or inviting a futurist, a technology futurist, I think you're very brave to do so. I am not a physician. I did start out my career as pre-med, but got a little waylaid on technology as my parents had landed and built their home in what was later to become Silicon Valley. And of course, that began to dominate my career choices. And since that time, I've been working with the founders of some of the largest, most innovative companies in the world. So it's my pleasure to come here today and to talk to you about some of the things that might be coming down the pike. I normally give about an hour and a half presentation followed by 30 minutes of live Q&A, and we don't have time to do that today. So I just wanna let everyone know that after my presentation, I'll kind of hang around and try to answer your questions because I'm sure there'll be many of them, or at least I hope there will be. So I've kind of broken our presentation down today to what's going on today in your neck of the woods, what lies ahead. By what lies ahead, I don't really get out into space travel and fixing leg and knee and ankle problems on the moon. But I'm more looking at things that might be hitting you and disrupting the way that you service patients and how you conduct your business in the next one, three, five, and 10 years. My job today is in the next 45 minutes to turn all of you into futurists, and we're gonna see if I'm able to do that. And then I'm gonna talk a little bit about a game plan. I'm not gonna be able to go into how to approach innovation and technology in a sensible and pragmatic way because so many things are gonna be hitting you in the next couple of years that you need some kind of business system in order to stay on top of these changes. And I don't like, you won't hear me use the word disruptive very much because I feel that when we talk about disruptive science and disruptive technology, we're talking about a failure to evolve. And as you'll see, I'm a big proponent of evolution, making small incremental changes along the way so that the final correction you have to make is not painful. So by a quick show of hands, and I'd like you to keep your hand in the air for just a minute. How many of you have noticed just in the last three to five years that caring for ankles, feet, the new techniques that are coming out have become much more complicated and complex? Can I see a show of hands? Look around the room. You can skip your therapy appointment this week. Yes, it has become very complicated, but you're not alone. This was the diagram that General McChrystal put up before the American people and said when we understand this diagram, we win the campaign in Afghanistan. This is how a claim is processed under the Affordable Care Act. Let's see, let me see if I can go backwards here. These are the low income government resources for people who need help, assistance. This is our global food system. This is a chart of how medical records move through a typical hospital. As you can see, it's become so overwhelming in every industry, and I speak at conferences all over the world, and we're all encountering the same thing. We're just overloaded. In fact, medical information is now estimated to double every 73 days. That's the environment that you are operating in, and to keep up with breakthroughs in your field, you'd have to devote 160 hours a week just reading journals in your particular area of medicine, and 81% of physicians now report that they have less than five hours, actually, when they're not attending to their practices, and so it's estimated that only about 20% of all decisions that physicians make and surgeons make is really evidence-based. The rest of it is really things that people have told you, your own personal experiences, your own sort of algorithm that you've developed through years of experience, and in the foot and ankle medicine area, honestly, you have so many things hitting you at the same time. There's an inherent complexity of foot and ankle diagnosis and treatment that doesn't exist in other areas of medicine that have been around for a little longer. I was mentioning to Dr. Haddad that this medical conference has an air of humility to it. I love the fact that these sages who get up on this stage, they say things like, we don't know how to do that yet, and we don't know how to fix that yet, and we don't know, really understand the outcomes, and you don't get that necessarily when you go to conferences in other areas of medicine. You get the sages saying, this is how you do it, and don't do it any other way, and so it's very interesting that you're currently, because this is a new area of medicine, let's face it, ankle and feet, we still have a lot to learn, and so we're navigating a thicket of complexity. Now, I happen to be, my dad asked me once, well, what's your area of specialty? What are you a subject expert on? And I said, well, Dad, I'm the world expert on fast adaptation in high failure rate complex environments and he said, what kind of job can you get with that? And I said, well, I'm not sure, but I really like solving complex dynamic problems, and he said, yeah, paying rent is a good thing, and so you can kind of tell, I came from a working class family where my dad was very concerned that my interests might not line up with paying rent, and fortunately, because we were in Silicon Valley at the time, that didn't materialize. In a complex environment, and this is a simple way to think of it, really, the number of wrong ways you can go far outnumber the number of right options you have, and the number of wrong options are exponentially growing, so instead of looking for a needle in a haystack, you're really looking for a needle in a stack of needles, a particular type of solution for a particular type of