Conversations Are the Source of Truth in Healthcare with Abridge CEO Shiv Rao

In this episode of No Priors, Elad and Sarah chat with Shiv Rao, MD, founder and CEO of Abridge. They dive into how Abridge is reshaping healthcare by creating AI tools that enhance clinical documentation and improve doctor-patient interactions. Shiv shares his thoughts on building trust with established healthcare systems, giving agency and time back to clinicians, and what makes the healthcare AI opportunity different today. They also discuss Abridge’s approach to developing and launching AI products, along with Shiv’s journey in founding Abridge. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @ShivdevRao Show Notes: 0:00 Introduction 0:35 Abridge’s Story and Vision 5:30 Strategy for Customer Choice 7:41 Healthcare AI Opportunities 11:24 Navigating Incumbent Partnerships 14:26 Doctor-Centric AI Solutions 19:54 Abridge’s Future Plans 22:13 AI’s Impact on Healthcare 28:43 Shipping and Iterating Products 32:50 Shiv’s Journey to Abridge

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:05Sarah Guo?

  Hi listeners and welcome to No Priors. This week, we're speaking to Shiv Rao, CEO and founder of Abridge, an AI company that processes medical conversations to unburdened clinicians from clerical and financial work, allowing them to focus on patient care. A practicing cardiologist at UPMC, Dr. Rao has recently led Abridge to secure a $250 million Series D raise. Join us as we explore how AI is transforming healthcare delivery. Shiv, welcome to No Priors.

• 0:32Speaker 01

  So excited to be here. Thank you a lot. Thank you, Sarah.

• 0:35Speaker 02

  So Abridge has been around for about seven years. Can you tell us a little about how the company has evolved over time, what your starting point was and what you're focused on now?

• 0:42Speaker 01

  Yeah, absolutely. So we started Abridge in 2018, so it's been a minute. And everything that we've been building since then is really based on the same thesis. So that hasn't changed. And the thesis for us in healthcare delivery is that we don't think doctors or nurses are going to get fully automated over the next 10 years. And so what's the first signal in healthcare delivery? We think it's a conversation. It's a dialogue between a professional and a patient. And we believe that those dialogues are really upstream of so many workflows in healthcare, and that's where we focus. And so we focus on clerical work first, but then that's a sort of wedge for us to expand into any number of different value propositions over time.

• 1:21Speaker 02

  Could you tell us a little bit more about some of the products that you have currently and how people use them day to day? And what sort of customers you work with? Just to give context to our listeners in terms of what business do you have? And what do you focus on?

• 1:31Speaker 01

  I guess, sort of, starting at the top, what we do is we unburden clinicians from all the clerical work that crushes their souls at night. And a little bit more color on that. So two out of five doctors don't want to be doctors in the next two to three years. And 27% of nurses, per a JAMA article that was published last year, don't want to be nurses in the next 12 months. And so we have this crazy supply and demand mismatch. It's a really, it's a public health emergency. Patients are having to drive five, six hours from rural health settings to see a clinician in an inner-city setting that could save their life. And so we've got to do something about it. And I think that's where technology has a role. Is finally sort of being recognized and acknowledged at the highest level.

• 2:14Speaker 01

  Like, the entire healthcare industry understands now that they just need to find a way to assist, augment, and automate any number of different workflows. And so where we come in is that we unburden clinicians from a lot of that clerical work that they hate to do. So they can walk in a room, they can, you know, hit a bridge, have a normal conversation, and talk about any number of different topics in whatever order. But when they hit stop and swivel their chair, there are notes there. But it's not the note that you might expect, you know, that my 14-year-old daughter could sort of create using, you know, an off-the-shelf model. It's a note that checks off all the different boxes across not just who the clinician is, what their specialty is, what system they're a part of, who this patient is, what insurance plan do they have, and what geography.

• 2:59Speaker 01

  Not just like the clinical note, but also what the billable note is, if that makes sense.

• 3:03Sarah Guo?

  Can you actually explain the difference between those two things? Like, a clinical versus a billable note?

• 3:08Speaker 01

  It's a great question. So, in this country, we're not compensated as doctors for the care that we deliver. We're compensated for the care that we documented that we deliver. So every single one of these notes is actually a bill. And that's why there's just like so, these are really high-stakes artifacts, not just from a clinical communication and patient outcome perspective, but also from a revenue cycle perspective. But I think another key insight for us that's served us well for these last several years has been that healthcare is not homogeneous. And, you know, that healthcare industry umbrella underneath it, on one end of the market spectrum, there's a direct primary care doctor down the street who's taking cash payment out of pocket off the insurance grid.

