No Priors Ep. 108 | With Abridge Founder and CEO Shiv Rao, MD

1. 0:00 – 0:35

   Introduction

   1. SGSarah Guo0:00

      (instrumental music plays) 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 unburden 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 dollar Series D raise. Join us as we explore how AI is transforming healthcare delivery. Shiv, welcome to No Priors.

   2. SRShiv Rao0:32

      So excited to be here. Thank you, Elan. Thank you, Sarah.

2. 0:35 – 5:30

   Abridge’s Story and Vision

   1. SRShiv Rao0:35

   2. EGElad Gil0:35

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

   3. SRShiv Rao0:42

      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 gonna get fully automated over the next 10 years, and so w- what- what's the first signal in healthcare delivery? And we think it's a conversation. It's the, 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- 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- any number of, like, different value propositions over time.

   4. EGElad Gil1:21

      Could you tell us a bit, 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?

   5. SRShiv Rao1:32

      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-demand mismatch. It's like, it's a really, it's 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 up, about it, and I think that's where technology has a role that, uh, uh, is finally sort of being recognized and acknowledged at the highest level. Like, uh, uh, the entire healthcare industry understands now they- they- 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 Abridge, have a normal conversation, and talk about any number of different topics in whatever order. But when they hit stop and swivel their chair, their note's there. But it's not the note that you might expect, you know, s- 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, in what geography, not just like the clinical note but also what the billable note is, if that makes sense.

   6. SGSarah Guo3:03

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

   7. SRShiv Rao3:08

      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, there's- there- 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- I think another key insight for us that's served us well for these last several years has been that healthcare is not homogenous, and- 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, there's an independent PCP, a really small provider groups 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 this 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 doctor down the street. And the reason why we went there is that the barrier- 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, like, advantages, a lot of our, like, differentiated muscles, 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- pretty amazing, like, team of machine learning engineers and- 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- the barrier to entry, the bar for good enough is a lot harder, 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-

3. 5:30 – 7:41

   Strategy for Customer Choice

   1. SRShiv Rao5:30

      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.

   2. EGElad Gil5:38

      You're a- 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've had, you have kind of like a who's who of customers in terms of Kaiser, Sutter, you know, others.

   3. Um, so I'm a little bit curious, like, how this has impacted your strategy in terms of you yourself being an MD and physician.

   4. SRShiv Rao5:55

      So a little bit a- 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 UPMC.

   5. EGElad Gil6:05

      Sorry to hear that.

   6. SRShiv Rao6:05

      (laughs) Um, so I, 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 where we started a machine learning and health program, and that's where actually I met Zach, our CTO. And we're not a spinoff. Um, we didn't, like, spin out of, like, UPMC. I quit that job to start the company along- alongside some other folks from Carnegie Mellon. But a couple 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 I'm on phone call as well, so just for, like, emergencies, like heart attacks in the hospital that I need to come in and sort of, um, help address. But it- it's, like, an incredible privilege. 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, 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, um, in a better and more differentiated way, but also kind of understand go to market. Like, how are we gonna sequence where we focus over time? And you mentioned some of our health systems, like Kaiser and Sutter. We're live in 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 multiyear agreements, um, I think is pretty historic. I don't think I've ever seen anything like this. When I was investing at UPMC if, if a startup had, like, a handful of logos a year, it'd be like high fives all around the room, like amazing, bottles of champagne. And so this is a really, really different moment right now,

4. 7:41 – 11:24

   Healthcare AI Opportunities

   1. SRShiv Rao7:41

      I think, AI and healthcare.

   2. EGElad Gil7:42

      How would you explain that? Um, sit on the board of a healthcare technology company now, I'm- I believe in this. I'm in this boat. But for over a decade, um-

   3. SRShiv Rao7:52

      Yeah.

   4. EGElad Gil7:52

      ... looking at healthcare technology, uh, as another VC on the outside, um, it- 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 the abstract level AI, right? But, like, how does that play out for your business?

   5. SRShiv Rao8:09

      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.

   6. EGElad Gil8:26

      Mm-hmm.

   7. SRShiv Rao8:26

      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, hire another clinician is, like, close to a million dollars and it takes a long time and, um, 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, but-

   8. EGElad Gil8:53

      Yeah, it's not new. Yeah.

