The Exchanges

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

A I think it comes back to that thesis. And so if you, if, if you really, you know, believe as we do that healthcare is about conversations, that it's like one of the first, you know, original signals in healthcare, then you start to see that any number of different workflows are beyond it. It's not just clinical notes. It's also orders. After I see a patient, I might say to my patient, like, uh, let's start you on metoprolol or let's get a CT scan. And so we talked about an order. So we can distill, we can extract those orders. We can Structure them, and we can place them in the medical record. What's after orders is a claim, is a code, is a bill that goes to the insurance company. There's all things revenue cycle. There are clinical trials that come up in a conversation as well. Whether I know it or not, maybe this patient in front of me has inclusion and exclusion criteria for some trial that could save their life. And so what if some, I had the superhero power and in the moment, at the point of care, I was being told, By a technology at the right time, like, hey Shiv, like this patient in front of you has inclusion and exclusion criteria for something that could save their life. Do you want to bring it up? Here's the information. So that's another sort of aspect of, of where we're going already. But then there's clinical decision support. And so in, in many ways, I'd say clin…

AI assessment note: “you start to see that any number of different workflows are beyond it.”

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

A I think it comes back to that thesis. And so if you, if, if you really, you know, believe as we do that healthcare is about conversations, that it's like one of the first, you know, original signals in healthcare, then you start to see that any number of different workflows are beyond it. It's not just clinical notes. It's also orders. After I see a patient, I might say to my patient, like, uh, let's start you on metoprolol or let's get a CT scan. And so we talked about an order. So we can distill, we can extract those orders. We can Structure them, and we can place them in the medical record. What's after orders is a claim, is a code, is a bill that goes to the insurance company. There's all things revenue cycle. There are clinical trials that come up in a conversation as well. Whether I know it or not, maybe this patient in front of me has inclusion and exclusion criteria for some trial that could save their life. And so what if some, I had the superhero power and in the moment, at the point of care, I was being told, By a technology at the right time, like, hey Shiv, like this patient in front of you has inclusion and exclusion criteria for something that could save their life. Do you want to bring it up? Here's the information. So that's another sort of aspect of, of where we're going already. But then there's clinical decision support. And so in, in many ways, I'd say clin…

AI assessment note: “you start to see that any number of different workflows are beyond it.”

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

A Well, there are APIs, but I think if you're really trying to differentiate where, like, three, five percent error rates can make a huge difference. Our ability, for example, to lean into the way a doctor pronounces a new oral oncology drug, an oral oncolytic, and, you know, I'm convinced no doctor knows how to pronounce any of these medications, and they all have their own way of saying these drugs, but we have to lean in and actually recognize the way they say them. And we have to recognize all the different symptoms, medications, diagnoses, and procedures across all the different specialties. And we also have to be multilingual because, you know, sort of like a bit of history of the voice game in healthcare is that before this world of generative AI and conversations and dialogues, there were dictations. And that's where I would go into a clinic, I'd see a patient, and then afterwards I'd pick up a dictaphone or maybe my phone, and I'd start to just rattle things off. As fast as I could. I'd say like, twenty-five-year-old female with a past medical history of diabetes and hypertension who presents with shortness of breast. Next line. Next line. Next line. You're just kind of go as fast as you possibly can. You're going through, like, 20, 30 dictations in the course of, like, 30 minutes. Um, and it's lossy. Because what you're dictating off of is chicken scratch. Like, stuff t…

AI assessment note: “where, like, three, five percent error rates can make a huge difference.”

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

A Well, there are APIs, but I think if you're really trying to differentiate where, like, three, five percent error rates can make a huge difference. Our ability, for example, to lean into the way a doctor pronounces a new oral oncology drug, an oral oncolytic, and, you know, I'm convinced no doctor knows how to pronounce any of these medications, and they all have their own way of saying these drugs, but we have to lean in and actually recognize the way they say them. And we have to recognize all the different symptoms, medications, diagnoses, and procedures across all the different specialties. And we also have to be multilingual because, you know, sort of like a bit of history of the voice game in healthcare is that before this world of generative AI and conversations and dialogues, there were dictations. And that's where I would go into a clinic, I'd see a patient, and then afterwards I'd pick up a dictaphone or maybe my phone, and I'd start to just rattle things off. As fast as I could. I'd say like, twenty-five-year-old female with a past medical history of diabetes and hypertension who presents with shortness of breast. Next line. Next line. Next line. You're just kind of go as fast as you possibly can. You're going through, like, 20, 30 dictations in the course of, like, 30 minutes. Um, and it's lossy. Because what you're dictating off of is chicken scratch. Like, stuff t…

AI assessment note: “Well, there are APIs, but I think if you're really trying to differentiate”

Q this, I'm in this boat, but for over a decade, um, looking at healthcare technology, uh, as another VC on the outside, um, It, it moves really slowly, right? In general, there are lots of reasons the market has been hard. Like, what do you think is different today? I mean, it's easy to say the abstract level AI, right? But like, how does that play out for your business?

