey, on other aspects
03:30is really part of the problems that patients can encounter.
03:34And maybe, Abbas, here we can start with you.
03:38Thanks, Alize.
03:39So, when you think about treatment, there's two kind of levers to look at.
03:45One is safety and the other is efficacy.
03:48And when you talk about personalized medicine, you're looking at how effective is that medicine
03:55and how safe is it?
03:57How effective is it for everybody or is it for some few individuals based on genetics?
04:05Now, this takes us back to genetics.
04:07And genetics is really that code that makes you who you are.
04:12So, many of us here might understand software, right?
04:15And for you to have a software tool, you would have code.
04:19Somebody would have put together JavaScript or whatever.
04:22And then that's how you have the features on the software.
04:26Genetics is the same thing for human biology.
04:29It's the code that determines kind of why we look like we look like,
04:33why we have the diseases we have,
04:37and why some people don't get some diseases.
04:40And so, when we start trying to treat people, because we're so diverse from a genetics perspective,
04:48you can't do a one-size-fits-all approach anymore.
04:52Because most of today's diseases don't require one-size-fits approaches.
04:58Now, cancers have shown us that.
05:00Today in cancer, if you have breast cancer, there's lots of different types of breast cancer.
05:05It could be BRCA1 or 2-driven.
05:07It could be HER2-positive, progesterone-receptor-positive, estrogen-receptor-positive.
05:13And each of those have different treatments.
05:16But sometimes, where you're from also determines what types of disease you have
05:23and what drugs work for you or are safe for you.
05:28And I think that when you look at precision medicine,
05:31that's where we are going to, because it already exists today.
05:35And in your opinion, because, I mean, digital code is very popular out there.
05:40You can see it at Vivatech.
05:42What, in your opinion, is preventing us from really using genetic code
05:46as a way to deal with treatment today?
05:49So, I think we're still in a phase where we are trying to understand the human biological code.
05:56You know, we had the whole human genome project, thinking that by analyzing a few individuals,
06:02we'll understand the code.
06:04But what all of those studies have shown us is that the more we have done that,
06:09the more we've seen the need to diversify our genetic data sets.
06:14In today's world, the genetic data sets, you have Caucasian populations making up about 80% of the genetic data
06:24sets.
06:24And groups like Africans, where you have the most genetic diversity because of historical factors,
06:31such as the fact that modern humans existed first in Africa,
06:36only represent about 2% of those data sets.
06:40And so, you can't really get to the source code, if you would.
06:44So, we are in a phase today where the world is trying to understand the full human genetic sequences.
06:52And all of that is going to power how we're able to understand diseases,
06:57how we're going to diagnose them, and how we're going to treat them.
07:02I love that.
07:03So, understanding humans.
07:04And I think that's, to go back to you, Kali, humans are not only genes, right?
07:09We are also people, we have emotions, we have fears, and healthcare can be scary, right?
07:16So, how do you see more on the journey part with Livy, this personalization going forward,
07:24and what's really triggering your motivation to make it more seamless?
07:30Well, what we do as a company is, of course, to try to provide great healthcare for everyone.
07:36And what is important to understand is that that's not the same thing for everyone.
07:40So, that's where we come to the personalization bit.
07:42And apart from the sort of genetical setup that I was about to explain,
07:46the differences can rely on where do you live, what's your resources,
07:51are you close to a healthcare setting or not?
07:53And that's one part of it, to increase accessibility towards getting into care,
08:00which we do with digital tools and our own technology.
08:03But the second part of that is to tie and knit together the sort of journey
08:08that a patient typically goes through in healthcare.
08:11And that relates very closely to what Abasi is speaking about,
08:14because if we know that a particular drug, for example, is the best treatment for a patient,
08:19we also need some way of distributing that drug.
08:22And we need to be able to follow the patient and the patient's journey
08:26through different care instances to make sure that we follow up what they've been given,
08:31don't give it twice, and so on.
08:33So, we are working a lot with the technological basis for following the patient
08:38through instances of care, primary care, secondary care, specialized care,
08:43even interventional care.