   9. SRShiv Rao8:54

      It's not new, you know, uh, but I think it's- it's not, it's not lip service anymore. Now it really, really matters and, um, i- if that was one star that aligned, I think the other one was generative AI and ChatGPT coming out in early '23. So we started in 2018, three months after Attention Is All You Need, and- 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 pretrained models in healthcare, and specifically in relation to these sorts of workflows, these clerical workflows. And so we published any number of different papers, um, you know, Zach and team won Best Paper at EMNLP I think in 2021. So we did a lot of really deep research, but w- when we started with BERT or Buyert, or LongFormer, or Pegasus, all these other pretrained 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. EGElad Gil10:23

       Huh.

   11. SRShiv Rao10:23

       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 th- where- where I think we YOLOed it in 2023 is that y- y- we could have decided to go to the small mid-market or 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 years probably. You don't get another shot on goal for a really, really long time, so you got to hit it out of the park. 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, um, across- across the country.

   12. EGElad Gil11:23

       One of the other challenges

5. 11:24 – 14:26

   Navigating Incumbent Partnerships

   1. EGElad Gil11:24

      in that end of the market is there's a lot of incumbency in the existing systems and, you- 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?

   2. SRShiv Rao11:38

      Thinking about ecosystems is super important and, um, 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, th- the stakes are high. Two days ago, um, 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 gonna 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. They're not making money anymore because, like, sort of explain that these notes 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 mar- uh, 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? Um, and it's easier said than done, but, um, in 2022 as an example, like, we had won that paper, that- that EMNLP 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'd heard that we had something that worked. Now the... You know, who we're- who we're competing with is Microsoft. That's who we essentially almost, like-... um, 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, 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, um, 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. So it was really, like, Indians in Bangalore who were listening to audio and writing the note and Wizard of Oz-ing it back into the medical record, and it w- there was like... Uh, it would take time, you know, for all of that to, that workflow to go down. And so that's why people would always ask us, like, "Put your hands up. Is that real?"

   3. EGElad Gil14:03

      (laughs)

   4. SRShiv Rao14:03

      And again, like, these weren't even LLMs yet in 2021. We were using, like, BERT and BioBERT and, like, you know, all those other pretrained models and, and T5 and, and other sort of summarization techniques. So when LLMs came out and, and when we started to really work with them in a serious way in late '22, '23, um, oh man, like, game was totally on for us and we were able to really, you know, take it to the next level.

6. 14:26 – 19:54

   Doctor-Centric AI Solutions

   1. SRShiv Rao14:26

   2. EGElad Gil14:26

      Now that you've gotten to all of these systems, um, 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?

   3. SRShiv Rao14:36

      Absolutely. So maybe it may be useful to sort of break down the stack a little bit, and then we can kind of talk about where we're going and, and where, where our research team is focused. So at, at, at a really high level, like, the core part of the stack is speech recognition, and so that, 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.

   4. EGElad Gil14:57

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

   5. SRShiv Rao15:07

      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-

   6. EGElad Gil15:27

      (laughs)

   7. SRShiv Rao15:27

      ... 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, uh, like, a bit of history of li- in, of the voice game in healthcare is that before this world of generative AI and conversations and dialogues, there were dictations. 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 line. Capital B. Past medical history colon. Next line." You 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, um, 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, what his, like, symptoms were, and then it would, like, start to mesh with another patient who had the same symptoms, and so-

   8. EGElad Gil16:38

      It's not encouraging, yeah. (laughs)

   9. SRShiv Rao16:39

      Not encouraging. Not good for doctors. Terrible for patients. Not good for a, 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 medicalese, 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, in Haitian Creole. Today in, um, Boston, we'll do thousands of conversations in Brazilian Portuguese, in 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, our job is to create the note in English within seconds and put it right into the med- medical record and all the different discrete fields for them to trust and verify. So part of what we do in the speech recognit- 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 ev- 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. Um, but then obviously, we're on this treadmill of always improving, always recognizing the latest FDA-approved drug or, or, or the, you know, the latest pronunciation. That's just speech recognition. I think a- as we move past speech recognition in, like, the core part of our stack, you start to get into all the text and language, um, 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? 'Cause 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? 'Cause these are bills. And I, I think a part of the reason why clinicians have burned out, are burning out, is that, uh, 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 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, uh, 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 very information extraction models where we pull out those symptoms, medications, diagnoses, procedures. We map them to data dictionaries. And then there, of course, there's summarization, and the way you summarize for anyone looks different. So if I wrote a note as a, 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 that to them, I'm... Understandably, I'm gonna get, like, blown up, right? I'm getting the emails and phone calls asking, "What was that term? You never said. I looked it up. It sounds scary." And so what we can do is do that sort of style transfer across all the, you know, the stakeholders that clinicians serve and sort of meet all of their different needs. Um, and that, I think, you know, h- has allowed us to serve the executives, like the buyer personas in large health systems.

