AI-Native Healthcare: 100M Doctor Visits, 10–20 Hours Saved, Prior Auth in Minutes — Janie Lee & Chai Asawa, Abridge

Introduction

0:00Okay, this is a special crossover late in space on Supervised Learning Pod. Very excited to do this. Once a year at this point, we get together. Once a year. And this is a fun occasion to get to do it on. I really wanted to talk to Abridge, but I felt very underqualified because healthcare is not something we cover very intensely. And it just so happens that RipPoints are big investors and supporters of Abridge. Anytime you want to have a portfolio company on your podcast, please, by all means.

Guest Introduction

0:31So we'll introduce our guests. Chai and Janie, welcome to the pod. Thanks for having us. We're excited to be here. Thank you. Yeah, so for listeners, what do you guys do just to situate you guys in the company? Abridge is a clinical intelligence layer for health systems. We really started with documentation and building for clinicians. And we think that, you know, as we think about reducing the burden that clinicians have, they're spending 10 to 20 hours a week on documentation. There's a massive doctor shortage in the country. We also think that conversations

1:03between patients and clinicians are probably the most important workflow in healthcare. It's obviously where care is given and received. But if you think about the 20% of our GDP that goes towards healthcare, almost everything is a derivative of that conversation, whether it's the claim, the payment, the actual diagnoses given the treatment. And we've started with a conversation to reduce the burden for doctors on documentation. But we're really excited about the path ahead as we become this broader clinical intelligence layer.

1:34I'm Chai. I work on clinical decision support at Abridge. And so I think, as Jenny said, that we had this, we're uniquely situated where we started off with the clinical note. What I'm really excited about and where we're expanding towards is what are all the things you can do before the conversation, during the conversation, and after the conversation if you did have access to all the context about patients, pair guidelines, medical literature, and put that together and to serve, you know, how healthcare could look fundamentally different. Yeah, and that's like the context engine that you guys have? Is that what it's called? Okay.

2:05So historically, as I understand it, the company started in 2018. A lot of people would be familiar with like the AI voice notes form factor that doctors would be like, well, do you consent to be being recorded? It replaces handwriting and what have you. But it sounds like more recently there's been a big transition in the company. Or just tell me about like the broader transition.

Company Transition

2:26Yeah, so from a transition perspective, we really think about our journey is how do we, you know, first chapter was, first act was how do we help save time? And that's where a lot of that original product was. Which like, by the way, one of those interesting stats on your landing page was like, people spend, doctors spend like time after hours. They call it pajama time. Okay, why is that pajama time? Doctors after work in their pajamas at home are just writing and catching up on their notes every day. And, you know, we think some of our favorite customer love stories,

2:57we have a Slack channel called Love Stories. We have clinicians telling us, Abridge has helped us, you know, from retiring earlier. We're now finally able to go home and eat dinner with our kids for the first time. Save their marriage and something. Yeah, one of your quotes was like, we're not divorcing anymore. And I'm like, why? Because they're working too much, I guess. Yeah. But in terms of where we're going and where we're expanding, we really think about our second and third acts around how do we help health systems save and make more money? Health systems are operating with, you know, record low operating margins.

3:29It's getting harder and harder to serve patients. And they have regulatory, some tailwinds, but also a lot of headwinds coming their way. And we think AI is ripe for helping on the saving and make more money piece. And then ultimately, how do we help save lives? The fact that our software and our product is open millions of times a week before, during, and after a patient walks in the room gives us massive opportunity with products like clinical decision support, which I is building, but so many others to actually improve patient outcomes and probably one of the

4:00most important workflows and problems to be going after right now.

Chai's Background

4:04I mean, I think one thing that's so interesting, Chai, is obviously you came over to a bridge from GLEAN. And I think about clinical decision support, which is, you know, for our listeners, is basically, you know, in the context of a visit, helping a doctor figure out the right type of care. It's really a search problem in many ways, right, of going through lots of different data sources. Very analogous to your previous role as one of the earliest engineers over at GLEAN. And I'm sure a lot of our listeners are curious what's similar about the problem set you're going after now and what feels different now that you're in healthcare. Yeah, very similar.