• 3:50Speaker 01

  There's an independent PCP, a really small provider group, like mid-market, you know, that kind of stuff. But then on the other end of the spectrum, there are the large health systems. There are the integrated delivery networks, the academic medical centers. And what we decided to do, and I think what served us incredibly well, is we made the strategic decision years ago to actually run into the hardest part of the market, that large health system end of the market, as opposed to the small practice or the mid-market or the independent, you know, DPC. See doctor down the street. And the reason why we went there is that the barrier to entry and the barrier to good enough, I should say, is really, really high. And that's where we felt like we could flex a lot of our advantages, a lot of our differentiated muscles.

• 4:34Speaker 01

  Like, we have a lot of science at the center of our company. Our chief technology science officer is this guy named Zach Lipton. He's a professor at Carnegie Mellon. He's full time with us, but he's been able to recruit, you know, a pretty amazing team of machine learning engineers and scientists who can really kind of reach their hands deeper down into the stack to be able to sort of meet that bar for all these large health systems, where we need to be good enough for not just the individual doctor in whatever specialty. We have to be good enough for all the different doctors and all the different specialties and all the different settings, outpatient, inpatient, urgent care, emergency rooms, and also in all the different spoken languages. And so the barrier to entry, the bar for good enough, is a lot harder.

• 5:17Speaker 01

  But running into that end of the market allowed us to sort of compete with just pretty much one other company, while a lot of the other startups were starting mid-market or, you know, down-market individual, like, primary care doctors, with the hope probably over time that they could recruit the people and aggregate the data and, you know, do the post-training or whatever else to be able to swim upstream over time.

• 5:38Speaker 02

  You're a practicing cardiologist yourself. I'm a little bit curious how that's informed with building this product, what customers to focus on first. I mean, you have kind of like a who's who of customers in terms of Kaiser, Sutter, you know, others. So I'm a little bit curious like how this has impacted your strategy in terms of you yourself being an MD and physician.

• 5:55Speaker 01

  So a little bit of a story about the company and myself. Like, we started in 2018. Prior to that, I was a corporate VC at a large health system called DPMC.

• 6:04Speaker 02

  Sorry to hear that.

• 6:08Speaker 01

  So I played VC. I was a faux VC. I was a faux investor and put a lot of money into startups, but also a lot of capital into Carnegie Mellon. We started a machine learning and health program. And that's where actually I met Zach, our CTO. And we're not a spin off. We didn't like spin out of like EPMC. I quit that job to start the company alongside some other folks from Carnegie Mellon. But a couple of lifetimes ago, I went to Carnegie Mellon as an undergrad in the middle, became a cardiologist. And I still see patients. So this last weekend, I was on call in the hospital. I do about one weekend a month and I'll do every Thursday night on my phone call as well. So just for like emergencies, like heart attacks in the hospital that I need to come in and sort of help address. But it's like an incredible privilege.

• 6:50Speaker 01

  It's helped us not just have this sort of scientific center in our company with folks like Zach, but also have this sort of like for clinicians, by clinicians ethos where I think we sort of get workflow and have that domain expertise to not just build that product in a better and more differentiated way, but also kind of understand go to market. Like, how are we going to sequence where we focus over time? And you mentioned some of our health systems like Kaiser and Sutter. We're alive and over, I think it's like over 110 health systems right now. And the speed with which we've been able to like land these multi-year agreements, I think it's pretty historic. I don't think I've ever seen anything like this. When I was investing at UPMC, if a startup had like a handful of logos a year, it'd be

• 7:35Speaker 01

  like high fives all around the room, like amazing bottles of champagne. And so this is a really, really different moment right now, I think, AI and healthcare.

• 7:42Sarah Guo?

  How would you explain that? I'm sitting on the board of a healthcare technology company now. I believe in this, I'm in this boat. But for over a decade, looking at healthcare technology as another VC on the outside, it moves really slowly, right? In general, there are lots of reasons the market has been hard. Like, what do you think is different today? I mean, it's easy to say at the abstract level, AI, right? But like, how does that play out for your business?

• 8:09Speaker 01

  A few stars getting aligned at exactly the right time. And one star is like post pandemic, the amount of burnout that was in the, that's been in the industry still. And we just sort of like stretched, I think clinicians so far beyond their limits that they're leaving the profession. And then health systems didn't know what to do. And all of a sudden, so many hospitals were just shutting down because they couldn't staff them anymore. And so I think, and the cost is sort of like hiring another clinician is like close to a million dollars. And it takes a long time. And so I think that star is a really important one because people have talked about clinician burnout. People have talked about trying to, you know, create a better user experience in healthcare for I don't know how many decades.

