Can AI cure doctor burnout?

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1:39William McDonough, the sustainable architect, had visited Carnegie Mellon in my junior year, and he told this story about how we're all designers of our world. And one of his examples was this ophthalmologist in India who designed this revolving platform that he sits on, and he does cataracts. These patients come in, they're sitting on these chairs around him. He does a cataract. Five minutes, he says spin. And his staff spins and he does the next cataract. And he's just spinning all day long. And by the time I'd heard this lecture, he'd given eyesight to over a million people. Shiv Rao went on to become a cardiologist and has now founded the AI healthcare company Abridge.

2:19But it was this moment, back as an undergrad at Carnegie Mellon, that really started his journey. So I remember leaving that lecture thinking that that's like insanely incredible, world-changing impact that he was creating. That was my hard pivot to, okay, I think I want to be a part of something that number one is in healthcare and can do something so profound, but also two, I wanted to be a part of something that could really create value at scale. Schiff's company, Abridge, is valued at$5.3 billion. And while you may not recognize the company's name, your doctors probably do. Abridge has partnerships with over 200 major healthcare systems.

2:59Among other things, it automates medical note-taking using AI. Basically, their platform cuts out the time-consuming bureaucratic work doctors do so that they can spend more time with patients. To me, this is a really powerful AI use case. Abridge deploys AI to do important yet mundane tasks so that we can build stronger human connections. And they're doing so on a massive scale. There's so much to talk about with Shiv, from how AI can be a solution to doctor burnout to why vanity matters when building a business. So let's dig in. I'm Rana El-Khalyubi, and this is Pioneers of AI, a podcast taking you behind the scenes of the AI revolution.

3:53Hi Shiv, welcome to Pioneers of AI. I am so excited for our conversation. Hey Rana, it's such a privilege to be here. Thank you. So you have a long career in health. What was your path to entrepreneurship? Yeah, it wasn't that I was in college thinking I want to start a company. It was though thinking about wanting to be a part of a team that could create this impact and wanting to create impact at scale. I sort of pivoted pretty late to wanting to go to medical school, went to medical school, felt like it was rote memorization, didn't see a lot of room for creativity. And I was kind of disillusioned on like what I'd signed up for, to be honest with you.

4:32But then went to residency, I went to University of Michigan in Ann Arbor, and I just fell in love with patient care because you're just thrown right into the trenches as a resident and you're in these life and death moments. But while I was in residency, I programmed something in Ruby on Rails for the program director. And I remember experiencing some peasant level of product market fit because he said like, oh, this is useful. Like, you know, how should we pay you for this? And I remember thinking, well, all that clinical research I was thinking about or that I was doing, I'm just going to focus all of my energy at this new thing, this new intersection of software and healthcare.

5:05Yeah. Okay. So I want to talk about a bridge. So you saw this big problem in medicine, which is basically doctors burning out because of all of the administrative burden and paperwork that they have to deal with. How serious is this problem anyway? It's a public health emergency. It's that serious. It's impacting all of us in some way, shape or form. And we have to find a way to leverage technology to solve for it. Schiff says that part of this public health emergency is burnout. According to one study, one in five doctors want to leave the profession in two years. Other studies show that a quarter of all nurses plan on leaving the field.

5:47When you ask them what is the reason, they'll tell you that they went to medical school or nursing school to serve people, to creatively sit down, build relationships, and help folks in their most vulnerable moments and help them be the healthiest versions of themselves. And so much of the business of healthcare actually pulls them away from what they want to do, instead pulls them into processes that are important and are necessary, but can really create overhead. Processes around billing, processes around revenue cycle, processes around clerical work, essentially. And so it shouldn't be a surprise that we're losing people and they don't want to come back to this profession.

6:30And it shouldn't be a surprise that there are rural health systems that are shutting down because they can't staff them anymore. And that means patients are having to drive five, six hours into the inner city hospitals to see the rheumatologists or other specialists who can save their life. So we have to do something about this. And it's this moment, I think, is so exciting because I think the entire industry of these last few years has woken up to this idea that technology is our way out. There's actually no other way out. We have to find a way to assist and in spots to actually responsibly automate and to unburden people and help the system treat patients the way they need to be treated.

