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

**Swyx** (0:03)
Okay, this is a special crossover Latent Space Unsupervised Learning Pod.

**Jacob Effron** (0:07)
Very excited to do this. Once a year at this point, we get together.

**Swyx** (0:10)
Once a year.

**Jacob Effron** (0:11)
And this is a fun occasion to get to do it on.

**Swyx** (0:14)
I really wanted to talk to Abridge, but I felt very underqualified because healthcare is not something we cover very intensely. And it just so happens that Redpoint is our big investors and supporters of Abridge.

**Jacob Effron** (0:26)
So anytime you want to have a portfolio company on your podcast, it's by all means.

**Swyx** (0:31)
So we'll introduce our guest, Chai and Janie, welcome to the podcast.

**Janie Lee** (0:34)
Thanks for having us, we're excited to be here. Thank you.

**Swyx** (0:36)
Yeah, so for listeners, what do you guys do just to situate you guys in the company?

**Janie Lee** (0:42)
Abridge is a clinical intelligence layer for health systems. We really started with documentation and building for clinicians. And we think that, you know, as we think about reducing the burden that clinicians have, they're spending 10 to 20 hours a week on documentation. There's a massive doctor shortage in the country. We also think that conversations between patients and clinicians are probably the most important workflow in health care. It's obviously where care is given and received. But if you think about the 20% of our GDP that goes towards health care, almost everything is a derivative of that conversation, whether it's the claim, the payment, the actual diagnosis given the treatment. And we've started with a conversation to reduce the burden for doctors on documentation. But we're really excited about the path ahead as we become this broader Clinical Intelligence layer.

**Chaitanya Asawa** (1:34)
I'm Chai. I work on Clinical Decision Support at Abridge. And so I think as Janie said that we had this, we're uniquely situated where we started off with the clinical note. What I'm really excited about and where we're expanding towards is, what are all the things you can do before the conversation, during the conversation, and after the conversation? If you did have access to all the concepts about patients, care guidelines, medical literature, and put that together to serve how health care could look fundamentally different.

**Swyx** (2:01)
And that's like the context engine that you guys have? Is that what it's called? So historically, as I understand it, the company started in 2018 A lot of people would be familiar with the AI voice notes form factor that doctors would be like, well, do you consent to being recorded? It replaces handwriting and what have you. But it sounds like more recently there's been a big transition in the company.
Or just tell me about the broader transition.

**Janie Lee** (2:26)
Yeah. So from a transition perspective, we really think about our journey as how do we...
First chapter was... First act was how do we help save time? And that's where a lot of that original product was.

**Swyx** (2:37)
Which, by the way, one of the interesting stats on your landing page was like, people spend... doctors spend time after hours.

**Janie Lee** (2:43)
They call it pajama time.

**Swyx** (2:44)
So why is that pajama time?

**Janie Lee** (2:46)
Doctors after work in their pajamas at home are just writing and catching up on their notes every day. And we think some of our favorite customer love stories... We have a Slack channel called Love Stories. We have clinicians telling us Abridge has helped us from retiring earlier. We're now finally able to go home and eat dinner with our kids for the first time.

**Chaitanya Asawa** (3:08)
Save their marriage and stuff.

**Swyx** (3:09)
Yeah, one of our co-hosts was like, we're not divorcing anymore. I was like, why? Because they're working too much, I guess.

**Janie Lee** (3:16)
But in terms of where we're going and where we're expanding, we really think about our second and third acts around, how do we help health systems save and make more money?
Health systems are operating with record low operating margins. It's getting harder and harder to serve patients. And they have regulatory, some tailwinds, but also a lot of headwinds coming their way. And we think AI is right for helping on the saving and make more money piece. And then ultimately, how do we help save lives? The fact that our software and our product is open millions of times a week before, during, and after a patient walks in the room gives us massive opportunity with products like clinical decision support, which Chai is building, but so many others to actually improve patient outcomes. And probably one of the most important workflows and problems to be going after right now.