Ethan Cohen

I’m excited to share that Ease Health is coming out of stealth with a $41M Series A led by @a16z, alongside @AbstractVC, Sunflower, F3, and @BoxGroup, to build the AI-native operating system for behavioral health. Behavioral health runs on some of the most fragmented infrastructure in healthcare. Providers are forced to stitch together EHRs, billing systems, CRMs, schedulers, documentation tools, and compliance workflows just to operate. We started Ease because this is not a tooling problem. It is an architecture problem. So we rebuilt the system from the ground up. Ease is a single platform where AI is not a bolt-on. It is foundational. -Documentation is handled by an ambient AI scribe. -An AI voice agent can manage intake and scheduling. -Our CRM auto-enriches leads and logs activity automatically. -Front desks can instantly match patients to the right provider. -Charts are audited continuously. -Utilization review and prior authorizations can run autonomously. When data is entered once, it flows everywhere it needs to go across clinical records, billing workflows, and reporting. One through-line of truth across the entire organization. Since launching, we have grown quickly and now support hundreds of behavioral health providers across inpatient, outpatient (IOP, PHP, MAT), and residential. The behavioral health crisis is not a demand problem. Providers are buried in manual documentation, claim denials, compliance friction, and disconnected systems. We are building the system designed to remove that burden. We are grateful to Andreessen Horowitz for leading this round, and thankful for the support of all of our investors and partners who believe in rebuilding healthcare infrastructure the right way. Your conviction and partnership mean a great deal to our team.

🆕 How to Kill The Code Review latent.space/p/reviews-dead the volume and size of PRs is skyrocketing. @simonw called out StrongDM’s “Dark Factory” last month: no human code, but *also* no human review (!?) in this week’s guest post, @ankitxg makes a 5 step layered playbook for how this can come true.

Introducing agentic trial emulation: Biomni scaled clinical trial emulation using 12M patients EHR data at Mount Sinai. Researchers used Biomni to autonomously emulate multiple landmark anticoagulation RCTs end-to-end within their EHR—turning months of manual target trial analysis into scalable, agent-driven workflows that can extend across large numbers of trials and diseases. Running many emulations in parallel enabled something new: learning a stable institutional transport signal that quantifies how published trial effects systematically translate in local care. Read more: phylo.bio/blog/scaling-a… Exciting work with @BenGlicksberg @joshualampertmd Justin Kauffman et al