No Priors Ep. 130 | With OpenEvidence Founder Daniel Nadler

1. 0:00 – 0:08

   Daniel Nadler Introduction

   1. SGSarah Guo0:05

      Danielle, thanks for doing this.

   2. DNDaniel Nadler0:06

      Happy to be here.

2. 0:08 – 1:54

   OpenEvidence’s Success

   1. DNDaniel Nadler0:08

   2. SGSarah Guo0:08

      So, uh, give us a sense of this incredibly viral sensation that has been OpenEvidence, uh, in terms of what type of, um, coverage it has of American doctors today.

   3. DNDaniel Nadler0:21

      As much as we would like to think that it's going especially well for us, I would sort of say as a qualifying point that, um, in all of the sub-industries of AI, you're, you're seeing an acceleration and compression, right? So the, the adoption cycles, even outside of OpenEvidence, before we get to OpenEvidence, in other fields of knowledge where encoding and so on are hyper compressed, right? It used to take, you know, half a decade or a decade for something to become standard, and now it seems to happen in two years or, or a year. So the same thing's happened with OpenEvidence. In about 18 months, it's become the operating system for clinical knowledge in the United States. Uh, it is used something like 20 times more than the next most used platform of any kind in our specific segment, which is high stakes clinical decision support for doctors. So high stakes clinical decision support for doctors is a specific category of medicine. It's distinct from, say, paperwork, or it's distinct from scribing. Um, those things are, you know, part of the workflow of being a doctor, uh, but the stakes and the consequences, uh, are different. Um, if you get it wrong, you can go back and do it again. Uh, that's not the case with a patient. Uh, you have to get it right, and you have one shot to get it right. And so clinical decision making, uh, of which clinical decision support, uh, is in service of, is unquestionably the highest stakes area of medicine. We're probably the only company working at the tip of that s- spear. Most people have self-selected themselves out of the problem of high stakes clinical decision making, uh, certainly through an AI lens, um, because they view it as

3. 1:54 – 6:35

   How OpenEvidence Works

   1. DNDaniel Nadler1:54

      ambitious.

   2. EGElad Gil1:54

      And could you explain an order of evidence? Because I think fundamentally, it's about taking information and then translating that into specific either recommendations or diagnoses for a patient. Can you tell us more about how that works?

   3. DNDaniel Nadler2:03

      Yes. One way to sort of simplify it down is at its foundation, it's a search problem, but it's a very semantic search problem. Uh, so most search traditionally works with keywords, right? So like, you know, flights to Barcelona or hotels in Barcelona. Most of the, you know, most of the keywords there can be captured in, like, a couple of words, and certainly in a sentence. And that's sort of traditional Google search. Even if you were to think about clinical decision support as a search problem, simply describing your search query, if you want to think about it that way, usually takes many sentences. So an example I like to give is you have a 44-year-old female patient, she has moderate to severe psoriasis. That's the red stuff on your skin. Um, y- you know, you're a dermatologist. That's so far, so simple. You would just prescribe one of the many creams you see commercials for on television. Except, uh, she has, um, MS. Uh, so now it gets interesting because you want to treat her psoriasis, um, but you don't want to make the MS worse. And you are not a neurologist, you're a dermatologist, so neurology is not your specialty. Um, but you don't want to go refer her to a neurologist because you want to treat her psoriasis and, and if you just keep referring people in circles, medicine never happens. From the ether, you might have heard as a dermatologist that the new classes of psoriasis treatments, um, which are biologics, they're IL-17 inhibitors and IL-23 inhibitors, might have some interactivity, uh, with the neurological dimension of a patient's condition. That's about all you know. Um, you didn't learn this in medical school because IL-23s were FDA approved in 2019, right? Uh, so one of the great themes of OpenEvidence is that this sort of golden age of biotechnology is sort of the dark ages of physician burnout because it's just impossible to keep up with all the new drugs and all the new mechanisms of action and so on. So y- you know, it was approved in 2019, you might have graduated medical school in 2005, right? (laughs) So y- you didn't cover it in medical school and that's it. That's kind of, that's what you know. So your question then is, you know, for a 44-year-old female patient with moderate to severe psoriasis, is an IL-17 inhibitor or an IL-23 inhibitor more appropriate and more safely tolerated with respect to not aggravating the MS? Now that's, that's not a academic question. Um, that's a very consequential question. IL-17 inhibitors will actually make the MS worse. Uh, IL-23 inhibitors are safe and well-tolerated in case of MS. Th- that's an example of where medicine can go wrong because even five or 10 years ago, um, either you're referring that person to a neurologist, in which case you're just getting r- referrals in circles and medicine is not happening, or unfortunately, what would more likely happen is they would just 50/50, and that MS might be aggravated. And, y- you know, it's well-known and it's been often repeated that medical error is the third leading cause of death in the United States after heart disease and cancer. But even that kind of, that statistic kind of understates it because that's just looking at death, right? In the case of my, in my example, um, this patient is not gonna die as a result of taking an IL-17 inhibitor. She's going to have a, a relapse of MS. And so it's not just that medical error historically was a leading cause of death, it's that, uh, as many people died from medical error, probably a factor of 10 to 100 as many people had a, a comorbidity or condition that became aggravated and got worse and so on. So coming back to your question, that whole string is the search query. And so you can't just do search in a traditional way where you sort of say, you know, IL-17, 'cause that's not really what the question's about. Um, nor does the physician have the time to go read book chapters on this stuff. What you need is a semantic understanding of the, of the query in the way that another human physician would semantically understand that query. And then it's actually quite-... deterministic and simple after that. Um, once you semantically understand the query, uh, you can from the world of published biomedical literature, you can find the exact snippets in a phase 3 RCT, Randomized Controlled Trial in The New England Journal of Medicine that tested each of these things and found that one aggravated MS and the other didn't, right? So once- once you have a semantic understanding of the query, uh, the rest is fairly deterministic and it's almost a search problem. Um, but all of the- all of the juice is in, you know, connecting the very complex semantic meaning of a medical scenario to the answer where the answer might be in a phase 3 RCT in The New England Journal of Medicine and a snippet in- in, not even in the- in the abstract, but in the methodology section or in the population

4. 6:35 – 11:37

   Dealing with Ambiguity

   1. DNDaniel Nadler6:35

      section.

