because the patient had two other conditions, or was seventy-eight, or was pregnant, and got excluded from the trial. Then they showed up anyway, and somebody had to decide.
Brigham Hyde co-founded Atropos Health out of research that began at Stanford, where a doctor could ask what the data said about the patient in front of them and get an answer back in a day or two. This year that work moved into the EHR workflow itself. We get into what that takes. What a health system needs in place before go-live, who reviews the evidence before a clinician sees it, which specialties adopted first, and how the conversation with CMIOs has changed in three years.
Brigham Hyde, PhD, CEO and Co-Founder, Atropos Health
Megan Antonelli, Chief Executive Officer, HealthIMPACT Live
00:00:00 Intro: Welcome to Digital Health talks. Each week we meet with healthcare leaders making an immeasurable difference in equity, access and quality. Hear about what tech is worth investing in and what isn't as we focus on the innovations that deliver. Join Megan Antonelli, Jenny Sharpe and Shahid Shah for a weekly no BS deep dive on what's really making an impact in healthcare.
00:00:30 Megan Antonelli: Most of the decisions made in the clinic today will not be backed by evidence about a patient like the one in the chair. Not because nobody studied it, but because the patient had two other conditions or or was seventy eight or was pregnant and got excluded from the trial. Then they showed up anyway and somebody had to decide. This is Megan Antonelli, CEO of Health Impact Live, and my guest today is Brigham Hyde CEO and co-founder of Atropus Health. He has spent fifteen years building health data businesses. The company he co-founded came out of a project at Stanford where a physician could ask what the data said about the patient in front of them and get an answer back with the methods attached fast enough to be useful. Hi Brigham, how are you today?
00:01:13 Brigham Hyde: Hey, Megan. Thanks for having me.
00:01:15 Megan Antonelli: Yeah. So excited to have you here. You know, uh, my background in terms of we worked for a long time with the, with node health and network of digital evidence around evidence. And that that was ten years ago now. So it's so exciting to see so much of this work really coming, you know, coming to the forefront and excited to, to have you here today. Um, we always start with the same question. Uh, a new question we've been asking, but we convene a lot of rooms here, uh, a health impact. And I like to start there with kind of what, what's the best room you've been in? Could be a dining room, a conference room, or, you know, a wine room in Tuscany.
00:01:53 Brigham Hyde: Best room I've been in. I mean, uh, it's tough to beat wine country, I would say. Um, But, you know, I've also had a chance to, to host some dinners and, um, you know, one of the things I try to do at health every year the health conference is host a dinner at Carbone and we've been doing this the Sunday before the, you know, the, the meeting starts and, uh, every year it's been awesome, uh, to pull in sort of thought leaders in our space and always leads to a, a great conversation. Um, you know, one of the things that we talked about last year, and we have folks from health system leadership, from pharma, from venture, uh, from policy. Um, one of the things I love last year is everybody talked about sort of why they got into doing this. Um, it's sort of an interesting choice to decide to work in healthcare, but then also healthcare data. Um, and every single person seemed like had a story of patient impact that inspired them. Um, and it's cool to work amongst people that kind of share that, you know, wanting to try and make that impact, um, you know, and I also find ways to run successful businesses that can do that. Um, so always inspiring. I always learn something in that room. So also that is my favorite carbon. If people are carbon fans. Nice. Vegas. It's awesome.
00:03:11 Megan Antonelli: Yeah, I haven't been I'll have to put it on the list. I well, you know, I've been to health many times and it's such a great event. And what is great about it is, is how it brings together all the different stakeholders, which is hard to do, uh, in a, in an, at a healthcare event or even in a single room. Um, but you also have been on other side, you know, sort of the different sides of healthcare, which I love about your background. Tell us a little bit about, you know, where you sat and, and your background, you know, starting with, um, you know, in grad school and I think, uh, concert AI and then, uh, so tell us a little bit about that.