• 8:53Sarah Guo?

  Yeah, it's not new. Yeah.

• 8:54Speaker 01

  It's not new, you know, but I think it's, it's not, it's not lip service anymore. Now it really, really matters. And if, if that was one star that aligned, I think the other one was generative AI and chat GPT coming out in early 23. So we started in 2018, three months after attention is all you need. And if Zach was here, he'd be, you know, very quick to say, well, everyone knew about transformers before that paper came out. And certainly like the research community was already interrogating it. But I think when we started the company, a part of what we wanted to do was interrogate all things related to these pre-trained models in healthcare. And specifically in relation to these sorts of workflows, these clerical workflows. And so we published any number of different papers.

• 9:39Speaker 01

  And Zach and team won best paper at EMLP, I think in 2021. So we did a lot of really deep research. But when we started with Bird or buyer bird, a long former or Pegasus, all these other pre-trained models. We, we got to the point where we had a product that worked. And I remember in 2021 and 2022, I think to your point, like we were demoing. And it was just like, oh, cool story, bro. Like people would look at the demo and be like, put your hands up. Was that real? And then be like, okay, cool. I'll call you in like five years. It was like, it was like, what are we doing here? And really felt like we were eating glass, but things really started to shift. And I don't think I recognized until 2023 that we were actually pre-selling, you know, the whole time. Like 2021 and 2022, we were like preparing. The market. And then when ChatGPT came out, they called us back.

• 10:23Speaker 01

  You know, all these CIOs and CMIos called us back and said, oh, I get it. You were talking about generative AI. You had a dinner about generative AI in 2022. I get it now. Like, let's try it. Let's do a pilot. Now, I think where I think we YOLOed it in 2023 is that we could have decided to go to the small mid-market or to the independent PCP. But we were like, no, let's just, let's go to the large academic. Knowing full well that the amount of virality on that end of the market is insane. Like, all these CMIos and CIOs are on WhatsApp groups every single day and talking to each other. And if you screw up with one of those health systems, maybe two of those health systems, you're kind of done for like a couple of years, probably. You don't get another shot on goal for a really, really long time. So you gotta hit it out of the park.

• 11:07Speaker 01

  And so we started with University of Kansas Health System, then Emory, and then Yale. And they were all home runs. And then all of a sudden, we saw like we were starting to, yeah, kind of go viral, if you will, at the enterprise, like executive level across, across the country.

• 11:23Sarah Guo?

  One of the other challenges in that end of the market is there's a lot of incumbency in the existing systems. And, you know, you, you were on the provider side. You understood this well. How did you think about navigating partnerships and like the systems people already had?

• 11:38Speaker 01

  Thinking about ecosystems is super important. And really, like, the only currency that ends up mattering in healthcare is trust. Like, can you somehow find a way to be trustworthy very, very quickly? Because especially on the provider-facing side of technology, like the, this is, the stakes are high. Two days ago, I'm just coming back from a red eye from like Vegas, where there was like a big healthcare conference called HIMSS. And while we were there, we met with an executive at a health system who was sort of asking us about like our stack, our infrastructure, how we're going to be able to scale and like redundancy. And he was explaining to us that we are now part of his, his health system's infrastructure. Like, we are core infrastructure. So if we go down, the entire health system goes down.

• 12:23Speaker 01

  They're not making money anymore because I sort of explained that these nodes are essentially bills, at least the way that we generate them. Thinking really hard about that responsibility and then figuring out if we're going to market on that end of the spectrum, then how do we also sort of partner with the right players, earn the trust of like the right ecosystems so that we can sort of absorb some of that trust? And it's easier said than done, but in 2022, as an example, like we had won that paper, that EMLP best paper. But in 2022, folks from like large healthcare technology companies had sort of started to take notice, not just because of that, but because of introductions and they had heard that we had something that worked. Now, the, you know, who we're, who we're competing with is Microsoft.

• 13:06Speaker 01

  That's who we essentially almost like always have to do a head to head against. And it's usually like three to four weeks and then we sort of move on from there. And, and so far we've never lost a head to head in these last like few years of, of doing this. But when we kind of like enter into a health system, I think that being able to, to demonstrate that you can kind of integrate with their stack is so important. And so we were able to forge relationships with players like Epic, you know, as an example. In 2022, we demoed up and down, I feel like the entire company and we were able to build trust. And at that point in time, like the large competitor, they had a solution in the space, but it was humans in the loop.