A A few stars getting aligned at exactly the right time. And one star is like post pandemic, the amount of burnout that was in the, that's been in the industry still. And we just sort of like stretched, I think clinicians so far beyond their limits that they're leaving the profession. And then health systems didn't know what to do. And all of a sudden so many hospitals were just shutting down because they couldn't staff them anymore. And so I think, and the cost is sort of like hire another clinician is like close to a million dollars and it takes a long time. And, um, so I think that star is a really important one because people have talked about clinician burnout. People have talked about trying to, you know, create a better user experience in healthcare for, I don't know how many decades.

AI assessment note: “A few stars getting aligned at exactly the right time. And one star is”

Q this, I'm in this boat, but for over a decade, um, looking at healthcare technology, uh, as another VC on the outside, um, It, it moves really slowly, right? In general, there are lots of reasons the market has been hard. Like, what do you think is different today? I mean, it's easy to say the abstract level AI, right? But like, how does that play out for your business?

A A few stars getting aligned at exactly the right time. And one star is like post pandemic, the amount of burnout that was in the, that's been in the industry still. And we just sort of like stretched, I think clinicians so far beyond their limits that they're leaving the profession. And then health systems didn't know what to do. And all of a sudden so many hospitals were just shutting down because they couldn't staff them anymore. And so I think, and the cost is sort of like hire another clinician is like close to a million dollars and it takes a long time. And, um, so I think that star is a really important one because people have talked about clinician burnout. People have talked about trying to, you know, create a better user experience in healthcare for, I don't know how many decades.

AI assessment note: “A few stars getting aligned at exactly the right time. And one star is like post pandemic”

Q One of the other challenges in that end of the market is there's a lot of incumbency in the existing systems. And, you know, you've, you were on the provider side, you understood this well. How did you think about navigating partnerships and like the systems people already had?

A Thinking about ecosystems is super important. And, um, really like the only currency that ends up mattering in healthcare is trust. Like, can you somehow find a way to be trustworthy very, very quickly? Because especially on the provider facing side of technology, like the, this is, the stakes are high. Two days ago, I'm just coming back from a red eye from like Vegas, where there was like a big healthcare conference called HIMSS. And while we were there, we met with an executive at a health system who was sort of asking us about like our stack, our infrastructure, how we're going to be able to scale and like redundancy. And he was explaining to us that we are now a part of his, his health systems infrastructure. Like we are core infrastructure. So if we go down, the entire health system goes down. They're not making money anymore because I sort of explained that these nodes are essentially bills, at least the way that we generate them. Thinking really hard about that responsibility and then figuring out if we're going to market on that end of the market, that end of the spectrum, then how do we also sort of partner with the right players, earn their trust of like the right ecosystems so that we can sort of absorb some of that trust. Um, and it's easier said than done, but Um, in twenty-twenty-two, as an example, like, we had won that paper, that, that EMNLP best paper, but in …

AI assessment note: “partner with the right players, earn their trust of like the right ecosystems”

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

A We have some people we call mutants, uh, in our company, uh, who are doctors who are also engineers. Like we had this one, for example, we have an engineer who was like a, a principal engineer at like Meta, who's also a clinician. Um, we have doctors who are like in the weeds of like just prompt engineering on a daily basis. Um, but then we have others that can go like even, even more scientific. We have others that also work on, um, other aspects of like partner success or go to market as well. And so I think we try to find those interesting, um, combinations of people cause it helps us go faster. They're having like Interdisciplinary and multidisciplinary meetings in their own mind, and we just don't have to, we can just, like, skip steps, I think, with those folks sometimes, but in general, I'd say, like, where we've, like, really invested, like, we've raised over, like, like, five hundred million dollars now, and so, like, where is that capital going? I think so much of it, 80% of it, should continue to go into R&D, and so it's just figuring out, like, what's next on this roadmap? What else can we build? And, you know, our ability to, sort of, Reach down, lower into the stack, and also, like, get into new, new workflows and user experiences at the top, I think, has served us really well. You were having this very successful career, uh, in corporate venture. Prior to that, y…

AI assessment note: “We have some people we call mutants... who are doctors who are also engineers.”