00:03:50 Brigham Hyde: Yeah, I was inspired to get into healthcare. Um, you know, I was, I guess, uh, good at the math and science side of things was a chemist in undergrad. But, you know, I had had a family impact from cancer and, you know, sort of thought about as a young person like, okay, great, I want to cure cancer. That's a great idea. Right? Um, you know, what you realize about people that go into that is it's a long journey and a battle we will always fight. But in that, it's a great thing to dedicate to. But I at first I was like, okay, you need to make new drugs to cure cancer. I was a chemist. And then then I realized, okay, well, these drugs have to be tested in clinical trials. And so I got a PhD in clinical pharmacology, which most people do that. Um, our clinical researchers work at the FDA design trials, and I learned a lot about the research side of things there. But along the way doing that, and I started my first company actually in grad school at Tufts, um, and went on to become relay technology management. Um, it seemed like data was going to be important. So in the early days of data, you know, we were starting to apply algorithms and mathematics to that in those days. That company was backed by nature publishing group Macmillan. We were focused on the early days of drug discovery and trying to find from the data we had, were there molecules or pathways that could be targeted? Um, you know, to create the next therapy. And so, you know, my earliest, uh, iteration of this was really on the drug discovery side. Um, I would tell you the data was pretty thin at that point. Um, there wasn't a lot. And so we were sort of, uh, rubbing sticks together in, in a sense. Um, but, you know, throughout it's sort of been chasing that idea of, you know, there's got to be answers in this data to help the next patient, whether that's developing a therapy for them or, uh, even optimizing their treatment. And so that's where I began, you know, I've been building, uh, healthcare data businesses and AI businesses across, uh, companies like DRG, Clarivate, where I'd sold my first business to, we, we launched, um, sort of healthcare data platform there and began assembling large reams of healthcare data for use by pharma and providers and payers. Um, I worked at Symphony AI, which is a venture fund in the West Coast that we were, you know, started an AI focused fund in twenty sixteen, which was early on that, um, although incredibly, uh, prescient by our founder. Um, and we founded concert AI and the oncology referral data space partnered with Asco cancerlinq, you know, uh, sort of cancer is a great place to, to leverage data. And, um, you know, we, we built that as a fast follower to Flatiron Health, um, and scaled that business dramatically. Um, you know, I worked at Eversana after that, a larger company, private equity backed, but again, trying to work with data to identify opportunities and patient services and hub and omnichannel, but also enrolled evidence and came about because my co-founders and I had gotten to know each other when they were working on this within Stanford. and it was the first time I saw, you know, an opportunity to bring. Uh, these these insights, this evidence back to the point of care and the Green Button project that my co-founder Nigam Shah had started was the first real use case that I'd seen. The key to it, and this really predates LLMs. And ChatGPT was building core automation, um, to be able to speed things up because getting the data was hard at that point, even if you had the data, turning it into evidence took, uh, weeks and months, best case scenario. And that just doesn't work for the point of care. You know, the patients here today, and I need to give them an answer. So we had shortened the time it took to produce evidence down from months to days within Stanford. And we spun it out. We've now, uh, allow us allow users to run new studies in seconds. Um, and even turning that automation into scale and producing answers even before somebody asks. So, um, it's sort of from my starting point of, you know, doing bench and clinical research and taking years to do a study to now we have these answers. Um, you know, at great scale and automation, it's just sort of amazing. And what inspires me there is that that means the answer for what to do for this patient is now, uh, out there impossible. And it's a matter of just getting it in front of the physician or the patient at the right time.
00:08:20 Megan Antonelli: Mhm. Yeah. An amazing story. And I mean, what, you know, he he was part of, uh, he certainly spoke at DMC. And, and I think he was just recognized as, you know, top minds in AI or top influencers in AI. You know, he's been doing such great work in this, in this area. And, you know, it occurs to me in terms of it's like the promise of all of this technology that we've been talking about forever. I mean, to go back to your very, you know, beginnings where it's like, we want to cure cancer. Well, that's, you know, we've been trying to use the data, use the technology to get there. It seems like for the first time we're kind. We're there in terms of being able to harness the power of technology to get to that evidence gap, you know, and we talk about it a lot. Tell us a little bit about, you know, when you see it and, and where you see it in action in terms of, you know, sort of solving problems. Um.