   2. EGElad Gil6:35

      How do you deal with that ambiguity actually 'cause I feel like in the context of medical information, there's things that are, uh, in pre-baked clinical guidelines, you know?

   3. DNDaniel Nadler6:41

      Yeah.

   4. EGElad Gil6:42

      Certain types of conditions, we're gonna do XYZ and that's where the recommended path. There's stuff that's kind of recently published, so is there evidence in a certain direction or maybe it's by the label or something else, and there's a bunch of stuff that's a bit more TBD in terms of there's clinical trials that they contradict each other a little bit or maybe other information that may be a bit more sporadic. How do you- how do you deal with that third bucket of ambiguity and how do you think in general about capturing that broader knowledge purpose over time?

   5. DNDaniel Nadler7:05

      So the first way to deal with that third bucket of ambiguity is ensure that your users are physicians and not patients, so we've made that strategic decision. And we keep thinking we're gonna change that decision and, uh, we've been talking about changing that decision since the inception of the company, and so far have not changed that decision. (laughs) For all the reasons implicit in your question, there's an enormous luxury that we have as builders, um, in having doctors as users because the MD is attached to their name, right? So they need to protect that MD, and they're going to use us as a tool in the same way as a Wall Street trader might use a Bloomberg terminal. If a Bloomberg terminal, for example, produced, you know, an inaccurate quote on a bond that was very obviously inaccurate, you know, it was off by an order of magnitude, and the- and the trader, you know, in a hedge fund just sort of went, "Well, I mean, that's odd."

   6. EGElad Gil7:50

      You indicated in the user interface that hey, there's some ambiguity around that circumstance of the evidence and here's the...

   7. DNDaniel Nadler7:56

      Absolutely. So there are areas of medicine where there's a lot of conflicting evidence and- and that's indicated. And- and it's not presenting answers, you know? We're used by 40% of doctors in the United States daily on average. It's about 20 times as- as much usage as the- the- the next thing that could be described as a clinical decision support platform. It's become the default operating system of clinical knowledge. And a lot of the value proposition early on is that we made references and citations a first-class citizen, uh, before that was in ChatGPT. So we were actually providing references and citations six months or nine months before ChatGPT started doing that. Um, that was a big reason we had adoption because people could interrogate and audit the source, right? So right there, there's a difference because then it's not an answer engine. It was never presented as an answer engine. It was always presented as a search engine. The way we did frame it was, um, as part of the long continuum of- of search and- and Google. Uh, you know, we're a Google portfolio company, and I've always framed this as part of the very long continuum of search engines as opposed to something net new, um, because I do view technology as a progression, a continuum. And that created a certain social contract with the users who, in addition to being physicians and have that MD that they need to defend, um, on top of it, viewed this as a, uh, a router to the phase 3 RCT in The New England Journal of Medicine, and maybe the conflicting phase 3 RCT in JAMA, right? And we'd route them to both. Very useful for each other-

   8. SGSarah Guo9:16

      So users do look at source material some of the time.

   9. DNDaniel Nadler9:19

      All the time. I would say that's almost the default behavior of a user to start with some complex query that you could not put into Google for the reasons I mentioned because it's a paragraph long, and then have it produce within, you know, from a search space or a surface area of 35 million biomedical publications, the exact three to five, you know, canonical landmark phase 3 RCTs or guidelines or other sources of information that, um, are responsive, not answers, but that are responsive to their question. And then I would say almost the default behaviors, then they go out. You know, I think we're one of the largest sources of referral traffic to The New England Journal of Medicine after Google and, uh, you know, the ranking, I don't know if we're number two or three or four, but we're- we're one of the largest sources of referral traffic to our partner in The New England Journal of Medicine. That's a testament to the way people use it. Historically, it was very hard to do two things. It was hard to describe a complex patient scenario or case, um, into a search engine and have it come out with anything useful, and it was hard to find, um, from the tens of billions of tokens, if you wanna think of it as an engineer, uh, that constitute the world of peer review public medical literature. It's very difficult to find, you know, the seven snippets that are directly responsive to a question and to the semantic meaning of the question as opposed to a few key words. So we just did those two things, just did those two things extremely, extremely well. We framed the right social contract. We picked our audience extremely well, you know, and all of those things start to stack, um, into something that looks more like a Bloomberg terminal for doctors, uh, where it's just a pro tool. They're using this because it has, you know, the right data that goes in 'cause AI is gold in, gold out, garbage in, garbage out. So they know this is not training on tweets. They know this is training on New England Journal of Medicine and JAMA and the rest. They know that we have these partnerships, these strategic partnerships with the, um, you know, gold standards of medical knowledge. They know that they're not gonna get an answer from OpenEvidence. They're going to get a routing to a- a source that answers the question. And so I think all these things sort of stack into something that feels just like a-

5. 11:37 – 15:53

   Treating Knowledge Workers as Consumers

   1. DNDaniel Nadler11:37

      a pro tool.

