Ambience CEO Nikhil Buduma on AI in Clinical Workflows

19:31Julie Yoo:You put a coder on that problem, you ask them a question. Versus you have both of them in the room with 10 minutes to debate before you ask them the question, you get three different answers. And so defining quality is actually just a fundamentally hard problem and one where it has to be solved with the intelligence layer and it's not being solved by the foundation models today. And then I think the last piece is that for these companies to be successful, as capabilities are evolving really, really quickly, you've got some companies in vertical industries where iteration speed is naturally fast.

20:11for us, you have to have really, really deep relationships

20:15Julie Yoo:to be able to go from prototyping something to deploying something in TST, to turning it on in prod, to then being able to actually make live learning loops with end users. And I think one of the things that we've solved as an organization is how do you build extremely deep relationships with marquee organizations to be able to go from concept to live and deploy learning with users within like less than 30 days, which is unheard of in our category. So I think there's like all these things that you have to solve for companies to be valuable in this category that are just far beyond clinical intelligence, if that makes sense.

20:48Nikhil Buduma:So the floor will stay lava for much longer is what you're saying.

20:50Julie Yoo:Yeah, 100 percent. 100 percent. Maybe a question for you is, imagine you lived in a world, like if you were to rebuild Kairos today, right? How long would that take? And then if you had an environment where someone had solved the integration problem, had solved the data integrity problem, had solved the sort of relationship with the health system problem, how long do you think that would take for you?

21:11Nikhil Buduma:I mean, this is definitely a question I think about all the time. And I see lots of companies that are actually doing, you know, forms of what I would have done. But it kind of gets just to, you know, rehash some of what you said. I think there's two components that I think a lot about. One is, what is the form factor of the product that we would have built? It would have been fundamentally different, right? Like we were a legacy enterprise SaaS product that got deployed into a call center setting where we had to train the humans to use the software workflow in the right way, you know, to get the right outcome.

21:37Nikhil Buduma:And as we know, in those environments, the churn rate of employees is just naturally super high. So as soon as you train one batch of people, they leave, and then you got to retrain them again, and you just have a huge sort of range of compliance rates, let's call it, with which people are using that software. So if you can actually make an agent do the work instead, you know, that removes a ton of the potential drift in outcome. And so that's, you know, one thing that we see a lot of these days is like, instead of just giving tools to a call center, why not be the call center and build, you know, voice AI agents that are effectively doing the work based on the rules of the road that you want, you know, kind of systematized across the entire healthcare system.

22:15Nikhil Buduma:The second thing I think about is exactly what you said. and I have a question back to you on this is, you know, the data layer, right? Like one of the biggest impediments to being able to do scheduling efficiently, which is what we were trying to do, is that there is no source of truth. There's no standardized way for representing clinical schedules. And not only that, it's like individual physicians, like, you know, to their credit, they obviously all have very different styles, very different, you know, preferences, different ways they want to practice. And there's no health system has an incentive to tell every doctor to, you know, systematize it in one way because, you know, these doctors are very scarce.

22:51Nikhil Buduma:They want to retain them. They want those doctors to attract the patients, you know, that they want to see. And so there's, you know, the heterogeneity of the underlying data was like a huge impediment to getting it right at the system level. And so, you know, if we were to build that company today or even, you know, what Ambience is doing, you're effectively creating the new system of record, right? Like you have a full texture of data that has never existed before in the healthcare system ever, right? conversation grade resolution on what's being said between a clinician and a patient. And then to your point, how does that translate to what actually gets documented in an esoteric fashion and creating those links?

23:27Nikhil Buduma:Like that's never existed before. Same thing with scheduling, right? Where, you know, as appointments are being booked and as you hear what was the preference of the patient and then how do you bump that against the preferences of the doctor and then, you know, create a semantic that links those two things together. Like that's a de novo set of information that like never existed before. So I think therein is another opportunity set for any company that's building in this day and age is, you know, how do you not only think about the work that needs to be done, but, you know, creating an entirely new set of data that not only trains your models better to like, you know, perform better in the future, but effectively could become an entirely new system itself, right?

