Our mistake in thinking about it was that we thought it was the beall and endall that that the key maneuver was if we could just have our data in digital form that will make healthcare better and safer and more convenient and drive a consumer revolution of people shopping for health care lower costs. Virtually almost none of that happened. And I think what I've come to recognize is that just digitizing the data and to some extent connecting some of the parts although imperfectly was foundational to the the revolution we need. It did not lead to the revolution we need. That's not that shocking if you think back about it because the foundation of the internet was really important but it took a several years before Door Dash emerged, Airbnb emerged, you know, Whimo emerged etc. You know, the companies built on the capacity of the internet didn't emerge when the internet emerged, but they couldn't have emerged if the internet the internet wasn't a thing. Same thing is true in medicine. >> All right. Well, Dr. Robert Walker, thanks for joining us today. >> It's a great pleasure, Mike. Thanks so much for having me. >> So, maybe as a as a way of of bio, uh you've written a bunch of books, as we were just talking about before we started, and I think the first one was in the early 90s. Um and then uh most recently a couple of books which we're going to talk about a lot today. A giant leap which just came out this year about AI and healthcare and then uh the digital doctor which I think was 2015 all about sort of the EHR revolution. I I'm curious uh because your day job has only gotten busier I would suspect since that first book in the9s. You now run uh UCSF medical which is one of the biggest academic uh hospitals and medical centers in the country. Um, so what motivates you to moonlight uh as a journalist and and when did the technology side of healthcare start to interest you? >> Well, first of all, thanks for having me on. Um, what motivates me is about every 10 years I seem to find an issue that I just cannot let go of. It it seems I'm a still practicing physician, although most of my job is leading a very large department at an academic medical center, but um they tend to be issues that are pretty close to the ground that involve real doctors and real patients and nurses and all and how we diagnose and how we treat and how people think about medicine and and and health and healthcare rather than kind of cosmic issues about insurance policy. I I I don't get all that jazzed about those. And it seems like about every decade an issue comes along that I just think, "Wow, I need to learn more about this." The explanations I'm hearing for things that I don't understand make partial sense to me, but always feel like they're coming from a variety of different perspectives. Uh, and that there needs to be somebody who can kind of disentangle the whole thing. And my first two trade books, layoriented books, I kind of just wrote them based on what I knew. When I decided to write the digital doctor and sort of do a deep dive into medicine going from paper to digital, my wife who's a journalist said, "You have to do this journalistically." And I said, "What does that mean?" She said, "Means have to go to talk to people." And I said, "I hate people." She said, "I know that, but so it turns out not to be true. I actually love the process of being amateur journalist. I went out for each book and interviewed over a hundred people." And you know, you know, from your work, I mean, that's how you learn a variety of perspectives and everybody knows some part of the story, but not the whole story. And I kind of pull it all together and try to articulate in a way that's useful. I have in some ways an advantage that it that used to give me imposter syndrome, which is I don't really know very much about the technology itself. I think people that are tech experts tend to get just lost in the details of the tech. And I I'm what happens when a political science major becomes an academic physician. So, I think about the big picture, the people, the politics, the money. Uh, and and I think not really being a techie person per se liberates me from getting too deeply in the weeds. Terms of your other question, how do I sort of make it all work? Uh, first of all, I'm a very good delegator. I'm surrounded by amazing people in my day job. There's some synergy between kind of what I'm studying and what we're trying to do, for example, at UCSF where I work. Um, and I'm a very efficient writer. I can write at, you know, on nights and weekends. I I'm an adherent to the old Hemingway line of write drunk, edit sober. So I can write for 10 hours, get something down, come back the next day and say, "This isn't very good, but I think I know how to make it better." And uh so yeah, it it this is an issue. And and finally, the tech issue. I've been very interested in just how to make the health care system better because it's not very good and it needs to be better. Um, and so if you were interested in that, and I've been studying medical mistakes for a long time, you had to be thinking like, if we could just digitize the damn thing, it would make it better and safer and more convenient and less expensive. And the digital doctor happened because we finally did. And only after the federal government bribed us with $30 billion of incentive payments. And I was flabbergasted by how badly it went and how everybody was unhappy with their electronic health record. And I kind of decided I wanted to learn more about that. And that's what led to that book. this book. It was sort of obvious as when the new AI happened on November 30th, 2022 with GBT, it was clear that this was going to be a revolution. My concern about writing a book about it was, is this going to be out of date 5 minutes after I'm done? And luckily, my wife, my publisher, a few other smart people said, "If that happens, if that's the book you've written, you've written the wrong book." So, you really need to try to helicopter up 10,000 feet and really say, "What are the big picture issues emerge when this new generative AI hits the healthcare?" And so, uh, I tried to do that and I'm glad most people seem to think I got it pretty pretty right. >> Well, we're gonna start with the the EHR book. So, I'm gonna ask you to do the thing you probably don't do in a lot of your current interviews, which is go back to the last book you wrote 10 years ago. Um, but I I do think, you know, the the thing that was pretty revvely for me in reading that book was a just learning all the kinds of ins and outs and crazy stories of of how we ended up where we are, but also realizing how much that is the scaffolding for the AI and healthcare uh experiment we're running today and then what we're going to talk about later, this sort of what this series is built around this this theory we're positing that there's going to be a continuous data revolution coming in the next 10 years as well. And I'll say the thing that that really struck me initially in the EHR story was how recent and how rapid it was. So I think the stat was something like in 2008 there was something like one in five hospitals were using an EHR. >> One in 10 actually one in 10 in 2008 and by 2017 one in 10 were not. So over a decade we went from a paper industry to a digital industry. >> Yeah. So that that pace of change but also the recency. I mean, if you would asked me what year was one in 10 hospitals using an EHR, I would have said 1995. I would not have said 20. 2008 is a year after the iPhone came out just to help people sort of place that. Uh, and so in that time, 90% of of a lot of medicine was still happening sort of on on paper. What I'm curious about is okay, so that happened over 10 years. We're now 10 years out from when you wrote that book. And the thing I kept wondering reading the book is where are we now? What has happened? And so I want to walk through a couple aspects of that uh which I think will kind of get us into the AI story. And the first I'm curious about is the government and regulatory side because as you mentioned the government threw $30 billion into this really seemed to be the impetus for that rapid change. Um it wasn't coming necessarily from industry. It was coming primarily from the government driving that but was also the source of so many of the challenges that came up through high-tech through meaningful use. So I'm wondering if you can just talk talk a little bit about where we are now in terms of the government's involvement in health IT if we want to take that sort of broad lens. We're going to talk about AI and government's role in that or lack of role in that in a little bit. But just in terms of how the government is interacting and and mandating uh how healthcare is using digitization now where are we? >> Yeah. I mean in some ways the main government regulation that that that governs uh our use of health information technology I guess the two main ones are the role of the FDA in regulating digital tools uh where you know the FDA is not perfect at by any means but you know knows how to regulate a new drug and sort of knows how to regulate a new device a new X-ray machine or a new pacemaker etc. um and but but we'll talk about this, I'm sure, but has a really hard time figuring out how to regulate AI, which has a very different animal than anything's had to regulate before. The second sort of main rule that we operate under is HIPPA, which relates to uh the privacy of data. And HIPPA, of course, was written and put into effect, I think, in the mid '9s, so really before anything was was digital. And uh it's one thing to be talking about what you're going to do with a piece of paper that has patient data on it. It's another obviously to talk about what happens when you have data flowing everywhere. Whether it's in my electronic health record at work or in coming out of your watch or your ring. I think some ways it's very clear that