   2. SGSarah Guo11:37

      I want to rewind for a minute. You were already a successful entrepreneur before you started OpenEvidence. Um, you wanted to build an impact-driven company. Like, you wanted to work in health. What was the moment of decision to serve physicians versus consumers? Because you also think a lot like a consumer entrepreneur in terms of growth.

   3. DNDaniel Nadler11:56

      Well, I served both. So this was a hack. I wanted to build a consumer internet company for knowledge workers, and I don't think that had ever been done before.So, I didn't want to build a healthcare company, uh, at all. Uh, I love, uh, Sequoia's quote that, um, Open Evidence is a consumer internet company masquerading as a healthcare company. Uh, I had zero interest in building a healthcare company. Open Evidence is not a healthcare company. I wanted to build a consumer internet company, but I wanted to do something that no one had ever done before, which is treat knowledge workers like consumers. So my whole career had been, you know, prior to this, dealing with knowledge workers, right? And, and people have a reductive view of consumers. They think of con- you know, they think of, like, 14-year-olds on TikTok, and that tends to be like their archetype of what a consumer is, and it's one type of consumer. Um, but, you know, traders on Wall Street are consumers and people, lawyers are consumers and people, and doctors are consumers and people. And what I realized is no one had ever treated doctors that way before. Doctors were just kind of treated as these appendages of health systems. I was like, "Hmm, it's an interesting way to organize the medical system and the health system." And, you know, you start to investigate and pull the thread a little bit, and you start to understand why, you know, there, there are very few things that people can agree about in America. They can agree Congress is dysfunctional, and they agree that American healthcare is dysfunctional. It's like bipartisan, universal consensus. But you start to really investigate and y- you know, you come across two or three things, and you're like, maybe that begins to explain the dysfunctionality, right? And to me in particular, the idea that doctors who were the fighter pilots, who were the knowledge workers, who were the people who have that MD on the line and have to make that high stakes decision weren't even their own gatekeepers as far as the technology they used. That was, that was pretty profound (laughs) realization. And so we did something that had never been done before ever, which is we treated them as consumers and as people that, uh, could go onto the app store and download, uh, a free app and start using it. And it sounds so stupidly simple, but it was, it was really (laughs) profound and it was really effective because no one had ever done that before. It's, it's kind of almost analogous to in relationships, whether friendships or romantic relationships. People can get caught in these sort of cul-de-sacs where there's a rigidity to their dynamic and to their relationship, and then there's a breakthrough where one person says something that they've, it, they've just never said it before or they've just never said it in that way before. And then there's like a breakthrough. It hits different, right? And in, in th- in, uh, in psychiatry or psychology and therapy, a lot of th- that field is encouraging this behavior in others, is to just sort of break free of cul-de-sacs, um, of, of dialectics, of relationship dynamics, and just say something in a way that's never been said before or do something that, you know, that hits different. And, um, long story short, you know, we, we, we did that with doctors, and, and it was, it wasn't the complexity of the idea. It was just no one who had ever addressed them as consumers before, and, um, you know, we had this realization, which is pretty obvious, that while this wouldn't have been possible 20 years ago, today every- virtually every doctor in America is walking around with a computer in their pocket that they own, called an, uh, you know, an iPhone or an Android phone usually, and they own that computer, right?

   4. EGElad Gil15:22

      Yeah, it's really cool. Yeah, I mean, the, the velocity of it and-

   5. DNDaniel Nadler15:24

      Yeah.

   6. EGElad Gil15:24

      ... the usefulness and value is reflective in that velocity.

   7. DNDaniel Nadler15:26

      The scale and the speed of it. And more common cases are cases in which the leadership of the hospital system are very avid users. So the entire, you know, the, this whole senior leadership of UCSF, of MGH, of Mayo Clinic, of Cleveland Clinic, of NewYork-Presbyterian, um, Mount Sinai, Cedar-Sinai, you know, right up to the, the chief medical officers, the chief physicians, and the CEOs in many cases are personally avid users.

6. 15:53 – 19:28

   Balancing Keeping Patients in the Loop

   1. DNDaniel Nadler15:53

   2. EGElad Gil15:53

      The reality too is that people are basically using Google for some of these use cases, or they're using probably tools like this where carrier. I, I have a sort of slightly separate question, which is maybe back to the consumer versus medical or physician side of this because, you know, I started a digital health company maybe a decade or 15 years ago, and one of the thing- and we were basically initially providing, uh, really key genetic information. We had a physician in the loop at all times. But one of the things we ran into is, um, what I characterize as almost a journalistic viewpoint in the medical community towards what information their patients should and should not get. And I think part of that was real concern about what the patients could do in terms of misacting that information, but I think a lot of it was just wanting to be a gatekeeper, or part of it was just not knowing how to deal with the questions of the patient. How do you think about that philosophically in terms of what in- what type of information should patients have access to versus not? How much should patients be able to advocate for themselves?

   3. DNDaniel Nadler16:42

      So I've, I've experienced both sides of this. So, uh, I've e- I've been on the patient side, and I'm very sympathetic to that, um, because the reality is medicine is not perfect. Uh, if it were, you know, everyone would be living to 80 or 90 years old. So clearly medicine is not perfect, and, uh, in a world where it's not perfect, patients should definitely have some, some role and agency in that. Um, what we, what we have done is encourage physicians to use Open Evidence to generate patient handouts, and that's actually a very, um, widely used secondary... It's mainly clinical support, but we have all these secondary use cases like prior authorization letters and, uh, insurance appeal letters, and one of the most common of those sort of secondary use cases is generating these patient handouts. The other side of this that I can appreciate is it, it took me personally taking my first graduate level statistics course at Harvard to really understand these clinical trials.