patient, and in a complex high failure rate environment, a number of symptoms start to show up. There are organizational symptoms, but there's also personal symptoms. The first one is the dilemma of overchoice. Having too many options is the same as having none, and you can get locked into the paralysis by analysis where you just really don't know when to change over. When's the right time to abandon the way you've been doing something and change over to something new? You don't want to be late. You also don't want to be the pioneer with the arrows in your back. The confusion between empirical facts and unproven beliefs. The more complex an environment gets, the more hearsay and opinions begin to outweigh empirical data, and there's a tremendous amount of research that's been done that shows that even when we are proven to be empirically wrong, and this is wired in our DNA, even when we're shown to be wrong, we have a tendency to double down. We don't reverse. We don't course reverse. We also are very Pavlovian. Whatever we've done to arrive at the success that we have today, we are wired to do that over and over again, even when it isn't quite working as well or even stops working at all. And then, of course, there's many other symptoms. So here's the real question. How did we get here? Well, every two days, we create as much data as we did from the dawn of civilization to 2003. So if you think about data as the four Vs, the velocity, the speed at which new information is coming at you, we talked about that just a minute ago, the volume of data, how big these files are. It used to be when you saw a new scientific study, as some of the people here were talking about earlier, you used to see one or two names associated with a breakthrough. Now you might see 30 people, some in China, some in India, some in the United States, that are all collaborating globally on new medical research. And so that speaks to complexity, the fact that so many people have to be involved. Of course, there's the variety of the data and also the veracity. For every one study you read that says one thing, you might find 20 that say the exact contradictory information or outcomes. Now our brains, as you all know, because you're all doctors, are not equipped for this deluge of data. We're not equipped to make these kinds of complex decisions in a fast-moving environment. And we're not gonna get new brains any time soon. What's more, when we look at the kinds of problems people are having with their ankles and their feet, we were designed to be quadrupedal. Modern man's only been around folks for 200, 300,000 years. And so the problems we're having largely stem from the fact that we haven't been walking upright all that long. And the fact is is that when the quadrupeds, about 60% of their body weight is supported by those extra two feet, that might explain why we're discovering that it might be better for people to run barefoot. It also explains why athletes like Andrew Luck will retire at age 29. This is shocking, everyone, but our parts weren't really designed to go through the kind of rigor that we're putting them through right now. So I'm gonna talk a little bit about how technology is gonna solve some of these problems. And first, let's talk about the data. I don't know if some of you, did anybody watch the 2011 battle between IBM's Watson and the two most winning contestants of Jeopardy? Anybody? Well, I'm a real data wonk. So I threw a big party. I know most people throw Super Bowl parties. I throw parties when the new GOES weather satellite is going into outer space, because I know we're gonna get five times the resolution and what the implication will be for being able to get ahead of weather events. But this was a real showdown. And at the end of it, of course, IBM Watson won. As a result of it, IBM went back and said, hey, we've got something that's far bigger than a computer that can win a game show. What we've got is a computer that can go out over the entire internet and look at all the medical information and unstructured data, conversations that are going on on Facebook, Twitter, different groups, and bring it back, and we can give statistical odds of what the diagnosis is. So this happens to be a case for Memorial Sloan Kettering. And you can see that somebody has put into Watson, doesn't matter if it's a nurse, it doesn't matter if it's a surgeon or a physician, doesn't matter how many years experience, they can put into Watson what is known about the patient. This is what we empirically know. And Watson will come back and say, hey, it's 91% this, 48% this is the problem, 29% this is the problem. However, and this is where the real payoff comes, if you go get me this data next, I can improve my diagnosis by X percent. That's what big data analytics has done for us. It basically elevates everyone's ability to do diagnoses based on what information about the patient is known. And more importantly, it makes the data immediately actionable and gives us a pointer as to what's the next most important piece of data that we need. And according to IDC, of course, big data analytics is gonna grow to 203 billion by 2020. Crowdsourcing also has become very, very popular. And that's going to play a role in just a minute. I'm gonna talk about catastrophizing and how you might develop algorithms or ways of sort of testing the water yourself on every patient. It turns out that rare diseases aren't all that rare. Each individual disease is rare, but when you smush them all together, your odds are pretty good. One in 10 diagnoses is a rare disease. And the average to actually correctly diagnose it is about 7.2 years right now. This fellow founded CrowdMed. He has a great TED talk that you can get on the internet. And he developed a series of weighted algorithms that