 Hi everyone, thank you for turning up on Sunday afternoon. So what we thought was, you know, talking about future health, it would be great to have a primary introduction to the underlying issues and why health care is where it is today. So that was the idea, so I'm not going to talk a lot about future as such, but I'm going to talk about what are the drivers, you know, and why we are here. So if we start by looking at what we want the future of health to be, so what is ideal health care, what should we aim for? So the first thing would be to prevent disease and disability. So the ability to predict disease before it happens, before it becomes difficult to reverse, you know, identify it as early as possible and fix it. So that would be one goal. How do we do this is a key challenge for the future of health. The second is longevity. So we want to avoid diseases, we want to avoid disability, but we want to do this for longevity. And longevity is sometimes a controversial topic when you talk about longevity for hundreds of years. So we are okay for longevity which is 120, then after a while it gets a little bit controversial, but any individual doesn't want to die. So longevity is, you know, we would want everyone to live really long. I mean, it's not enough that we live longer. We want to enhance how we live. We want to enhance our capabilities which is very possible and there are already indications of this. So we want to enhance our capabilities to be super good. And this is one of the goals. It's not enough to live long but you want to live well and have as many experiences as you can have. And we want this for nothing. So ultimately we don't want this to be available only for billionaires. We want this to be available for everyone. And we would have a situation, I guess, where it would be available only for millionaires and billionaires. It may not be available for everyone and we are already starting to see indications of that. So we want to get to a point where it is almost free. So these are our goalposts for the future of health and we can see how this can be achieved and we can look at what are the challenges, why this is not the case already today, right? Why, because, you know, data is kind of free now. There are so many things that are dramatically improved what is the challenge with healthcare that's causing us to be here. So if you look at the hierarchy of, you know, so kind of repeating this point, if you look at the hierarchy of our focus with healthcare, I mean, at the very basic level we want to avoid premature death and disability. Then we want to move to wellness and then we want to look at reversing aging and then we want to be super human. So that's kind of the hierarchy of our goals. So what I thought would be very interesting is to look at some interesting graphs and stats and concepts that might help us understanding healthcare a little bit better and get an insight in terms of how we could change it. So what I find this graph really interesting, so this is the death rate by age group, right? And if you see, it's really, you know, aging is the issue, right? So if you cross the one-year mark without problems, then for the next 30, 40 years, you're okay, right? Typically okay, right? And then it starts to go south in terms of how, you know, all sorts of things happen. It's not just death, but it's the same with hospitalization. So hospitalization dramatically changes after your 45-50. So it's really at the core, it looks like an aging issue, right? So if you are able to deal with aging better, if you're able to age healthily, you know, that's kind of what we are effectively looking for. So then you ask the question, so how do you measure health or how do you measure, in this case, the burden of disease? So this is what we are trying to avoid, right? So the idea of the burden of disease is the number of years lost due to disability or death. So this is a great way to measure across diseases, across a population to look at what is the impact of various diseases in one number. So very valuable point of data that we can measure and it's been measured for a while now. And so if you look at, so this is Dali, disability adjusted like, you know, years lost. And so between 1990 and 2010, what you've seen is there's almost a 25% drop in the disease burden. And so that's like a massive achievement. So in 20 years, we have removed quarter of that burden and with increasing population, you know, with all this, we have done this really well. So this is Dali per 100,000 people. So we have done this really, really well for an increased population. And we have done this well across all age groups, right? So we have done this well for, somebody who is less than five all the way up to 70 plus. So we've made progress everywhere in the spectrum, right? So that's the good news. And if you see where did we make those gains? And where did, what were the gains and what were the losses, right? So this is, I'm going to try and explain this. So what you see on the bottom are where we have decreased the burden, disease burden. What you see on the top is the increase in disease burden. So this is over the last 20 years. So I think this is 1990 to 2010. So what you see here is all the things that are in the red are infectious diseases, right? So we have done really, really well with infectious diseases, right? I mean, except AIDS, which I think we would be in the next graph, if you do an update, you would have done really well. So you've been very successful when it's a pill that you need to pop or a vaccine or something like that. It's really easy to do. But where are we failing? Where are we failing? Including AIDS, it's a lifestyle issue. It's really, you know, we are failing with heart disease, stroke, back pain, diabetes, neck pain. You know, those are things where we are really losing the burden, right? So we are moving from infectious to preventable diseases, right? And this graph shows so clearly where the problem is. So we are moving, you know, the problem has moved from stopping somebody from getting malaria or TB or AIDS to affecting their lifestyle and changing the way they live. So how can we affect this? How can we affect health as such? So if you look at