7:07Yeah. This clearly affects the physician's ability to provide quality patient care, but this administrative burden also affects the patient experience. And one of the things that comes to mind is the series of studies where they showed the doctors who communicate with empathy are less likely to be sued, right? Yeah. So I would love to hear how this whole problem affects the patient experience as well. Yeah. Yeah. So our entire thesis is that healthcare is about people, and we don't think that's going to change in the next five to ten years. We think that no matter what happens with AI, there will still be a place for clinicians, for doctors, for nurses.

7:48My father-in-law recently was in the hospital. I spent some time there. It was very eye-opening to see how important that role is that nurses play, for example, on a daily basis. And no matter what kind of technology you might have in the operating room, you'll still probably want someone overseeing that technology. If that is true, then conversations, dialogues are one of the original signals in healthcare. They're one of the most historically untapped signals. And you can, we believe, build a platform on top of it. You can create all kinds of value on top of it. And so I have like a really quick story that is in some ways, you know, part of our founding story.

8:25In March of 2018, I saw a patient, just as we were starting the company, I saw a patient in a weekly clinic that I had then. And she was 50 years old at the time. She had a 10-year history of breast cancer. And she was coming to see me because she was recommended to start doxorubicin, a specific chemotherapy that could affect her heart muscle. And she needed clearance. She was super nervous and anxious, like crawling out of her skin. So at the end, I asked her why, and if there's something I did or something I said to make her feel so clearly uncomfortable. And she told me that for the last 10 years, her husband had come to every single visit with a new kind of doctor.

9:00He just couldn't make this one for whatever reason. So I asked her, well, 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. But she told me that him taking notes meant that she could feel free to be fully present, to make eye contact, to build that relationship with me, knowing that when she got home, she wouldn't have to be paranoid about how much she remembered when someone asked her, what did the doctor say? And what they would do when they got home, they would actually unpack all of his notes. They would Google all the big words and rewrite their stories in words they could understand so that they could go to the next doctor and retell it feeling like the main characters as opposed to someone looking in from the outside.

9:39So much of what she shared was about agency. Agency for herself, you know, for her family to feel like that main character. But then you think about the other side of the room and clinicians burning out, doctors and nurses leaving the profession. And really what they're wanting is agency as well. Agency to serve their patients. as best as they can to do the best possible job. And every competing force burns them out. And so I remember when we first raised our seed round in 2019, our deck, which sort of looks like a high school yearbook photo now. It's like our design ethos has leveled up for sure 10 times since then.

10:16But there was a slide that still is like our true north slide. It's just like a clinician on one side of the room, the patient on the other. But as people, they're engaging, but we're creating value for both sides. The clinician is unburdened getting agency, the patient's getting a summary that can help them better understand and follow through. That's awesome. When did you recognize that AI is gonna be a kind of key component of the solution for both sides, right? Yeah, absolutely. We're very much an AI native company, an AI first company. We started with that as a big part of our why now thesis and why us.

10:50So prior to starting this company, I was an executive at a large health system called UPMC in Pennsylvania. And I joke, but it's true. I sort of violated Peter's principle 10 times in five years. I got to lead their provider facing innovation portfolio and also their investment portfolio. We were putting money into companies, startups of different stages, but also putting money into venture capital funds. And so I was getting exposed to a lot. I was learning osmotically about the different dimensions of business and healthcare. and one of our investments was into Carnegie Mellon. We put a lot of money into Carnegie Mellon CS department to start a machine learning and health program.

11:27We got to fund professors doing different types of research at the intersection of AI and healthcare. The first person I funded is my co-founder and CTO now. His name's Zach Lipton. He's an associate professor at Carnegie Mellon and being able to ride shotgun with those professors, I don't even know if it was shotgun, but to ride along and to learn from them was an incredible kind of privilege for me. It helped me kind of, I think, sense and feel and my bones have that intuition that like something is going to happen, but also that something was happening in 27, 2018 that maybe was worth the bet, was worth taking a risk.