allowed people who had been diagnosed with a rare disease to look at patient's information. These are doctors from all over the world that specialize in specific rare diseases. And if you diagnosed a patient who was having trouble getting a diagnosis correctly, then your weighted average kept going up until those who were correctly diagnosing had all the say, and then those who maybe weren't so good didn't have a say. And he has now cut that time down. He's 50 times faster than conventional methods for diagnosing. So let's talk about the next great leap. The four Vs, the velocity at which data's coming, the veracity of which to believe, all of the volume of the files that are coming, the general complexity of the data has made it very, very difficult to adapt after something has occurred. I would argue that even though I am an expert at fast adaptation, I don't believe you could be fast enough. I think that people who try to time it right, it's like trying to time a stock on Wall Street right. I don't believe it's possible to do. So we're entering a period where predictive analytics is really going to help do the diagnosis for us, because it's able to take billions and billions of dots, something the human brain cannot do, and put them all together, and to see patterns that we didn't know existed before. And not only that, through machine learning, every bit of information that we learn is getting added to those algorithms. So the algorithms are becoming more and more precise. And this is allowing us to get out ahead of change. Because the minute we know, based on the data we have amassed to this point, what's going to happen, then we can take an action in the present to prepare for it, or avert a danger, or change an outcome. And I call this pre-adaptation. I don't believe you can adapt to change. I believe that we know what the change is going to be, and that's mostly what I'm going to talk to you about today. What's coming down the pike? How do we get ready for it so it's not disruptive? Well, when you think about the future, we know more than we've ever known before. It used to be a couple decades ago when a woman was pregnant, we didn't know if she was having a girl or a boy. I mean, some people did. I guess some people thought it was the shape of the belly, or what kind of snacks she wanted in the middle of the night. But the fact is, is that we didn't completely, now we know 100% about what the sex of the child is going to be, and even about diseases the child may be born with before the fact, before the child's even entered the earth. And the automobiles today, think about it. They're anticipating you're going to have a crash. They're getting out ahead of you. In fact, on the MKZ Lincoln, there's an algorithm built into the car, and it knows when you're falling asleep because of the way that you tap the brakes and steer the steering wheel is different than you do when you're stuck in traffic. And so it says, hey, maybe you want to pull over and avoid an accident. Genetic testing has opened the door to pre-adaptive medicines. We were talking about this earlier. There will come a time in the not-too-distant future, because we know science is accelerating and how fast it's changing. In the not-too-distant future, you will be able to look at a newborn and say, given a normal amount of activity, it is very likely you may have problems with your ankles around the age 21. We're going to get there. In fact, today, we know all about these diseases. We know that a person, from the moment they are born, and even in cases, many cases before they're born, about diseases they are predisposed to, and now, through genetic medicine, we're able to offset and prevent some of these diseases as well. And many people are shocked to learn that we can now predict, using algorithms, we can predict a person is going to trip and fall in the next three weeks with 86% accuracy. And you know if we're at 86% now, we're going to go to 90, 95% until we get to a point where I could look at a person and say, there's a problem, you're going to trip and fall tomorrow. And you're going to hurt yourself. And how do we know that? Because it turns out there's a 3 to 5 centimeter change in a person's normal walking gait. And you better believe that Silicon Valley is now developing a Fitbit for a person's ankle that will identify instantly that change in the walking gait. So that we can alert a person, particularly the elderly, and alert their caretakers and say, hey, they've got a high risk of falling within a three week period, within a two week period, within a one day period. And just ping their telephone and say, hey, stay off of the stairs. Use the handrail. You might want to use some support, a cane, something else. Think preventatively, because let's face it, most of the elderly lose their ability to live independently. And there's a third of the US population that's going into retirement in the next 10 years. This is going to be a huge problem if we don't get on top of allowing people to live independently longer. And this is a big step toward that. And you know, I know that us ladies, we don't want to put things on us that make our hips look bigger. This isn't very attractive, but it might be a temporary solution. If somebody has a high statistical odds of falling, I'd wear them. We also have sensors inside of canes, walkers, and other devices, which can indicate that additional pressure is being put on the wrists in a particular way. The algorithms have gotten so sophisticated, we know that the way the pressure is being used in accordance with the walking gait indicates certain kinds of spinal issues are developing. And again, all of these measures have allowed us to potentially extend the amount of time that an elderly person can live independently by somewhere between two and three years. So think of foreknowledge as power, the power to do something. And we know the probability of an event or an outcome is very, very high and growing higher. There are a number of providers who are really investing heavily in predictive modeling. 