24:04Nikhil Buduma:And I mean, that would be the question to you is, as you think about the future of the EHR, like to your point, the EHRs are trying to vertically integrate into these workflows themselves. themselves. You know, I think obviously I'm betting against them being the ones who win that game. But, you know, is this the moment? Like all of us have been waiting for decades for the moment where we can finally disrupt, you know, that incumbent EHR layer and have a shot at potentially introducing new tools that, you know, number one, bring those systems into the modern era, but also enable startups to have a shot at, you know, kind of owning that layer.

24:38Nikhil Buduma:Do you think this is the moment or, you know, what needs to be true for us to start to kind of eat into the layers of the stack that are owned today by these kind of monolithic ERP players?

24:48Julie Yoo:I think this is the moment and it's the moment for two reasons. The first is deeply related to what you were talking about, which is the organizations that figure out how to build the practice service area of the future and the administrative stack of the future have to be able to unlock this sort of level of AI to product clock speed that is bottlenecked by the fact that Today, we've built all these legacy systems on a legacy architecture that's not actually optimized for AI at a clock speed to product clock speed. And so one of the innovations of Ambience is we've actually built out a layer that sits on top of the EHR that pulls all the data out of sort of the systems of record, puts it in a form that makes it easy to build AI products on top so that the incremental cost of building a net new use case dramatically drops.

25:40Julie Yoo:Because you can imagine every single use case and application area is leveraging the same underlying systems of record. And you don't want to recreate that same infrastructure over and over and over again, right? And so for us to go from like two products to 12 products to next year, 24 products out in the market. I think once you've had that infrastructure layer that we've created, it fundamentally changes your product clock speed as an organization. So that's probably the first insight. And it's not a trivial piece of infrastructure to create. Like for us, it took several, several years of deep R &D to be able to even do this in the first place.

26:17Julie Yoo:I think the second is the following. The physics of the world have fundamentally changed with the set of capabilities. And so you were talking a little bit about how if you were to build a Kairos today, like would the form factor even be the same, right? And I think that's happening across every use case across the span of the entire system, right? RevCycle is probably one that you're really, really deep in as we are too. And one of the questions is like today, when you have a RevCycle problem, how does system solve it? And it's really twofold. Either they invest in training to teach clinicians who don't want to learn coding to try to get them to do the right thing.

26:54Julie Yoo:Or you've got massive back office teams to try to like review as much as you possibly can and then try to correct a clinician afterwards, which obviously clinicians hate as well. and is extremely expensive and inefficient. And so the question for us is, well, if you could distill that expertise into a model, which is extremely hard to do, but if you could, and then you can distribute it at the cost of software across every interaction where RevCycle expertise is relevant, and you then own the window in front of that user, how do you then leverage that set of capabilities to make doing the right thing easy?

27:28Julie Yoo:How do you make doing the right thing obvious? And all of a sudden, that starts to disintegrate a lot of the underlying assumptions for why RevCycle is constructed the way that it is, right? So like building a product for pre-bill, does that make sense anymore in a version of the world where you can work closely with the clinician to create a more accurate source of truth and automatically adjudicate all of it in real time? Yeah.

27:52Nikhil Buduma:The next obvious step from everything that we're talking about is, you know, autonomous AI doctor, right? So you guys are today a co-pilot, every use case, I believe, that is still being used in the wild. requires a physician to basically sign off on the notes or the documentation, etc. What's between, why shouldn't your platform be an autonomous AI doctor? Like what's between, you know, the capabilities today and, you know, the future, you know, reality that we will likely have a lot of this being done, you know, in a fully automatic fashion, doing clinical, like actually making clinical judgments.

28:23Julie Yoo:We live in a world where the demand for healthcare is just rising so quickly. We have 10 ,000 people aging into Medicare every single day. and we just can't train doctors fast enough to take care of all these people. And on top of that, the sort of cost of adding one more patient to a doctor's panel today is so painful. And so I think one of the promises of these technologies is how do we do more with less? How do we make it painless for a clinician to say, I do want to see more patients. I do want to increase access to the care and expertise that I have. do it in a way that is long-term sustainable.

