Inside Abridge: The AI Listening to 100 Million Doctor Visits — Abridge's Janie Lee & Chai Asawa

Janie Lee and Chaitanya “Chai” Asawa of Abridge join us for a crossover episode with Redpoint’s Jacob Effron 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 ultimately save lives • Why conversations between patients and clinicians may be the most important workflow in healthcare • 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 se