• 13:49Speaker 01

  So it was really like Indians in Bangalore who were listening to audio and writing the note and wizarding it back into the medical record. And there was like, it would take time, you know, for all of that to, that workload to go down. And so that's why people would always ask us like, put your hands up, is that real? And again, like these weren't even LLMs yet in 2021. We were using like Birt and BioBirt and like, you know, all those other pre-trained models and T5 and, and other sort of summarization techniques. So when LLMs came out and when we started to really work with them in a serious way in late 22, 23, oh man, like game was totally on for us and we were able to really, you know, take it to the next level.

• 14:26Sarah Guo?

  Now that you've gotten to all of these systems, you obviously had to get to a certain quality bar to get deployed anyway. Like, what do you think about what is next in terms of being able to use that scale?

• 14:36Speaker 01

  Absolutely. So maybe useful to sort of break down the stack a little bit, and then we can kind of talk about where we're going and where our research team is focused. So at a really high level, like, the core part of the stack is speech recognition. And so that's where we have like an in-house model. It's really a set of models that create best-in-class output for healthcare conversations.

• 14:57Sarah Guo?

  Can you help us understand that? Because like, you know, an outsider looking at AI and trying all of these, you know, voice-based experiences might say like, it looks like a solved problem. There's an API for that.

• 15:07Speaker 01

  Well, there are APIs, but I think if you're really trying to differentiate, where like 3%, 5% error rates can make a huge difference. Our ability, for example, to lean into the way a doctor pronounces a new oral oncology drug, an oral oncolytic. And, you know, I'm convinced no doctor knows how to pronounce any of these medications. And they all have their own way of saying these drugs. But we have to lean in and actually recognize the way they say them. And we have to recognize all the different symptoms, medications, diagnoses, and procedures across all the different specialties. And we also have to be multilingual. Because, you know, sort of like a bit of history of the voice game in healthcare is that before this world of generative AI and conversations and dialogues, there were dictations.

• 15:51Speaker 01

  And that's where I would go into a clinic, I'd see a patient, and then afterwards I'd pick up a dictaphone, or maybe my phone, and I'd start to just rattle things off as fast as I could. I'd say like, 25-year-old female with a past medical history of diabetes and hypertension who presents with shortness of breath, next line, next sentence, capital B, past medical history, colon, next line. You're just kind of go as fast as you possibly can. You're going through like 20, 30 dictations in the course of like 30 minutes. And it's lossy. Because what you're dictating off of is chicken scratch, like, stuff that you wrote on a piece of paper while you were in the room. And, you know, later that day, or maybe that night, and the doctors call this pajama time, you're hoping that you'll remember the details. Sometimes I would write on a piece of paper, "tall guy in the Mets hat." And that was supposed to trigger all my memories around who that tall guy was and what his, like, symptoms were. And then it would take

• 16:36Speaker 01

  Start to mesh with another patient who had the same symptoms.

• 16:38Sarah Guo?

  It's not encouraging. Yeah.

• 16:39Speaker 01

  Not encouraging, not good for doctors, terrible for patients, not good for like revenue cycle or billing, so lossy, you know, and so I think in this new world, what we have to do is recognize all those words, those medical ease, like all those medical terms. We also have to recognize all the different languages because it's not a dictation, it's not a monologue, you have to lean into whatever the patient speaks. And so today in California, we'll probably do 50,000 conversations at least in Vietnamese and Haitian Creole. Today in Boston, we'll do thousands of conversations in Brazilian Portuguese and Spanish. Today in Indiana, there's a doctor who's speaking in Punjabi to her truck driver patient population at Reed Health. But regardless of what language anyone speaks,

• 17:22Speaker 01

  our job is to create the note in English within seconds and put it right into the medical record and all the different discrete fields for them to trust and verify. So part of what we do on the speech recognition side is we're like sampling the audio so that you can have these polyglot conversations where you're speaking in like 10 languages in the same conversation, not that that has ever happened, but we'll still do a good job because we've been able to bias the model towards whatever the language is that that we're hearing at any given time. But then obviously, we're on this treadmill of always improving, always recognizing the latest FDA approved drug or the, you know, the latest pronunciation. That's just speech recognition. I think as we move past speech recognition in like the core part of our stack, you start to get into all the

• 18:04Speaker 01

  text and language work that we do. So there are models that in a sense sort of abridge the conversation where we're trying to like distill what would the doctor need to communicate to other doctors and nurses? What would the doctor need to communicate with the patient? Because that's also like an artifact that's created, it's called an after visit summary. And then what would the doctor need to create for revenue cycle? Because these are bills. And I think a part of the reason why clinicians have burned out or burning out is that they're serving multiple stakeholders all the time. And so it's really hard for them to sort of focus on the one person they went to medical school or nursing school to actually serve, the patient. Instead, they're always thinking in the back of their head, what would a rev cycle