   4. EGElad Gil17:37

      Mm-hmm.

   5. DNDaniel Nadler17:37

      Right? And so I'm sympathetic to the idea that a patient simply finding some clinical trial published in The New England Journal of Medicine because it was mentioned on CNN or Fox News and then going and trying to read it, especially through the lens of fear or hope, is not necessarily going to result in a, in the most sort of constructive decision-making process. I mean, there, there's no good answer. The reality is it's very tough, right? You, you want to give patie- you want to enable...... patients with all the answers that are clear in consensus-

   6. EGElad Gil18:13

      Mm-hmm.

   7. DNDaniel Nadler18:14

      ... and certainly, you want to give them the tools to make sure that their physician is not missing anything.

   8. EGElad Gil18:19

      Mm-hmm.

   9. DNDaniel Nadler18:19

      At the same time, you, you, you know, you don't want... You can imagine all the failed cases where that could go wrong, where they're coming and saying, "Well, why aren't you putting my mother, you know, on this drug with this, with their own handouts?" And the answer might be a very technical answer, right? The answer might be that, um, because your mother also has this other comorbidity, and if you look at the p-value, the p-value of the, uh, e- efficacy of this drug is not statistically robust in the presence of this other comorbidity. And the patient is like, "What's a p-value?" But they're not gonna just stop at what's a p-value, they're gonna get really upset.

   10. EGElad Gil18:57

       Yeah.

   11. DNDaniel Nadler18:57

       It says, in this case, that this other treatment is effective, and then, then you're just in this endless circle where, where the physician, who has, by definition, taken at least one graduate level statistics course, is trying to explain to a civilian what a p-value is. And, and I think that's a, that's probably not a constructive outcome, so it, it's a balance. We, we encourage physicians to use open evidence to produce page- patient handouts, especially where guideline-based medicine is concerned.

7. 19:28 – 22:12

   How Technology May Shape the Future of Medicine

   1. DNDaniel Nadler19:28

   2. EGElad Gil19:28

      So I think you mentioned something really interesting earlier, which is the velocity at which your product got adopted was incredibly fast, and I think part of that was just it's incredibly valuable and

   3. SGSarah Guo19:36

      Mm-hmm.

   4. EGElad Gil19:36

      ... a lot of these

   5. SGSarah Guo19:37

      Mm-hmm.

   6. EGElad Gil19:37

      ... different tools. Uh, and I think that's one of the almost underappreciated aspects of this wave of AI is not only is there a fundamental technology shift that's enabling all sorts of new products, but also there's this massive shift in terms of the openness of adoption of people in organizations to new technologies. And that's in terms of what you've been doing with open evidence, it's to your point of the medical scribing thing, it's companies like Abridge or Commure or others. Um, if you think ahead 10 or 20 years, and this may be impossible to extrapolate, how do you think the change of medicine or the state of medicine changes in general? Like, are we, are you still going to the doctor's office for visits? Are you interacting with some online tool and it's backed up by a doctor? Uh, are drugs developed differently? I'm just sort of wondering at a high level how you think about the whole industry evolving or changing, given that so many markets are open in ways that they weren't before, but also there's new technology ways that are gonna impinge on markets.

   7. DNDaniel Nadler20:26

      It's getting difficult. The, the definition of a, of a singular event horizon is you cannot even project, you know, into the near future, let alone the far future, and I think we're, you know, we're probably in the midst of something like that. With respect to doctors in the loop, planes have been able to land themselves for a very long time. It's a peek into in a, in a way of future by analogy, because that's a, that's a, that's a domain or an industry where there's no debate really, uh, as to whether the technology is there, and yet you don't see this sort of mass movement, um, of (laughs) airline passengers to get the pilots, uh, out of cockpits. There just isn't. I- I'm not aware of one mass movement to get pilots out of cockpits. Then the question was why, and, and of course, uh, that is a, uh, attribute of, of human psychology that we are anthropologically tribal and, uh, we don't abstract trust well. Um, we, we, we personify trust, and we trust things that we personify and anthropomorphize, um, and there's a whole history-

   8. EGElad Gil21:29

      So aren't you doing a lot with the chatbots, right? In other words, there are people who-

   9. DNDaniel Nadler21:32

      Yeah.

   10. EGElad Gil21:32

       ... effectively view themselves as being in relationships with...

   11. DNDaniel Nadler21:35

       Yeah. They don't have bodies yet. I mean, you could start to reason by analogy. Would there be any more of a mass public movement to have computers land planes if in, if you still had a cockpit, if you just removed the two seats? No one wants that. Okay? What if you keep the two seats but they're empty? I still think no one wants that. What if you keep the two seats and there are mannequins, essentially-

   12. EGElad Gil21:58

       Mm-hmm.

   13. DNDaniel Nadler21:59

       ... that act as visual surrogates for the computer system and what it's doing? I think if you were to poll people, that'd be the first time you see this little uptick in willingness. I think it would still be the minority.

8. 22:12 – 30:40

   How Technology Will Change Medical Education

   1. DNDaniel Nadler22:12

   2. SGSarah Guo22:12

      Can I ask a question, if we're talking about the near future? Um, uh, you've, you've mentioned before, like, we are in an era of, um, you know, uh, in an amazingly optimistic way, like, explosion of biomedical knowledge-

   3. DNDaniel Nadler22:25

      Yeah.

   4. SGSarah Guo22:25

      ... which should accelerate. You've mentioned before that the half-life of the knowledge you learn in med school as a physician is decreasing rapidly.

   5. DNDaniel Nadler22:33

      Yeah.

   6. SGSarah Guo22:34

      Do you think that's gonna change? Like, how you are educated as a doctor?