27% are predicting readmissions at higher and more accurate rates than ever before. Many are predicting patient deterioration, sepsis, general patient health, and also using it in administrative decision making. And when you think about it, I really enjoyed sitting here and listening to a number of the conference presentations, even though I'm not a physician, because when you think about it, there's a growing list of precursors to problems, which you are now learning. You're still a new field of medicine. But every year you learn about things that indicate that there could be a problem, there may be a problem. And as we begin to combine all this knowledge together, we'll get very, very good at being able to predict outcomes and predict diagnosis and problems. In fact, I read a report not too long ago that we've now discovered that the severity of the pain that the patient feels four weeks following treatment, surgery, physical therapy, doesn't matter what kind of treatment that they went into, is actually a better indicator of recovery than the severity of the injury itself. And that's going to get us to the catastrophes that get admitted as well. In the same way that we can use data and predictive analytics to prevent 80% of deaths from strokes, because we knew what the stroke factors were, we can also stop opioid addiction in its tracks. It turns out that about 130 people die every day in the U.S. from opioids. And most of those people got started on a doctor's prescription and abused that prescription. But there's a company called Fuzzy Logics. They developed both a behavioral questionnaire, as well as look at some of your medical records and your background information, talk to your surgeon. And they've been able to predict who will be predisposed to addiction. Because let's face it, we don't have a cure for addiction. We can mitigate it. We can manage it. But we don't know how to cure it. There's no cure. So the only thing we can do is try to identify people who may be predisposed. And they can identify almost up to 85%. And that, again, that number grows every day in terms of accuracy. Now let's talk about catastrophizing, shall we? Social media, it's a gold mine. It's the canary in the coal mine. You know, it's how we're catching terrorists. It's how we're catching people that are depressed and suicidal. We're going into publicly, voluntarily shared information. We're not asking people to disclose anything that they don't want to disclose every minute of every day. I don't understand it, frankly. And if you go on my social media site, all you get is science and tech stuff. And you might get a picture of my dog every now and again. In fact, in Houston, Texas, they were looking at medical billing as a public information that was available. And they were able to see that a particular pharmacy was giving out opioids way above the norm of any other pharmacy. And they were able to take care of that. There are danger words. We have researched them. We know that they lead to catastrophizing. Physical pain, depression, suicide, heart disease, diabetes. These are the words. And every day we add words. You can just simply take an algorithm, say, may I have access to your social media account? And you can find out who's catastrophizing. Like that. Lickety split. These are the well-being words. These are the patients who are going to have a better likelihood of recovery. And the outcomes will be better for them. The one I love here is ah-ha. Or ha-ha. Ha-ha. Ha-ha. If you see ha-ha, good chance. Good chance. Predictive models are being used to accurately point everything. Insurance companies are all over predictive models. Because they want to know the outcomes. They want to know the potential for readmissions. Because that allows them to gauge how they're going to set their premiums. Now if we know what's coming, why don't we just get out ahead of it? There are a plethora, a plethora of new exciting techniques and technologies and sciences that are coming your way. I'm really excited about this. First of all, everything's tied together by low-cost sensors. Low-cost sensors are measuring, you know these from Fitbit, and I've told you about a Fitbit for your ankle that would predict you're going to fall. These low-cost sensors have really revolutionized medicine. In fact, if you go to the store now, there's many smart labels. You can, you know, there's only so much you can get on a pharmaceutical label. There's only so much you can get on a jar label. And yet people want to know, you know, what the sources were, what farm certain ingredients came from. The consumer really wants more information, and so, you know, you can't make the labels any bigger. What are you going to do? Well, these smart labels are really thin films, and all you do is you just walk up with your phone, you tap it, and you can get all the information you want. It goes on and on, all the way back to the farm who grew the coffee bean and what their labor practices were. Did they use child labor? What kind of organic standards did they meet? So on and so forth. It's quite spectacular. Pharmaceutical labels are going to move to the same thing. I don't know if the last time you had to actually go and get a drug for yourself, but you get these pamphlets that are multifold, and they go on and on and on, and if you're on two or three drugs, it's really, it's daunting. It's daunting to the patient. I was in Paris, France, where I saw the first self-expiring pharmaceutical bottles. I love these