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

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Abridge did not start as an “GPT wrapper”. It was founded in 2018, years before the Cambrian explosion of AI application layer companies. OpenAI launched ChatGPT publicly on November 30, 2022 and by then, Abridge had already spent years doing the unglamorous work of building trust for one of the highest context, most important workflows in healthcare: the conversation between a patient and a clinician.

Abridge’s original wedge was clinical documentation. Listen to the visit, generate the note, reduce the clerical burden, and let clinicians spend more time with patients instead of the EHR. By focusing on how doctors actually document, how health systems actually buy, how EHR integration actually works, how clinicians verify outputs, and how missing context during a visit turns into downstream friction across billing, prior authorization, quality, and follow-up, the adoption of LLMs became a force multiplier on a workflow already optimized for sensitive context gathering.

The company has scaled fast: Abridge says it is projected to support 80M+ patient-clinician conversations this year across 250 large and complex U.S. health systems, with support for 28+ languages and 50+ specialties. It raised $300M at a $5.3B valuation in June 2025, after a $250M round earlier that year.

Today, Janie Lee and Chaitanya “Chai” Asawa of Abridge join us for another crossover pod with Redpoint’s Jacob Effron (who is on the board of Abridge) to dive into how Abridge is building the clinical intelligence layer for healthcare starting with ambient documentation, then expanding into clinical decision support, prior authorization, payer/provider/pharma workflows, and eventually real-time agents that act before, during, and after the patient conversation.

We go inside the product, data, infra, evals, workflow, privacy, and org design choices behind bringing AI into one of the highest-stakes enterprise environments from 100M+ medical conversations and specialty-specific evals to real-time alerts, EHR integration, de-identification, clinician-scientist teams, and why healthcare may solve some of the hardest AI problems first.

We discuss:

Why Abridge started with clinical documentation, “pajama time,” and saving clinicians 10–20 hours a week

The transition from ambient scribe to clinical intelligence layer: save time, save money, and save lives

Why conversations between patients and clinicians may be the most important workflow in healthcare (patient visit summary feature)

Chai’s “healthcare-coded Glean” framing: context is king, but healthcare raises the stakes on safety, evals, and rollout

Why Abridge wants AI to feel like “air conditioning”: always in the background, but only interrupting when it truly matters

The prior authorization example: turning a denied MRI weeks later into real-time guidance while the patient is still in the room

Why payer policies, EHR data, medical literature, and hospital-specific guidelines make the problem hard, and also create the moat

How Abridge thinks about ambient form factors: mobile, desktop, in-room devices, nursing workflows, multimodality, and future AR

The multi-sided healthcare customer: CMIOs, CFOs, CIOs, clinicians, patients, payers, and pharma

The hardest AI problem at Abridge: high-quality, low-latency, low-cost real-time support in a high-stakes clinical setting

When Abridge uses frontier models vs proprietary models, and why its unique data from medical conversations matters

Why “every agent is a coding agent underneath,” and how the EHR can be thought of as a filesystem for healthcare agents

How Abridge approaches personalization across individual doctors, specialties, and health systems

Why “AI slop” is AI without context, and how edits, memories, and clinician preferences create a data flywheel

Abridge’s eval stack: LFDs, LLM judges, in-house clinicians, third-party evaluators, specialty-specific evals, and progressive rollout

HIPAA, PHI, de-identification, one-way anonymization, customer contracts, and learning from healthcare data safely

What changes when you operate at 100M+ conversations: reliability, cost, post-training, model routing, and infrastructure optimization

Why the same clinical conversation can serve doctors, patients, payers, pharma, and future clinical-trial workflows

How Abridge works with EHRs, and why deep interoperability is table stakes for clinician adoption

Why healthcare AI has regulatory tailwinds, why 80/20 does not work here, and why high-stakes domains may drive AI forward

Why Abridge embeds “clinician scientists” into product and eval teams

What Chai learned from Glean about search, quality, and durable AI infrastructure

Why the future of AI infra may look like context layers, event-driven systems, Kafka, Temporal, sockets, CRDTs, and tools built for humans

Why Janie changed her mind on “PRDs are dead,” and why crisp written clarity matters more in complex AI products

How Abridge uses Claude Code, Cursor, and coding agents internally

Abridge:

Website: https://www.abridge.com/

X: https://x.com/AbridgeHQ

Janie Lee:

LinkedIn: https://www.linkedin.com/in/janiejlee

Chaitanya “Chai” Asawa:

LinkedIn: https://www.linkedin.com/in/casawa

Timestamps

00:00:00 Introduction and what Abridge does

00:02:05 From ambient documentation to clinical intelligence

00:04:04 Clinical decision support and context as king

00:06:57 Alert fatigue, proactive intelligence, and prior authorization

00:12:36 Ambient AI form factors and healthcare customers

00:16:59 The hardest AI problems in healthcare

00:18:26 Frontier models, proprietary data, and model strategy

00:21:07 The EHR as a filesystem for agents

00:24:03 Personalization, memory, and clinician preferences

00:30:40 Evals, LLM judges, and progressive rollout

00:36:47 HIPAA, de-identification, and privacy

00:39:21 100M conversations and operating at scale

00:44:10 EHR integration and the clinical intelligence layer

00:46:39 Healthcare regulation, latency, and high-stakes AI

00:50:11 Clinician scientists and long-tail quality

00:53:04 Lessons from Glean and durable AI infrastructure

00:57:03 The future of agentic healthcare workflows

00:57:34 PRDs, product clarity, and building serious AI products

01:03:11 AI coding tools at Abridge

01:04:06 Outro

Transcript

Introduction: Abridge, Clinical Intelligence, and the Latent Space x Unsupervised Learning Crossover

Swyx [00:00:00]: Okay. This is a special crossover Latent Space Unsupervised Learning pod.

Jacob [00:00:07]: Very excited to do this.

Jacob [00:00:08]: At this point, we get together once a year.

Swyx [00:00:10]: Once a year

Jacob [00:00:11]: And this is a fun occasion to get to do it on.

Swyx [00:00:13]: I really wanted to talk to Abridge but I felt very underqualified because healthcare is not something we cover very intensely. It just so happens that Redpoint’s our big investors and supporters of Abridge.

Jacob [00:00:27]: Anytime you want to have a portfolio company on your podcast

Jacob [00:00:29]: Please, by all means.

Swyx [00:00:31]: So we’ll introduce our guests. Chai and Janie, welcome to the pod.

Janie [00:00:34]: Thanks for having us.

Chai [00:00:35]: Thank you.

Janie [00:00:35]: We’re excited to be here.

Chai [00:00:36]: Thank you.

Swyx [00:00:36]: So for listeners, what do you guys do, just to situate you guys in the company?

Janie [00:00:42]: Abridge is a clinical intelligence layer for health systems. We really started with documentation and building for clinicians and 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 healthcare. It’s where care is given and received but if you think about the 20% of our GDP that goes towards healthcare, 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.

Chai [00:01:34]: I’m Chai. I work on clinical decision support at Abridge.

Swyx [00:01:37]: Yes.

Chai [00:01:37]: And so as Janie said, 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 context about patients, payer guidelines, medical literature and put that together and to serve, how healthcare could look fundamentally different.

Swyx [00:02:01]: And that’s the context engine that you guys have?

Chai [00:02:04]: Yes.

Swyx [00:02:04]: Is that what it’s called? Okay.

Swyx [00:02:05]: 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 “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. Tell me about the broader transition.

From Documentation to Clinical Intelligence: Save Time, Save Money, Save Lives

Janie [00:02:26]: So from a transition perspective, we really think about our journey as The first act was: how do we help save time? And that’s where a lot of that original product was.

Swyx [00:02:37]: By the way, one of those interesting stats

Swyx [00:02:39]: On your landing page was, doctors spend time after hours.

Janie [00:02:43]: They call it pajama time.

Swyx [00:02:44]: Why is that pajama time?

Janie [00:02:46]: Doctors after work in their pajamas

Swyx [00:02:48]: In their pajamas. Oh

Janie [00:02:49]: At home are just writing and catching up on their notes every day.

Janie [00:02:53]: 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 early or we’re now finally able to

Janie [00:03:06]: go home and eat dinner with our kids for the first time.”

Chai [00:03:08]: Save the marriage in some cases.

Swyx [00:03:10]: One of the quotes was “We’re not divorcing anymore.”

Swyx [00:03:12]: I’m asking, “Why?”

Swyx [00:03:14]: Because they’re working too much.

Janie [00:03:16]: 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 AI is ripe 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 improve patient outcomes and probably one of the most important workflows and problems to be going after right now.

From Glean to Healthcare: Context Is King

Jacob [00:04:04]: One thing that’s interesting, Chai, is you came over to Abridge from Glean and clinical decision support, which for our listeners is, in the context of a visit, helping a doctor figure out the right type of care. It’s really a search problem in many ways, going through lots of different data sources. Very analogous to your previous role as one of the

[truncated for AI cost control]