7. 19:54 – 22:13

   Abridge’s Future Plans

   1. SRShiv Rao19:54

   2. EGElad Gil19:54

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

   3. SRShiv Rao20:06

      I think it comes back to that thesis. And so if, if you, if, if you really, you know, believe, as we do, that healthcare 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 workloads 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, uh, "Let's start you on 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 I know it or not, maybe this patient in front of me has inclusion and exclusion 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, of where we're going already. But then there's clinical decision support. And so in, in many ways, I'd say, uh, 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, and billing and revenue cycle, 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, Shiv, 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 sarcoidosis, and thus you should skip to the cardiac MRI and not screw around with a 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 inspiri- or in- you know, get more insights into what the differential diagnosis could be." Like, that's, I think, a big part of, of what we're pushing and the infrastructure that we're building, uh, is all gonna, you know, amount to.

   4. EGElad Gil22:12

      I think

8. 22:13 – 28:43

   AI’s Impact on Healthcare

   1. EGElad Gil22:13

      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 little 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 corpus of everything that's happened to people get- seeking out healthcare and then providing recommendations? I'm just sort of a little bit curious, like, what is the big-picture view of where all this is heading and, and on what timeframe?

   2. SRShiv Rao22:47

      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, and it's hard to predict where we're gonna 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, um, and learning already. And, and being at scale, by the way, and I think maybe this, we were getting at this earlier, is, is really magical now. Like, we're live, we're doing millions of conversations, um, like every couple days. Like, it- it's, it's real scale. 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, 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, the, 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. And I- I think where we are now is that those best possible artifacts still get edited, you know? And, and we're seeing in the metrics that we use these validated instruments that we're, 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, uh, with, we reduced that by like 50% sometimes and 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, it's a pretty awesome moment. But, uh, now that we're getting these edits, 'cause like nothing's perfect, you know, and we don't claim to be perfect at all. Like we, we, absolutely, we're creating drafts that people can kind of leverage and take from there, but we save them, hours a day, um, with these drafts. But now that we h- we have these edits, we really, like, we're going to town on all things related to, you know, post-training. And, 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. E- even if we're never gonna 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, that's sort of like the, the, the big game for us.

   3. EGElad Gil25:26

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

   4. SRShiv Rao25:31

      Yeah. Yeah.

   5. EGElad Gil25:31

      So a long time ago. And what I've observed is that 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, uh, some of the early, your research, you know, Med-PaLM 2, for example, came out, I don't know, what, two, three years ago now-

   6. SRShiv Rao25:50

      Yeah.

   7. EGElad Gil25:50

      ... 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. 'Cause the technology curve is clearly there-

   8. SRShiv Rao26:08

      Yeah.

   9. EGElad Gil26:08

      ... but the adoption curve is starting through things like a bridge. But there are- there do seem to be these almost, like, systemic obstacles...

   10. ... uh, to adoption of new technology in healthcare.

   11. SRShiv Rao26:18

       I totally agree, and I-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 in, like, high-frequency workflows, like, that's probably not gonna get absorbed into, like, the healthcare system proper very, very quickly. But when it's lower stakes, higher frequency, like our, you know, workflow, um, 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 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 GPT 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 diag-, like, tr- treatment plan?" And oftentimes, I'd say it was, like, 100%, you know, correct off the bat, but, um, maybe just as often though, it was, like, a dialectical experience, where, like, two 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 (laughs) , you know, together with it. And so I-I think that clinicians, um, I think medical trainees, residents, um, medical students, you know, like, they're figuring this out faster than maybe the older generation of attending doctors and consultants out there. Um, so I'm super optimistic that as those clinicians sort of, like, mature in their careers, it's gonna be, like, game on and they're gonna all be leveraging this technology be- to be even better. Um, and there's also no question in time, like, uh, I think, you know, in any of our minds that, uh, like, this technology is gonna get to the point where it's gonna be able to, like, 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- I think when most of us get really sick, we're probably still gonna wanna see, you know, a real-life doctor to sort of parse through information and use tools like this to figure out what the

9. 28:43 – 32:50

   Shipping and Iterating Products

   1. SRShiv Rao28:43

      care plan is.