bottles. They're modeled after how a banana gets blackened. So you know, the labels fade on your pharmaceuticals, and you don't really know what's in your medicine chest and what's expired and what has lower efficacy anymore, but these bottles, as you get closer to the expiration date, they start getting black spots on them, and then when they pass the expiration date, they're completely black like a rotten banana. I think this is so cool, and the same thing is for blister packaging as well. There'll be an alert on there so that the elderly will know when they've expired. Smart bandages now are very, very thin, and more importantly, they can be painted on. They're breathable. Another big problem with flexible bandages is the flexing, and this has been painted on and flexed over 10,000 times. It's breathable, and it can monitor a lot of different kinds of activities, particularly around the ankles and feet. We can monitor people's activities and movement, and then basically generate that on a screen and see how someone is moving around when they're not in front of you. The end of UTIs, urinary tract infections, these diapers with thin film sensors are in there. I was at one hospital. I said, these are less than a dollar a piece now, and what's wonderful is you think about all the things that we measure with urine, all the diseases that we ... All the data that we get from urine, and this explains why people move away from me at cocktail parties. I'm going, we're wasting urine. There's so much we could do with this. Every day in hospitals, they've got these diapers for children and also for the elderly, and all you have to do is open it up, take your smartphone, take a picture of the sensor, and it will tell you dehydration, what vitamins and minerals a person is lacking and needs to be supplemented, whether they have bacterial infections. It's fascinating, and a dollar a piece. Of course, there are cities being built right now that now have smart sensors all over them. It'll be interesting to see how we use that information to provide better care, and all of this is leading to something you all have heard of, the Internet of Things. The Internet of Things just means everything's going digital, and everything's talking to everything else without humans involved. Now, it's very hard to put everything in a cloud or in one centralized area, and in Silicon Valley, I'm old enough to have been through many, many generations of this. We centralize and then put a bunch of dumb devices out to get central data, and then we go, no, we need more computing power, and then we decentralize. We went through this once with IBM mainframes, then we decentralized with servers, and then we re-centralized with the cloud, and now we're decentralizing again with something that you'll hear about, fog and edge computing, where the devices have a certain amount of computational capability and independence. You can see that a number of companies, up to 35, 36 percent, are currently looking at fog and edge computing. I'm not going to go a lot into this, but if you're interested, please step up and ask me about it, and I'll be happy to talk more about it. And speaking of smarter, more capable devices, most of you are familiar with 3D printers, but you may not know all the applications for 3D printers. Like for example, inside my doctor's office in Silicon Valley, he's now printing crowns and dentures and replacements right there while you're in the office. And now, using scanners of ankles and feet, we can now print replacements, prosthetics and replacement parts. We can do metal extraction, plastics extraction, all types of materials can be extracted. So that these kinds of support devices can be developed in your office, the same way that the dentists are now adopting 3D printers to do those kinds of things. You see that these kinds of mechanical devices will be customized, you'll measure, you'll scan the person's foot, ankle, and you will print the exact specifications right there in your offices. In fact, I was at a pharmaceutical conference, I don't know, three years ago, and I told them that they will no longer be selling pharmaceuticals in the bulk, in a big jar anymore. What we'll be doing in the near future is we'll be doing a metabolism test, and we'll be printing each pill for each individual physiology. Because when you think about it, I get the same doses as a guy who's 400 pounds and he's an athlete. And how can that be? The dosing is wrong. Dosing is dependent on how quickly my body and my physiology will metabolize, and how quickly the physician wants it to metabolize. In the case of an epilepsy drug, we want a large pill that has a large surface-to-volume ratio and has very loose porosity, so that it enters the bloodstream quickly. But in the case of a pain medication, we want a lot of compression, we want slow release. And add to that how quickly my body and my physiology will metabolize that pill. So what is going to happen is that same 3D printer that's printing the prosthetic, or the part, the surgical part that you need, will also print the right and appropriate pills. You'll get a pod from the pharma company, it'll load into a 3D printer, and you'll print the exact pill that that patient needs right there in your office. We're also printing spare parts for machines, custom clothing, and furniture for your offices. Even food is being produced by 3D printers. Your offices will eventually be printed. This is an office that was created, 600 to 800 square foot structure. It was created in actually a little over one day. And this is a two-story. They're using Vulcan 3D printers in China. You can see this on YouTube. By the way, everything I tell you about, you can go on YouTube and watch a one-hour video on just to confirm it. In China, they're