   7. DNDaniel Nadler22:39

      I think medical education is gonna radically change. I, I, I, I think doctors are, are gonna be in the loop for a very long time. They have been in a loop since, you know, the ancient Greeks, if not, you know, the ancient Egyptians. I think they're gonna be in a loop for a very, very, very long time, and for, for the rest of our lifetimes, if not longer. Medical education is gonna change radically, um, because, um, it's just, you know, the, the statistic I, I cite, and all of this is in peer-reviewed public, uh, publicly available medical literature, the rate of doubling of medical knowledge as measured by citations, uh, in 1950 was every 50 years. So every 50 years, the number of total citations of peer-reviewed medical literature doubled. Uh, today, it's every 73 days, uh, by an estimate in the British Medical Journal and one in Nature. I think that methodology was a little bit aggressive, uh, because they were looking at the totality of all publications. Not all publications are equal. But, you know, we came up internally with a more conservative one, 'cause we didn't wanna, you know, we didn't wanna drink the Kool-Aid, so we said, "Okay, let's just look at the top quartile of peer-reviewed medical literature, and let's, let's pretend that physicians never need to read the bottom three quarters of medical literature," which is not really true, but-

   8. SGSarah Guo23:51

      (laughs)

   9. DNDaniel Nadler23:51

      ... let's just, let's, let's, let's, let's do this with one hand tied behind our back. And if you do it that way, it's every five years. So, um, if you, if you use the more conservative methodology, it's not every 73 days, but every five years, uh, the, the, the, the total sum of the top quartile of peer-reviewed medical literature by citations, uh, doubles. Now, you could say, "Well, look, luckily for"... for humans, medicine has become specialized, so your dermatologist doesn't, you know, n- need to read everything in urology. That was my initial example and now they have Open Evidence so they can bridge some of this stuff. Um, so why don't we go even more conservative still and say if a physician just needed to read the top 10% of peer-reviewed medical literature in their own specialty, so now this is very conservative. There's no cross-functional, interdisciplinary medicine at all. Everybody's hyper-specialized. It's not a great outcome, but let's just pretend that's the case. Um, what would that mean? Well, now you're in the realm of doable. Obviously, every 73 days and every five years is not doable. But now you're in the realm of doable but you, the, but that physician would need to spend on average nine hours a day just reading the top 10% of peer-reviewed medical literature just in their own discipline. Uh, of course they would never see patients, he'd spend time with their family, uh, and so on. Now you can sort of keep going more and more conservative with these methodologies and realistically, not everything even within pediatric cardiology is relevant to every pediatric cardiologist and so maybe it's not nine hours, maybe it's four hours. Maybe it's three hours a day. But y- there's some point at which it's going to be like you'd want them to know all this stuff even i- uh, the, you know, narrowed down all the way and it still is kind of, uh, impractical. At minimum, I think that this framework of medical school being a very defined period in time and then having continuing medical education which has kind of historically been this sort of, like, uh-huh, okay, sort of, you know, wink wink kind of thing, that is going to more or less invert where the continuing medical education is going to be the majority of medical education. Um, and that's already happening. That's not a future projection, right? If y- if you speak to really phenomenal, you know, world-class physicians, they will tell you very openly that 90, 95% of what they practice they learned post-graduating medical school and in, and in most cases post their fellowships, if they had fellowships, and residencies, if they had residencies. And some of the greatest physicians that I've ever s- met and spoken with tell me, you know, extreme things like the majority of what they practice today they learned in the last two years. And I've had, I've had a 70-year-old physician tell me that. Now these are world-class people, but what that shows for everybody is that, um, you, you're gonna need to invert the construct of medical education.

   10. EGElad Gil26:44

       Does that change the nature of a residency or... 'Cause the, the way that physicians are trained is very structured today-

   11. DNDaniel Nadler26:49

       Yeah.

   12. EGElad Gil26:49

       ... in a very specific sequence of steps that was based in some part on how you should train somebody 50 years ago.

   13. DNDaniel Nadler26:55

       Yeah. No, it's gonna ch- it's, it's gonna change. It is, it is changing. There, there are these very, um, avant garde-

   14. EGElad Gil27:01

       Mm-hmm.

   15. DNDaniel Nadler27:01

       ... approaches to residency at some of the top places like Mayo, Cleveland, UCSF, which are trying to deconstruct the 50-year-old model and, uh-

   16. EGElad Gil27:09

       What do they do differently?

   17. DNDaniel Nadler27:10

       They encourage evidence-based med- medicine, not just guideline-based medicine. Um, they, uh, encourage the curbside consults. Um, they basically try to solve the problem of information overload through, uh, you know, a distributed hive mind. So-

   18. EGElad Gil27:24

       What, what does a curbside consult mean?

   19. DNDaniel Nadler27:26

       So a curbside consult, uh, sounds fancy but it just means, you know, go ask some other physicians who might know something about this. I mean, all of these things sound obvious. Who wouldn't want evidence-based medicine?

   20. EGElad Gil27:36

       (laughs)

   21. DNDaniel Nadler27:36

       Who wouldn't want physicians asking a panel of other physicians who might also know something about it, you know, about the thing? The demands on a knowledge worker are highly correlated to the number and complexity of the tools available, right? Like, in 1917, in, you know, at the end of World War I, your tools were basically nothing. You know, you had gauze and some scissors, right? So this is all very, very new that getting into my early example, like IL-17 inhibitors or IL-23 inhibitors and biologics in the treatment of psoriasis where someone has a neurological comorbidity, like that's all the last, like, five seconds from a historical perspective. So of course the profession has to change and it's gonna change evidence-based medicine, curbside consults, um, distributed decision-making. You know, that's a big part of it. Like, a lot of what's so incredible about all these famous places that are rightly famous, Mayo, Cleveland, UCSF, MGH, um, others, um, is they really are sort of at the, at the vanguard of thinking about distributed decision-making. Like-

   22. EGElad Gil28:40

       Mm-hmm.