• 18:48Speaker 01

  person think of this note? Oh, I'm gonna get a bunch of emails for how crappy this thing is, or I didn't like elucidate exactly, you know, where the symptom was or what the differential diagnosis was. And so that's part of the challenge. And that's what we're doing in the background. And obviously, these are like agentic systems in the background that are listening for all the right things. And so distilling and then structuring data. So those are information extraction models where we pull out those symptoms, medications, diagnoses, procedures, we map them to data dictionaries. And then of course, there's summarization. And the way you summarize for anyone looks different. So if I wrote a note as a cardiologist, and in my note, I wrote transcatheter aortic valvuloplasty as a recommendation for my patient. And then my patient sees that term, and I never said

• 19:30Speaker 01

  that to them. Understandably, I'm gonna get like blown up, right? I'm gonna do emails and phone calls, asking, what was that term? You never said it. I looked it up. It sounds scary. And so what we can do is do that sort of style transfer across all the stakeholders that clinicians serve and sort of meet all of their different needs. And that, I think, you know, has allowed us to serve the executives, like the buyer personas in large health systems.

• 19:54Sarah Guo?

  Think about what's next and like, you know, greater ambition for a bridge. Do you have to choose to go down one of those paths first in terms of that translation? Or you just choose like totally different clerical workflows?

• 20:06Speaker 01

  I think it comes back to that thesis. And so if you really believe, as we do, that health care is about conversations, that it's like one of the first, you know, original signals in healthcare, then you start to see that any number of different workflows are beyond it. It's not just clinical notes. It's also orders. After I see a patient, I might say to my patient, like, let's start showing metoprolol, or let's get a CT scan. And so we talked about an order. So we can distill, we can extract those orders, we can structure them, and we can place them in the medical record. What's after orders is a claim, is a code, is a bill that goes to the insurance company. There's all things revenue cycle. There are clinical trials that come up in a conversation as well. Whether

• 20:50Speaker 01

  I know it or not, maybe this patient in front of me has inclusion and exclusion criteria. Criteria for some trial that could save their life. And so what if some I had this superhero power and in the moment at the point of care, I was being told by a technology at the right time, like, hey, Shiv, like this patient in front of you has inclusion and exclusion criteria for something that could save their life. Do you want to bring it up? Here's the information. So that's another sort of aspect of where we're going already. But then there's clinical decision support. And so in many ways, I'd say clinicians, I think, you know, they see that as the real holy grail where what if we could not just sort of level out or like raise the bar on the quality of documentation and billing and revenue cycle.

• 21:34Speaker 01

  But what if we could raise the bar on the quality of decision making? What if at the point of care, a bridge could say, hey, shit, like this patient in front of you, like Sarah, actually, she looks like 10,000 other patients in California that have been seen in the last few weeks. And for them, people have decided that this is amyloidosis and not sarcoedosis. And thus you should skip to the cardiac MRI and not screw around with the CT scan. And also maybe consider this therapy and, you know, look into this New England Journal of Medicine study to get, you know, more inspiring or, you know, get more insights into what the differential diagnosis could be like. That's, I think, a big part of what we're pushing. And the infrastructure that we're building is all going to, you know, amount to.

• 22:12Speaker 02

  I think you have a really unique perspective as a clinician, a cardiologist, an AI entrepreneur, you know, somebody who's actually operating at scale in terms of the application of technology to healthcare. I'm a bit curious how you think about the impact of AI more generally to healthcare. Is it anybody can log into a website and access the equivalent of the world's best doctor? Is it tooling for physicians in really rich ways? Is it, to your point, sort of mining the purpose of everything that's happened to people seeking out healthcare and then providing recommendations? I'm sort of a little bit curious, like, what is the big picture view of where all this is heading? And on what timeframe?

• 22:47Speaker 01

  I think it's all of the above, but like the timeframe piece is the key thing. And obviously, like, all of our time machines are broken right now. And it's hard to predict where we're going to be in, you know, even a year or six months, you know, with how fast things are moving. So much of what I was describing earlier around like what conversations are upstream of, I used to think that was like a three-year roadmap and we're building all of that right now at the same time and deploying it across all of our health system customers and learning already. And being at scale, by the way, and I think maybe this we were getting at this earlier, is really magical now. Like, we're live, we're doing millions of conversations, like, every couple days. Like, it's real scale.