00:09:19 Brigham Hyde: Yeah. So solving that evidence gap is the mission of what we focused on from day one. It's the through thread and everything we do, um, to define what that is. Um, the stat that we use is only about fourteen percent of daily medical decisions that doctors have to make are backed by high quality evidence. Right? What do I mean by that? I mean, somebody ran a clinical trial or an observational research study compared treatments in a certain setting or patient group. And, you know, did the statistical analysis required to prove that it was actually caused or the effect was caused by the therapy of choice or decision of choice. And that's a big gap. Um, that's not totally surprising that that exists. There's a bunch of good and bad reasons why there's a gap. Like trials are expensive and take a long time to run. We're not going to run a clinical trial for every single patient setting or every patient subgroup. There's also certain populations historically, which just haven't been studied by the classic machinery, um, you know, comorbid patients, people with multiple diseases, which are most patients, by the way, um, other groups, uh, women's health is understudied. You know, an area that we look at a lot is pediatrics. Geriatrics just don't run trials in those groups. Um, there's other demographic groups which have been disenfranchised by that system historically. So there's just a whole bunch of reasons why that exists. But then you get to the question of, well, okay, how do we fill it? Well, in the early days of Atropus, the idea was that if you went to the literature or the guidelines or you might say up to date, maybe nowadays you say, um, you know, ChatGPT and you ask ChatGPT about what to do for this patient. Well, if there is no study out there, it doesn't matter how good your LLM is. Like it's not going to have an answer to bring back. And so what we focused on was running those studies in the early days. You could ask us that question. We'd run a study in real time and, you know, produce an answer. And we still do that. And many researchers use our tools to, to produce that evidence. But we also about two years ago sort of said, well, you know, one, we, we had a lot of studies. We'd run hundreds of thousands of these. And that started to mean sometimes when somebody asked a question, we already had the answer. We'd already run a study. And that made us say, well, why are we waiting for somebody to ask a question? Like, hey, we know a lot about the questions people are asking, but also like, we could look at that evidence gap and say like, okay, well, where are the questions in there that are worth answering and would be valuable? And we set out to do that. We sort of ate our own dog food. We started producing studies even before somebody had asked using our system. And the big breakthrough for that was last year. We published a study around an eval. We created our benchmark called Answered with Evidence. And this benchmark looks at thousands of physician questions and determines, can you answer it directly with evidence? And what do I mean by that? I mean, you take into consideration all the aspects of the patient, like not just is g o p one better than metformin, but is it better for this patient with this history for the forty five year old female Hispanic with CKD? And is there evidence specifically for her that this drug should work better? And so by publishing that eval simultaneously, we also ramped up what we call our library of studies. It's called Alexandria. And last fall we announced that we produced thirty three million new studies and or study equivalents. And just to put that in context, there's about thirty seven million studies total in all of PubMed or the literature ever. And so already this year, we've eclipsed the size. If you just think about size in terms of the amount of evidence that exists in all known published literature. Um, this starts to have an interesting implication in the current LM environment. Right. So we talked about, um, you know, ChatGPT recently announced that, you know, you can plug into epic, right? And you can basically pull in the patient's medical history into the context of a chat. Well, if you do that, or if you go to ChatGPT health and upload your, you know, your records and start asking questions. If ChatGPT doesn't have a study somewhere that matches to your exact history, there's no answer they can give you. There's a big demand coming from