   23. DNDaniel Nadler28:40

       ... if there's a patient with a complex fact pattern, let's bring in sort of interdisciplinary... Let's bring a group of doctors, you know, across disciplines and look at this in an interdisciplinary way. Let's have a cardiologist and a neurologist and an oncologist look. Now the issue is that's very expensive. As I'm describing this, I'm just thinking in real time, like it's really expensive to do. So then there's this, um, equity issue where it, it's pretty clear what the right way to practice medicine is in 2025 in light of this explosion of treatments and the golden age of biotechnology. It's not clear how to pay for that because now it's not just one extremely expensive specialist, now it's three or four.

   24. SGSarah Guo29:20

       And the availability. We don't have that many specialists, that many hours. Yeah.

   25. DNDaniel Nadler29:22

       We're not making more oncologists at any faster rate than we were-

   26. EGElad Gil29:25

       Do you think all this translates into sort of AI-driven tooling or things like that that help augment that?

   27. DNDaniel Nadler29:29

       The, the hope, and this is kind of where we're in the midst of this, is that, um, in under-resourced areas as an example, um, you know, we have, uh, we have, we, we have physicians using Open Evidence in, in every state, electoral county and ZIP Code in the United States including rural Alaska and Southwestern Georgia. And, you know, we, we get letters from doctors because when you, when you make something awesome that's free... When you make something awesome that has a subscription, I think people like it but they don't s- send you fan mail. When you make something awesome that's free, they send you fan mail. So we get fan mail from, you know, southwestern rural Georgia from an oncologist who's like, "I'm one of two oncologists in a 50-mile radius serving..."... a 75% African American population with a median household income of $43,000 a year, and I use open evidence as my curbside consult, which he, by which he means, you know, as my panel of other... So that starts to bridge it, uh, and, and I think increasingly, certainly in rural areas and healthcare deserts, at the fringes and edges of healthcare in the United States, that's absolutely how certainly open evidence is being used, and how AI, I think broadly is going to be used at least to sort of bridge, bridge that gap. Um, and, and I think that's a, that's a real clear silver lining or positive s- side of AI right now.

9. 30:40 – 36:02

   Examining Consumer Adoption of Preventative Health Measures

   1. DNDaniel Nadler30:40

   2. SGSarah Guo30:40

      What do you think, um, consumers might do productively in the future in terms of like preventative health? Like, you, you're treating doctors and knowledge workers as consumers.

   3. DNDaniel Nadler30:53

      Yeah.

   4. SGSarah Guo30:53

      Um, there's not enough of them. Hopefully you will multiply their productivity-

   5. DNDaniel Nadler30:57

      Yeah.

   6. SGSarah Guo30:57

      ... dramatically.

   7. DNDaniel Nadler30:58

      Yeah.

   8. SGSarah Guo30:58

      Um, w- d- do you imagine consumers will be responsible s- for some piece of their own health differently?

   9. DNDaniel Nadler31:04

      This is not going to be a, uh, a popular answer or a, or a politic answer, but, um, if you go spend five seconds in Japan, I'm obsessed with Japan. I named my first company Kensho when I was in Japan two months ago. I've been to Japan, like, a dozen times. I'm obsessed with Japanese culture. Um, the difference in why... There's so many differences, some of which are genetic. But a big difference in why they're so healthy in Japan is they just do all the things that everyone know, and I'm not generalizing to all Japanese, and there's now Western food and Western culinary traditions that have entered Japan, and it's all complex and we live in a globalized world, but J-

   10. SGSarah Guo31:40

       Disclaimer, disclaimer, disclaimer.

   11. DNDaniel Nadler31:41

       Disclaimer, disclaimer, disclaimer.

   12. SGSarah Guo31:42

       Yeah.

   13. DNDaniel Nadler31:43

       But there isn't some net new list, right? So I was in Japan a couple of months ago, and it is striking, it is shocking the extent to which, um, especially if you go outside the big cities and go to places like, uh, Kyoto or smaller cit- cities like Hakone or so on, just they're all walking. They're all just the average Japanese p- And at all ages. You have 70, 80-year-olds who are walking 10,000, 15,000 steps a day. It's a walking culture. And, and it's not just my sort of romanticized illusion as a white Westerner looking at it. Like, I've gone pretty deep on this. I've, I've, I've been there, e- again, like a dozen times. I had long conversations with people that are there, not just academics and scholars, but just ordinary people on the street, taxi, ta- taxicab drivers and so on. You know, they like walking. And also, the older they get, the more they like walking. The younger kids actually are... You know, the, the ones that are 65 and 70, they'll just go walk t- four miles, um, to work. They don't retire. They don't fetishize retirement. Um, uh, they have concepts in their culture of, um, you know, what, what, what Plato called, you know, a good life, but in, in, in Japanese culture, a good life is inextricable, uh, from a life with purpose. You know, an idle life cannot, in Japanese culture, be a good life. Like, those are, those are, um, incompatible notions, you know, idleness and, uh, and fulfillment. And so, uh, there's no concept of fetishizing like, "I'm just going to work really hard and make a lot of money, and at 65, you know, I'm going to hang out on the beach." That's just not a concept really in at least the traditional culture absent the Western, recent Western influences. So people work past 65 into their 70s, into their 80s. You know, that's when it really matters, right? Like, the, you know, that's when, that's when risk of mortality starts to go, to go up a lot, and then, of course, famously, diet sort of... It, it's not just, you know, a sort of pescatarian scoop diet, but it's also the fact that, um, you can, you can almost eat anything if it's in the right, uh, portions. Um, you know, they, they don't, they don't gouge themselves on food. They, they eat until 70, 80% full, all these things that are famously known. And I think at least we're having a conversation about it now in the United States. For the longest time, you had things that every doctor believed. No one would d- There's no, I have never met a doctor who disagrees that, you know, as you, you get past a certain point in body weight, your risk of all sorts of things goes up, but, but 10, 15 years ago, no one wanted, no doctor would have wanted to say that out loud, 'cause it sounded like...