that our thinking about data privacy has to be uh has to be modernized. I mean the the the the story of the electronic health record and the reason I thought it was worthy of a book to tell the story is you're absolutely right. Every other industry that I know of digitized a decade or two before medicine. um despite the fact that medicine is now 20% of our GDP um is a is a an industry that fully depends on data and and and whether it's data about your health or data from the medical literature um and it was a massive market failure that that just didn't happen on its own. The federal government, I think to its credit, recognized that it wasn't happening on its own. That in 2008 in the average American hospital, if I saw you, if I was your doctor, I would be scribbling down my observations about you on a piece of paper. There were there your laboratory results would be on computer. Uh I would probably have scribbled a prescription that periodically hurt people when somebody couldn't read my handwriting. So kind of crazy that that didn't happen on its own when the travel industry and the retail industry and the financial service industry had all computerized a decade or two earlier. Um the feds sort of an amazing story in 2008. It was in the state the the great recession that the federal government decided that needed to throw $700 billion at the American economy to try to get out of the recession. And it was only almost the happen stance that there happened to be some health policy adviserss in the White House who saw that as an opportunity to throw $30 billion to get us to digitize. We were sort of ready to do it, but if you were the average hospital, you said this is going to cost $und00 million. It's going to be massively disruptive. Um it's going to screw up not just the way we kind of collect data, but it's going to change every process about the way we've organized the way we do work. And that's just too hard. when the incentives came out, they didn't fully pay that hundred million or $500 million, but maybe they paid 10 million or 20 million, and there was a now a threat that if you didn't do it in the next couple years, we were going to ding you on your payments for Medicare. So, it got us all to to go very quickly. The meaningful use regulations were a whole bunch of standards that these computer systems had to meet. Some of them were perfectly sensible, some of them were bureaucratic overkill. And um you know my own take on the overall era is a lot of people were unhappy about the electronic health record, unhappy about all the trivia that we had to record about sort of the bureaucratic documentation requirements. But to me it was absolutely ne necessary to get us digitized. Our mistake in thinking about it was that we thought it was the beall and endall. That that the key maneuver was if we could just have our data in digital form that will make healthcare better and safer and more convenient and drive a consumer revolution of people shopping for health care, lower costs. Virtually almost none of that happened. And I think what I've come to recognize is that just digitizing the data and to some extent connecting some of the parts although imperfectly uh was foundational to the the revolution we need. It did not lead to the revolution we need. That's not that shocking if you think back about it because the foundation of the internet was really important but it took a several years before Door Dash emerged, Airbnb emerged. uh you know Whimo emerged etc. You know the companies built on the capacity of the internet didn't emerge when the internet emerged but they couldn't have emerged if the internet if the internet wasn't a thing. Same thing is true in medicine that that we needed digitization. We needed our data to be in digital form in order to change the fundamentals about how you get your health care and now increasingly how patients can take control of some aspects of their healthcare that they used to depend on the system. But it didn't do it automatically and I think that's where I got it wrong and where most of us got it wrong. It was just sort of the first step. And now where we are today is, you know, we now have these new tools in the form of generative AI that do a bunch of things that prior AI and prior digital tools could not do. I'd say most importantly, uh, is is read language. Um, you know, data that were computable until the four years ago were numbers. you could compute or you could compute you know the corre the connection between a patient's blood counts and whether they ultimately got cancer for example but anything that required that you understand the conversation that I might have with a patient or what I document in my note uh or the medical literature we didn't have the capacity to do that we do now and I'm you know we'll talk about sort of the government's role now but I as you well know the government has taken a very hands-off approach to regulation of the new AI. Personally, I think that's not a bad thing. And I'm not anti-regulation in general, but I think this thing is moving so fast. I think in healthcare systems like mine, there's enough guard rails that already exist. We're not going to implement an AI tool that we think is going to be dangerous, that we think is going to hurt people. We don't do it on moral grounds, but we don't do it as well because we know we'll get sued if something bad happens or if we leak data. So, there's I think some built-in protections in the system that mean aggressive government regulation of this thing uh I think has more downsides than upsides, but we'll have to see because at some point this these new tools will harm some people and of course there'll be an uproar. And I think we're already beginning to see a little bit of that in the mental health sphere where you start seeing some examples of the AI, you know, saying some bad things and kids hurting themselves or killing themselves and of course you get a massive uproar and appropriate under, you know, it's an appropriate response where people say we need to regulate this. >> How much is HIPPA still a sort of impediment to what we're trying to do? Has HIPPA modernized to the degree that it needs to? >> It it has not really modernized at all. The rules, as far as I know, are pretty much the same as they've been for about 30 years. I think people sometimes pin things on HIPPA that really are not HIPPA's fault. you know, they say, "Well, you know, part of the problem is we can't move the data from my Apple Watch into my Epic, you know, my into my chart into" and the answer is you can or that health care systems say, you know, we can't have a agreement with a company that could take our data or take your p you know, my patients data and do magical analyses of it. And it turns out you can you need a you know you need a business agreement between the two organizations but I think every it makes everybody pretty riskaverse about you know about hoarding data and I do think there's a fair amount of data hoarding where people use HIPPA as the excuse you know I cannot send your data uh to this company whereas you as a patient might want it sent because that company might give you some insights about about your health or maybe guide you to a place where you could get cheaper care. And it may be that a healthcare organization doesn't really want you to do that because they want you to get your MRI from them, not from uh from Joe's MRI down the street. And they'll blame HIPPA. They'll say, I you know, we're not going to send your data around because we're worried if there's a data leak or breach. The fines are enormous. And sometimes when they say HIPPA, what they really mean is it's not to our business advantage to do that. And I think that's the thing we we have to disentangle. My background is in nutrition. I thought I knew everything, but it wasn't until I was tracking that I had to ground truth my assumptions with what was actually happening in my real life. The only way to actually know what works is real data. Using levels has been life-changing. For one, I've learned how food individually affects me. Even though I know as a physician how stress impacts your glucose levels and your cortisol levels, it was very eye opening to see right in front of me in real data that my glucose levels go through the roof under certain forms of stress. You can see how food, stress, sleep, and exercise impact you personally to make smarter choices that improve your health. With features like AI powered food logging, real-time blood sugar tracking, comprehensive blood testing, and personalized dietitian support, Levels gives you everything you need to optimize your health. Whether your goal is losing weight, having more energy, or choosing the right diet for you, Levels gives you the full picture of your metabolic health. Stop guessing. start knowing. Check out Levels and click the first link in the description. Now, back to the episode. >> I also want to talk about the how the doctor experience has changed maybe since that that book was written. I mean, I think one of the things the book really does well is it's easy as an outsider to look at the digitization of health information when you say, "Oh, there's a whole book about HR." And think, "Well, how difficult can that be?" As you said, other industries did it. What's really clear throughout the book is like, oh, this is orders of magnitude more difficult because the depth of process, the the density of process that surrounds everything that happens in a medical system, you know, the the fact that you guys had to like flip a switch and one day it was paper and one day was digital was like giving me the shakes. I thought, oh my god, that to live through that had to be had to be crazy. and and that the the sort of rapidity again of that change seemed to be what drove a lot of the the uh discern early on. You had a quote that I really liked um that I think was from