29:02Julie Yoo:I think most organizations want to increase access to patient populations. And the question is, how do you do that without asking your clinicians to do more? Well, a big part of that is, how do we start to offload some of that work to sort of virtual care team members who can sort of take the next step on behalf of the clinician? And so already we're starting to experiment with before the visit even starts. So before a visit today, Ambience has sort of anticipated everything this doctor would need to know before they even see this patient, right? So imagine a clone of yourself, you're an endocrinologist, a clone of yourself has poured through all the data for hours and hours and hours, and has put together a summary of everything you need to know for this patient.

29:44Julie Yoo:Now imagine you move that one step further upstream, which is you've got an agent that has access to all this context that can also anticipate, well, what are the most likely questions that are top of mind for the clinician? and you start asking those questions before the visit even happens. And then you've loaded that also up into the summary for the doctor. And then on the flip side, you had a great conversation with your physician. The clinician sort of wrapped up with you with an after-visit summary that gets sent back to you through the patient portal. What happens if you turn that into an agent that can have a continuous conversation with you?

30:15Julie Yoo:Answer questions. Double check. Did you pick up your medication? actually make sure that you got that sort of that lab test done at Quest Diagnostics? Did you pick up your Xanax for your claustrophobia because you have a CT scan before seeing the oncologist and you're anxious about that CT scan? All these things. What does it look like for now there to be a virtual care team member to actually help quarterback all of those things on your behalf? That is what the true promise of these capabilities is.

30:41Nikhil Buduma:So let's say that I'm the CEO of a hospital system. And like what you're describing sounds incredibly exciting. But today I've got five different vendors who are all vying for, you know, that like that pie, essentially. So you got your I have my EHR. I've got, you know, the foundation model companies who are all doing, you know, launching health care products and, you know, claiming that they're going to get into the space. I've got ambience, you know, kind of starting with AI scribe, but expanding rapidly. I've got my AI revenue cycle players. I have my AI clinical decision support players. You know, those are kind of like, to me, the five major categories of players that are kind of all converging on this vision.

31:17Nikhil Buduma:what's your pitch to me? Like tell me how I grok this whole space and how do I think about making investments against those categories in such a way that is durable for me you know for over multiple years like a five-year time horizon as opposed to me being in a situation where I need to rip and replace vendors like every six months.

31:35Julie Yoo:Yeah. The way that I would think about it from the shoe of the operator is probably twofold, two lenses. The first is there's a lot of excitement and noise around AI But there's a big difference between marketing and noise versus if I bought this, will my clinicians actually use it? Because if you don't have the right level of adoption, nothing really matters, right? And so I think that's probably where right now the jury is looking really negative for a lot of AI use cases, which is these companies are very good at pitching a vision. But then when it comes to brass tacks, the adoption utilization of these technologies is extremely underwhelming.

32:18Nikhil Buduma:And that's one of the cool things about this era is that you can literally just let the doctors use it and kind of put with their own feet, right?

32:22Julie Yoo:A hundred percent. And I think that already is, it's so clear for any organization that either works directly with ambience or just talks to an organization that works with ambience. And most of these have tried every single one of the players in the category that we've done an incredible job of actually owning the window of care in front of every single clinician side of the enterprise. And if you can't do that, that's table stakes. And so that's the first lens is, can you actually get the level of adoption utilization? I think the second is, look, health systems are not traditional enterprise BSAS enterprises.

33:02Julie Yoo:You don't have hundreds of millions of dollars of cash lying around to be able to invest in toys, in cool toys. And so the question then becomes, how do I actually fund these things? And so the second lens that we take is, at the end of the day, one of the reasons why AI is exciting is because this is the first time that we think as an industry that there is a class of technologies that can fundamentally change operating margin. And I think if you get this right, the organizations that unlock their AI product clock speed, they'll be able to adopt AI. They'll be able to unlock new forms of operating margin.

33:39Julie Yoo:That operating margin allows them to invest more in tools that attract better talent. That better talent means more volume. More volume equals more revenue. More revenue equals the ability to invest in more AI. You get sort of unlock this crazy flywheel. And I think from the CEO's perspective and the board perspective, the organizations that figure out how to do this effectively, they compound and become the destination of choice for patients. And the ones that don't, they're at risk of consolidation. And so then the question really becomes, who can I actually work with to change operating margin?