what causes, what are the factors that affect health, and you see there's only 10% which is clinical care, right? Only 10% is clinical care, right? And the rest is really outside the control of the hospital, right? It's really outside the hospital, right? So 10% is biology and genes, right? The rest, so the big ones are the socio-economic factors and health behaviors, right? So it's almost like health is as much an economic social challenge as a medical challenge, right? So healthcare has to be much broader. If you want to deal with this really well, it's really the issues outside the hospital. We are being successful with the hospital with infectious diseases, but we have not been with preventable diseases. So this is, you know, this is a diagram that really shows if somebody is poor and they are in a bad housing, you know, basically poor, right? If it changes, it makes it very, very, very difficult for them to be healthy, right? So health is very related to poverty and the socio-economic conditions. And this is, this kind of is a very interesting graph which shows, so this is on the bottom is the income per person, right? Not from 100 dollars to 100,000 dollars, probably more. But if you see, the lower you earn, the lower the life expectancy. So it's, you know, the richer will live longer. So that's the idea up to a point, right? So if after a while it stops being effective because that's only so much we know about medicine today that we can't really, with money, change, change the outcomes, you know, state jobs exhibit A, right? So, you know, you can have billions of dollars but you can't really fix and some things like that go wrong. You know, this is another aspect of health care which is really interesting. Basically, what it shows here is the 50% of population spends, this is US data, 50% of population spends about 36 billion. So this is private spending. And the rest of the 50% spend 1.2 trillion, right? So it's like a massive difference. And the top 5% spend 50%, right? 50% of all the spending is by the top 5%. So, you know, it's a very different problem if you look at it like this. So these are some of the ideas that I thought would be interesting to note and influence how we think about health. So to ask what's wrong with health care today, right? So the first one I'll say is, you know, you all think that ounce of prevention is better than a pound of cure. But not really. Not really for all cases. So you're being good at doing this for some areas, particularly infectious. Its prevention is definitely better than get the economics of that as well established. But it doesn't really spread to other aspects of health care really well. And this is a big challenge. And if you see a lot of insurance companies, you know, I've been looking at prevention and the economics of prevention, what you're able to see very consistently is insurance companies and many payers, including governments, sometimes think it's actually cheaper to let people get sick and then fix them. It's actually cheaper, right? And this is a challenge. So this was a Mark Cuban's tweet a couple of weeks ago. So he tweeted basically saying everybody should get, if you can afford, you should do a quarterly blood test. So you should kind of start to build, you know, you should go and do your blood screening every three months, every four months, and then it became a big controversy. And the controversy is not completely unfound. There are issues in terms of cost of screening, false positives, false negatives, you know, testing leading to unnecessary intervention. You know, this is a mess. So it's very, very difficult to justify the cost of testing. It's very difficult to deal with false positives and false negative tests and the interventions that come out of it, right? So you could really affect somebody. It's almost like you went to a police station to file a complaint and you got shot accidentally, right? So that's how it can happen in many cases where the screening can lead to issues. So there's that on prostate cancer death. So you have to almost test about 1,000 people for a 10-year period every year to get to say 1% dying from prostate cancer. That's a very difficult economic argument to make anyway, including the patient, right? So, but, you know, this is the current state of affairs, right? So we don't really know precisely what to look for when it is benign, when it is not, when something is out of range, how to treat your very little clue about this, but this should change, but this is the current state. And the issue is also, you know, let's say somebody is diagnosed and somebody is given medication. It doesn't work for everyone. So our medication is really population-based. So we, I mean, if you look at how clinical trials are done, how drugs are discovered, it's really based on a statistically significant population getting better from that drug, right? It doesn't really matter to an individual whether it is statistically significant or not. It's what matters is whether it works for that individual or not, right? So, I mean, for the same diagnosis, for the same prescription, for some, you know, it will be toxic and not beneficial, right? So for some, it will be not toxic and beneficial. So this is the group that we want, but there are other groups that are paying and taking medication and suffering side effects, right? So there is people who are toxic, but benefits them, not toxic, not beneficial, based on money, right? So this is a big challenge. So we spend a lot of money on medication and works for a fraction of people who take that medication. Because there is no way for us to personalize medication consistently everywhere. You know, the sixth largest reason for death is medical errors, right? And besides death, if you see side effects, if you look at wrong diagnosis, if you look at people having side effects, it's just the numbers are much, much more larger and we have no idea today because there's very little data on this, right? The more we know, the more we are scared about doctors, right? So any doctors here? So, you know, so we kind of realize how little we understand the biology, right? And how little we, you know, we can predict what's going to happen, right? And, you