David Blumenthal that said the implementation of health it is not a technical project it's a social change project and I wondered uh how that social change project is going today versus how it was going in 2015. Are doctors still grumbling about the HR? Have you worked out these processes that surround digitization in a way that you just hadn't in 2015? >> Well, I think the in some ways the most fascinating thing or one of the most fascinating things about the modern era of AI is the many of the early use cases are designed to fix the problems that the last digital revolution created. And so, um, the first kind of ubiquitous tool that we're all almost everybody is using is what's called an AI scribe or sometimes called ambient intelligence. And basically what it is is if I if if you come in to see me as as as your doctor, you may have noticed over the last 15 years since we have an electronic health record that my head is down in my computer and I'm typing away and the patient gets the sense the doctor's paying more attention to the computer than than than to me. and the doctors are completely unhappy about this, the fact that they've become an expensive data entry clerk. Um, that was a problem created by the electronic health record because the electronic health record could now make the doctor do stuff. When I was scribbling on pieces of paper, nobody could make me do anything. If if anyone wanted wanted to audit whether I was providing highquality care or I was writing my note in a way that created the the best bill for my hospital, they'd have to pull this paper chart and review 300 pages. Whereas now, of course, that can be judged in real time and can be uh and and the EHR can be created in a way that makes me do a whole bunch of stuff designed to optimize certain things. And one of the things that really pisses doctors off is those things often aren't really clinical care. They are designed to optimize the bill that we then send to Etna, which I mean, we're part of the problem because that bill partly pays our salary. But what it did then is create a a documentation requirement that was massive and did not exist before where I have to when when the doctor's looking down at their at their computer, what they're doing is they're checking a whole bunch of boxes. They're they're putting in certain terms that are buzzwords that create a better bill. And so one of the early most successful use cases for AI is most of us are using AI scribes now. And AI scribe as you come in to see me today and I'll say to you, is it okay if I record our conversation just for the purpose of documentation? If you say yes, uh I'll put my phone down, it will record our conversation and then create that note. still optimized again for billing and malpractice prevention and all the things, but at least prevents me from having to look down at the computer. I can now look at you. You may think that's not that big a deal. Like we've had voicetoext translation for 20 years, but it turns out that wasn't good enough. A transcript of our conversation is worthless. Actually, it needs to do a whole bunch of things that are very special and highly and formatted a certain way. So, but I think you know the broader point is one of the main sort of themes of the first book was what's called the productivity paradox of it. And the productivity paradox is something that's been seen forever, which is a new technology comes into an industry, and I'm talking here about general purpose technologies, things that really transform the whole way the work is done. And there's great hope that and great hype that's going to be fantastic and improve quality and improve productivity. And the thing comes in and lo and behold, nothing happens. And everybody's left shaking their head. Um, one Nobel Prize winning economist said in 1986, not talking about medicine because we didn't have electronic health records, we wouldn't have them for a generation, but if you went to the factory floor or you went to the trading floor of Wall Street of Goldman Sachs, he said, you can see the computer age everywhere except in the productivity statistics, meaning there are computers everywhere, but it's not yielding what we hope. What the lesson of that mantra is is that just plunking in a new technology into a complex workflow does not achieve what you think it will achieve. The ones that eventually do yield benefit and the good ones do often take a decade and the decade is partly the technology getting better but much much more importantly the changing of the nature of the work the workflow in some ways the governance the leadership may need to die off to have new people come in to say why are we doing it this way and the answer is oh because we always did it that way and we put a computer in um so that's sort of a fundamental problem in healthcare and I think in in in organizations in general general this one is easier and when I say this one I mean AI meets healthcare as opposed to the electronic health record for a bunch of reasons. One is there are a lot of individual problems that can be solved with sort of point solutions meaning uh that documentation problem. Okay, I'm going to buy this thing called an AI scribe. It's not that hard to use. I don't have to go to a 10-hour training course. kind of turn it on and it's obvious what it does and it solves and immediately solves a painoint for me and actually the patients like it too because patients see that their doctor's paying attention. Part of what I need to do before I even see you as a patient is I need to review your old medical record. Uh, one out of five patients has an old record longer than Moby Dick. So, if you've forgotten your great literature course, that's 600 pages. The idea that I'm going to be able to review 600 pages in the two minutes I have to do it is a joke. It's impossible. I now can press a button and it will do that in 30 seconds. It's not perfect but it's better than I am. And so these are the sort of and again takes no learning curve on the part of the of the organization. Now what is going to be much harder is weaving all these tools in together into sort of a holistic whole you know sort of integrated thing. So it's not a complete mess of 50 different tools that we're using, but the overall act of moving from paper as the way we do our work to an electronic health record, which really transformed every bit of work and needed one big monolithic integrated solution as opposed to what we're all doing now, which is sort of picking out different things that are annoying and don't work and trying to solve them. I think what we're doing now is just easier. The tools are pretty good, pretty intuitive. Where things will get dicey is trying to weave it together into something that really works as a holistic thing because there's a lot of what we do that's interconnected and then as we get to the topic your topic dour we're talking about a whole new set of problems which is like what happens when we're not just talking about taking the data that I gathered from you when you came in to see me in clinic which is a single blood pressure reading a single measure of your glucose a single measure of your oxygen but instead we're trying to deal with data flows that are coming continuously from your phone or your watch or your ring or cameras in your house or your Alexa or who who knows. I mean that we don't have the foggiest idea how to begin doing that. But that's going to take a whole new way of thinking about data massively overwhelming the system. The data we already have overwhelms the system and we haven't even begun to think about how to deal with that kind of data flow. Before we get to that and and we will I want to just finally touch on the sort of patient experience and how that's changed. You know, one of the things that hit me reading the book was of course is obvious in hindsight, which is that my ability to access my medical information was only possible once this got digitized. Like that was a really fundamental change in the sort of experience of healthcare. went from being this absolute black box where I went to a hospital, I went to the doctor, you wrote down a bunch of things and then I didn't really know what that was to I can log into my chart and I can see my labs and I can see your notes and I can see all this stuff. At the same time, I was just on my chart this week. It still looks like an interface from 2015. You know, it does not look like a modern tool. We still do not have any kind of universal health portal. I was recently I got some vaccines and I was trying to see when I had got my last tetanus vaccine and I've changed you know health systems insurers a few times over the last 10 years. I had no idea where to find that information and I started trying to create one. I thought all right Apple health I'll just make that my center and I was trying to dump in CVS vaccination records and one medical vaccin and there was no way to do it. They would not. Google's tried this, right? What 10 plus years ago with Google Health, Amazon wants to own it. >> 2005 completely flamed out Microsoft, right? Everybody's tried this. Uh, and there is still that still doesn't exist. So, how has the the patient experience of of sort of interacting with their data? Where has it gotten better? And and why is it still in these kind of ways I'm describing so bad? Yeah, as you describe that, it strikes me, Mike, that that that there are the sort of two revolutions on the patient side. And one is, as you say, very imperfect revolution, which is access to your data. And you can now get your data from UCSF, but that's not the same as your data from Walgreens or CVS and might not be the same as your data from another doctor that you saw in a different state. And the feds have tried to create some rules and standards to make it easier to weave that stuff together. And it's been massively hard. Um, part of it is our, you know, in