Conversations Are the Source of Truth in Healthcare with Abridge CEO Shiv Rao

**Elad Gil** (0:05)
Hi, listeners, and welcome to No Priors. This week, we're speaking to Shiv Rao, CEO and founder of Abridge, an AI company that processes medical conversations to unburdened clinicians, from clerical and financial work, allowing them to focus on patient care. A practicing cardiologist at UPMC, Dr. Rao has recently led Abridge to secure a $250 million Series D raise. Join us as we explore how AI is transforming healthcare delivery. Shiv, welcome to No Priors.

**Shiv Rao** (0:32)
So excited to be here. Thank you, Elad. Thank you, Sarah.

**Sarah Guo** (0:35)
So Abridge has been around for about seven years. Can you tell us a little bit about how the company has evolved over time, what your starting point was and what you're focused on now?

**Shiv Rao** (0:42)
Yeah, absolutely. So we started Abridge in 2018 So it's been a minute and everything that we've been building since then is really based on the same thesis. So that hasn't changed. And the thesis for us in healthcare delivery is that we don't think doctors or nurses are going to get fully automated over the next 10 years. And so what's the first signal in healthcare delivery? We think it's a conversation.
It's a dialogue between a professional and a patient. And we believe that those dialogues are really upstream of so many workflows in healthcare. And that's where we focus. And so we focus on clerical work first, but then that's a sort of wedge for us to expand into any number of different value propositions over time.

**Sarah Guo** (1:21)
Can you tell us a little bit more about some of the products that you have currently and how people use them day to day and what sort of customers you work with? Just to give context to our listeners in terms of what business do you have and what do you focus on?

**Shiv Rao** (1:32)
I guess sort of starting at the top, what we do is we unburden clinicians from all the clerical work that crushes their souls at night, and a little bit more color on that. So two out of five doctors don't want to be doctors in the next two to three years, and 27 percent of nurses for a JAMA article that was published last year, don't want to be nurses in the next 12 months. And so we have this crazy supply demand mismatch. It's like it's a really public health emergency. Patients are having to drive five, six hours from rural health settings to see a clinician in an inner city setting that could save their life. And so we've got to do something about it. And I think that's where technology has a role that is finally sort of being recognized and acknowledged at the highest level. Like the entire healthcare industry understands now that they just need to find a way to assist, augment and automate any number of different workflows. And so where we come in is that we unburden clinicians from a lot of that clerical work that they hate to do, so they can walk in a room, they can hit a bridge, have a normal conversation and talk about any number of different topics in whatever order. But when they hit stop and swivel their chair, their notes there. But it's not the note that you might expect that my 14-year-old daughter could sort of create using an off-the-shelf model. It's a note that checks off all the different boxes across not just who the clinician is, what their specialty is, what system they're a part of, who this patient is, what insurance plan do they have and what geography. Not just like the clinical note, but also what the billable note is, if that makes sense.

**Elad Gil** (3:03)
Can you actually explain the difference between those two things, like a clinical versus a billable note?

**Shiv Rao** (3:08)
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 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 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, that kind of stuff. But then on the other end of the spectrum, there are the large health systems. There are 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 DPC doctor down the street. And the reason why we went there is that the barrier to entry and the barrier to good enough, I should say, is really, really high. And that's where we felt like we could flex a lot of our advantages, a lot of our differentiated muscles. We have a lot of science at the center of our company. Our chief technology science officers, this guy named Zach Lipton, he's a professor at Carnegie Mellon. He's full time with us, but he's been able to recruit a pretty amazing team of machine learning engineers and scientists who can really reach their hands deeper down into the stack, to be able to meet that bar for all these large health systems, where we need to be good enough for not just the individual doctor in whatever specialty, we have to be good enough for all the different doctors and all the different specialties and all the different settings, outpatient, inpatient, urgent care, emergency rooms, and also in all the different spoken languages. And so, the barrier to entry, the bar for good enough is a lot harder. But running into that end of the market allowed us to sort of compete with just pretty much one other company, while a lot of the other startups were starting mid-market or down-market individual, like primary care doctors, with the hope probably over time that they could recruit the people and aggregate the data and do the post-training or whatever else to be able to swim upstream over time.