4:35And I think taking a step back, I think with every wave, there's a lot of, like, very similar patterns that happen across different products. A lot of social networking products look the same. A lot of, like, credit-based products look the same. And I think we're seeing that's very similar in the agent era with many companies, of course, in Redpoint's Portfolio and so forth. And the key insight between both companies is that, like, you have amazing models, but, like, context is king. Context is what actually puts them to work. So I see it in a lot of ways, a lot of similarities, and, like, this is a healthcare-coded

5:05version of GLEAN. But I think the differences are really interesting. A couple of things that come to mind. First and foremost, like, the rigor in the setting we are in. The downside risk is extremely high here in healthcare. It can actually be fatal in some cases. You prescribe something that the patient is allergic to, for example. Whereas at GLEAN, it's like, oh, you got the question wrong. It wasn't the end of the world in most cases. And so what does that mean? That shapes our evaluation strategy, both offline evaluation,

5:36progressive rollout, and there's a lot more we could kind of go into there. Second thing that comes to mind is, like, vertical versus horizontal. In both cases, there's a large variance. But when GLEAN is a much more horizontal company, there's a variance of personas, companies that you're working with. We also have a variance of personas, different types of specialties, different hospital systems. But the variance is a little more narrow. So from a product perspective, you're able to focus far more, especially when you have a maturing technology and you're building new products

6:07that never existed before. It lets you go specific, go after them much more easily. And especially in healthcare, where so many problems were solved with labor and process, that's actually extremely ripe for AI to keep helping augment and enable.

6:21And then the final thing that I think that's really interesting, a bridge specifically compared to many other companies in the AI area, is the modality we started with. We're ambient and we're always listening in the background. And I think many more AI products will go that way, but it's actually how we started. And I think that's actually the greatest form of AI we can create. AI that's actually seamless. You're not actually looking at your screen. It's always there. It's always helping you out and being proactive. You know, the Jarvis vision, that like every hackathon I went to over the past decade, there was always a Jarvis competitor.

6:52But I actually think a bridge very much started from the opportunity and continues to go that way. And one thing I think is super interesting then from a product perspective is you have this always on, seamless in the background. And then you have to decide like when do you kind of break the wall almost and like say, hey, you know, clinician, like you might not have thought about X or whatever it is that you want to do. And obviously, I think in healthcare traditionally there's this idea of alert fatigue and just like a million pop-ups and then a doctor just ignores all of them. It's probably a pattern that a lot of builders are thinking through now. How do you think about like the right way

7:22to intervene or to pop up in a doctor visit? Yeah, it's such a good question. I think alerts are notorious in healthcare specifically. I think over 90% of alerts are ignored. I think the first and most important thing is context is everything as Chai alluded to. And I also think about how do we go from being reactive alerting to really proactive intelligence at the point at which it matters most. One thing we like to say is we want our product to feel like air conditioning. It should be in the background

7:53just making things better. And maybe if and if there is something that has great clinical risk and we're acutely aware that intervening now and not later is incredibly important, we should decide to act. But I think if you think about proactive versus reactive, instead of alerting a clinician during a visit when they're with their patient having a pretty serious and sensitive conversation, how do we actually prep a clinician before they walk into the room with that patient? And so historically,

8:23clinicians might have to manually go through charts with a patient that they've had over the course of months or years. and they'll try to suss out what are the things they should be doing. You can imagine a world with a bridge will summarize all of the most recent contacts for you, tell you based on the reason for a visit the patient is coming in for, the types of things you should be discussing. And so you're actually going into that conversation prepped rather than walking in cold to that patient visit and then having this product interrupt you five or ten times

8:54throughout the visit. And there might actually be times where it's really important to interrupt. We have a product called Prior Authorization and so this is when you may go into a doctor's office with knee pain, they'll prescribe you an MRI and I think so many of us have had this experience before where in four weeks you'll get a call saying, hey Sean, that MRI that you were prescribed wasn't approved and why don't you come back in? We'll figure it out. In a world with a bridge, we might choose to actually quietly

9:24but still alert a doctor in that visit and alert is probably not even the word we would want to use before a patient leaves. We would want to tell the doctor, hey doctor, before Sean leaves, you should ask him, has he had physical therapy and has his pain lasted for more than six weeks because the Aetna plan that he's on in California requires six things. We've already confirmed four of them have been met because we have all the contacts but these two last criteria, if you can address with Sean

9:55before he leaves the room, we could actually guarantee that your MRI is approved before you leave. And so when you think about clinical usefulness impact to the patient, I think there are instances in which if we can catch a doctor while the patient is still in the room, you know, as we think about save time, save money, save lives, we kind of get to check all of those boxes but when, you know, doctors have 15 minutes between visits, we have to be really, really thoughtful about when it actually matters. I think there's this interesting product opportunity that AI can have

10:25is reduce latency in the world. For example, prior authorization is an example of where care gets delayed and so great AI can reduce that and I think the problem with alerts before partially is a technical problem like it's the quality of your alerts really matters. They're going to get ignored if you get alerts that, similarly in engineering where they're noisy alerts that you can't act on but if you can make really high quality alerts with both the context as Jenny said and really high quality models, then I think you can create a whole another game. Yeah, and I really like that experience because I think it kind of starts