some ways HIPPA and the privacy rules. You know, you can imagine a world where all of your data were organized around your social security number, for example, or some unique patient number. And it doesn't matter where it's coming from, it automatically flows into this one portal that you have access to. That would be a spectacular thing. But the obstacles to that, there's actually a a federal law against a unique a single unique patient identifier. And so, and none of the entities, you know, my health system, your health system, Walgreens, CVS, etc. don't have any great incentive to spend money or political capital on making that happen. So, I mean, you want it to happen. I want it to happen, but it's actually going to require stronger government intervention and probably some government funding to make it happen. I'm a little skeptical that it'll happen because I don't see the government sort of stepping up to the plate there. So, I think you have a fundamental problem with the democratization of care, which is you're going to have some access to your data, but it's going to be quite peacemail and therefore not give a complete holistic view of your entire health. Now that said, I think for most people, if they're being followed largely in one health system, the stuff that's in my chart or in your electronic health records is probably a reasonably complete view of you. Maybe not not perfect, but reasonably complete view of you. But the second revolution, which I think is a very much more recent revolution, is until a few years ago, you had access to all of that data. So the asymmetry between the data I had as a doctor or a health system and the data you had was really profound and was really breached by now things like my chart. But you still had a massive asymmetry between what I know and what you know because I went to four years of medical school and three years of residency and two years of fellowship and I've been practicing for 40 years and you are quote a novice when it comes to you know a lot about you and and the things that are important to you but but really don't understand medicine. that asymmetry has now been breached by the by virtue of AI. So the tools that you have in your pocket if you're using whether GPT or Gemini or or a more healthc care specific tool uh makes you at least potentially as smart as I am when it comes to medicine. Uh now you might say all what the hell do we need doctors for anymore? It turns out that uh me using the same tool that you're using and I'd say you generically obviously as a science and healthcare journalist you know a lot but but for the average patient when I use a tool I will get better answers than you get and the reason is I know what to put in. Often when I'm seeing a patient, I might have 200 pieces of data at my disposal. Everything from you came in because your throat is sore, but you're on these 10 medicines, the these family history, you just went hiking in uh in the mountains and drank fresh water from a lake, etc., etc., etc. You had an appendecttomy when you were 14. the act of taking that and creating a prompt that I might put into AI that this is a 62-year-old man with a history of diabetes who has a comes in with a fever, a white counter creatin of 1.7 and an ALT of 12. I mean that's Latin to you I assume uh but completely natural language for us as physicians and that is what you need to put into the G into the AI to get the right answer. So there still is some asym asymmetry, but it's weird asymmetry because by all appearances, the asymmetry is gone because you have access to a tool in your pocket that is as smart and medicine as I am. Actually smarter. It will it knows more medicine than than your doctor does. So we're kind of in a weird time where you now have access to most of your information, but maybe a little bit of peace meal, but that's actually true for me, too. uh but now you have knowledge tools that really take away this hierarchy of I know stuff and you don't know it but still has a little bit of hierarchy in a more subtle way because there is some subtlety about what you put in to get the right answer that I know and you don't know so it's sort of a bizarre time and then layered on top of this we're going to have these new data flows that we're going to have to figure out where that goes and how to make sense of it >> maybe going back to the the sort of doctor experience or how AI maybe is a bridge to how AI is now being used in within your system. You have an anecdote in digital doctor um a really vivid anecdote that kind of illustrates why digitization of information doesn't isn't necessarily a panacea for all mistakes and in fact can cause mistakes. I'm wondering if you can tell the story of Pablo's overdose and and then talk about how that might be different. How would that situation play out today with the sort of AI tools that are available and if the answer is not much different how it might play out in say three years or five years? >> Yeah, I I don't know if it would be different today. That's a good question. Uh that sort of the the in some ways the spine of that book that 10 years ago the digital doctor was a case that we had at my hospital where we gave a kid I think 16 year old kid a 40fold overdose of an antibiotic and and it was because of our electronic health record um because it turns out not to be all that hard to toggle between the it gets a little complicated but the dose that you want to give of x number of milligrams. And what we do in kids, which is we say you should always put in the dose per the kid's weight. It doesn't matter if for an adult whether you weigh 200 lb or 160 lb. The doses are the same. But for a kid, it might be you're talking about a three lb preeie versus a 130 lb adolescent. The doses better be very different. So the computers are sort of programmed to put in the doses per the kid's weight. In this case, through a series of sort of almost almost comical mishaps, uh the computer was set for the dose per the kid's weight, and the doctor didn't realize it. So, the doctor put in what she thought was the whole dose, and the computer then multiplied that by the kid's weight, which was 40 kg, and therefore it was a 40fold overdose of a common antibiotic. Then things really got crazy. And by that, I mean, this is a dose that is absurd. This is a dose that any doctor or nurse would look at and say, "Are you kidding me? 40 Septra tablets?" That's that's crazy. The equivalent would be if you were driving the highway and saw a sign that said the speed limit is 2500 miles per hour. That is a 40-fold overdose. It's sort of it's patently absurd. You would know it's absurd. And yet, people had such trust in their computer that they saw this dose and they said, "Huh, it must be right. there must be something I don't understand because the computer has all these checks built in. Uh it went to the pharmacy but of course in the pharmacy now there is a computer check but the computer check is not that this is not an absurd dose. It's is the dose that the that the computer that the robot has has pulled out of the bottles. Is it what the doctor wrote for? So if the doc if if the doctor wrote for a 40-fold overdose, the computer's job is to make sure that that uh that it it pulls up the quote right dose, which is the absurd dose. And the final step, which was sort of the most amazing thing, was a young nurse who happened to be floating on on on a floor that she usually wasn't on for a bunch of happen stance reasons, sees this dose, says, "This is kind of weird." And yet then she barcodes the the pill, which is this modern computer machine that we have to be sure she's given the right dose. And she barcodes pill number one. And the computer says, "Well, that's one out of 40. You need to do 40 doses." So she has to barcode 40 pill things to in order to give it to the kid. I mean, basically what had happened is she had turned her brain off. She no longer trusted her judgment. She trusted the judgment of the computer more than more than her. gives this kid this massive overdose. He has a seizure, spends a week in the ICU, and just dumb luck that he doesn't die. And so it beca, you know, I think in some ways the computer has made care safer. It's gotten rid of doctor's handwriting on a prescription. It it my prescriptions now go directly electronically to Walgreens or CVS. That is ultimately a safer system. But I think that case pointed out that these systems can also cause their own problems, problems that did not exist when we were on paper. And part of the problem is that the humans will turn their brains off quite naturally. Will tend to give the computer undo amounts of trust and sort of no longer trust their their gut. And so what does that mean for the current era? I think the current era is that on steroids is a as AI gets better and better begins taking over certain of our decisions or making making suggestions to us whatever it's doing whether it's it's it's writing my note for me or reviewing your 600page chart or suggesting diagnosis or suggesting the right treatment for you if the system is one in which we want the human in the loop meaning the computer will make a suggestion but ultimately the choice the final choice is made by the doctor or I'm supposed to review the note that it just drafted for me. This is probably true in your writing as well that there are a number of flaws in that model but probably and one of them is deskilling which is over time as I become more and more dependent on the computer I'm less good at the thing than I was in the beginning but probably just as importantly maybe more importantly if you think about the tasks that humans really stink at I would put remaining eternally vigilant when I've come to trust a technological tool pretty high on the list it's like if it was right the last 20 times are you going to be are you asleep at the switch on time number 21? The answer is yes if