12:05So we started our company just months after, for example, Attention is All You Need was published. That's the paper that basically spurred all these transformer models that are now at the core of all of the large language models we're using, right? And day one of our company, we were leveraging pre-trained models that we were fine-tuning, like BERT, BioBERT, Longformer, Pegasus, T5. We published a paper in late 2021. Zach, as first author, he published a paper about what you could do with those models to actually unburden clinicians with a clinically useful note. And so we were sort of like early, And there is a refrain in Silicon Valley that being early is being wrong.

12:43But we're here to say that not if you don't die. You know, if you don't die, if you stay standing, it can sometimes lead to not just first mover advantage, but also it can lead to just being a more prepared mind with all this potential energy on what this opportunity actually is. So when the sky opens, you can run right into it. In a minute, Shiv walks us through why his product is so innovative and how they provide real day-to-day value to clinicians. Stay with us.

13:24So walk us through how a bridge works. So say I'm a patient, I'm coming in to see you. what is Abridge taking care of and what do I get as a patient? What do you see as a physician? Yeah. So if I saw you in clinic, the first question I would ask is, hey, Rana, do you mind if I use this note-taking solution called Abridge? It's going to help me focus on you instead of all the distractions that get between us. The backdrop of our company is really trust. That is the currency that matters. And so even at that individual user level, it's about trust. It's about to hit record. And so it makes total sense that you'd want to do this.

14:01But then once the patient consents, and by the way, almost no one says no anymore, the zeitgeist, the culture has shifted so much over these last few years. You hit record and you have a conversation about anything in whatever order. So it could be about family, it could be about the games this last weekend, the weather, and your hypertension, and then about something else, and then your congestive heart failure. But regardless of how you talk, when you hit stop, you should be able to swivel your chair and your notes there. It's waiting for you inside the medical record for you to trust and verify, edit, adjust as you see fit.

14:34Amazing. Was there a lot of pushback when Bridge went to market early on? Yeah. In 2019, we weren't yet ready to deliver an enterprise-grade solution. The bar for good enough there is high. There's so many boxes you've got to check off. But we were ready to put something out there on the other side of the room. So we actually led with a consumer facing app, believe it or not. Interesting. I didn't know that. Yeah. So totally different go to market, totally different motion, you know, but it's been fascinating these last three or four years. It hasn't been hockey stick for us. It's been more like telephone pole growth.

15:10And we've had to build a lot of muscles across different parts of the organization almost overnight. And generally speaking, should take a little bit more time to develop. And it's been awesome. Like we have a saying in our company, pressure makes diamonds. and we're under tons of amazing pressure. It's a privilege. But over these last four years, what we've also seen is just like AI move so quickly. So even that scenario I described to you, the real differentiation and the hard part is in the details and oftentimes the invisible details. It's what we're doing behind the scenes, like how we might be pulling information from your chart to understand who is Rana.

15:47Like what's Rana's story? What are her allergies? What are her medications? What's her family situation like? Also pulling maybe information from your insurance plan to better understand what this note needs to look like so that you can get the procedure you need as fast as you need it. So cool. Pulling information from guidelines, from medical textbooks to help the clinician hopefully ask the right question. So if I'm in front of you, I would love to feel like I'm a superhero, like I know everything. and anymore, both of us, all of us have access to these new AI primitives that allow us, you know, to like be that much more informed and equipped to help me think about what the right diagnosis could be.

16:29It's a lot of this stuff that ends up being invisible and that's where a lot of the hard work happens. I want to go behind the scenes. This is an AI podcast and we love to go behind the scenes and unpack the AI powering a bridge. So are you building foundation models from scratch or are you fine-tuning these foundation models? For us, it's about fine-tuning. It's about post-training. It's about being able to leverage what's out there, being able to distill into our own models as much as we possibly can. But certainly we are here to put the best possible product out. And so that generally involves a playbook.

17:06It involves fine-tuning. It involves post-training. And now that we're at scale, So next year, maybe we'll do close to 100 million conversations, let's say. And if we touch 100 million patients, for all those clinicians and patients who are involved, the edits that they might make, the adjustments a doctor might make to a note in the orthopedic surgery space. They might want a different style, a different structure. They might need something different for revenue cycle. We might get edits from a coding expert. Coders are folks who are creating the bills that end up getting to the insurance company.