know, these are preventable medical errors. There's so much human interaction which leads to a lot of these errors. And, you know, so based on the fact that we don't have personalized medication, we don't have, you know, normal drug doesn't work for everyone very well, we have to do trial and error, right? So we are going to, somebody is going to get a medication, see if it works, if it doesn't work, we are going to change it. So it's very difficult to predict upfront what's going to work for you. Like what exactly is the problem and what's going to work for you? Very little data. So, and, you know, there's very little data and wherever there is data, it is disjointed, it is not connected, it's all over the place. It's, you know, we do a lot of tests over time, but we don't get the benefit of all those data. You know, there is a diagnosis made, there is an allergy. Part of this data is missed, right? Every time it's missed. And, you know, so there's one more slide which I want to talk about just a bit more. On the other side, if you see, you know, drug development has become more and more and more expensive, right? So this leads to a situation where unless there is enough people who would buy a drug, there is no incentive to discover those drugs. Unless we change how we, you know, discover drugs, how we make drugs, it's going to become more and more difficult. You know, not just drugs, but every procedure, every aspect of healthcare is inflating at a crazy rate, right? It's becoming more and more a bigger and bigger part of the GBP. And, you know, between the patient, the employer, the government, the provider, the goals are very misaligned, right? So we have very different goals and sometimes it's in conflict with each other. And this is a, this is a crazy problem as well. And this is a structural issue, nothing to do with medication. Patient experience, again, you know, the patient experience in the typical healthcare system is bad, right? And it is, again, it's a structural issue because it's not easy to provide great patient experience the way we are set up today, right? So, and so this is, you know, we have intelligence leaking out of the system all the time, there's no way to accumulate this intelligence, make use of this intelligence so that we can make better and better use of this data. I mean, if you look at aviation, if a flight goes down, you know, the amount of work that happens after that flight goes down, there's so much research and analysis that's done on why it went down, then everybody benefits. The whole aviation industry benefits from that one mistake, right? Whether it is a hardware issue or a software issue or, you know, the pilot locking the door, these issues are systematically addressed and everybody gets to know it and then as a result, aviation becomes more and more safe. If medicine was as safe as aviation, it would be fantastic, right? I mean, medication is not safe at all, right? I mean, in the, they say in the 18th century, if you got sick, you have two choices, you could go to the priest or you could go to the doctor and if you went to the priest, you are better off, right? Because you are not going to be subject to unnecessary errors because we didn't know much then, right? So things have improved, but there's still a long way to go. So this is, I mean, so we saw some of the issues and we saw some of the challenges that we have. Now, you know, what I wanted to show are some trends that are positively changing the way healthcare is done. You know, not all of them are moving at the same pace. You know, you would expect some breakthroughs in some technologies for some of this to get really quick. So we're moving, we want to move from being reactive to preventive and I think as the cost of testing becomes less, as the algorithms finding out what the issue is becomes more, as we become, as we are able to connect all the data points together so that we can make the prediction that we can find the right drug, it gets, you know, this can happen more and more. So we can move from a spray and pray medicine to a precision process, right? So same factors influencing this. So I mean, one more thing I want to talk about here in terms of precision. So, you know, let's say when it comes to lifestyle, right? One of the areas which I'm very actively looking at now as a way to prevent diseases, the problem is we have very general statements about what one should do. So we would say something like, you know, walk 10,000 steps or, you know, have, you know, this diet or that. But what we lack is precision, right? What we lack is the ability to say for you, you know, we need to walk 2,000 steps between 8 and 10 to get the maximum effect, right? So, I mean, because effectively what we're doing is spray and pray, right? And that's why we are not getting sufficient engagement. When people are able to see exactly how it impacts, then we would start to get really better, right? So population medicine to a personalized healthcare, you know, your medicine is to be able to look at how to prevent, how to live a healthier life, how to optimize for productivity, right? What do you do to be at the best you can be? You know, instead of episodic, you know, so currently the healthcare system is like a barrage, right? So you don't go to the barrage unless your car is broke, right? You go when once it is broke, right? So we can move away from that, like some of the cars are already doing, having a computer on board. You start to analyze data and then you can go and fix before things get bad, right? So we need onboard computers via nanotechnology or sensors. You know, as these technologies get better and better, we would have that onboard computer where somebody remotely can watch and talk to us only when there's a need, right? To say, okay, you need to come in for something and then we don't have to worry about managing that. And, you know, healthcare has to really move from healthcare facilities to homes and offices and in the neighborhoods. So, I mean, these are the trends that we want and we're already seeing these trends happening slowly but surely, right? With technologies, what is really interesting going forward is that not that there's one technology that's going