you're human. If you're busy, of course you are. And so we just have to think I think the fundamental issue as we move into a new era of AI is if the AI was right half the time, it would be worthless. If it was right 100% of the time, it would be great. I'm worried about what we're all going to do for a living, but that's a different issue. But I think the the the the the moment we find ourselves in is the AIS that we are implementing in healthcare, whether it's suggesting a diagnosis or or drafting my note, are now correct often enough to be useful, really useful, and wrong often enough that they do need a human final arbiter to check its work. And that is a system that sounds better than it is. We're going to have to really think carefully like what is that diad? What is this kind of this co-orker or wingman? What is how does that actually work? How do we be sure the humans still have agency, keep their brains engaged, still have judgment? This is going to be true for patients too as they begin to use these tools. Um I think we've only just begun thinking about that. We focus so much on what the tools themselves can do and haven't really focused efficiently on how does the whole thing work together. um you know because it's not going to be right all the time and you're going to want your clinician to stay stay engaged and keep their brain on >> that. I I think they call that automation bias. I forget what the bi the specific biases but where we tend to trust the machine and as you say we see that all over with AI. Now the couple of other things I thought about and again maybe this was because I I read the first book first and the and the or the second book first and and the EHR book afterwards. So I was thinking about these AI systems when I was listening to that anecdote was one you could imagine a world in which there was AI integrated into all of those kind of dumb computer processes, right? The barcode scanning, the robotic pharmacy filling that would be intelligent enough to flag that kind of a dosage and just say, "Wait, no, this is crazy. Somebody step in and double check this." I The other place I thought that that I could imagine this being different, you know, part of the story of that nurse was she didn't want to bother anybody, right? She's on a shift that's not her own. She doesn't want to wake anybody up. It's late at night. And I thought, well, could she in today's era, she could pull out GPT and say, I'm about to give this kid 38 pills or 40 pills. Does that sound right? And GPT would surely go, that's crazy. Don't do that. Go ask somebody. Do you think that are either of those kind of instances, either AI systems within the kind of what I call dumb kind of computer processes there today? And even at sort of the nursing level, you talk a lot about the sort of doctor use of AI today. at the nursing level, are they pulling out GPT or open evidence and integrating it into their work? >> The answer on the latter is yes. I think we're all using now AI tools to to sort of mitigate our uncertainty. I I you know, I use open evidence, which is sort of GPT built for clinicians. Um, as I the way I frame it in in my new book is is I use it as my curbside consult, meaning lots of patients. I'm a generalist. I'm something called a hospitalist. So, I take care of sick people in in hospitals, but I'm a general internist. And so, many times on rounds in the day, I have a question that, you know, I don't need a full-on consult. I don't need a cardiologist to come to see the patient. But I'm 90% sure of the right thing to do, but not 100% sure. And in the old days, I kind of would have hoped that I ran into my colleague in the cafeteria, and if I did, I'd say, "Joe, can I run a quick case by you?" But now, that's what I use AI for. And I think the nurses are beginning to use it for that purpose. So the answer is yes. It's nice to have an advanced knowledge tool. A 40-fold overdose of uh of this antibiotic does not require fancy GPT. I mean that is like textbook 101 from 1970 says don't do that. So the problem here was less sort of an information deficit. And the computers by the way did fire alerts periodically saying this seems like a weird dose. The problem and AI's it'll be interesting to see if it gets this better. If it makes this better. The problem is if the computer fires every time it sees something that either it knows it's wrong or it doesn't quite match the normal template. You will have an overwhelming number of alerts because there's a lot of stuff that happens in medicine that is a little bit outside of the norm. >> >> And the challenge of course is alert fatigue, which is a a a nurse researcher at my hospital several years ago did a study where she asked in in our intensive care units, how often does the monitor next to the patient's bed, their that's monitoring their blood pressure, their oxygen level, their EKG, how often does that thing fire an alert? And the answer was across our 70 or 70 80 70 ICU beds. It fired 2.5 million alerts per month, one every eight minutes to the point that she told me the story where she was standing by the bedside of a nurse in the ICU. An alert is firing every five or six minutes. And the ICU nurse seemed completely chill about each of the alerts. And finally the researcher said to the nurse like, "What would make you worried? These alerts are going off. You seem not to be bothered. what would make you worried that your patient was in trouble? And the nurse thought for a second and she said, "Silence." She said, "If there w if there were no alerts, I'd be really worried something was wrong." So, the challenge is can we? And so, we as doctors and nurses, particularly if we have any experience, have learned to just ignore a whole bunch of alerts that in our experience are always false alarms or 98% false alarms. The problem is every now and then there's one that's real. So the question and I don't think the answer is obvious is is embedding sort of new fangled AI into our system so much better than what we've had that it will be better at calibrating when the alert is real and you know giving you a real live super duper alert uh when it really is is necessary and does not fire when it's going to be a false alarm. I think we can get there, but it's not like we turn on some fancy GPT button and that automatically happens. It's a really complex problem of calibration because there's a lot of stuff that we do where if you say does this violate the sort of standard textbook way you do this thing the answer is yes and we do it all the time because we know empirically that it works. I mean, one anecdote that I put in the last book was I spent a couple a day at Boeing and spent with the cockpit engineers and they told me about the way they they deal with alerts in the cockpit and they were just flabbergasted when I told them that 2.5 million story. They were just unbel they they couldn't believe it because they know that every unnecessary alert increases the probability that the pilot's going to do the wrong thing and kill everybody on the plane. And so there may be lots of stuff that's going on that goes to the ground engineers so that they see how the performance of the plane is going, but they would never ever dream of of mainlining that information to the pilot because it would be a distraction. And and the the story that stuck with me in forever is I said and then when we do give the pilots alerts, there's a gradation of seriousness. Whereas our alerts often are generic. Doesn't matter whether it's, you know, the patient shouldn't take this medicine with grapefruit juice or the patient's about to die. The alerts often look the same. AI should get that better. But the story they told me was if the plane is stalling and you're, you know, everyone's going to die if they don't do the right thing. A red signal comes up on the computer. It's the only time we ever use red in the cockpit. The uh the steering wheel starts to shake. A voice robot voice comes out say stalling, stalling. Design all designed to get everybody's attention. And I said and they said, "Then there's an level down where the color is yellow. There's no steering wheel shaking." And I said, "What's an example of that?" And they said, "Well, let's say one of the engines is on fire." I said, "That's not the highest level alert." They they said, "No, you know, it will extinguish itself, but we think the pilot should know." It's like, "Are you kidding me?" So, there's sort of a much more sophisticated way of thinking about this. In other industries, in healthcare, there's so much kind of chaos that I think we have to figure out how to calibrate, how to deal with the false alert problem. I think AI can help us with that, but it's going to take a lot of work, a lot of thinking, a lot of engineering. >> Yeah. the the comparison between your alert system and all the stuff in alert fatigue was was frankly kind of terrifying to realize that so many of those alerts were being ignored and then the clarity of the of the Boeing example uh was really illustrative but it it raised that question for me about judgment right about about the role that >> that AI can play not just in sorting information or searching information right which it's really good at but in actually making calls about things so one could be that Right? You could turn an AI system loose on the alert ecosystem and have it judge uh which time should things should be red versus yellow. One could imagine that. The other place I thought about this was in you talk about the concept of a differential diagnosis and this maybe takes us back a little bit to how consumers use AI as well. And you know, it occurred to me that the way that the models basically work today when you're talking to them about health, maybe GPT health will be different, but they don't think in terms of differential diagnosis, right? And you can explain what that is, but they they they basically give