building 10 new buildings, office buildings, a day, a day, using 3D printers. They're no longer using traditional construction methods. But 3D printing isn't just reserved for construction. You're going from screens and Skype kinds of interfaces and interfaces on your cell phone to actual holographic images that come out of the phone where you are a 3D doctor or nurse or surgeon. We can also get a doctor or nurse on demand in some of the hospitals. They're experimenting with 3D. And it's interesting, when we look at cognitive measures, we see that when people are interfacing with a 3D hologram, they have the same pleasure center's relief lowering of anxiety as they do with the real doctor. And of course, internal medicine will be radically changed by VR. It has already started to happen in many hospitals. You'll see a lot of 3D diagnosis and assist occurring if you're not using that now. But I wanted to get to this because I want to show you where 3D is going. Is that the coolest thing you ever saw? That's where 3D teaching is going. Think about your own medical school experiences and then think about a cadaver talking to you in 3D, right, and being able to do surgeries in 3D. New opportunities abound. One area which you might be interested in, because it's not very costly. You can buy facial recognition software for $100, $200. There's 43 muscles in the face, and this really reveals our inside feelings and thoughts. And what's important about this is when you're sitting down with a patient, wouldn't you love to have a camera on that patient? Have them sign a release, a photo release. Wouldn't you love to have a camera on that patient that told you whether the patient was telling you the truth about their pain? because I was in the hospital not too long ago and I wasn't a great deal of pain and I remember the nurse came to me and she asked me a very I'm a scientist by training so this was a very curious question she said to me on a scale of 1 to 10 how much pain are you in and and I and I thought about it and I said well does anybody ever see anything but 10 10 is the right answer why would I say 2 or 5 and and she said well you have to gauge it well a much more accurate way would be to have facial recognition on my face because this is how we humans express pain and deception and what about those people that are in a lot of who we don't talk about who are in a lot of pain but they don't want to bother you they don't want to trouble you you know I can I can I can grid it out and when you need to know about that because something may be going wrong but I I'll tell you my mom was one of those she wouldn't tell the doctor so that was that was that was a real problem for us it's also very good for interviewing people you can tell who's deflecting when you ask a question who's exaggerating and facial recognition is very good in the classroom when you're teaching because you can zoom in on who's tuned out and in one classroom in California they have a monitor in the back kind of like almost a Wall Street ticker thing and it's telling the teacher in real time how many students got that got what they just said and if it's 29% the teacher says hey you know what I'm gonna go I want to go back and review what I just talked about because most of you didn't get it that's that's fantastic think about think about how often a teacher just skips over something and and nobody in the classroom really understood what they were talking about and in one particular hospital they have a camera on all of the patients that sign off on it camera turned on or turned off and instead of giving them rounds of medication on us on a time-based schedule they look to see using facial recognition when the pain when the patient is beginning to experience discomfort and when they start to see the slightest signs of discomfort it rings the nurses station to get some pain medication to the to the patient they're not waiting for this for that for the bell to be to be rung now at MIT I'm going to be at MIT Media Lab you may have seen this recently on 60 minutes they have a device that clicks that clicks onto your head and it can tell it will vocalize the word you're thinking before you say it because it turns out you think the word before you say it and and this is a great truth teller and I have often said this will be the end of all marriage I don't think this was a good good there are some inventions I'm not excited about and and that might be one of them it's difficult to imagine a future without drones and you may have seen many drones out that are that are out there for kids to play with and that kind of thing but Stanford Blood Center was the first to be cleared by the FAA to opt to bring blood to where it's needed so when there are situations emergencies they dispatch a drone in a which has a secure part compartment to wherever it's needed the military is now experimenting with large-scale emergency transport so you might be thinking about where where an ER or a patient might land in your parking lot and of course Amazon is deploying up in Canada these Mercedes-Benz that have landing pads on the top Mercedes-Benz wants to get more into the commercial vehicle area and so they have these drone vehicles and what the drones do is they just pick the packages up while the van is driving on the freeway they deliver it and then the drone will relocate where the van is fly back pick up the next package so the van never stops driving it just keeps going while the packages are being delivered so this is kind of these drones are talking to each other they are not being controlled by humans at all what the humans have told the drones to do is to build a specific type of structure to a specific type of height and now the drones are talking to each other and working in a coordinated