YouSaid · the spoken record

Omar A. Khokhar

lines on the record: 70
first: 2026-03-04
most recent: 2026-03-04
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“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. Like if 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 product's actually meaningfully change the lives of the clinicians we serve. And so that's a responsibility on us too to keep doing that quarter after quarter after quarter.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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 just even a little bit. To a point where it's just fun when we change the nature of how they view technology.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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'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. Like, you know, this was a person who had, you know, been considering quitting their job, basically, after all the terror of COVID and, you know, and everything else, compounding on top of that that you're talking about. So I 100% agree with you that after nothing but technology being a burden to this whole industry and even when I was deploying my own software back in the day, doctors would groan and they'd be like, oh, yet another tool? Like, why are you stepping this down my throat? All of a sudden to an era where they can actually see sunshine.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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 niche 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”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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. There is a pathway for the job of being a clinician, being a nurse, to be 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.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“That was a good question. There is a specific level of just humility and honor and 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 ambience comes in because they were a loved one or had an experience with the health system or works inside of the health system. Just one of those industries where The Outlook looked bleak for quite some time.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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. 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 even begin thinking about what the right framework is?”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“It does. I think generally on the platform side, you want people who can think really deeply about long-term architectural choices in C-Round corners. And 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, really good at requirements gathering. And that's actually the bottleneck to building great products and features. Internally, we use AI 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?”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Cool. And one last question for me that's more internal facing of 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 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?”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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 super natural and that could extend to our customers, but it could also extend to the broader ecosystem.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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?”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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 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 go have teams that can go tackle the problem is the bottleneck more than anything else.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think we're at a place where it's hard to disentangle 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 some of that work is is still a little tricky. I think generally like predictive modeling is not particularly well solved yet by this class of models”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“That hasn't really changed. Yeah, yeah. But the capabilities have definitely evolved. 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 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?”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“As like the bar has gone up on the things that AI can do. There's a lot of things that I remember 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 come along far enough for us to do XYZ. And then here we are, those things are actually very, very feasible.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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. So I think I'm long-term optimistic on this one, even if the short term seems a little bit tricky to navigate.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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 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 of fidelity with clear audit trails, it's not just a win for the organization on the health system side. I think long-term it ends up being a win for the payer as well because on the flip side, 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 and the health system side. But I think what ends up happening is once you have a shared source of truth, then”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Margin. 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. And the sort of steel cage deathmatch of providers implement AI, payers implement counter AI, and then we have a bought-on bot crime around RCM. That's actually playing out as we know. And people are actually talking about on their earnings calls even. With some of the large national pairs, where do you see that playing out? Like, how do you see, and if you guys are doing any work along those lines, it would be great to hear just like real life case studies of how do we solve that challenge.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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. 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 cache 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. 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.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Category. It's interesting too because I know 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 positions 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 to actually heart ROI in phase two.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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. 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.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“And so then the question really becomes who can I actually work with to change operating margin? 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. Our success is going to be dependent on your ability to help you change operating margin.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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. 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.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“100%. And I think that already is so clear for any organization that either works directly with ambience or just talks to an organization that has worked 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 inside 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 BSaaSaaSaaSa enterprises. You don't have hundreds of millions of dollars of cash lying around to be able to invest in toys and cool toys. And so the question then becomes, how do I actually fund these things? And so the second lens that we take is”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“The way that I would think about it from the shoe of the operator is probably twofold, two lenses. The first is a lot of excitement and noise around AI. 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.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“So let's say that I'm the CEO of a hospital system and what you're describing sounds incredibly exciting, but today I've got five different vendors who are all buying for that pie, essentially. So you got your, I have my EHR, I've got the foundation model companies who are all doing, launching healthcare products and claiming that they're going to get into the space. I've got ambience 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. Those are kind of like to me the five major categories of players that are kind of all converging on this vision. 