10:56to tease apart like what makes this so hard and unique. I think like one to make that prior authorization example possible, think about all the data that you need to have, you need to integrate within with electronic health record to know all of the patient context. Do we have access to your previous labs, previous imaging? And then to actually match you and to know that you're on Aetna, we have to collect all of the different payer policies and they vary by state. Some of these payer policies live on websites,

11:27some of them live in unstructured 50-page PDF files. I thought this episode was to make sure we didn't scare people from health care. But I think when you think about the things that make it hard, it also gives you the moat. And then I think the second is the AI and the model quality we need to be able to hang our hat on. And so the bar, I think similarly when I worked at Opendoor, I worked on pricing models. Like every outlier wiped out the margins of 30. And so similarly here in health care, the bar for accuracy

11:57is so high. And then I'd say the last is workflow is everything. You know, if insurance companies deploy AI, it typically happens too late. And this is when you have the notorious kind of like comical examples of AI just fighting each other when it's too late. But if we can pull forward the use of both the AI, but also the ability to solve problems when the patient's in the room, you can start to collapse what typically takes weeks or months after your visit, ideally down to minutes or real time.

12:28And I think it's where health care is both very difficult, but also extremely rewarding if you can crack it. Just to get some baseline on the form factors, because I've seen some videos on your website and stuff. You've got to talk a lot about ambient AI. Is it primarily on the phone? Is there any other form factor that people get a bridge in? Like, is there like a bridge room set up where it's just like always on? Like, I don't know. A bridge podcast studio.

12:58Primary phone factor is mobile and desktop. Usually clinicians are walking in and out of rooms with mobile, but at the end of the day when they're closing out their notes or wanting to prep for the day ahead, they might use desktop. We have been having a lot of really interesting partnership conversations with a lot of these in-room device companies as you think about what is, you know, the power of multimodality and even more data as you think about all of the what is today not captured context. Really, really fascinating to think

13:29about, especially even as we go into building and scaling our nursing product. It's one where nurses constantly, you know, as they're walking in to check in on a patient for two minutes or maybe even 30 seconds, starting an abridged experience is probably going to take longer than the visit. And so what can we do with in-room devices that are always on? Starts to beg really, really interesting and fun product questions. Like the way in tech companies we have all these Google Meet things, we might as well set up

14:00entire rooms with just a bridge tech. Very much. And I also think similarly about like actually also air glasses and so forth. I think it's also quite relevant where part of it is how do we bring it in a way without like a screen, but like also bring the information to the clinician in real time, but also let them focus on the patient. Do you think they want that? I'm just like very, I tend to be skeptical of AR, but, you know, I'm curious what you've tried. You know, admittedly, it's not a near-term product roadmap by any ways, and I'm here being far-fetched.

14:31There's some sick AR stuff for surgeries actually, when people are trying to visualize like the, you know, you're about to make an incision, but you want to see like what the cut might look or what the body might look like inside, and they can basically layer in imaging. Yeah, that's cool. Yeah. Yeah. Some point in the future. There are a lot of our largest customers and at the largest health systems integrating already, and so even as we think about building into it, I think unlocks a lot of product capabilities. Yeah. And just to establish terminology, sorry, and I know I'm asking

15:01basic questions somewhat for myself, but also for the audience who might be less integrated. When you say health systems, it's like the Johns Hopkins, the Kaiser Permanente. The Kaisers of the world. These are your customers, right? And the outcome that you deliver for them is happier doctors, reduce like whatever cost, I guess, of like processing, reduce mistakes. I think it's weird in a sense that I feel like there's also like a secondary customer, like the customer of the customer. And I don't know if you, do you think about it that way? Yeah, we have, I think the other interesting and

15:32complex part of building product is we have our buyers who are the chief medical information officers, the chief financial officers, the CIOs of these large health systems. Our users today are clinicians, but if you think about who downstream has impacted, it's patients. And so as we build with every product in mind, we think about who are we building for, who's the secondary user, and what does that mean either in terms of experience, security compliance, ROI that

16:03we have to make tangible. And so like you said, like time savings is one of them, but for CFOs, they care a lot more than just time savings. We have to show for every dollar you put into a bridge because you have more compliant documentation or because you have fewer queries coming from your billing team. We actually save or add real dollars to your bottom line or top line, I think, are things that we're constantly thinking about because of the dynamic across all three sets of users. And I think there's a whole other axis too

16:33with the payers and pharma as well and like connecting all these three big stakeholders in healthcares. Do the payers ever see your data? Sorry, the payers mean in the insurers, right? Yes. They also see a bridge data? No, they wouldn't see the raw bridge data, but when you're working together on something like prior authorization, whatever information they need, we'd communicate to them. Oh, right. Yeah, that's cool. I guess I would love to dig into just obviously you have to solve a lot of