   14. EGElad Gil34:12

       Well, how do we break that culturally? Because I think ultimately, to your point, you know, physicians are viewed as people who have extra knowledge.

   15. DNDaniel Nadler34:20

       Yeah.

   16. EGElad Gil34:20

       Who are supposed to be helping patients, and obviously, they're very focused on that. My sister's a doctor, you know.

   17. DNDaniel Nadler34:24

       Yeah.

   18. EGElad Gil34:24

       Like, I think it's, it's, uh, you know. For many people I know, it's really core to why they became a physician.

   19. DNDaniel Nadler34:29

       Yeah.

   20. EGElad Gil34:30

       But at the same time, political culture took over and prevented them from speaking their minds on things that were really clear on the evidence side of it, that had a huge impact for their patient population, yet nobody would stand up and say, "Actually, it's really bad that we're glorifying the fact that, you know, being dramatically overweight is healthy."

   21. DNDaniel Nadler34:46

       I think the pendulum swings back and forth. I think all these issues are, are deeply entwined. I think that, um, we're now for the first time in a long time having a more open conversation that is not just reduced through the lens of identity politics around, um, health, uh, life choices, and it's not just obesity versus, or, or it's not just overweight versus, um, not overweight. You know, let's use something that has nothing to do with weight. Um, uh, neurodegenerative. Now, n- there's a g- there's a strong genetic component to neurodegenerative, and there are definitely people who have never used their brain in their entire life and never get Alzheimer's. That's obviously true. But no serious neurologist will dispute the fact that a mitigant to neurodegenerative disease is to continue to use your brain over the course of your life.

   22. EGElad Gil35:38

       Mm-hmm.

   23. DNDaniel Nadler35:38

       It just feels like, you know, now at least you can have this sort of more open conversation around like, you know, if you want to at least mitigate the risk of neurodegenerative disease, you know, continue to do all the things Sanjay Gupta taught us to do. (laughs)

   24. EGElad Gil35:51

       (laughs)

   25. DNDaniel Nadler35:51

       You know, if you're, if you're left-handed, write with your right hand once in a while. If you're right-handed, write with your left hand once in a while. Like, just silly things like that that will form n- you know, n- new neural pathways.

10. 36:02 – 37:27

    Lessons for Other Fields

    1. SGSarah Guo36:02

       This is a different type of AI application, um, and you are getting adoption with the type of knowledge worker where people are surprised by the pace, generally, con- sort of conservative industry-

    2. DNDaniel Nadler36:13

       Yeah.

    3. SGSarah Guo36:13

       ... has gatekeepers, everything that you described earlier. Um, does it...So, what do you believe about what would happen, what can happen in other fields? Or like are there lessons for, you know, lots of entrepreneurs that listen to this podcast?

    4. DNDaniel Nadler36:25

       Well, medicine is obviously very specific. The human psychology is not.

    5. SGSarah Guo36:29

       Mm-hmm.

    6. DNDaniel Nadler36:30

       And everything that was true and that we've seen through this sort of hyper-pace consumer internet growth curve adoption by the most traditionally skeptical knowledge workers shows that in any, um, industry or subfield that tech might want to touch, um, the same basic rules of the game psychologically a- apply, which is if you address people, um, as people and as consumers and if you speak to them in a way they've never spoken to before and it sort of hit them different, you know, in a way that no one's ever kind of come at them in that way before, um, that at minimum will be very refreshing and, and different and will lead to them, uh, considering the thing with an open mind, and in all likelihood, um, will break the mold that has typically been the rate limit of the adoption curve of whatever had defined

11. 37:27 – 41:13

    Rationalism vs. Will

    1. DNDaniel Nadler37:27

       that industry.

    2. SGSarah Guo37:27

       I think for a long time, like if you build it, they will come has been just like laughed at as an idea, um-

    3. DNDaniel Nadler37:35

       Mm-hmm.

    4. SGSarah Guo37:36

       ... amongst much of the tech community. Why, why do you think there's such skepticism when like there are the cases of, you know, consumer internet companies or things like OpenEvidence?