17:40Not software engineers. Yeah, exactly. And so we can learn from all of that. And so that's, I think, the playbook that probably our other AI native companies across industries are pursuing as well. So do you use these 100 million conversations to train or post-train the models? What do you do with this data? Yeah, so the edits, absolutely. Like the post-training piece, we'll always work with these health systems to have permission. It actually took us a long time, perhaps 18 months end to end, to build a de-identification pipeline that allowed us even normalize data to the point where we could leverage these edits to deliver a better product more dynamically, more continually to any given user.

18:20The industry needs this technology so desperately. There are necessary barriers to be able to do this in a safe way. And some of those barriers, they just take time and we've been able to navigate them. And I think that's also part of our early year story. That's very cool. So it sounds like you have many models that need to come together to work accurately and effectively. And that's the contextual reasoning engine. Can you tell us a little bit more about how that works? It's essentially context engineering, or what people call that now. And it's being able to pull information context from seemingly disparate sources, but then orchestrate that information through pipelines so that we can create the best possible output.

19:02So just to maybe give you a sense of the complexity here, let's say there's a quality metric in healthcare called the PSI-9 and that looks at perioperative bleeding after you have a procedure and historically if a patient was on a blood thinner the only way to make sure that they weren't misclassified was to use a specific diagnosis code and that described a hemorrhagic a bleeding disorder code that never really matched what doctors would say like what how they would speak to the patients in front of them so then these folks downstream of the doctor-patient interaction, those are the folks we were talking about earlier, they might be called clinical documentation improvement or coders.

19:44They'd have to chase that stuff down and it would create so much friction because then they'd send me an email or what's called a query and be like, clarify this, what actually happened. In August of this year in healthcare, there were new diagnosis codes that went live, Z7901, you don't want to know, this stuff, but like these new codes came out and now all of a sudden when a doctor just says, Hey, this patient's on this blood thinner or they take aspirin every day, what a bridge can do dynamically in the conversation is map that spoken language or ever colloquial directly to that new like cutting edge, you know, diagnosis code that just came out.

20:21So no more workarounds, no more queries, no more paper cuts back to me. We're shifting a lot of workflows upstream into the conversation itself. By the way, has this, has a bridge and kind of helping with these workflows, has that affected the job of the coders? I'm sure you get that question all the time. Yeah. Healthcare is one of those industries where we also, as a system, we can't hire humans fast enough to do all the jobs that are out there. So I would argue that it's the most kumbaya industry, at least for the foreseeable, for AI to be implemented. Like everybody wins when we have more automation, I would say.

20:58There aren't too many casualties of war. Now, certainly there were people who would literally, all they would do is what they call scribe. They would stand in the corner and write notes for people. And yeah, that job certainly, I think, has seen a pretty dramatic kind of change over these last few years. I also think that there are so many jobs to do and so many new places to focus human creativity in healthcare that it's going to be a while until like we see this be like controversial in any way. Yeah, very interesting. So I'm trying to think of the environment like where a doctor's visit, right?

21:34Like sometimes the environment is noisy. The doctor's saying these big and complex words. You've got different accents. I'm Egyptian and my PCP is Egyptian and I love her. So we often banter back and forth in a mix of Arabic and English. How does a bridge deal with all these complexities? Yeah. I was listening to an interview with the Shopify CEO this morning where he talks about how lucky you feel when you choose a problem you'll never have the perfect solution for because you can just work on it forever. And I think part of what AI companies do, especially AI companies who are ambitious and want to really build for years.

22:13And we tell our health system partners that we want to be their partner for the decades to come is you choose these problem spaces where you know you're always going to be on a treadmill. You're never going to be able to get off this treadmill once you get on. And languages are a really good example of that. We have to support all the languages out there. So today there will be tens of thousands of conversations using a bridge, but spoken in Farsi, in Los Angeles, in Mandarin, in Vietnamese, in Haitian Creole, in Brazilian Portuguese, and Boston, we have to support all these different languages.

22:45We benchmark how good we are on as many as we can find the bandwidth to do that for. And I think we're over 30 on that front, but we can support over 90. And so Arabic is a really good example. But once you're in these conversations, you can be jumping from multiple, like in and out, and we'll still be able to create that clinically useful, but also compliant output on the other side. Yeah, fascinating. We're going to take a short break. Stay with us.

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