   2. EGElad Gil28:43

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

   3. SRShiv Rao29:06

      Totally.

   4. EGElad Gil29:06

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

   5. SRShiv Rao29:21

      So for us, on that hard end of the market, w- 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 CMIO, the chief medical information officer. That's the person who sort of re- represents all the doctors and the nurses inside the system. Then there's the CIO, the chief- chief information officer, and so that person a- is representing sort of, like, the long-term technology investments for the system. They're worried about sunk cost. 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 the- there are a certain set of challenges there. And then there's the CFO, and the CFO just wants to make sure that 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 look so different than, uh, a primary care doctor's note or, like, a- a surgeon's note. And so, uh, all these different specialties, they're- there's different stylistic sort of preferences. There's different structures to the note. There's different content that actually 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, paged 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 disti- discontinuous conversations, and now we're doing that, um, uh, like, we're working to do that for the- the broader care team, um, stitch 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.

   6. EGElad Gil31:26

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

   7. SRShiv Rao31:36

      We have some people we call mutants, uh, in our company, um (laughs) -

   8. EGElad Gil31:40

      Okay. (laughs)

   9. SRShiv Rao31:40

      ... who are doctors who are also engineers. Like, we had this one, for example, we have an engineer who was, like, a- a principal engineer at, like, Meta, who's also a clinician. Um, we have doctors who are, like, in the weeds of, like, just prompt engineering on a daily basis. Um, but then we have others that can go, like, even- even more scientific. We have others that a- also work on, um, other aspects of, like, partner success or go to market as well. And so I think we try to find those interesting, um, combinations of people 'cause 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. 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 into R&D. And so it's just figuring out, like, what's next on this roadmap? What else can we build? And, you know, o- our ability to sort of reach down lower into the stack and also, like, get into new, new work flows and user experiences at the top, I think has served us really well.

   10. SGSarah Guo32:49

       You were having this

10. 32:50 – 38:42

    Shiv’s Journey to Abridge

    1. SGSarah Guo32:50

       very successful career, uh, in corporate venture. Prior to that, you know, uh, 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 Abridge come together?

    2. SRShiv Rao33:02

       Like late 2017, like it was clear already, like deep learning was starting to take off, at least on the research side of things, um, 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 mo- nodules on, on a scan. And I think now act- actually that stuff will, is starting to take off in a, like a more real way. It's gonna 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. 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 you, 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 doxorubicin, which is a chemotherapy that can affect her heart muscle, 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, 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, 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, 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 side, on the doctor side is about agency. There's an American Journal of General Internal Medicine article from last year that suggests that doctors need 30 hours a day to get all of their work done.

    3. SGSarah Guo35:33

       (laughs) .

    4. SRShiv Rao35:33

       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, and so you don't, you don't have any control, like over your time. 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. 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, th- th- th- the doctor and the patient, or the nurse and the patient, people who matter most in healthcare, um, is really like what we're aspiring to do, and now we're, now we're doing it. Like, um, 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 gotta do better," or like, um, "You didn't recognize the way I said this me- me- medication," or, uh, "I'm a gastroenterologist and I would never, 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 gonna retire anymore, and I- 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 years, uh, 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 Abridge 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 gonna be able to eat dinner with us every night now.'"

    5. SGSarah Guo37:49

       Aww.

    6. SRShiv Rao37:49

       And we get feedback like that, like every day, you know? And so like w- there's, there's dopamine hits, you know, in hypergrowth, and th- like those are awesome, but I think that they get us through like sprints, 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, it hits me at least a little bit different.

    7. SGSarah Guo38:14

       Me too. Um, you know, uh, congratulations on all the amazing progress with Abridge, Shiv, and keep climbing.

    8. SRShiv Rao38:20

       Awesome. Thanks so much, Sarah. Thank you, Yoda. (instrumental music)

    9. SGSarah Guo38:24

       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: 38:42