17:04problems on the AI side. And so maybe to start at the highest level, what's one of the hardest problems you have to solve in AI at a bridge today? Yeah, to make things simple, let's take like building off the prior auth example. So one thing Janie talked about is like, okay, this data is all over the place and there's this combinatorial explosion of like procedures, payer policies, and even sometimes different health systems, there can be some cross product of all of these different considerations you have to take into account. But what's really, really, really hard about this problem is actually doing it

17:35real time in the conversation. So, you know, in any AI product, usually the three KPIs you care about are quality, latency, and cost. Now, what we're saying is we want you to do this real time in the conversation guiding the clinician. How do we do it in a way that does not break the bank? But we're using, but we also need very intelligent models because you're working with this cross product of data and just like all this context layer as well. So you need high intelligence and high quality because you don't want the alert fatigue, but you

18:06also need to be fast and cost effective. And so that's where I think a lot of clever engineering goes, actually. It's like, okay, without getting into all the details here, can you model these policies in some intermediate representation or other things that you can do that can actually make this problem tractable? And, of course, the Pareto frontier is always changing, but we're also trying to do this now. What implications has that had for what you take off the shelf and say, you know, we don't need to be world-class at X. We'll just take this from the model providers or from some infrastructure player and what you're like, no, this is where we spend most of our time focused on?

18:38Yeah.

18:40This is the fun challenge in AI, right? Of course, with the shifting landscape. We try to be extremely thoughtful on predicting the trends of where third-party models are going and where we can uniquely go. And, you know, sometimes I feel like when you talk about AI models, we're like the models are just going to get infinitely better. But I don't think it may be in the grandness of time you could say that, but actually within every month, every quarter, there's specific ways they're getting better. You know, they're training on a lot more coding data to be

19:10better coding agents, for example. And so we have to think about where the things that unique data that we're uniquely training on or actually to step back a little, like where is a proprietary model bringing an advantage to us is if it can give higher quality or lower cost and latency for similar quality, very similar to many other companies. And when we can do that is when we have proprietary data. So, for example, we have on the order of 80 million or hundreds

19:41of millions actually now getting close to of medical conversation. It's insane. This is a unique data set. And this data set, it's very interesting because this data set is effectively a large part of the trace between the patient and the provider. That's where the quote unquote debugging happens in healthcare. We actually have these traces at scale as in like as our CEOs even called it, an exhaust that comes out of our product. And so when you have these traces, that's how you can actually train better agents on certain use cases, whether it's your

20:11transcription diarization use cases or so on, or like note generation models. And we can do that much cheaper and faster. But we're always also working with these third party model providers, you know, we closely collaborate with them. And that's how we kind of predict where the trends are go. The thing that I think about a lot is that I know that the model providers are going to train much more on like agentic workflows and so forth. So that's great. So that you have a better agentic harness. But the other thing that's interesting is you know that the model providers, because a large class of the consumer model providers

20:42is healthcare queries, you know that they actually might optimize to train a lot of healthcare data to actually encode the knowledge in its weights. And I think this is just a great thing for us as well, where the off-the-shelf models can keep getting better at general healthcare information, such that what our strategy is, we have a constellation of models, we can use something for this, that, and like we only care about, at the end of the day, the best product experience. And obviously you have like overall capabilities improving. I'm curious, like as these models get better, is

21:12there something you look at and you're like, you know, three months ago we really couldn't do that, but God, like the, you know, the latest models really allow us to do it. So here's something interesting that I've kind of been toying with. So all models are, this wasn't super, super obvious a year ago, but now it's become clear and clear that almost every agent is a coding agent underneath, underneath the hood, right? So you, you give it whatever a file system, it can write its own code and so forth. So when you think about within, within healthcare and the use case that we have, you can think of the EHR effectively like a

21:43file system. It's just, it's a storage of all this information. It's actually a lot of information there. They cannot fit into the context window, at least of today's models. And you want to use that context effectively for all these product use cases we're talking about. And so if you have better agents that can actually like manipulate data, read that data, treat it as a file system, as we see they're going, and we know model companies are investing this way, then that actually very directly benefits us. Yeah. Yeah. Okay, cool. Again, just establishing basic things, but we're

22:13going back to the model stuff. I'm really interested in double-clicking more on like the real-time element, which is pretty important for both of you. Is it, is real-time basically just batches of like every one minute, every five minutes? Is that how we actually do it? Or is there some more native, like genuinely real-time in the sense that OpenAI has a real-time API or Gemini has a real-time API? Yeah, yeah, yeah. So today it is more on