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

A We have some people we call mutants, uh, in our company, uh, who are doctors who are also engineers. Like we had this one, for example, we have an engineer who was like a, a principal engineer at like Meta, who's also a clinician. Um, we have doctors who are like in the weeds of like just prompt engineering on a daily basis. Um, but then we have others that can go like even, even more scientific. We have others that also work on, um, other aspects of like partner success or go to market as well. And so I think we try to find those interesting, um, combinations of people cause it helps us go faster. They're having like Interdisciplinary and multidisciplinary meetings in their own mind, and we just don't have to, we can just, like, skip steps, I think, with those folks sometimes, but in general, I'd say, like, where we've, like, really invested, like, we've raised over, like, like, five hundred million dollars now, and so, like, where is that capital going? I think so much of it, 80% of it, should continue to go into R&D, and so it's just figuring out, like, what's next on this roadmap? What else can we build? And, you know, our ability to, sort of, Reach down, lower into the stack, and also, like, get into new, new workflows and user experiences at the top, I think, has served us really well. You were having this very successful career, uh, in corporate venture. Prior to that, y…

AI assessment note: “We have some people we call mutants, uh, in our company... doctors who are also engineers”

Q One of the other challenges in that end of the market is there's a lot of incumbency in the existing systems. And, you know, you've, you were on the provider side, you understood this well. How did you think about navigating partnerships and like the systems people already had?

A Thinking about ecosystems is super important. And, um, really like the only currency that ends up mattering in healthcare is trust. Like, can you somehow find a way to be trustworthy very, very quickly? Because especially on the provider facing side of technology, like the, this is, the stakes are high. Two days ago, I'm just coming back from a red eye from like Vegas, where there was like a big healthcare conference called HIMSS. And while we were there, we met with an executive at a health system who was sort of asking us about like our stack, our infrastructure, how we're going to be able to scale and like redundancy. And he was explaining to us that we are now a part of his, his health systems infrastructure. Like we are core infrastructure. So if we go down, the entire health system goes down. They're not making money anymore because I sort of explained that these nodes are essentially bills, at least the way that we generate them. Thinking really hard about that responsibility and then figuring out if we're going to market on that end of the market, that end of the spectrum, then how do we also sort of partner with the right players, earn their trust of like the right ecosystems so that we can sort of absorb some of that trust. Um, and it's easier said than done, but Um, in twenty-twenty-two, as an example, like, we had won that paper, that, that EMNLP best paper, but in …

AI assessment note: “partner with the right players, earn their trust of like the right ecosystems”

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

A It's a great question. So in this country, we're not compensated as doctors for the care that we deliver. We're compensated for the care that we documented that we deliver. So every single one of these notes is actually a bill. And that's why there's just like, so there, there, these are really high stakes artifacts, not just from a clinical communication and patient outcome perspective, but also from a revenue cycle perspective. But I think another key insight for us that served us well for these last several years has been that healthcare is not homogenous. And, you know, that healthcare industry umbrella underneath it, on one end of the market spectrum, there's a direct primary care doctor down the street who's taking cash payment out of pocket off the insurance grid. There's an independent PCP, a really small provider group, like mid-market, you know, that kind of stuff. But then on the other end of the spectrum, there are the large health systems. They're the integrated delivery networks, the academic medical centers, and what we decided to do, and I think what served us incredibly well is we made the strategic decision years ago to actually run into the hardest part of the market, that large health system out of the market, as opposed to the small practice or the mid-market or the independent, you know, DPC doctor down the street. And the reason why we went there is that …

AI assessment note: “But I think another key insight for us that served us well”

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

A It's a great question. So in this country, we're not compensated as doctors for the care that we deliver. We're compensated for the care that we documented that we deliver. So every single one of these notes is actually a bill. And that's why there's just like, so there, there, these are really high stakes artifacts, not just from a clinical communication and patient outcome perspective, but also from a revenue cycle perspective. But I think another key insight for us that served us well for these last several years has been that healthcare is not homogenous. And, you know, that healthcare industry umbrella underneath it, on one end of the market spectrum, there's a direct primary care doctor down the street who's taking cash payment out of pocket off the insurance grid. There's an independent PCP, a really small provider group, like mid-market, you know, that kind of stuff. But then on the other end of the spectrum, there are the large health systems. They're the integrated delivery networks, the academic medical centers, and what we decided to do, and I think what served us incredibly well is we made the strategic decision years ago to actually run into the hardest part of the market, that large health system out of the market, as opposed to the small practice or the mid-market or the independent, you know, DPC doctor down the street. And the reason why we went there is that …

AI assessment note: “But I think another key insight for us that served us well”