08:45And what you're mentioning is actually really interesting.
08:48So, following them, providing support, a lot of studies have shown that a lot of people
08:54are just not following treatment, and that's what's triggering a lack of effects of those treatments.
09:00So, to be able to follow and adjust those treatments and those journeys,
09:05you also need healthcare professional rights.
09:08So, how do you guys see the role of healthcare professionals, of pharmas as well,
09:14into this journey toward personalizing medicine?
09:18Well, I usually say that there is no lack of data in medicine.
09:21That's not the problem that healthcare is having.
09:24The problem is that the data is siloed.
09:26So, you have an incomplete data set at every sort of care instance where you work.
09:32And if you can combine that, you usually need actually less data than today,
09:36but you can utilize that data to make much better decisions.
09:41And that means that if we can get the sort of data that your company is producing, for example,
09:46to tailor what treatment do we want to give to this patient,
09:50we also need to make sure that that data is distributed across the patient's journey.
09:56We have to remember that, I mean, most patients do not come to healthcare once leave it treated.
10:02It's usually a journey for a continuous time.
10:07It's a lot of patients that have chronic diseases and so on.
10:10And this becomes more critical to more diseases you have and the longer they last.
10:16And Dr. Betti, you want to add, because Kali was talking about data, that's a bit your thing, right?
10:22I mean, yes.
10:24So, to Kali's point, I think that, you know, the data, there's a lot of data.
10:31It's just not a representative data set.
10:35And healthcare professionals have a role in how that data is gathered.
10:40So, both on the research side, you know, when you're trying to understand from a research perspective,
10:46but also from a real-world evidence perspective, you know, so when you go to hospitals,
10:52healthcare professionals trained enough to know that they need to be capturing real-world usage of the data.
11:00Because sometimes, in certain ecosystems, the doctor may not really know that what they are seeing can be helpful from
11:10a general perspective.
11:11You know, they go in and they're just looking to provide services.
11:15But we also have to start using doctors as a way to pull back information for each treatment.
11:22And I think that's kind of more what Kali's, you know, company does.
11:26But even on our own side, you know, we can't really build the data sets we build without doctors.
11:32Because we need to go into the hospitals.
11:34We need to look at multiple diseases.
11:37We need to work with specialists.
11:39You know, we need to build protocols around cancers and cardiovascular diseases.
11:43And so, you know, we can't do anything we do without the healthcare professionals.
11:49And to your point, I think both of your solutions are ready or could in the future really trigger some
11:57change in the way that the medicine is being given to patients.
12:01So, how would you describe already what's existing maybe for Levy and more in the future for you, Bassi, the
12:09future role of an exponential and data-driven doctor in the future?
12:17Because we know there's going to be a lack of doctors.
12:20More and more we have shortage of healthcare professionals.
12:23They have very few time to dedicate to each patient.
12:27So, how do you see that changing in the future through a good and really enhanced usage of data services?
12:36Well, I think there are two things that we need to accomplish as a society to come around that problem.
12:43And one is that a lot of healthcare that is being done today is being unnecessarily done.
12:49And that relates to the fact that patients go to see three or four or even five doctors for the
12:55same disease and keep repeating what their complaints are.
13:00And the doctors have no way of sharing the data between themselves.
13:05So, that's half of the answer.
13:06If we can limit that by sharing the data in a better way, we can also reduce the number of
13:11visits and our clinicians will last for more patients.
13:14The other aspect is, of course, the more direct aspect, which is that if we have better data sets that
13:21we can use to suggest to doctors what the right treatment is,
13:27then we can become better at treating patients and they will hopefully lead less care.
13:32The important thing to understand that is, back to your point, that we cannot expect that a doctor can sit
13:39in front of a huge data set and then figure out for themselves what's the right thing to do here.
13:43So, we have to serve that treatment to the doctor.
13:48Yeah, you just touched on a point I was thinking about, which is, you know, as you have a lot
13:54of information coming out, and that's where we are today.
13:56There's a lot of information being produced by the data sets.