way just wanted you to get some idea of where artificial intelligence is going and the Internet of Things so to utilize all of these devices and all of this exciting new technology that's coming up we're going to have to make a switch from classical computing the computers that you use today to quantum computing and I'm not going to get into how quantum computing works because you kind of have to be a physicist to even understand it took me a long time to understand it but it's based on the fact that an atom can exist in two states at one time actually in multiple states at one time and it's going to speed everything we know about computing up by many magnitudes how much faster in 2017 D-Wave systems ran a system where they took 10,000 taxis in Beijing and they wanted to calculate the fastest route to the airport in back and it took our the fastest classical computing systems about 45 seconds and quantum computing took about a fraction of a second that's how fast and it'd be hard to talk about the future without talking about artificial intelligence you know you start I had somebody here say you know five years ago you ever never heard about AI now it seems like that's all you hear about well that's because between 2017 and 2021 the number of inquiries and questions and problems being resolved automatically so think more in terms of are there questions general frequently asked questions general services that can be automated the pharmacist didn't get my prescription I need to change my appointment you know simple things could be very easily automated and the technology is not expensive to do that by AI by the numbers there's 250 million smart speakers in people's homes and those are powered largely by AI and so people are kind of getting used to it they're getting used to speaking to a machine like Alexa and as that cultural revolution occurs they'll get used to not speaking to you as the first line of defense as well or a live human being and speaking of not speaking to a live human being I've done many many conferences in association with Sophia we're kind of pals now Sophia is the first AI powered humanoid and she has the 43 muscles in her face she nods she expresses empathy when she gets stuck and doesn't know what you're talking about she nods very politely and says indeed there are robots being used in healthcare mainly in Asia at this particular point in time reception desks are being manned by them nurses stations are being manned and it's still a novelty item it's still a little bit cost-prohibitive but over time we'll begin to see more and more human looking robots that are powered by AI and then speaking of robots we can't forget that there's functional robots as well those that help with lifting a patient that that needs to get into physical therapy those that help to dress patients those that help to deliver information and also when we talk about biomechanics these biomechanics devices will be tied in wirelessly to AI and will be tracking the way a person moves the pressures that they get it will start to learn about the person using the biomechanics and then be able to facilitate and be even better and of course we can't forget about nanobots when I talk about this it always sounds like something that isn't happening yet well nanobots smaller than a human cell have been designed and injected into Norwegian rats to now travel through the bloodstream and eventually remove the plaques that are associated with Alzheimer's disease that's the precursor to Alzheimer's disease so we're now programming these nanobots to go after cancers scar tissues and eliminate these things from the inside out so that we're not having to be invasive eventually like a video game you'll have a nanobot that will be getting in and doing some of the surgical processes from the outside in now I've talked about a number of pre-adaptive measures I could talk about more obviously you could tell I I live and breathe technology but everything doesn't have to be disruptive or revolutionary or even involve technology we've now discovered that even though sitting is associated with negative health comes health outcomes like obesity and cancers lengthy standing is very damaging as well it leads to feet spine muscle cardiovascular problems and also lower product productivity and so even introducing something like the wearable chair you might smile and laugh and say I'm not going to put that on oh yeah you will oh yeah you will throughout hospitals in California people are they're fighting over who gets these they're very comfortable they look a little awkward right now but again you know the first generation of things doesn't always look great but that but the fact is is that you strap them on and while you're walking it's not bothering you and then when you go to make a sitting motion it may immediately locks into place so the secret is getting started so let's talk a couple minutes about a game plan I mentioned earlier on that you are designed not just you we humans are designed to repeat those things that are successful it was vital to our survival when it during prehistoric times it is vital to our survival today and so when something's working for us when we're making money when the patients are happy we just want to stay in that lane for as long as possible so how do we get out of that how do we cause ourselves to take a risk how do we foster an innovation based culture and and how do we convince ourselves to move forward well like everything else in business you gotta have a systemic approach when when you hear about something new you don't have any vessel by which to take it in you don't have any system that's there and so it does it it's all it's always awkward and disruptive and I'm a big believer you can set up those systems not today I don't