the LM side for more content, more evidence. And so, um, we have begun using that Library of Alexandria, the thirty three million from last year, but we've actually expanded that now to over five hundred million, and using it as training material for LMS so that they can answer those questions. And so in the modern world, you know, if you had told me five years ago, we would be, you know, quintupling the size of all known medical literature in the span of months. And then that would be used as training material for a foundational model. I don't know that I had that on my bingo card, but that is the way, the way it's gone at the end of the day. What are we talking about, though? We're talking about the ability to answer these questions with precision. And, you know, when we went through this past year or so of LMS gotten quite good at, you know, answering high level questions like, what's the standard of care, first line therapy for hypertension. Every arm in the world now can go look at the literature, figure that out and pull back a nice reference for you on that. But the minute you introduce one, one level of sort of patient complexity to that. Like, okay, I want first line hypertension, but I only want it for an eighty five year old with lupus. What's the recommendation there? If you don't have access to this precision evidence that studies run in patients with eighty five year old with lupus, then you really can't answer that with any evidence that could be used to back a decision. So we view this as an increasing precision or personalization of care. And the expectation is now that you're able, uh, you know, able to answer these questions that you answer them with precision evidence. So our goal is to exceed two billion, uh, study equivalents this year. Uh, and, uh, we will have recently published by the time this comes out, our performance on the answer with evidence benchmark, which we've updated to include precision evaluation. So the, the benchmark we're putting out literally takes patient history and a question about treatment and determines, did you actually answer this, including the context of that patient with direct evidence or not. And our ability, our five hundred million, uh, scale library is outperforming significantly all sort of base open LMS simply because we just, we have an answer when a lot of people don't. And we think that matters a lot in this stage of the market.
00:16:13 Megan Antonelli: Yeah. Wow. I mean, just the, the quantity of the studies and, and sort of that, you know, ability to answer the questions when it comes to where, where is the data coming from, where, you know, how, how are you building these studies? Explain that to me.
00:16:31 Brigham Hyde: Sure. So, um, our technology installs on patient level data within health systems. Um, you know, and it's a federated technology, so the data doesn't have to move around. There's sort of less friction around HIPAA and security. It can stay where it is. And then what we're doing is running these studies and increasingly rerunning even our library locally on these data sets. So your you can take a health system, take our system, rerun all five hundred million studies locally like. Here's the answer. A health system, a health system, B. And so that localization is not just to patients, but to settings of care, which we actually think is really relevant. We also just over time, going around working with health systems and other data partners, we have what we call our evidence network, which is the network of these nodes of data we've installed on. We have hundreds of millions of patients data they're de-identified that can be used to run new studies and generate this content, uh, EHR data, claims data, omics, and other specialty data sets. So, um, we have access to a ton of data to do that, that enables us to answer all these questions and produce the answers and evidence out of them.
00:17:43 Megan Antonelli: Amazing.
00:17:45 Break: You're listening to digital health talks. When we return, we'll continue our discussion on how technology is revolutionizing healthcare delivery. Stay with us to hear more insights on creating sustainable, patient centered digital health solutions.
00:18:03 Megan Antonelli: And in terms of the workflow of it, right. I mean, you know, this is the goal, right? That is right time, right place, right right information. How is the physician actually interacting with the system? Is it, you know, where does it live?