14:01You know, we are beginning to understand quite a lot about ourselves, why we are protected, why this person is
14:10not protected.
14:11And all of that sometimes is happening in real time.
14:13So, your physician of the future might be AI-driven, right, because there is a lot of data that's going
14:21to be generated.
14:22And there will be a need, right, to be able to understand and parse all of that information.
14:29Even today, when you go to the clinics or to the hospitals, many doctors are struggling with all the new
14:35information that is coming out.
14:37Because when you go through medical school, you're taught one thing.
14:42But by the time you're out of medical school, a lot is changing, right?
14:46And so, maybe it's a future where there is AI plus a human healthcare professional, but definitely there's going to
14:55be a need to understand all the information.
14:58I would say we are already there within our company, not in that we use algorithms and so on to
15:05decide upon a treatment.
15:06But we can definitely use it to suggest what could be a potential way of handling this situation.
15:12Then it's still up to the clinician and the doctor to make the decision upon that.
15:17And I think that is sort of the future that we will go towards, that we will serve with opportunities
15:25for potential treatments and so on.
15:27And I also think we have a huge potential in utilizing algorithmic work and data in order to take away
15:35the time that doctors are spending right now on, like, gathering this information.
15:41If you look at any sort of study in the westernized societies, at least, doctors and nurses spend more or
15:48less half their time on other stuff than seeing patients.
15:52And I think you're touching something very important, which is adoption, right?
15:56How do you get the best tools that's on the market in the hands of doctors so that they actually
16:03use them?
16:03So how do you make them part of the process about designing those tools?
16:09How do you train them, make them easy to manipulate so that it's actually making a difference?
16:15So are you guys already maybe in the process of either training or working with some doctors so that you
16:22are making sure that's something that's going to be really out there and useful?
16:26Yeah, I mean, we employ about 7,000 doctors within the company.
16:30So we, of course, are the perfect testing ground for ourselves.
16:34And I would say that if the tools that we are developing, which we do in-house, are not easy
16:41to use, then I think that's a failed development.
16:44So that's simple in itself, and we should be able to do that.
16:46The problem that we have is that usually our clinicians sit in front of 10 different systems,
16:52some of them legacy systems that's been in place for many years in the region where they're working and so
16:57on.
16:57And we have to be very careful with adding to that.
17:01So there's just the 11th system coming on.
17:04And that's where the challenge really lies in how can we either lie on top of legacy systems that we
17:12still need for reasons such as they are regulated to be used by the region you're in,
17:18or can we take them away and replace?
17:21I think we should be very careful with just adding.
17:26And so I have another question that you just touched upon, which is we talked about AI, we talked about
17:34medicine, treatments, genetics.
17:36It's a lot of different expertise, right?
17:38We are in an era where the next wave of innovation is really going to be around how to bring
17:45together the different sciences,
17:47the different expertise to be able to unlock new solutions.
17:52So I guess today already, Dr. Abassi, in the way that you're using AI genetic algorithm, you're already starting to
18:02make a proof of that.
18:03Maybe you can tell us a bit more.
18:05Yeah.
18:05So, you know, the work we do as a company, my company, we build data sets in highly diverse populations.
18:16And in order to do that, we typically have to work with lots of ecosystem partners, so doctors within the
18:23hospital, within the countries, the academics.
18:26But for us to make sense of all that data, whether that's biological sample or clinical records, we have a
18:34very diverse team, both geographically diverse as well as technically diverse.
18:39So we have a bunch of doctors, we have, you know, wet lab scientists, we have data scientists, AI type
18:47folk, you know, we have nurses, we have operation supply chain, of course, because you have a lot of movements.
18:57And so in order to bring together this precision medicine reality, it's really a combination of multiple, you know, different,
19:08you know, skill sets.
19:11And that's something we see today.
19:15Yeah, I would completely agree on that.
19:17And our setup is also very diverse.
19:19And as I already said, we are thousands of clinicians and we are also several hundred developers within the company.
19:25And I would even go as far as saying that the times in medicine is such that technology must be
19:33regarded as a scalpel.