have time to go through the seven steps that you can take when you get back to your offices when you get back to your practices there are steps that you can put into place that will help you to absorb these changes as they occur one after the other just like you have an accounting system you need an innovation system and a method for receiving them and introducing them into your day-to-day practices one thing that you must do whenever you hear about something new that you want to get involved in or you think will affect your your your future in medicine is that you want to divide the innovation into something that is market-driven something that you can put into place three six months or something that is a moonshot speculative not all worked out yet but has to but is going to be the future and here's the reason you have to do this this looks like a complicated chart but the most important thing is the is the red line called parity parity is there's no difference between what you're doing and what everyone else is doing it's standardized it's standard practice there's no differentiation now let's say that you're a physician and you're doing what everybody else is doing with ankle or feet issues and you have there's no difference between the special the special treatments you offer in anyone else and then let's say you're the first people to offer online billing you say oh yeah you know what we think online billing is going to be given the thing 10 years ago but and and you Sarah we're gonna we're gonna be the first to offer online billing that's fine that's a nice differentiation for a very very short amount of time because guess what everybody else is going to offer online billing and pretty quickly because it's not hard to do so that can't be your differentiator what the only thing that can be your differentiator and allow you to command higher prices higher margins different clientele open up your practice if that's what you're interested in has to be a moonshot it has to be something that is that has risk associated with it and is it has a longer development window if you don't have that switching mechanism what you're likely to do is to take market driven innovations and move them too slowly you're going to treat them like 3d printing parts or pharmaceuticals in your office you're going to try to treat online billing that way and you're going to be slow to the market and pathetically slow on the other hand if you don't separate moonshots you're going to be impatient and the likelihood is you'll prematurely try to get out to the market before it's been properly vetted and here's the real reason why and by the way you'll have access to all these slides because I don't have time to and no nor do you want me to read you can read the slides but there's a big difference between a moonshot in a market-driven innovation in that a moonshot will always cannibalize some part of your business by cannibalize I means it's going to cannibalize some method or process or product that you're offering it's going it's a replacement for that and so that's what makes the switch so hard and so risky because it is it will cannibalize some part of your business it also typically will involve some regulatory aspect right the regulatory issues have not maybe completely been worked out that would be a clue that it's probably a moonshot it's going to be more expensive than a market-driven innovation that would be another clue that it's a moonshot the fact that you're taking a preemptive strike the fact that you have to educate people that you have to explain to a client what it is you have to explain to your peers the people working in your office what it is and how it works that might be a clue that that's a moonshot as well managing risk is very different from managing strategy risk management focuses on negative threats and failures rather than the opportunities and successes and I believe that what predaptation does is it allows you to get out ahead of negative outcomes negative events and and it allows you to seize opportunities that are known and are on the way so in conclusion the first person to live to be 1,000 years old is already walking the planet according to Aubrey Debray who's a genetic mathematician he has looked at the rate at which medicine is changing and I made the mistake of opening up a pension fund conference with this slide and practically got booed off the stage they said we don't even have a pension program for a hundred-year-old what does it mean it means there'll be more people more data and more ways more ways to treat more ways to diagnose but as an evolutionary biologist let me just say this the highest instrument of our evolutionary inheritance is to be able to preview to do thought experiments to preview future outcomes and then to avert danger or seize opportunity it's the reason human beings climbed of all millions and millions of species we climbed to the top of the living pyramid because of this frontal cortex that allows us to to get ahead of change we learn quickly and then we make amazingly accurate predictions and now with technology the future really is going to be known the outcomes are going to be known whether someone's going to have a problem and what kind of problem they're likely to have is going to be known in a very very at a very very high statistical rate predictive technologies and business models are changing the future of health care and again I want to thank you so brave of you to have me here and thank you so much for the very important and good work that you're doing for so many

technology futurist

healthcare industry

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artificial intelligence

3D printing

robotics

revolutionize healthcare practices

pre-adaptation

innovation-based culture