you what they think is the most obvious answer unless you really press them as you probably would as a doctor to say, "Don't just tell me what you think these symptoms are. Give me the 20 most give me the 20 things they could be, and then give me a probability ranking of what you think this actually is." I'd say give me a probability ranking and then give me a a ranking in terms of worst case scenario like what is the thing that could kill the patient. So this is like if you do a Yelp search and you search and then you say give me by rating and then give me by distance for example. Those are the two sorts that I want to see in terms of thinking about the list of potential diagnoses. >> And how good are the AI tools today at that level of of judgment? >> Pretty pretty good, but only if you prompt them to do that. And that again is something that I will naturally do because that's the way my brain works and a patient might not naturally do. And one of the things that's been interesting as we sort of looked through the literature about research on AI and healthcare is you know the tools that we are all using this tool called open evidence is a company that didn't exist a few years ago. It really is GPT built specifically for for clinicians and mines not the entire literature and and therefore the onion and Reddit but al but is really focusing what it's mining to get its answers on the medical literature on on respected journals and on guidelines that come out of respected societies and in the space of two years it's become almost sort of ubiquitous in in in in medicine there last month it had close to 30 million searches but when I use a tool like that. Again, I know what to put in. And when I look at its output, I know, you know, I have a sense like that first diagnosis it gave me. Oh, yeah, that's good. But I thought of that. Number two, I hadn't even thought of that. That's good. That's really helpful. Number three, no, that's absurd. I'm going to ignore it. Patients have no real ability to know what to put in and how to interpret the results. It doesn't make them stupid. It makes them normal. They're they're normal humans. They've not gone what, you know, from novice to expert, which is what you hope your physician has gone through. Does that mean that it's feudal? Not not at all. I think that the question is, can tools be built that act more doctorish than than the current tools? Meaning, you say, I woke up this morning with a sore throat. I would never give you what I think your diagnosis is. I now I have about 10 questions I need you to answer about, do you have a fever? Are your lymph nodes swollen? Are you on any medicines uh you know that suppress your immune system, etc., etc. I can't even begin to answer what's going on. and create that differential diagnosis, meaning the likeliest thing is this, but I'm also worried about this, this, this, and here's the tests I'm going to do to sort that out. Can't even begin to do that until I have the answers to those questions. I may also need to do a physical exam, which obviously the AI can't yet do. But where this gets interesting on the consumer side, there was a wonderful study that was in NA, the journal Nature out of Oxford last year, early this year, where they created prompts that had obvious answers in the medical world. Here's a good example. Patient woke up this morning and had the work worst headache of her life. You tell that to any doctor worth his or her salt. They will say to you that is a bleeding around the brain. It's called a subberactid hemorrhage until proven otherwise. We learned that on the second day of med school and the patient needs to go to the ER like stat. Do not pass go right away. When they put those prompts into GPT, that's exactly what GPT said. This is a subaractid hemorrhage. Go to the ER. What they then did was they gave the prompt to the patient and said,"Now interact with GPT." And the patient took, "This is the worst headache of my life and put into GPT." I woke up this morning and I had a really bad headache. And GBT said, "Oh, I'm sorry to hear that. You should take some Tylenol and rest." So, because the patient had no idea that the worst headache of your life is actually a term of art that we use to differentiate bad headache from awful headache. So that's you know I'm quite hopeful that these tools will be more and more helpful. Part of it is as GPT for health or claude for health now do part of it is they now have the capacity to for you to put in your past record and for them that to know about your background which sometimes is quite useful because a headache in one person might have a very different meaning for a headache in a person who was on imunosuppressive medicines or had a history of cancer. So part of it is knowing enough about you to give you a more customized answer, but I think in some ways more importantly is is it acting does it have the judgment? Does it know the right questions to ask that a good doctor would have? I don't see any reason the tools can't develop that, but the generic tools aren't not quite there yet. >> I think that's a good bridge into uh the kind of continuous data world that that we want to talk about before we end here. So the the thesis behind this series that we're doing is that we're trying to imagine a world in which uh healthcare data is much less episodic and much more continuous. And so you can you can start to see the little glimmers of this in the Apple watches, the Whoops, the consumerization of continuous glucose monitors. And even in in the kind of breadth of things we can measure. So Abbott just announced a a you know continuous ketone monitor to go along with the glucose. There's a whole bunch of labs working on various sensing modalities and we're going to talk to them as as part of this series of different ways to to do this kind of measurement. But then the question is what happens with all that information once it sort of gets into the system. Um and and maybe where I want to start there is I talked about sort of the sensors and the tools. I think that's a kind of push model of how this world comes to be and I think 5 years ago we would have said that's what's going to drive it is that the Abbotts, the Dexcoms, the new startups of the world are going to want to make these sensors. Now I see a real push pull part of this rather which is the foundational models companies want to get into healthare because it's an enormous market and they are ever more data hungry and so they're going to start to drive I think some of the the development of these tools that can bring them more data all the time and I'm curious where do you think the AIs today are held back by the amount of of data or the diversity or distribution of data I was thinking about the the male clinic platform and you can describe what that is which seemed to be a a bit of an effort in this direction to grab a more diverse array of data to inform uh its tools and how it's thinking. >> Yeah. Well, I I first of all I agree with your premise. I you know I think over the course of the next decade or two it seems inconceivable to me that we will not be leveraging this more continuous measurement system to have better insights about a patient's health what they need and better ability then to have an aector arm that in the case of a insulin pump sort of changes this medicine you're getting in real time. but in other cases may just deliver to you coaching to say, you know, if you change your diet this way or change your exercise regimen or sleep regimen that things will be better. There's a lot of steps between that vision and and where we are today. I'd say this is an area that's that's hyped a lot without a ton of data demonstrating that that kind of measurement really leads to improvements in patients outcomes. When you think about the patients that need this the most, the kinds of patients that, you know, I might see in my office have five chronic diseases and are in 10 medicines and are really at high risk of a health problem. That's very different than the patients that I think are most likely to be using these tools who are often relatively healthy young people who are kind of into the into a wellness mindset, which is all perfectly nice, but I think it's yet to be proven that taking all this data and putting it in some fancy AI machine and spitting out an answer is demonstrabably going to improve their health. I can't see a reason why it wouldn't. As we come to understand that this data better and understand the connection between, you know, your heart rate is doing this or your urine analysis from your wired toilet tells me this and we know that that is associated with this bad outcome 10 years from now. And we know that if you change your diet this way, your exercise this way, that that will decrease the probability of that bad outcome. That seems logical that that will happen. But there are a lot of steps between now and and that. And I I do worry a little bit about sort of overhyping this because I don't think we've made those connections yet. That said, how can it possibly be that the best way for me to manage your high blood pressure is I measure it once in my office every 6 months. And at that point, I adjust your medicines and I'll see you again in 6 months to see whether you got better. When you now have the capacity on your wrist or on your ring to have your blood pressure measured in real time to potentially even have your measure have your blood pressure adjusted with medication changes or lifestyle changes in real time to get you real life type coaching like doesn't seem like you took your medicine today or you know I think we agreed that your regimen should be you know 10,000 steps and it looks like you only had 2,000 today. I mean that could be annoying but it could I think the conceptual change here is up until very recently the only unit of both measurement and intervention was the doctor's