• 23:29Speaker 01

  And with every single one of these notes that are generated, we're getting edits. And so it's fascinating. Like, we have this contextual reasoning engine, we call it, that's sort of pulling in information, not just from the conversation, like, the core stack that I was describing earlier, but we pull information from other sources, disparate sources, not just like the clinical system, like, the electronic medical system, like, the past medical history or the problem list that the patient has. We're pulling information from insurance systems, we're pulling them from clinical textbooks. And so all of that information is sort of orchestrated together in the right way, in the right order, so that we can generate, like, the best possible artifact.

• 24:08Speaker 01

  And I think where we are now is that those best possible artifacts still get edited, you know? And we're seeing in the metrics that we use these validated instruments that we're reducing cognitive burden by, like, 60% within six weeks of a clinician using this. And clinician burnout, per one survey that Stanford came up with, we reduced that by, like, 50% sometimes in the first couple of months. And, like, no technology has ever done this in healthcare, like, had that kind of impact. So it's a pretty awesome moment. But now that we're getting these edits, because, like, nothing's perfect, you know? And we don't claim to be perfect at all. Like, we absolutely, we're creating drafts that people can kind of leverage and take from there, but we save them hours a day with these drafts.

• 24:52Speaker 01

  But now that we have these edits, we really, like, we're going to town on all things related to, you know, post-training. And for us, it's like preference tuning, like, DPO and reward modeling and reinforcement learning. And I think having this incredible amount of feedback coming in on a daily basis means that we're always, at least, you know, in our estimation, like, getting less imperfect. Even if we're never going to be absolutely perfect, we're getting less imperfect and it's worth it. Like, it matters at that end of the spectrum in healthcare. So that's sort of like the the big game for us.

• 25:26Speaker 02

  Yeah, part of the basis of my question was, you know, I started a digital health company 10, 11, 12 years ago.

• 25:31Speaker 01

  Yeah.

• 25:31Speaker 02

  So a long time ago. And what I've observed is the technology cycles are really slow in healthcare. So they're always a decade behind at least. And this is an odd example where actually certain health systems are ahead by using a bridge. And relatedly, if you go back and you look at some of the early research, you know, MedPalm 2, for example, came out, I don't know when, two, three years ago now.

• 25:50Speaker 01

  Yeah.

• 25:50Speaker 02

  On the older Palm models. And even then, it provided output that outperformed physicians in terms of predictability of a disease state or other aspects of care, but it never really got adopted. And so I'm a little bit curious about the adoption curve versus the technology curve because the technology curve is clearly there. The adoption curve is starting through things like a bridge. But there are, there do seem to be these almost like systemic obstacles to adoption of new technology in healthcare.

• 26:17Speaker 01

  I totally agree. And I think it's like finding the right wedge is so important. There's some kind of two by two that's always in my head, like when, when you have high stakes and like high frequency workflows, like that's probably not going to get absorbed into like the healthcare system proper very, very quickly. But when it's lower stakes, high frequency, like our, you know, workflow, because there is that clinician in the loop who's making those edits, making sure that things look right. I think that there's an incredible moment right now, like the window is open, especially if you can demonstrate increase in productivity, improvement in user experiences for doctors and for patients. And, you know, the biggest deal for us increasingly is like, we're also talking to the CFO at these health systems and demonstrating

• 27:00Speaker 01

  that if you used some other technology that didn't put all that work into this orchestration of different models, that you'd actually be losing money. And with us, actually, you're getting full credit for the care that you delivered. In relation to your point, so this last weekend while I was on call, I used GBT for a lot of my different patients and I played with Claude too. And what I would do is sort of try to distill the call that I got, the patient that I was about to see. And I would put in, like prompt all these different models and ask it, like, what do you think I should do next? Or what's the differential diagnosis? Or do you agree with this like treatment plan? And oftentimes I'd say. It was like hundred percent, you know, correct off the bat. But maybe just as often, though, it was like a dialectical experience where like two

• 27:42Speaker 01

  hands to clap, like it would be me and it going back and forth like three or four times before we got to something that really was the right thing. And the art was like getting it there or getting there together with it. And so I think that clinicians, I think medical trainees, residents, medical students, you know, like they're figuring this out faster than maybe the older generation of attending doctors and consultants out there. So I'm super optimistic that as those clinicians sort of like mature in their careers, it's going to be like game on and they're going to all be leveraging this technology to be even better. And there's also no question in time, like I think, you know, in any of our minds that like this technology is going to get to the point where it's going to be able to like

• 28:27Speaker 01

  take on some aspect of care. But I think when, when most of us get sick, but you can disagree if you don't agree with me, but I think when most of us get really sick, we're probably still going to want to see, you know, a real life doctor to sort of parse through information and use tools like this to figure out what the care plan is.