19:34I mean, that's natural that you have a scalpel if you're going to do surgery and you will need to
19:39have technology.
19:40And we also see that, I think at least, there's several examples of where you haven't got this multidisciplinary team.
19:47Then you just start throwing some random tech onto medicine and it rarely solves a real problem.
19:52So I think that's key.
19:55So we've talked a lot about treatment and also going to the doctor, having consultation.
20:02But personalization should also be brought maybe earlier, you know, in the whole medicine process, which is maybe diagnostics prevention.
20:11That's something we discussed quickly before this panel.
20:14So how's your vision of how we could leverage personalization also even before there's actually a disease to cure?
20:24Yeah, well, I mean, one thing is, of course, that if you know nothing about a particular person or a
20:30patient, it's very hard to do anything beforehand.
20:33We can, of course, offer easy accessibility, but it's a lot of responsibility up to the patient to actually gather
20:41information and share that about themselves if they want to be part of a potential prevention measure.
20:48Broad things like quitting smoking in a country, that's easy to do, but if you want to do it in
20:54a personalized way, we somehow need to get some information about the status of that patient.
21:00To me, that is a huge potential, of course, in terms of monitoring devices that many of us typically wear
21:08day to day right now.
21:09But I would still say that the patient would have to also find reason and want to be part of
21:16such a measure because that's not something the healthcare can solve on its own.
21:19That's a population question together with healthcare.
21:24Yeah, and I think where we come in is, you know, we try to find the biomarkers that the diagnostics
21:32companies should be looking for or the doctors should look out for to know that this person has this type
21:39of disease such that they need this treatment.
21:42So let me give an example of a study that was published less than a year ago.
21:47A group looked at a certain ethnic group in Nigeria, Yoruba women who have cancer, and they noticed that there
21:57was a new, well, it's not new, but they just observed it for the first time of a genetic mutation
22:05that was responsible for women who come from that ethnic group to have breast cancer in their 40s,
22:13more aggressive forms of breast cancer in their 40s.
22:17Now, why is that important?
22:20It's important because if a doctor or a diagnostics company does not know that I should also be looking out
22:27for this biomarker, I might be treating the wrong type of breast cancer, right?
22:32So the more we are able to look at more diverse groups and understand the various reasons for why they
22:39have certain diseases, we may not be treating the right disease.
22:43And it doesn't just affect the women in Nigeria, for example, because when you look at that particular ethnic group,
22:51they have contributed to the African diaspora in a large proportion.
22:56So when you see an African in the U.S., in France, in Germany, they might actually fall under that
23:04group maybe three generations ago.
23:06Even somebody who may not look like an African, but maybe three generations ago had that in their family line.
23:14And so for us, it's that type of research, understanding disease in multiple diverse populations that is able to help
23:23us know what to be looking out for such that we are treating the actual cause of disease and not
23:30the wrong ones.
23:32And it's actually a very good point because we don't always, as non-healthcare professional, understand the consequences, right, of
23:42such a small fact.
23:45And I think with COVID, we've understood also the limits of being able to communicate in an easy way to
23:53a large group of people science facts so that people can really understand and act on it.
23:59And you were mentioning, Kelly, that we will need the patient to also act on it.
24:04So on one hand, we have a lot of people really interested in their health.
24:08They have access to Internet.
24:10I think we have a stat like 60% of mobile users have downloaded an app that's health-related in
24:18their life.
24:18That's huge.
24:19But in the same time, we see that when we have governments showing guidelines, trying to explain science, we hit
24:27a wall, right?
24:28So how do you see in the future the role of the patient and how can we enable that empowerment
24:37of the patient so that they can become an actor of their journey and they can make it more personal?
24:43Yeah, good but complicated question.
24:45I think that we are actually making a little bit of a progress now, at least within the EU, with
24:49the European data health space that is just now forming.
24:54At least that will give some sort of grounds for operating with data in the healthcare sector.
25:01But there are, exactly to your point, a lot of regulatory hurdles that may not be that this is unsafe.