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 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.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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? Answer questions, double check. Did you pick up your medication? Actually make sure that you got that sort of 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? What does it look like for now there to be a virtual care team member to actually help quarterback all those things on your behalf? That is what the true promise of these capabilities is.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Clone of yourself has poured through all the data for hours and hours and hours and has put together a summary that of everything you need to know for this patient. Imagine you move that one step further upstream, which is you've got an agent that has access to all this context that it 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.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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. 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.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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. On top of that,”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“The next obvious step from everything that we're talking about is 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, et cetera. What's between, why shouldn't your platform be an autonomous AI doctor? Like what's between the capabilities today and the future reality that we will likely have a lot of this being done in a fully automatic fashion, doing clinical, like actually making clinical judgments.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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 could distribute it at the cost of software across every interaction where RevCycle expertise is relevant. 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? 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. 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? Of it in real time”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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 with 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 a 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. Or you've got massive back office teams that try to like review as much as you possibly can and then try to correct the clinician afterwards, which obviously clinicians hate as well and is extremely expensive and inefficient.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Two products to 12 products to next year, 24 products out in the market. I think once you've had the 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. I think the second is the following”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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 of 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. Because you can imagine every single use case in 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”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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 surface 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 clock speed to product clock speed.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Work that needs to be done, but creating an entirely new set of data that not only trains your models better to perform better in the future, but effectively could become an entirely new system itself, right? I mean, that would be the question to you as you think about the future of the EHR. Like, to your point, the EHRs are trying to vertically integrate into these workflows themselves. I think obviously I'm betting against them being the ones who win that game.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“You know, if we were to build that company today, or even 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, like that's never existed before. Same thing with scheduling, right? Where 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 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”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Why not be the call center and build voice AI agents that are effectively doing the work based on the rules of the road that you want kind of systematized across the entire healthcare system? The second thing I think about is exactly what you said. And I have a question back to you on this, the data layer, right? 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. To their credit, they obviously all have very different styles, very different preferences, different ways they want to practice. And there's no health system has an incentive to tell every doctor to systematize it in one way because these doctors are very scarce. They want to retain them. They want those doctors to attract the patients that they want to see. And so there's, you know, the heterogeneity of the underlying data was like a”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“For you, I mean, this is definitely a question I think about all the time, and I see lots of companies that are actually doing 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. 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, that removes a ton of the potential drift in an outcome. And so that's one thing that we see a lot of these days is like instead of just giving tools to a call center.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“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. For us, you have to have really, really deep relationships 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?”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“You put a doctor on an ICD 10 coding problem, you ask them a question, you put a coder on that problem, you ask them a question versus you have both 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 Has to be solved at the intelligence layer, and it's not being solved by the foundation models today. And then I think the last piece is that.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“And credentialing matters a ton. And then there's also the realization that so much of what ends up and needs to end up in the medical record is never verbally sort of explained In a visit, right? So, for instance, an oncologist may be walking through a decision tree on a care pathway in their brain, which is then expressing to the patient and words that they can understand what the next step is. But what goes into the clinical note is that trace of the decision tree, not the words that were spoken to the patient. And so defining quality as to what sub specialized oncologist expect Is fundamentally complicated. And then you've got certain use cases where For example, ICD 10 coding, you put two doctors in a room, they agree 60% of the time.”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Exactly. And it's especially challenging because most of the use cases are open-ended use cases. And they start as trivial as if you have multiple pieces of contradictory information in the chart. You got a patient where there's no indication of thyroid problems in the problem list, but this person is on thyroid medication that was prescribed six months ago. What does that mean about the state of this patient? Even just resolving truth at that level is tricky. Then what if you're in the inpatient setting and all of a sudden you've got four different clinicians doing a physical exam on this patient and you've got one very specific physical exam that happened about 48 hours ago that came in through a specialty consult? How much do you index on that versus the physical exam that happened two hours ago by generalists?”
2026-03-04 · a16z Podcast · Deploying AI in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source