office visit and that's got to change and and I think that's a very exciting era that that's that's going to change it now where does AI fit in first of all in taking all those data and trying to make sense of it which I think is partly a research question as opposed to an individual care question but once we understand the associations between those data and better health then yes trying to figure out ways that the stuff is measured you get insights into what's going on you have recommendations that are evidence-based about what you should do uh you have coaching that helps you do it better I mean that all I think has to happen where are the challenges here uh to the extent that the that what we need to know in order to make sense of your data is not just what's coming off your watch but what's in your electronic health record those two data sets need to get connected to the extent that what is being measured may at some point mean you actually need to see a doctor like what I your blood pressure now is way too high or way too low or you're now in atrial fibrillation. What's the connection between this ecosystem of ambient realtime measurement and actually connecting you to a health care system because what you have is scary. What can't happen is all of that data that you're now collecting in real time gets mainlined directly to your doctor. Your doctor's already overwhelmed by data. The idea that they're now going to get continuous data feeds on the 1,800 patients they're following, they'll quit this afternoon. So somehow AI needs to be monitoring all this, getting you the information as a patient that you need, connecting to the health care system when the algorithm says you're actually not doing well and in trouble and need to see a doctor. Know know what has to happen. Do you need to go to the ER or do you need an appointment with your doctor sometime in the next two weeks? If you need to go make an appointment, does it connect to the scheduling system and get you an appointment and then get the doctor the information that that is why you're being scheduled because this thing is being measured. So even even just saying that is exhausting. I mean the idea connecting all of those pieces together, I think that's like a decadel long foray and not clear who's going to do that. Is Dexcom going to do that? Is GPT going to do that? Is Epic, the maker of the electronic health record, going to reach out from the HR and do that? Is Apple going to do that? Each of those companies or is my health system going to do that? Each of those entities has some part of the puzzle here. But in order to make this whole thing work, it's all got to get woven together into something that's like this the the world's best air traffic control system, that's going to take a decade or two to get right if we ever do. It's really it's a pretty heavy lift. Yeah, that's what what brought me back to that sort of universal health portal question, right? If I can't even get all my vaccines into one place today, the idea that that my Whoop data and my CGM data and my whatever new sensor data that I have is going to all go together and then do this intelligent dance with my health system does sort of both seem like a a very plausible future as you say, but also seems really hard to imagine how we get from here to there. >> Yeah. But the it's just important to to to almost disentangle those two things. One is can you get all your vaccine data and the 10 medicines you're on in one place. Those are in some ways relatively static pieces of data that once they're in there, okay, they're in there and they're going to get they're going to get leveraged into sort of my health system. That's fine. There's sort of a second volume problem which is all right if we do that but we're no longer talking about 10 discrete pieces of data that maybe one of them changes every four months or I get a vaccine once a year it's got to update my flu vaccine but now it's a data stream and and 20 different data streams being developed constantly any change in which it might be trivial or maybe should influence the way I think about how many steps I take tomorrow but might mean also a life-threatening emergency that therefore needs to connect back to the healthare care system. I mean, that's a problem that's a a mind-blowing set of problems that it that you don't fully solve just by getting all your data into one place. >> Yeah. And that's that's the spot where I find myself wondering if my if if how difficult is for me to wrap my head around that is just me being limited in how I'm thinking about the capabilities of the AI tools or the laws of scale when it comes to compute, right? Because they're already doing things that 5 years ago we would have said were impossible. They're all obsessed with hyperscaling and until the the communities start really, you know, holding out the pitchforks in the data centers, they're going to continue to hypers scale. Um and and how much of it is that lack of imagination on my part and how much of it is really structural both sort of in in terms of the infrastructure is it just too much data to sort of do anything with intelligently and how much is it structural in the sense of we would be asking the AI to do something that it it it can't yet do that the kind of connections the kind of analysis it can do um it just can't do and that's what made me wonder about the sort of training data side of it right like it makes sense that open evidence can look at all the papers that are out there in the world and then give you a pretty smart differential diagnosis. If I'm asking it to look at a a complex array of values for me and then tell me something about it given the heterogeneity of humans, is that where we're going to bump into a thing where like, well, it's only trained on this little bit of data that OpenAI could somehow steal from the internet and it was all in, you know, 40-year-old white guys in San Francisco and now we're asking it to to look at a diverse array of people. Do you bump into any places now where AI seems to be limited by its lack of medical training data or do you see that as a potential hurdle coming down the road? >> Uh yeah, even just as you describe all this, you know, my my head is exploding on trying to figure out how all this works. I think it it it would be hard to as you look at the last four years hard to say that the lack of training data is going to be the obstacle or the lack of compute is going to be the obstacle. I think the obstacle or you know I think the integration of all your data into a single data pool that gives gives the AI a complete picture of your health that also seems surmountable. It seems like that's that's not completely infeasible that we could get there. I think where the real challenge here is that that strikes me as being maybe the hardest one of all is um the connect is is essentially medical research is the connection once assuming some some some mythical state of a decade from now your data all are in one place. We managed to connect all the dots. uh the limitation on collecting and and dealing with all this much data is not we've we've overcome that. We're connected to the health care system in a meaningful way so that you're able to self-care where you should and where you shouldn't and you really need to see a doctor or get a test or whatever that is connected in a way that the algorithms understand and it and and those wires have been laid. Where I think the real challenge is, and this is where I think things get a little bit in danger of being overhyped, is most of the interpretation of, you know, the meaning of your heart rate variability or your, you know, or or your temperature variability or your sleep patterns or whatever or your, you know, the the urine that's being analyzed by the the the smart toilet or your stool that's being analyzed by the smart toilet. most of those sort of connections between that piece of data and what your health outcomes are and what you should be doing to improve your chances of living better and and longer I think have not been worked out. I mean the the hypeers would tell you they have but they just haven't. We don't know what these things mean and even when we kind of know what they mean we don't really know what the interventions are. Now you might then say this is the field what we call real world evidence that let's get over this old fddy duddy idea that we need to do some double blind control trial of 10,000 people where they ate more cheese and 10,000 people that ate more tofu and to see which ones did better. You know let's that's the old way of thinking. We'll just analyze 100 million people and look at the patterns that emerge that tell us what the right things to do. It may be that's a really hard problem from a kind of medical research standpoint because of the heterogeneity of people because the confounders of someone who's living a healthier lifestyle in a lot of ways and then trying to take one piece of their heart rate variation or their oxygen saturation or their sleep pattern and say that is the thing that if you could just fix that they would live longer and better. There's a lot of steps between A and B there. And so I think that's the you know the things that we're talking about compute connectivity connectivity to health system connectivity of all your data pools into one place those strike me as sort of human problems and maybe technological problems but actually truly understanding what are the interventions that flow from that that actually are meaningful make a difference in your health and healthcare you know I think we'll get there I mean I think today I would like to know what your blood pressure is all the time rather than just when you're in my office every four months but for a lot of the other measurements that you're taking all the time and and for some patients obsessing over all the time, you go to your doctor and doctor