25:09I really don't think it is to a large extent, but rather that we have no rules for it.
25:13And that means that no one knows how to play the game.
25:16So I think that it's an immaturity in the governing in many aspects because technology has moved faster than we
25:25have with lawmaking and so on.
25:27So we will have to sort that out.
25:29And I really think that's a responsibility that we have as a society to put pressure on everyone who is
25:36involved with that in order to do it.
25:38If we do it, it's not very technically hard to allow for a certain patient to share what they want
25:47to share with a healthcare provider or company that could make use of the data.
25:52And if you speak to patients, my understanding, which is, I think, fairly common knowledge,
26:00is that patients are usually very much open to sharing their data for the benefit of themselves, but also for
26:07the benefit of a population.
26:09So that's not where the shortcoming is either.
26:14Yeah, I think it's a very tricky question as well.
26:19Because I think that if you want to see movement in any, like, adoption, right, patients really help with adoption.
26:30You know, we build data sets.
26:33It's really patients volunteering to be in studies.
26:36So we see that, you know, when you want adoption, you need the voice of the patient.
26:41You need to empower them because sometimes the government or the providers can actually be barriers or gatekeepers, if you
26:50would.
26:51Now, on the flip side, if you give patients too much information, how do you control it?
26:58You know, people start drinking bleach, right?
27:02So it's a balance that it's difficult to navigate.
27:07And I don't have the answers, but I know that the patient's voice needs to be part of it.
27:13But how do you sort of balance that is the key question.
27:18And also, I mean, who is the patient?
27:20Is that someone who has a disease now or is it all of us that potentially will have a disease
27:25in the future?
27:26So I would even say it's sort of a, it's a people's question for all of us, basically.
27:32So just to add a point there, in genetics, we don't believe any, you know, when you're doing studies and
27:40you want to have, like, healthy controls, there really isn't a healthy control.
27:45Because most diseases you're going to have is somewhere in your genes.
27:49It just hasn't happened yet.
27:51So, yeah.
27:54Yeah, I think we talked a lot about data today.
27:57And it's always tricky.
27:58I don't think we have the answer yet.
27:59But for sure, I think communication, trying to make the patient understand, okay, if I'm giving you access to that
28:06data, what am I getting out of it, right?
28:09Is it a better treatment?
28:11Is it easier access to the right professional or to the right treatment?
28:15But communication is definitely, I think, quite key.
28:18But communication is one.
28:20One, I think accessibility is something else.
28:23Because today, some people live just too far away from the doctor to be able to react in time if
28:29they have an emergency.
28:31Some of the facilities that you will need to treat your disease, they're just not accessible everywhere because they are
28:37pricey.
28:38So, how do you see personalization that's also seen as something that can be costly, that can be only accessible
28:46for only a happy few, becoming something on a health topic, which is such a critical topic, a society topic,
28:54something that's accessible, that's affordable for the mass?
28:59Yeah, I mean, one thing you have to always remember is that there is nothing as expensive as having diseases
29:07untreated that could have been treated earlier.
29:10So, inaccessibility is a huge problem for any society in terms of how we spend the resources.
29:17We have, for many years, lowering the threshold for accessibility by being able to provide a digital means of doing
29:26so.
29:27And, for example, in France, I mean, 30% of our patients come from a medical desert.
29:32That's an area where we have a low penetrance of healthcare professionals and so on.
29:36That's twice as much as the average.
29:39And that's true for most of the markets where we are in.
29:42That is, of course, one way to do this.
29:45But we need to go much further than that.
29:48We need to not only be able to increase accessibility to the fast contact with care, but to also guide
29:54the patients through to care, other care instances where they may need that, which is something that we do as
30:02a company right now.
30:03And have already, I would say, been able to show in large markets in Europe that we can do that
30:09more efficiently and at a lower resource consumption than traditional healthcare.
30:15And I remember you mentioned, so for those of you that are not familiar with Livy, that they are doing
30:21telehealth, so that you're doing with Doctali, for instance.