00:18:20 Brigham Hyde: Yeah, it's a really important topic. So we launched five years ago with sort of an app, right? And somebody could enter login, enter a question, and we would provide the answers and the evidence behind it that still persists. And there's even increased capability of those tools to do different things. But increasingly on the provider side, it it's there's been sort of a substantial jump in where health systems want this information showing up and the type of integrations we can have. We have a really big partnership with Microsoft, um, both on the tech side and on the ambient side. So with Dragon Copilot, um, we're actually now one of the first seven agents live in the Dragon Copilot, uh, general release. And what this allows us to do is the doctor is sitting there recording the visit with the patient. While that's happening, we're, you know, listening to that transcript, pinging the patient history. It hits our agent in the background and produces, uh, answers to questions that would be important for this visit, inserts it in epic in the note. And as the doctor swivels over, it's already there. So like they didn't even have to think of a question. And we're popping in questions that are relevant to that particular case, that visit, including the detail of that patient's background. So like, you might not have thought of it. I mean, some of the feedback we've gotten is people are like, oh my God, I didn't even think to ask that. That's great. Like I needed to know that for this patient. And the more interesting part of this from a user experience is like, it's not stop. Open an app, ask a question. It's like it just happens in the background and you're getting this evidence right where you want it. We've done that in the ambient workflow. We're also doing it in pre-visit summaries. And increasingly we're also doing it in prior auth support. So for instance, like, okay, I see the evidence you present and great, it says I should prescribe the GLP for this patient. The very next thing you got to do is go get that approved by the insurance company. So why not just package up that evidence, send it through for approval to the insurance company showing why this patient needs this therapy. So we think of it as going from a pull like somebody stops, ask a question to a push at the right moments throughout that interface. And I view it in terms of physicians as like, you know, if you put strong evidence, clear evidence in front of a physician, they know what to do with it, right? It's about getting in front of them at that right moment that's relevant to what they're doing and also not overloading them. I mean, like, we've learned a lot about this, like, what's the threshold? You want to give them ten alerts. They got to click away. Like, what are the really critical things you want to pop up and what drives engagement?
00:21:01 Megan Antonelli: Yeah. Well, and that's, I think there's an interesting piece to that in terms of, I think I was just reading, I think, uh, Ezekiel Emanuel, probably a post on LinkedIn because that's where I spend too much time reading. But it was about an article he wrote about, you know, what, what physicians cognition is needed and needed now versus needed in the future with all of these tools. Have you guys looked at that in terms of, you know, there was a time where they need to know all of this stuff, you know, in their heads and now the guidelines are going to be there. The evidence will be there pulling that. What is that? You know, does it change things about the practice of medicine that that you guys have looked at?
00:21:44 Brigham Hyde: There's a couple of things here that I think are relevant. Um, one, you're sort of articulating like, it's so easy to get the answer served to you now. Um, and it can happen at many different places, very automated. Right? So in order for that to work, clinicians have to trust what's coming through. And I think, you know, last two years there's been all this discussion about hallucinations. And I mean, we're kind of working through that. But we've always taken the tact that like trust is the key to this. And so what we do is we, number one, we published everything that we do and how we do it. So it's referenceable. We've also been peer reviewed hundreds of times, right? So using the peer review system to build trust. We also do things like data quality scoring. It's like right on the cover tells, you know, garbage in, garbage out. Can you trust this data for this answer? And that eval I mentioned answer with evidence shows up with every answer. It's a yellow green, red badge, right? That tells you like, is this, you know, going to work? And we publish of course, how that's working. And, you know, we think you have to put that front and center. The other thing that we're doing, which I think, again, will be out by the time this goes up, is that I'm producing five hundred million studies. Okay. On one level, that's really exciting. On the other level, the question I always get is, well, are those studies peer reviewed right now? Getting peer review of five hundred million studies is a lot of work. So one of the first things we did was we took one percent of those studies, just a slice across the library, and we paid to have it peer reviewed by an outside publisher. And basically using that, we can validate, okay, and use that as a test. Right? Of okay, I'm saying it's green on my badge. Did it pass peer review? And how do we factor those things together? So we always offer any answer we produce. There's a button you can one call in a clinician on my side if you want them to review it. And we'll do that within a day or two. Two you can submit the study. You've just run for peer review and it'll be done within two weeks. So like there's that