say, "I don't really care about that because I have no idea what its meaning is." And and there are hypeers out there will tell you, "You need to know that piece of data because it's massively consequential as to whether you live to 100." I I think we're going to have to prove that that's true. I don't think we're there yet. >> Yeah. I think one of the things we've seen, you know, we've sort of played in the the continuous glucose monitor space. So people without a diabetes diagnosis paying attention to their blood sugar and a whole bunch of benefits to that. But one of the things we've we've definitely observed is that it it risks pathizing normal variability, right? That people look at a 30 point spike when they eat blueberries and they go, "Oh my god, I can't eat blueberries anymore because they're not good for me." I'm curious, you mentioned blood pressure. Are there any other areas, especially as a kind of generalist, that strike you now as, "Boy, this would be better if I had longitudinal data rather than episodic data." Well, I I'm glad you brought up diabetes because I think diabet I think in some ways people are extrapolating from diabetes. You know, in an area, you know, that is a measure that we used to insist that patients I mean, I remember when I was in my clinic, patients would come in with this graphical spreadsheet where they'd put dots down on all of the and they're checking it by sticking their finger four or six times a day. And we now know that tighter control is better than looser control. and and and so the ability I mean if you told my my 30-year-old self that we'll have a place where we're able to measure that variable constantly understand its implications give that data to the patient and or their clinician and adjust the their insulin in real time through a co closed loop system I would say that's a dream I mean that's magical but I think there's a danger of extrapolating that this one metabolic parameter that we that changes all the time and we and we can influence in real time and we know that there is real live health impact to what happens with that piece of data. Uh it's a little risky to extrapolate that to your your heart rate variation or your sleep patterns or your urine etc etc. What are other variables like that? I'd say sort of your glucose level is the one that's sort of most obvious to me. your blood pressure is a little bit like that, but there I don't need a measurement every every second. I could use maybe once a week would be great. Uh certainly for the patient with a known pathology like atrial fibrillation, I would like to know how often they're in atrial fibrillation, which might ultimately influence their risk of a stroke and might influence my decision about how how to try to control it. But for a someone without known pathology, God, I I'd have to do a lot of head scratching to know what are the variables that I really want to see continuously versus episodically. Now it may turn out that you know the cholesterol I check on you uh every six months or every year. If I knew what its pattern was uh you know and it it goes up and down I might see that you know actually your cholesterol looked good be every time you come and see the doctor because you go on good behavior and change your diet or you actually do take your lipore the week before you come in but if I knew what it was two months ago you'd stop taking your medicine and and it was awful. that might be useful. But I I'd say it's important to say the glucose insulin example, at least in today's understanding of medicine, is by far the exception rather than the rule. And for a lot of the other things, I think it's a little bit of of of of handwaving to say we really know that more continuous measurement of this thing is super salient. And I'm thinking in medical terms, I mean, it may be continuous measurement of your mood is really important. And if we can determine that from your tone of voice or the number of words that you speak per second or whatever it is that that is super useful. You know there's there's stuff that that I can imagine being useful. But I think before we just sort of say it is because we can measurement measure you have to weigh the downsides of pathizing everything and making people crazy over the upsides of measuring it intervening on it coaching on it etc. And you know, I think right now that's a pretty short list. >> Maybe as a as a place to end, you know, at the end of of the first book, um, you I've heard you describe it as 25 grumpy chapters and one optimistic one. I have to say the book did not strike me as that grumpy. I think in both books, there's actually a real tone of optimism, particularly in the AI book, and a real tone of balance, and I wondered if your journalist wife sort of helped you with that. I feel like you did a very good job of threading the needle of of kind of, you know, objectivity and it didn't feel like a sort of angry screed around, you know, EHRs. I'm wondering where you sit right now is in in terms of that optimism as you look 10 years ahead. Do you think given where the all the things we've talked about where the government is in terms of its um intervention or lack of intervention in here where the relationship between the medical systems and the health tech companies is um where patient demand or understanding is right now? What's your sort of rosy picture for where this could be in in 10 years? Yeah, I mean this I I'd say my last book it wasn't super grumpy, but it was written largely out of frustration about like how badly the digitization went when in some ways as measured against the yard stick of how optimistic I was about it. It was that disconnect that I saw in a lot of my colleagues and that I saw that led me to write and I came to believe I think it ends on a note of optimism because I said I now see where this is going to go but we need better tools and just digitizing the record was not the answer. It was it was the foundation. So in some ways it anticipated the moment we're in now. The moment we're in now is one where I have a fair amount of optimism in that these tools are magical and can do things that we've never been able to do before. Uh but also the health care system is in such desperate need of help and has a number of problems largely related to the amount of data and it's only going to get either better or worse depending on your point of view when all of this new these new data sources flood into us that now we have tools that can address that. Uh to me that's extraordinarily exciting. It opens up massive possibilities for good. Um but the way I frame this is this is going to be the greatest experiment in the history of medicine. it could go off the rails in a hundred different ways. Um, the democratization of care is fantastic, I think, allowing people to get the things that they need, uh, be more have much more agency, be more empowered, probably do things less expensively and more conveniently. It also is the opportunity for immense amounts of mischief because you're no longer seeing a doctor who took the hypocratic oath. Uh but you may be getting your care from a company that's that's there to make profits off you and and the opportunity for miss and disinformation has never been greater within the health care system. You know, these tools can be used for for good, but they also can allow us to turn our brains off and lead to levels of deskkilling that we're giving the tools undue amounts of agency and undue amounts of trust. And I'd say the trust issue is particularly challenging with generative AI because it seems so humanish that you know we tend to trust people more than we trust companies. But these tools when you talk to them feel like you're talking to a a colleague and even a sicopantic colleague who will tell you how wonderful you are. And then on top of it, you have um the general mistrust of of of these tools of AI, which is growing um for reasons I completely understand. The backlash that's likely in every profession, including nursing and medicine, if people feel like their jobs are being threatened, uh the risk, the overall risk that we're all worried about of bioteterrorism, of you know, if we hit 15% unemployment, I think there'll be a revolution. All of that kind of stuff means that as as wonderful as these tools might be, the implementation of them in health care systems has to deal with the fact that there will be some push back by the incumbents. Uh has to do with the fact that even if the tools are really good, the I use the Whimo example a lot in the book. Whimo is demonstrabably safer than riding in a car that you're driving or Uber's driving. And yet six months ago, as you probably know, Whimo ran over a cat in San Francisco. became international headlines. Um, so we're going to hold these tools to a very high standard and at some point they're going to kill people and that will become a cause celeb and everybody's going to talk about how you know see we can't trust them which will lead to a backlash. So there's a lot of tricky kind of socopolitical ethical steps that probably to me are more complex than the techn the technology steps. uh but when I take a step back overall you have a health care system that is not delivering what it people need quality safety convenience uh the burnout among clinicians the cost of care and these are problems that I think if you net them out it's likely that AI will make substantially better compared to the status quo but part of my optimism is I think the status quo is pretty terrible and I can't see how we get to a much better place without embracing these tools and trying to do it thoughtfully and sensively and ethically. >> Well, I look forward to the book in 10 years, uh, reflecting on on how that revolution went. >> Yeah, I look forward to writing it. >> All right. Well, Dr. Bob Wter, thanks so much for joining us today. I appreciate it. >> Thank you, Mike. It was a joy.