30:25But you also mentioned you can, for instance, order your medicine through the app, so going a step further in
30:31the journey toward pharmacy, home delivery, right?
30:35So, in the future, how do you see, like, where does it stop?
30:40It doesn't stop.
30:42No, but what you can do already today using Livy is that you can access care, then you can be
30:48guided towards what you want to do, which may renewing a prescription if that's all you want to do.
30:53And then you shouldn't even, perhaps, have to see a clinician, thereby lowering resource consumption.
30:59You may not know what your problem is, but you have a complaint.
31:02Then we can guide you to the clinical profession you want to see, usually digitally first, but digital does not
31:09solve everything.
31:10So, we also have physical units within our company.
31:13We have specializations, we have mental health, we have physiotherapy, and that chain of what we do are only going
31:20to grow and grow.
31:21So, we will not do everything within our company ourselves, so we will partner with other companies, perhaps with you
31:28guys for getting a better understanding and data set of what to give the patient, perhaps with surgical units for
31:34providing surgery.
31:36But I really think that knitting this together is where the big bang for the buck is, basically.
31:43And I know, Abasi, so accessibility, affordability is really close to your heart, so maybe you want to tell us
31:49a bit more?
31:50Yes, I mean, it's close to my heart because, you know, we build data sets.
31:56So, it just happens to be that the most genetically diverse individuals also happen to come from some of the
32:05poorer countries.
32:07And so, you have to think about better ways to deliver, you know, the results of your work.
32:16And, you know, when you think about the healthcare experience, the most expensive part is the pharmaceuticals, is the treatment.
32:25Now, you want, the reason drugs are expensive is because pharmaceutical companies spend a lot of money in the R
32:32&D and in the development.
32:34And so, they have to, yeah, incentivize to go into those research areas, right, so that they can then charge
32:43that in the future and make their money back.
32:46But when you start thinking about a future, precision medicine doesn't have to be expensive.
32:52It's believed that it should be because you're treating subtypes.
32:56But the same tools that enable precision medicine also reduce the cost of development because it becomes as easy as
33:05running an algorithm to find a drug target.
33:08So, once upon a time, if I wanted to find what we call a drug target, I might work for
33:1410 years in the lab performing experiments.
33:18Today, if I have the right data set, I can run an algorithm and find that target within, you know,
33:25a few days.
33:26And then, I just have to start validating.
33:29So, we are cutting down the time it takes to discover a drug and to develop it.
33:35And so, with more advances in AI and genomics, hopefully, we can reduce the price of development such that we
33:43can reduce the prices that we charge different groups or subtypes of disease.
33:50And you just mentioned, so, pharma giants.
33:53Obviously, they play a huge role in the healthcare landscape, right?
33:57So, I guess today, you guys are more agile, you're quicker.
34:02Those big guys, they're a bit more rooted, I would say.
34:06So, do you see the big pharma's moving toward that?
34:10What's the latest moves around that?
34:13And how do you see partnerships, maybe, as a way to enable that change to happen, maybe, quicker?
34:19Yeah.
34:20So, we see a lot of movement in the bio-pharma space.
34:25I mean, right now, we are all experimenting.
34:29You know, that's the best way to put it.
34:31So, different people are trying different things, but we're seeing that the money is going to biocomputing, genetics, AI.
34:38Most pharma groups are beginning to revamp their R&D strategy to be data-first, target-first.
34:47And for us, you know, we are partnering with pharma companies to develop drugs now,
34:53using the data set that we build, and applying our bioinformatics capabilities.
35:01We're able to find targets, and we're able to develop them with pharma partners.
35:05I mean, these guys have a lot of experience, to your point.
35:08They have more resources.
35:10And so, it's a future where I believe everyone will move in that direction,
35:14but it will take some time.
35:16But we're seeing movement there.
35:19Yeah, and I think it really has to be together, right?
35:22Because different capabilities, different assets, but the same goal, I hope.
35:28And to go back to you, Kali, I think partnership is also quite key in your model,
35:33especially with state, because we're talking reimbursement.