human layer around it when needed and then taking a step to, to get it validated both with these benchmarks and with external review. So we think it's just crucial, like you have to, um, you have to do this validation and we try and lead front foot with it because if people don't trust it back to your cognitive overload question, then they'll stop using it, right? So you have to get that trust. I think one other part to the question you asked that I, I think about a lot is like, what am I really saying here with five hundred million study equivalents and precision study equivalents, right? Like a different piece of evidence for you than for me. Right? I'm sort of saying we're moving from this place of a doctor being able to say, okay, this is the guideline. I treat all the patients like this to saying, I might treat a patient different every single day because the evidence says so. And that's an interesting dynamic, I think health systems and value based care organizations are structured the opposite, like they want to reduce variation in care. However, if it leads to better outcomes, both financial and clinical and experiential and workload, then it is something we can follow through to. Ultimately, though, I believe it's the patients who are going to demand this. Look at the rise of, you know, longevity care, the function, health, even ChatGPT itself. People are going there and asking questions. People don't want to be told, well, this is the guideline based on a trial of one hundred people, none of which look like me. Right? Like they want a precision answer for them and they're going to demand it. Ultimately, the change here that'll be required is does the payment incentive system shift? And you know, I've spent time in DC talking to the administration about this. You know, like potentially now because we now have this evidence. So we didn't have before. Could you streamline a lot of the archaic processes that were simply related to the fact that sometimes there wasn't evidence to use this or that. And so we have these things like prior auth. We have these, you know, four letter words, if you will, on the payer side that people like hate. Well, what if we could reduce those burdens and still get better care just by having the evidence for what to do? So the ultimate arc here, you know, the Overton window, if you will, on the adoption of real world evidence into care, relates to the incentive and payment system, and making sure all parties can benefit from the fact that we have this this evidence. But I think you'll hear it from the patients because they will demand it.
00:26:20 Megan Antonelli: Right? I mean, it's that it's the vision, right? I mean, individuals now sit in a place where, whether it's ChatGPT or Google or however we've been, you know, using technology to kind of get answers to health questions at two o'clock in the morning. And, you know, you're on a Reddit stream for too long, not that I've ever done this, but, you know, it happens. Now what you're able to do is collect, you know, connect the individual to the body of evidence of actual medical practice as opposed to anecdotal, you know, sort of sharing. And some of that is all valuable. But, but that promise of that, to your point about what people really want is exactly where I think we're headed. And as we look at kind of connecting that data, connecting the data from our wearables, connecting the data from our, our lives and our socioeconomics and demographics and all of that to what treatment and what path of care works, then engagement increases. Right. I mean, that's what I, where I think we've always missed the mark, right? Because when it is, you know, just eat like this, do this. And it works sometimes, you know, but not always. Then you don't have that engagement. I mean, that's what you know, to, to go where they were.
00:27:40 Brigham Hyde: At this point. You bring up something interesting to me, which is like, you know, the whoops, the aura rings, you know, even even the genomics world. Right? Like the gallery test, whatever. Right. You know, the we're awash in data even as individuals now, but in making decisions for a treatment, you really need to convert that data into evidence. And what like that means you have to do the method correctly, right? As opposed to just getting a trend. I mean, you probably looked like your, your whoop app or your aura app and you're like, okay, there's like a trend here, but like it's a trend. It's not something where we've said holding all other things constant, changing only one variable and making sure we've looked at this across thousands of patients. Similar that this is the thing you should do, right? There is there will be a different outcome for you if you do this. That's the the leg we're heading into next. And that that word evidence and that method that that implies is actually how all of healthcare functions, every guideline in the world. Okay? Every piece of training is evidentiary in nature. It's not some data trend, right? And that's the key principle. And even if you look at like a payer policy and you look at why are they approving rinvoq for me or not? Right. It is cited back to a study on their medical policy document, why that decision is made. And so it's the need to create this new evidence is not simply to, um, you know, answer a question today. It's actually to sort of meet healthcare where it is because healthcare can't transact. A doctor can't practice unless there's evidence the payer won't pay for it unless there's evidence. Right. So converting this, this data layer we're in now into the evidence layer is very much what we're focused. And by the way, doing it correctly really matters. So back to trust. You know, you can do this wrong and doing it wrong as has bad implications.