• 28:43Sarah Guo?

  Can I ask one last question on just how like product and engineering and research work at a bridge? Just because you're, you're deep into the journey, you're at scale and away a few, few people are with these AI applications now. You mentioned you run headlong into the really tough piece of the market where the scope is large and the quality bar is high.

• 29:05Speaker 01

  Totally.

• 29:06Sarah Guo?

  And yet like today and forever, the product will be imperfect. How did you think about what was good enough, like minimum viable quality is? And it's like, you know, you continue delivering more and how to communicate that or negotiate that with users?

• 29:21Speaker 01

  So for us on that hard end of the market, we're always like threading a needle through a few different buyer personas and then the end users. And so on the buyer persona side, there's the, the C M I O, the Chief Medical Information Officer. That's the person who sort of represents all the doctors and the nurses inside the system. Then there's the C I O, the Chief Chief Information Officer. And so that person is representing sort of like the long term technology investments for the system. They're worried about sunk costs. They're worried about integrating with existing stacks. They don't want to have too many apps inside their ecosystem. Microsoft is probably like something that will never get them fired. And so there's a certain set of challenges there. And then there's a CFO and the CFO just wants to make sure that

• 30:04Speaker 01

  there's actual real tangible ROI. And so for us, we knew like in early 2023, we couldn't check off, we couldn't run the table on all three, but we could do two out of three. And that was enough for us. We were like, CMIO, CIO, awesome, let's go. And then the CMIO, the big challenge was, could we serve all the different specialties? And I'm a cardiologist. My note, my output looks so different than an oncologist. We just announced Sloan Kettering yesterday. And their notes at Sloan Kettering looks so different than a primary care doctor's note, or like a surgeon's note. And so all these different specialties, there, there's different stylistic sort of preferences. There's different structures to the note. There's different content that actually

• 30:49Speaker 01

  gets pulled into the note. And there's different workflows. Like in the emergency department, you go into one room, and I don't know if you're watching like Pitt, but it's actually like a pretty real, I think, like you go into one room and then you're like page into another, and then you go back into room one, and then you order an X-ray, and then you go to like room three, and you come back to one. And, and so what we had to do was figure out a workflow for the emergency department where we could stitch together these discontinuous conversations. And now we're doing that, like we're working to do that for the broader care team, just together all their conversations around one patient to create like one set of artifacts for that whole encounter. So I think like that was the barrier to entry for us.

• 31:26Sarah Guo?

  It sounds like a big barrier. Did you have that? Do you bring that expertise in house? Are you just working really closely with customers? Like you are not every version of that, that doctor.

• 31:36Speaker 01

  We have some people, we call them mutants in our company, Okay. who are doctors who are also engineers. Like, we had this one, for example, we have an engineer who was like a principal engineer at Meta, who's also a clinician. We have doctors who are like in the weeds of like just prompt engineering on a daily basis. But then we have others that can go like even even more scientific. We have others that also work on other aspects of like partner success or go to market as well. And so I think we try to find those interesting combinations of people because it helps us go faster. They're having like interdisciplinary and multidisciplinary meetings in their own mind. And we just don't have to, we can just like skip steps, I think, with those folks sometimes.

• 32:20Speaker 01

  But in general, I'd say like where we've like really invested, like we've raised over like like $500 million now. And so like, where is that capital going? I think so much of it, 80% of it, should continue to go in R and D. And so it's just figuring out like what's next on this roadmap. What else can we build? And, you know, our ability to sort of reach down lower into the stack and also like get into new new workflows and user experiences at the top, I think, has served us really well.

• 32:49Speaker 02

  You were having this very successful career in corporate venture. Part of that, you know, you continued to practice medicine throughout. And then you decided to take this giant leap and start this company. What prompted that? And how did how did a bridge come together?

• 33:02Speaker 01

  Like, late 2017, like, it was clear already, like, deep learning was starting to take off, at least on the research side of things with computer vision. And there were a lot of companies out there doing interesting things in like the CT scan world, for example, detecting pneumothoraces or being able to predict benign versus malignant, like nodules on a scan. And I think now, actually, that stuff is starting to take off in like a more real way. It's going to be exciting to see where that those technologies go, those products go. But at the time, it was clear there was like something out there that we could do. And I think that this idea, once we saw it, and once I saw it, it was like hard to unsee. And it was really easily like easy to get like super obsessed about it.