34:11Julie Yoo:Are the EHRs going to work with me in a partnership model where they're committing to change operating margin. I think it's a thing that's so scary for people to do because it requires your products to actually work. You have to actually be good at change management. You have to be good at measurement and attribution. But I think because we've built out all those capabilities, we're willing to go to a health system and say, we will actually put our money where our mouth is. And our success is going to be dependent on your ability, our ability to help you change operating margin. And so I think the combination of those two things creates a set of proof points in the beginning of a relationship where it's almost like a religious conversion that happens where now you walk into an organization that leverages ambience and you can't go from room to room without being stopped in the hallway.

34:55Julie Yoo:And that builds the level of sort of trust and excitement for the future that earns you the right to do more over time, right? So I think within six months of working with the Markey Academic Medical Center customers we have, we go from, hey, we're working on the scribe to we want everything on your roadmap as quickly as possible. And I think it's because of that sort of like excitement from seeing it actually work. And then the CFO for the first time looking at AI implementation and saying, this clearly pays for itself many times over. And we can take the new margin we didn't have. You know, one center we work with, they're projecting over$30 million of net new margin post attribution debate.

35:35Julie Yoo:That's just created by Ambience.

35:36Nikhil Buduma:Because they don't have to hire human scribes anymore? What's the attribution there?

35:39Julie Yoo:A big part of it is RCM. A big part of it is improving throughput and access. but RCM is a big part of it. And now you're like, well, I have all this margin to invest in more AI. And that's sort of the beginning of unlocking that flywheel. And so once you see that, I think it just fundamentally changes your perspective on the category.

35:56Nikhil Buduma:It's interesting too, because I know like even just a year ago, I remember the first wave of AI scribe adoption when I talked to C-suite executives at Hospital Systems. And I said, you know, what's the ROI? Like what made you purchase these products? They basically said, listen, Julie, actually there's not that much financial ROI. we're just doing it for retention. We want employee happiness. We want our physicians to feel like we are looking out for them and giving them a reason to come to work every day. And now it seems like it's very much shifted, you know, to actually hard ROI in like phase two.

36:26Julie Yoo:It's because people didn't know it was even possible at that point in time, right? To do this, you have to be able to track where user behavior is happening inside of the EHR. Map that user behavior down to a code, that code then being submitted, a CDI query prevented, a denial prevented, and then putting that all together to new cash you're collecting in reduction in cost to collect and doing that in a way that's going to actually meet the muster of the CFO's office. And for us to do that requires us to basically download the data warehouse and build an entire analytics stack that would actually work for the CFO's office.

37:02Julie Yoo:And so like in the absence of doing that, of course, the CFO is never going to believe it. But now we're seeing the real proof points across customers where CFOs will actually tell other CFOs how much value this category can create. But it takes a lot of work and thoughtfulness on getting the right use cases, actually moving coding performance, actually doing change management to move the marker on operating margin. Yeah.

37:26Nikhil Buduma:We've talked solely about the provider side of the market, but obviously some of the things that you're speaking about have implications for, certainly for payers at a minimum.

37:34Julie Yoo:Yeah. And, you know, the sort of steel cage death match

37:38Nikhil Buduma:of providers implement AI, payers implement counter AI, and then we have a bot-on-bot crime around RCM. That's actually playing out, as we know, and people are actually talking about it on their earnings calls even with some of the large national payers. Where do you see that playing out? How do you see, and if you guys are doing any work along those lines, it would be great to hear just real-life case studies of how do we solve that challenge.

37:59Julie Yoo:You probably know, we work with a lot of organizations that are integrated. They have a plan, close relationships with plans. I think what we're finding is that the world may, our view is more optimistic, which is if you've got a system that actually can understand source of truth and understand it really, really well, which is not just ambient listening, it's also deeply understanding all the past context as well. So it's like you need that sort of like layer on top of the systems of record. But if you deeply understand source of truth and you know exactly what happened in the visit and you can answer any question with high levels fidelity with clear audit trails, it's not just a win for the organization on the health system side.