00:29:46 Megan Antonelli: Yeah, that's I was absolutely going to say it all goes back to the trust. And that's, you know, that's literally why the scientific method exists. Everybody. That's what evidence is. That's what science is. And that's why we're all here, you know. But that's right. Yet, um, sometimes, uh, you know, we all forget that. But it is so important to put that rigor of, you know, the scientific method and evidence on, on all of this data that we have. But it seems to me from your background and from, from where you've been, that's like exactly where, where you live. So amazing that, uh, you know, you've, you've sort of been been taking us on that journey. But, but for you personally to have been in that kind of, um, data, data deluge of how do we take the evidence, this data and turn it into evidence from the pharma side, from that data side, and now to the provider side and bringing it right to the bedside. So awesome. Well, I, you know, I could talk about this all day. Literally. I'm excited that you'll be at Healthimpact in October in New York so we can chat about it. Our last question is always about kind of. We have a segment called Five Good Things. What. What's the good thing happening in healthcare that that you're, that you're excited about right now?
00:31:01 Brigham Hyde: I innovation on the therapeutic side is getting super interesting. You know, there were just two big approvals around cancer vaccines, Replimune and Merck, Moderna. Um, you know, it gets back to this precision trend. I mean, I don't think we're a long way off from being able to design very personalized therapies. My point about that would be you really need personalized evidence for personalized therapy, right? They should match together. But you know, it's that bending the curve on drug development of personalized medicine, both having a progressive FDA currently and in the past, I mean, FDA has been progressive, most of my adult professional life. And, you know, the potential of, of reducing, uh, and speeding up that process, reducing the burden of it means that I just feel like we're headed towards a world where, you know, you'll get your peptides and your your cancer vaccines made at the corner pharmacy and, you know, it'll, it'll all be perfectly matched to you. Um, so I'm very optimistic about that. Um, because there's been a lot of pent up energy, particularly driven by the data side that's been able to identify a lot of these opportunities is now being matched by regulatory and development process that can, that can speed those things forward. So super excited about where that can go. You know, the other part of it, I think, is, you know, the role of the patient in all of this is changing. Um, you know what these tools like ChatGPT and other things start to enable for them, you know, taking medicine out of the, the space where, you know, the, the quality of care was not equally distributed. Um, you know, uh, my wife's an oncologist, like, you know, she's the first one to tell you like you have a family member gets sick. Like which oncologists? The one you should talk to, you know, but even navigating, that's been so difficult. So if you can flatten the information, an evidentiary side of that with tools that give you access to that. I just think, um, everybody should be able to receive the same type of care, um, no matter where they are. Um, it should be evidence based.
00:33:02 Megan Antonelli: Yeah, one hundred percent and get, you know, accessing that evidence and, and the body of it that, that is that we're able to access because of the technology is really amazing. So, well, thank you for the work that you're doing on that. Let our audience know where they can find you and follow your work.
00:33:18 Brigham Hyde: Yeah. I mean, I'm, I'm fairly active on LinkedIn and I respond to people. So shoot me a note. Um, also, we'll be at health impact this fall. Um, and soon after this, uh, and that's prohealth dot com. Uh, you can try out our stuff, but a Microsoft shout out, you can actually go right now to Microsoft Teams and download the evidence agent and start asking you questions. Uh, so we're, we're live right in that environment and backed by all the evidence we talked about. So go try it. Check it out.
00:33:47 Megan Antonelli: Amazing. Well, that's I as an aside, I last I saw a thing about, you know, sort of vampire programmers that the engineers up in the middle of the night coding because now they can and it's so productive. Now we're going to be up in the middle of the night asking questions and getting real answers. But, you know, it's all for our health and it engages us more. So all good. Well, thanks so much, Brigham. So nice to meet you. Look forward to seeing you in October. To our audience, thank you so much for joining us. If this conversation got you thinking, that is the whole point. Share it with someone who needs to hear it. Subscribe to Digital Health Talks and follow us on YouTube at Health Impact Live. This is Megan Antonelli, CEO of Health and Back Live. And this is digital health talks. Let's keep fixing health care one conversation at a time.
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