• 33:44Speaker 01

  Interestingly, like we knew when we started the company that we wanted to serve both sides of the story. And there's a professional side of this, but like always keeping that patient in mind and like thinking about that bigger system was also a big deal for us. And, you know, in terms of thinking about not just the professional, the doctor, because that's my professional pain point. And also thinking about patients, I saw this one patient in clinic in March of 2018. And she had a 10 year history of breast cancer. And she was starting to see me because she was just prescribed doxirubasin, which is a chemotherapy that can affect your heart muscles. So she needed the clearance from somebody in cardiology to to move forward with that chemo regimen. And she was super nervous and anxious, like crawling out of her skin the whole time.

• 34:28Speaker 01

  Um, I was with her in the exam room. And so at the end, I asked her why, um, and if there was something I did or something I said. And she told me that for the last 10 years since she was diagnosed with breast cancer, her husband would come to every single visit with a new type of doctor. And he couldn't come this time for whatever reason. And so I asked her, what does he do that's not obvious? And she told me that he sits in the corner, he's quiet, he just takes notes. And she's an English professor at the University of Pittsburgh and allows us to tell this story. But she told me that him taking notes for her meant that she could feel more present with me. And she could make eye contact and and she could build a relationship. And then they could go home and unpack all of his notes and rewrite them in words they understood.

• 35:08Speaker 01

  And then go to the next doctor and feel like the main characters as opposed to someone looking in from the outside. And so so much of her story on like that patient side of the room, that story is about agency. It's about ownership, it's about control. And I think so much of the story on the clinician's side, on the doctor's side, is about agency. There's an American Journal, a general internal medicine article from last year that suggests that doctors need 30 hours a day to get all of their work done. And they broke down all the like where all that time needs to go. And so you're always paying debt on on work, you're never able to get ahead. And so you don't you don't have any control like over your time. And that's why they call this pajama time. This this affliction where like doctors are writing notes after dinner or after their kids are in bed or whatever it is.

• 35:52Speaker 01

  And so finding a way to thread that needle, as contrived as it might sound, but like build that bridge between like the two people, the two, the doctor and the patient or the nurse and the patient, people who matter most in healthcare, is really like what we're aspiring to do. And now we're now we're doing it like. Maybe one last thing I'll leave you with. So we use Slack as a company and inside of Slack, we have a channel called Love Stories. And so every day we're getting feedback from our doctors across the country, like feedback in droves. And I think it's his like, it's pretty heroic in general for a doctor to give you feedback like, hey, this sucked and you got to do better. Like, you didn't recognize the way I said this medication or I'm a gastroenterologist and I would never,

• 36:34Speaker 01

  you know, sequence my problems in my assessment and plan section of my note this way. It doesn't serve me well and makes me look like terrible as a doctor or whatever. We get that feedback. We love it. It's oxygen. But then we also get the feedback that's like, hey, this is amazing. And I'm not going to retire anymore. And I've got like years, decades left in my career now, thanks to this technology. But in this channel, Love Stories, all of that feedback, that positive feedback, we just get it like programmatically funneled. So any one of our people inside of the company can always go into that channel. And it's like purpose, you know, it's like fulfillment immediately, like you immediately understand why we're all working so hard. And why it makes sense because like being on this very telephone pole like journey these last couple of years

• 37:16Speaker 01

  is obviously like it's news for so many of us and we're all kind of building new muscles, but it's it's a lot of pressure. But this is my favorite bit of feedback. So this love story comes from a doctor at Tanner Health, which is a rural health system. And she wrote to us, she wrote, I was sitting at dinner last week and my son asked me, mommy, why aren't you working right now? I literally took my phone out and explained to him that a bridge is a new tool that lets mommy come home early and eat dinner with her family. I started to tear up and looked over at my husband, who then said, mommy's going to be able to eat dinner with us every night now. And we get feedback like that like every day, you know? And so like there's, there's dopamine hits, you know, in hypergrowth. And like, those are awesome, but I think that they get us through like sprints.

• 38:00Speaker 01

  But I think it's the oxytocin hits like this. It's the purpose, it's the fulfillment. It's like, that's, I think, what I think we're really after in this company. And so like, everybody's mission driven out, out there. But I think this mission, um, like it hits me at least a little bit different.

• 38:14Sarah Guo?

  Me too. You know, congratulations on all the amazing progress with Abridge and keep climbing.

• 38:20Speaker 01

  Awesome. Thanks so much, Sarah. Thank you a lot.

• 38:23Sarah Guo?

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