35:36Maybe you can tell us a bit more about the movement,
35:40and how you see slowly maybe states progressing towards personalized and maybe digital health as well?
35:46Yeah, I mean, partnerships are key with several partners in order for us to solve health care problems.
35:52For us, of course, the most important part is usually the public sector,
35:56who is excellent at doing parts of health care.
36:00So, we shouldn't take that away from them.
36:02And we definitely do not see ourselves as in competition with public sector health care,
36:09rather the opposite.
36:10They are our friend, and we want to knit them together with what we do.
36:15But partnerships with pharma, we have, of course, a lot of interest for that kind of partnerships as well,
36:20because there are three things we need to accomplish in order to get to a personalized treatment.
36:26And that is, what you explained, we need to understand what's the setup of this human being,
36:31and what is then the drug that we could provide to them.
36:33But then we also need to find this human being and to actually provide the treatment.
36:37And I think that everyone in health care is interested in the same thing.
36:42So, we just need to figure out ways to even better utilize our competences to get working at a broader
36:52scale now.
36:54Thank you.
36:56I'm seeing the time, and I want to keep a few minutes for questions, whether they're online or in the
37:02room.
37:03So, maybe one last question, and you guys have time to answer it, don't speed up,
37:07but what's your vision of the future of personalized medicine?
37:12Like, in a few years, what's your target as of what you would like to achieve
37:17and what you would like to see as maybe a new standard of care out there in the different states?
37:25Well, I think what we have to come to is an orchestration of health care, which is not as disjointed.
37:33That means several things.
37:35We need to be able to have an equality of care, regardless of where you live and who you are
37:41and what your resources are, so that you can access into health care.
37:45Then we need to, together, coming back to partnerships,
37:48make sure that we can help you navigate to the different aspects of care that you need for your disease
37:55and that your data and the information that you carry is carried with you to the next provider.
38:02And then, in parallel to that, we, of course, need to be even better at understanding the biology
38:08in terms of what treatment you should have for different sorts of diseases and so on.
38:14Yeah, and I see that there's going to be a lot of new treatments,
38:20so diseases that we've struggled with understanding.
38:23We will have more information for how to treat them.
38:28And, you know, as our genetic databases get more diverse,
38:33we will hopefully have treatments that work but are safer for most of us.
38:41It's going to take some time, but we hope that, at some point,
38:44most of this treatment will be safe for all of us.
38:48Thank you.
38:49And I made a mistake.
38:50You need to write your question online.
38:53But we already have one.
38:55So, for you guys in the room, if you have a question,
38:58I encourage you to write that on the app.
39:01There's a question in Cali that might be for you about patient-doctor interaction,
39:07and especially during digital consultation.
39:11What are the tools that are currently existing
39:14and maybe that you are developing to make it better?
39:17Because you were mentioning the fact that we need follow-up.
39:21Sometimes you're seeing several doctors for the same problem.
39:24So, how do we make this interaction more fluid and maybe more continuous as well?
39:30Yeah, within our company, you can actually have your own doctor
39:36or your preferred doctor that you repeatedly see over video or physically,
39:42depending on what you need are.
39:43The ways of interacting with your healthcare professional is video, of course.
39:48You can also do it over chat, asynchronous or synchronous.
39:53We text message with our patients and share documents and files that way
39:57through our own application that the patients will then have downloaded.
40:00And that's also where our patients save a lot about their healthcare data and so on.
40:05I think that to make the interaction even better,
40:09there is potential to, of course, connect monitoring devices
40:14that you can keep at home to the visit so that the doctor can watch you
40:18while they also get some physical measurements and so on.
40:22But I also think that the sort of decision whether it's suitable
40:27to see someone over video, chat or physically
40:31is quite clear to most doctors, at least when they've trained for a while.
40:36And most patients' preference will match that well.
40:39So, I don't see that as the major challenge.
40:43So, there's online, but it cannot replace entirely the real interaction, right?
40:48Definitely not.
40:49I usually say that digital when possible, but physical when needed.
40:54And that, I think, is true for all of healthcare, basically.
40:57And it's definitely true also towards building trust.