to make all the difference. What's going to make all the difference is the convergence, right? So it's really how so many of these technologies are coming together to create an impact, right? So it's just not just one piece of technology. It's not just genetics. It's not just a biomex. It's not just a mobile. All of them together dramatically changes the outcome, right? So this is, I took a picture of this from this book called The Creative Destruction of Medicine by Eric Topolso. You know, people who are interested in healthcare, I would definitely recommend that book. It's a very readable view of what's going to happen. So one of the great things about some of the technologies are they are digital at the very core, right? When something is digital at the very core, there are some implications, right? We have felt these implications in a few areas already. We have seen this in travel. We have seen this in banking. We have seen this in photography. We have seen this in many areas. So what happens when something gets digitized? The first thing is for a period of time, it's very deceptive. So there are a lot of progress happening, but you wouldn't feel that progress. So it is kind of under the radar for a long time before it suddenly starts to make itself seem. So I presume we are in a very deceptive phase in healthcare because it's going to take a while before these advances are going to make things seem. So this is a graph of how the cost of sequencing a genome has changed over time. So what was $100 million, not long ago, but in 2003-2004, to less than 1,000 now. So that's like a massive improvement in performance per dollar. And we would see this in many different aspects of healthcare. And together I think it's going to make a big difference. So what we're seeing is things are becoming easier and easier to access. Some of these devices provide critical data that you can have at your home without having to go to a hospital to do a test. Things have become very, very simple in terms of knowing data. And we're also seeing bionics and prosthetics which are getting really better and better. So if you had a hearing aid 30 years ago compared to what you have now, the difference is huge. We're going to reach a point where it's better to have the artificial hearing than your actual hearing because it's going to do much better. So it's already starting to, these are some examples which are very visible, great stories of people who lost their limbs and with these recording arms and legs are able to do much better. I mean, not much better yet, but starting to be becoming a part. The thing is this evolves very, very rapidly. Whereas the rest of the body evolves, takes millions of years to evolve. So as we start to replace these parts, it's going to get really interesting. So this is a bionic eye that we're talking about, brain implants that can stop seizures. So there are so many interesting technologies that are coming up which will address some of the challenges we saw. So with that introduction, hand over to Julian to deep dive into the digital health space. Cool. I mean, a lot of them are US data, so we don't have good data for, but I think should be representative. I can put some show notes on the, I'll put the links for these. What happens if prevention is less lucrative than the cure? The cure is less, sorry. What if it's better, it makes more money to treat a chronic disease than cure it or prevent it? That's the case today. That's the case today. And that's where, that's the challenge. So how are we going to solve the challenge? Because what I mean, I think like we saw with a lot of preventable chronic diseases, it's not so much about the pharma company doing something or the hospitals doing something. It's outside, right? It's what you do at your home, what you do. So there are now a range of companies coming to help on the lifestyle and this is going to make a big difference and they are outside. You know, it's not a hospital problem. Yeah, you're right. The current challenge is that treating a disease is very expensive. I'm pretty at home. And of course, people need to be in the middle of it themselves. We're sure it's going to be legal to government. But there's free stay all the way already and we're changing this. So that's a big move of that. We would be able to push with digital health. Okay, cool. I think you shared a little bit about the timeless components of what you're right for. People want and consumers want. And I think we also know that hospitals and doctors are kind of like, you know, driving to see what it is. But I think you mentioned a little bit about interest in conveying different goals from both the kind of medical side as well as the patient side. Can you see in the next kind of 50 years how you see the insurance? No, 50 years too long, maybe there's no insurance. Because if we are able to precisely predict risk, there's no place for insurance. I do feel a bit of work with insurance companies. All insurance companies will privately tell you, or CEO insurance companies will probably tell you that they know that this is one of the next 5-10 years of debt. Google will do insurance. Google is already doing insurance business. Car insurance. It's not bloody insurance yet. It's not far behind. Why? Because Google has access to the most wide-ranging data available. You know, everything about them. They know you're going to buy a house 6 months later as I'm buying a house. Why? Because you start living in houses and you'll suggest schools. So most insurance companies are stepping into healthcare at the moment. They're all stepping into healthcare. So AIA, for example, launched an healthcare accelerator in Hong Kong last month. They're all recognizing that they need it to define a new purpose for themselves. In the prevention space, maybe you want to mention also what the talk is about. Yes, sir. I mean, one of the things, I'm currently working on a program. It's a lifestyle intervention program for people with pre-diabetes. So what we're trying to do is a 6-month intervention where they can help somebody who is pre-diabetic to reverse and combine the pre-diabetic. Cool. Okay, good. Okay, so we'll take more questions than after Julian as well.