    5. DNDaniel Nadler37:45

       I don't think if you build it, they will come is true. I... And nor would I say that, you know, Apple or Steve Jobs is a story if, that if you build it, they will come. To me, you know, Apple or Steve Jobs is a story that, um, if you have extraordinary will to power and you see reality as malleable and, uh, you believe as Nietzsche says that, you know, ideas and rational thought are, are second order, you know, uh, after projections of the will, you know, then you'll succeed. (laughs) But that's not a, you know... (laughs) That's not a f- that's not a fairy tale that you can, you know, tell to Y Combinator kids or, or to MBAs, right? And there's this tension, um, and this has been discussed by many people at length, but there's this tension in the history of Western thought between, you know, rationalism and will, right? Reason and will or the intellect, uh, and will. And the Enlightenment was this sort of, um, Cambrian moment in the explosion of, uh, rationalism and ideas and this sort of, uh, faith, and it really is a faith because the irony of, of the Enlightenment is that the notion that reason is supreme was not arrived at through reason, but through faith. And there was this faith that reason would ultimately govern and that humans are in their first order, uh, rational and cogito, ergo sum in Descartes. And so much of everything that waterfalls down today to like what MBAs or Y Combinator kids believe which is just like, you know, "So tell me, Daniel, when you had the idea for OpenEvidence, were you in a coffee shop? What kind of coffee shop? What coffee were you drinking?" Like what, what was the circumstantial thing that gave rise to the idea, right? And all of that is actually just, you know, a, a, a derivative idea of Cartesian thought. And I think a more useful question for people than, you know, what coffee shop, what was the person drinking when they had the idea for something they admire is where can I find, um, a level of motivation that is almost, um, compulsive? Right? And that's different for different people. There's no one answer, right? There, uh, there are a lot of people that find that, um, from, uh, proving somebody wrong. Somebody said to, something to them when they were a kid that really just hit them in the right way when they were really psychologically vulnerable, and they've spent the rest of their life trying to prove that person wrong, whether that person is a parent or a friend or a teacher. I mean, how many famous examples are there of, you know, people trying to prove a teacher wrong that, you know, that is literally dead, you know? And that this person's... I've met these people. They're e- they're 75 years old and they're trying to prove a teacher wrong that's been dead for 30 years. But it turns out that those things work. Um, and those ingredients work. Um, and it doesn't need to be proving someone wrong. It, uh, it, you know, it could be, um, people that have, are born with an enormous amount of aggression and found a constructive way to channel that aggression out. You know, in my case, I was born with an, an, just an unbelievable amount of aggression and through a combination of training my intellect and just luck, um, I've found a more useful channel for that aggression. But you, you need to find this sort of perfect storm of things and it has very little to do with ideas. You know, the idea for OpenEvidence is the most obvious idea in the world. It's, it's the same as, it, it, it's no more creative than, uh, let's go to the moon. Let's do something really hard. What are the hard things?

12. 41:13 – 42:44

    Daniel’s Thoughts on Motivation

    1. DNDaniel Nadler41:13

    2. SGSarah Guo41:13

       Do you actively seek to find more motivation for yourself?

    3. DNDaniel Nadler41:16

       No. Uh, I, I, I, I... And, and actually the opposite. One of the things I think is unhelpful about the contemporary cult of psychoanalysis and, and psychology and psychiatry that sort of traces its origins to early 20th century and Freud and these guys is, um, it doesn't appreciate that in the analysis and description of something, you kill it. So I've actually resisted exploring trauma. I've resisted going back to the origins of my motivation. I've resisted going back to the origins of my aggression. I have kind of like a partially developed map from childhood and other experiences, but the second I feel myself going close to analyzing it, I, I, I resist the urge to analyze it because in, in the, in the analysis of something, um, is, is the deletion of it in a way. Um-

    4. SGSarah Guo42:09

       And you already have the well and the well is deep, so it doesn't... You don't need it.

    5. DNDaniel Nadler42:13

       I, I, I don't need more of it and quite the opposite. Um, I, I, I resist trying to discover,... a what the propulsion system is. You know, most propulsion systems originate from, from trauma. The, you know, the, uh, the, the, the, the, the s- what's now become the sort of famous, like, Sequoia methodology of, you know, Doug and these guys of, like, talking about your early childhood and all this stuff. I mean, I, I think there's a lot of truth to it, except, um, you don't want to go too close to that stuff because, because you'll actually kill the propulsion system in analyzing

13. 42:44 – 44:48

    Daniel’s Recruiting Philosophy

    1. DNDaniel Nadler42:44

       it.

    2. SGSarah Guo42:44

       What of this lens of motivation do you take to recruiting for your own team?

    3. DNDaniel Nadler42:48

       I quickly learned, w- in my first company even, that, um, there is a, there's only a moderate correla- there's like a .65 correlation between freakishly smart and output. I think you have to find people that are obviously exceptionally intelligent, but, um, to all the things I've been saying, have some propulsion system. They don't need to know where it comes from. But we've all met people that are extremely aggressive or extremely driven. They might have very little understanding of why they are. That's better, not worse. Better. And, um, and, and those are the people that end up, you know, that I try to recruit and that I seek out in recruiting, um, because, because then all the other stuff that is, uh ... You know, I actually don't like management, um, and I don't want to practice the art of management. And so much of management needs to come into play in the absence of those things, right? Like, a lot of this stuff, you know, I'm not an MBA by background. I've never ha- never gone to business school. I've never had ... I've never gone to one business school class. You know, but I have friends that have, and I've, I've ... There are people I respect that, that have done those things, and, uh, and, you know, a lot of that world is, like, how to motivate people, how to inspire people, like, how to give people constructive feedback and constructive criticism, and all of this stuff, and I think there's, there's definitely a body of knowledge there. Like, it ... You can definitely do better or worse at doing those things. But what I seek out in recruiting are the people for whom all of that is just entirely redundant, because there's just no, like ... (laughs) They're, they're just, they're driven on their own war path, and the best you can do is sort of get out of their way.

    4. SGSarah Guo44:26

       Awesome. Thanks for doing this, Daniel.

    5. DNDaniel Nadler44:27

       Thank you. Happy to.

    6. NANarrator44:29

       (Upbeat music playing)

    7. SGSarah Guo44:31

       Find us on Twitter @nopriorspod. Subscribe to our YouTube channel if you wanna see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. That way, you get a new episode every week. And sign up for emails or find transcripts for every episode at no-priors.com.

Episode duration: 44:48