38:40Julie Yoo:I think long term, it ends up being a win for the payer as well, because on the flip side, you know, we're talking about an AI versus AI arms race. We've already seen the labor versus labor arms race because you've got payment integrity teams being built out to sort of work with the RCM teams on the health system side. But I think what ends up happening is once you have a shared source of truth, then the ROI of RCM becomes potentially negative over the next five years. And there's a very real likelihood that the ROI of payment integrity also becomes negative over the next five years. And it just makes sense to collaborate.

39:12Julie Yoo:So I think I'm long-term optimistic on this one, even if the short term seems a little bit tricky to navigate.

39:19Nikhil Buduma:As like the bar has gone up on like the things that AI can do, like there's a lot of things that I remember like when we first invested, you know, we were kind of like, oh, that's a pipe dream. we'll have to do additional development and or wait until AI models, you know, come along far enough for us to do X, Y, Z. And then here we are, you know, those things are actually very, very feasible.

39:35Julie Yoo:Our pitch deck's about the same.

39:36Nikhil Buduma:The pitch deck's absolutely the same. It hasn't really changed. Yeah, yeah, yeah. But the capabilities have definitely evolved. Like, is there any remaining, like what's hard today? Like what's still hard to do? What are you hearing from your customers that they want to do that, you know, we're not actually able to do yet with, you know, the capabilities that are out there? And is it AI that's the bottleneck or is it all the other stuff that you talked about with respect to like last mile workflow, integration, RCM, all that kind of stuff.

39:59Julie Yoo:Yeah, I think we're at a place where it's hard to disentangle. Like, what do you consider like a bottleneck at the foundation model layer versus a bottleneck in post training versus a bottleneck in product? I think some of the use cases that are still tricky and hard for us are as we're thinking about cascading context across care settings. So how do you anticipate what's going to happen when you've got a patient who's admitted to the ED and now they're upgraded to the inpatient setting and now you're trying to make predictions on what next best action really is. Some of that work is still a little tricky.

40:34Julie Yoo:I think generally like predictive modeling is not particularly well solved yet by this class of models. But I think in general, what we're finding is that if you've built the right team with the right applied R &D expertise and the right sort of internal clinical subject matter expertise and RCM, subject matter expertise, to pair really, really well with applied R &D teams, we're in a world where like so much is, there's just so much to build that we don't really feel that bottleneck. We're almost just like, our ability to understand the problem and have teams that can go tackle the problem is the bottleneck more than anything else.

41:14Nikhil Buduma:Do you ever envision Ambience becoming like a true platform in the sense that you open up your capabilities to third parties to then develop on top of your system and you become kind of that back-end layer that the EHR plays today?

41:26Julie Yoo:It's an active conversation with most of our customers. A lot of the academics have internal product and engineering teams that want to build all sorts of stuff. Sometimes that's on roadmap for us and we talk about how do we want to think about making the right shared investments over time. There's a lot of great ideas that are likely just not on our roadmap. And so this ability for us to make it easy for others to build on top is supernatural and that could extend to our customers, but it could also extend to the broader ecosystem. Cool.

41:56Nikhil Buduma:And one last question for me that's more internal facing of like your experience as building ambience as an AI native company. And obviously, again, like being an employee at an AI company has obviously also evolved quite a bit over the last few years. Like what are some of the things that you're doing like fundamentally different today based on the availability of AI tools for your employees that you weren't doing like, you know, two or three years ago that you think has been a huge game changer?

42:19Julie Yoo:I mean, there's probably a number of experiences that are wild to folks if you almost like rewind back time two years ago. But on the engineering side, like the amount of work that an individual engineer can get done now with Opus 4.5 is quite insane. I think what we're finding is that you just need really smart thinkers and you don't necessarily need as many people anymore to get lots and lots of work done.

42:52Nikhil Buduma:I was going to say, has it changed like the profile of engineer that you hire?

42:55Julie Yoo:It does. It does. I think generally on the platform side, you want people who can think really deeply about long-term architectural choices and see around corners. And on the product on the product engineering side, what you're really looking for is the kind of person who can embed deeply in clinical environments, spend time with customers, work closely with subject matter experts, and do really, like a really, really good requirements gathering. And that's actually the bottleneck to building great, great products and features. Internally, we use AI a ton for research, for sharing context. One of the things we oftentimes think about is when a new employee onboards, how do we take almost our internal decision traces on how we make decisions as an organization and make it really, really easy for a new employee to be able to stand under the shoulders of giants?