41:01I mean, I think a lot of studies have shown that you are more trustworthy
41:06with a real human being than with a bot.
41:09And we're talking about health, right?
41:11About your future or maybe the future of someone you care about.
41:15So, we definitely need to keep the doctors in the loop.
41:19And I think the mix of real interaction and maybe online interaction is key.
41:25I certainly do not believe that the goal is to replace doctors or nurses with bots.
41:31I rather think that the goal is to replace some of the duties that they do today,
41:37which is not meeting the patient, not seeing the patient with a bot,
41:41so that we free up time for them to do that.
41:44And that's at least where we are developing a lot of technology in order to do that.
41:49So, I mean, the interaction between doctor and patient
41:53is typically not the largest challenge when it happens.
41:58The challenge is rather to free up enough time so that doctors and nurses
42:03and other healthcare professionals can see patients with that time
42:06and not do a bunch of administrative stuff instead.
42:11And while we are waiting for other questions,
42:13maybe I have a question still for you, Avasi.
42:16because in the end, if you provide that tool with the data set of the dynamic of the patient to
42:23the doctor,
42:24he will still have to explain a bit about that, right, to the patient.
42:29And that's something that's quite expert, quite complex.
42:34So, did you already kind of think, thought about that
42:38and how you could make it maybe a bit more comprehensible for patients too
42:43so that they trust in the process, trust in the treatment,
42:47and that the experience on top of the treatment is also being personalized?
42:52So, that's a good question.
42:56I think that, you know, first of all, we wouldn't give the doctors the data, right,
43:02because they don't have enough time.
43:04They don't have the expertise to analyze it.
43:06So, it's about being able to do all the research, analysis,
43:12and then providing it in a digestible format to the doctors
43:18in a way that they can connect the dots, right?
43:22Because what you really don't want to do is give the doctor a bunch of information
43:27and there's no inference, right?
43:29Or you're leaving them to sort of determine exactly what it is.
43:35You know, some of these doctors still require that you connect the dots.
43:40And ultimately, you know, I look at, like, the U.S. healthcare system
43:45and it's not the best healthcare system to learn from.
43:49It has its strengths and it has its weaknesses.
43:53But one of the things I've really liked about the U.S. healthcare system
43:57is how you can go directly to the patients.
44:00The patients don't prescribe the drugs.
44:03You know, it's the providers, it's the doctors.
44:07But somehow, the pharma companies have understood
44:10that they need to provide this information to the patient
44:15in ways that the patient can understand.
44:18And as such, they can have conversations with their doctor
44:22about their healthcare plans.
44:25So, essentially making the patient a partner in healthcare.
44:29And there's lots of ways to do that.
44:31But I think that in as much as we communicate with the doctors,
44:36we have to find easier ways to communicate with the patients as well.
44:41And do you think that media and events just like this one
44:46where you have crowds that are coming from health,
44:48but maybe others that are just from other sectors listening in
44:52can also help to a bit spread the word around the fact
44:56that there are some treatments that are coming to the market
44:59that are personalized and you need to look into it.
45:02Like, how do you see the role of internet and self-information also into that?
45:09Well, I think it's already clear that we have so much more information.
45:14And if any one of us have a disease or even a complaint right now,
45:17the first thing we do is probably to go online and look for information about it.
45:22And I'm with you, Abazi, that the challenge that we're having
45:26is the curation of this because you can find all sorts of stuff.
45:30And that's where I think that the healthcare professional,
45:34the doctor and the training they go through is a critical part.
45:39And that is, back to your point,
45:41we cannot just throw all of this onto the doctor and say, sort it out.
45:44We need to be able to help them with,
45:46this is based on that sort of data, you can trust this for this reason,
45:50and then they can help the patient to also navigate in this data overflow, perhaps.
45:56Thank you, guys.
45:58And I think we are done for this session.
46:00So thank you so much for listening in.
46:02And thank you, too, for all your insights.
46:05I think it was super helpful for everyone.
46:07Thank you, again.
46:08Thank you.
46:09Merci.
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