43:49Julie Yoo:Because if you join a new company, oftentimes you're like, you have no idea how this place works. You have no idea the historical context of decisions that were made. You have no idea if you're about to make a new decision, how do you then begin thinking about what the right framework is?

44:00Nikhil Buduma:What we tried before, and yeah.

44:02Julie Yoo:100%. And so I think still early on some of these use cases, but we're starting to build internal teams to think about if we were to internally make ourselves an AI-first company, what that would look like. Because our thesis is in the same way that you wouldn't, if you were building a health system from the ground up today, you would not do it the way you were building 10 years ago. If you're building a company today, you would not do it the way you would be doing it two years ago.

44:28Nikhil Buduma:What did we not cover that you want the world to know about ambience?

44:32Julie Yoo:That's a good question. There is a specific level of just humility and honor in being able to work on this problem. I know you've got your own personal experiences with the health system. I sure have my own. And every person at Ambience comes in because they were a loved one or had an experience with the health system or works inside of the health system. It's just one of those industries where The outlook looked bleak for quite some time. Folks have been talking about the system being at a breaking point. I think this is the first time where there's hope that, hey, there is a pathway to doing more with less.

45:17Julie Yoo:There is a pathway for the job of being a clinician, being a nurse, to be a fulfilling one. There is a pathway for the experience of a patient not being as confusing and full of despair as sometimes it is. And I think in some ways that makes this a special time to be working in healthcare. Whereas in the past, I think we didn't necessarily attract the best people and the best talent because it was unclear, could you even have an impact if you wanted to on healthcare? So this moment is very special. And I think there's just a lot of gratitude. I think we all have to be in a position to even be able to contribute to the problem.

45:54Nikhil Buduma:100 % agree with that point about just the exceptional talent that's coming into healthcare and the fact that I think health tech is now earning its right to be compared to best in class, broader tech, as opposed to this weird dish that just like always looks crappier than everything else. And then I would also translate that to the physician side. And, you know, my last story here is just, I remember it was probably three years ago when one of your customers asked me if they could share my email address with a doctor. And I was like, sure, what, you know, what's going on? Two days later, I got an email from that doctor saying I specifically asked for a contact information of one of the investors of Ambience because I just wanted you to know, thank you for investing in this company because I've never experienced the type of joy that I've experienced using this tool in my day-to-day job that has made me now want to remain a doctor.

46:41Nikhil Buduma:Like, you know, this was a person who had been considering quitting their job, basically, after all the terror of COVID and everything else compounding on top of that that you're talking about. So I 100 % agree with you that after nothing but, you know, technology being a burden to this whole industry. And even when I was deploying my own software back in the day, doctors would grow and they'd be like, oh, yet another tool? Like, why are you stuffing this down my throat? All of a sudden to an era where they can actually see sunshine. And I think the delta between the magic of the tools that they're experiencing in their consumer lives and what they do in their work has for the first time narrowed, you know, just even a little bit to a point where it's just fundamentally changed the nature of how they view technology.

47:21Julie Yoo:It's kind of wild when we even hint that we're about to release a new product. The energy we feel from the clinicians is almost like lining up for the next new Apple product. And like you've just never seen that kind of energy before. And I think for us, it feels great that every time we create something, there's almost like this level of magic that's created for the clinician that sort of builds up this anticipation. And I think we're also we understand that comes with a bunch of responsibility, too. right? Which is then once we do deliver, that the products actually meaningfully change the lives of the clinicians we serve.

47:55Julie Yoo:And so that's a responsibility on us too, to keep doing that quarter after quarter after quarter.

48:00Nikhil Buduma:Yeah. Amazing. Well, always a pleasure to talk to you about these things, Dekeel, and congrats on all the progress at Ambience.

48:04Julie Yoo:Thank you, Julie. And thank you for believing us since the very beginning.

48:07Nikhil Buduma:Yeah, absolutely.

48:11Julie Yoo:Thanks for listening to this episode of Raising Health. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. Follow us on X at A16Z and subscribe to our Substack at a16z.substack.com. Thanks again for listening, and I'll see you in the next episode. As a reminder, the content here is for informational purposes only. It should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast.

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