When I was 13, I lost my mom to breast cancer. I still wonder if it could have been prevented. When she found the lump, she did everything right. She went to her primary care doctor, and he told her it was nothing. My mom wasn’t convinced, so she went to a women’s health specialist for a second opinion and was told the same thing. A year passed before she saw a third doctor who was shocked that nothing had been done and ordered tests the same day. By the time she was diagnosed, the cancer had already spread. For the next three years, she went through chemo, surgery, and radiation. Specialist after specialist, asking the same questions again and again. After she passed away, there were two thoughts I couldn’t out of my head: (1) Somewhere, a doctor must have seen a case like my mom’s and caught it early. (2) Nothing that doctor learned had any way to reach her. Healthcare runs in my family. All four of my sisters are nurses or pharmacists, and my fiancée spent the pandemic working in a multi-organ transplant recovery ward. I’ve seen how hard clinicians work, and how much of that work requires understanding the patient and recognizing patterns from past cases. That’s why a doctor can practice for 30 years and get better every year, while a hospital can treat millions of patients without getting much better at treating the next one. What’s missing in healthcare is the infrastructure for it to learn from its own experience. If we don’t examine the cause and outcome of every interaction, how do we get better at knowing which signs to watch for? I spent years at Google and Upwork building AI from human expertise, then eventually left big tech to work on the one problem I couldn’t stop thinking about. I started a company called Concurrence (@concurrencehq) and raised $24M to build data and AI systems that enable healthcare orgs to learn from every patient they treat. Nothing learned is lost, and it reaches the next patient who needs it. We’ve already supported care for over 10 million patients worldwide. My mom deserved a doctor who had seen her case a thousand times. Every patient does.

Diagnosis is a small part of delivering care. Most of the work happens around the boring stuff: > Intake > Scheduling > Documentation > Follow-ups This work falls on healthcare workers who are already stretched thin. It’s also where patients fall through the cracks. A follow-up call never happens, or a medication change doesn’t make it from the specialist back to the primary care doctor. That’s where AI should start, and the record it builds there makes every later clinical decision better informed.

This is happening everywhere, not just neolabs. We are seeing it at the application layer. All scribe companies are becoming “platform” companies that then serve voice and front office and back office etc. “everyone is saying they can do everything for me” is something I hear from healthcare buyers daily.

If you think AI isn’t smart enough for healthcare, you’re wrong. The models are more than capable enough. What health systems lack is a way to learn from their own care. Every day, health systems generate enormous amounts of knowledge. Most of it is never captured. The next wave of clinical AI will be built on that record. The winners will build a system that turns vast amounts of healthcare data into actionable insights that continuously improve care delivery.

Let the healthtech talent wars begin

anyone building this pls dm me will fund. (Robot must run away within 30 mins of starting its shift or its not realistic)

All these simulation companies raising 100s of millions to predict human behavior… and then use it to help Pepsi do market research... what a waste

Life update: I just got engaged! Here's what it taught me about B2B SaaS👇

Check out the @databricks case study on how @concurrencehq runs clinical AI at trillion-token scale! databricks.com/blog/how-conc…

Everyone thought I was destined to fail. The first company I built was selling AI clones to coaches, sourcing revenue from a $20/month Slack community. People congratulated me when we hit $1M ARR But the truth is, we were burning capital to retain consumers The business model was unsustainable with a small TAM When I started building in healthcare, one enterprise healthcare deal arrived that doubled our revenue At that point, every advisor told me to keep the customers and revenue from AI cloning to fund the shift into enterprise I told them no. I knew dragging the old business along would ruin the new one So I churned 100% of our customers that very afternoon. I refunded every single customer so our engineering team could focus strictly on building AI infrastructure for healthcare Within the same year, we rebuilt from no revenue to $2M ARR with zero churn. Moral of the story: Don’t waste time saving a dead business. Bury it fast and move on to something better.

10,000,000 patients are already running on @concurrencehq. we’ve raised $24M from General Catalyst, Madrona, and Optum Ventures. we have more demand from major health systems and enterprise orgs than our current team can physically serve. I’ll give you $10k if you help me find my next hire roles: > head of engineering you will scale our infrastructure behind dozens of live clinical deployments. you will build the core engineering org training our Health World Model. clinical-grade reliability is the baseline here, not an afterthought. > enterprise sellers you will close health systems, payers, governments, and massive provider groups. our inbound pipeline has completely outpaced our capacity to close deals. > how we operate high agency, low ego, and direct communication. DM me if you know someone exceptional.

What do you get when you put a Stanford cancer surgeon, a Google DeepMind researcher, the engineering leader behind the world's largest clinical studies, and my co-founder in the same room? Come find out at the @concurrencehq event during SF Tech Week on Wednesday, October 7th! If you're an engineer or researcher curious about healthcare, RSVP here → partiful.com/e/RBjOKviZHlul9…

My mom lost her life to breast cancer when I was 13, but there's a world where she didn't The clinical knowledge required to save her existed somewhere, but two doctors dismissed her case for a year before she got diagnosed Over time, I realized they weren't evil or incompetent They reasoned in good faith from the cases they had personally seen in their careers. This is the unfortunate truth about modern medicine. The care you receive based on a lottery of which clinician happens to walk into your exam room. A doctor gets better over 30 years of practice. When they retire or leave the clinic, their clinical intuition leaves with them A hospital can treat 10,000,000 patients and be no smarter on the 10,000,001st than it was on day one. Healthcare doesn't fail because clinicians do not care, but because the system has no institutional memory. Every patient starts from scratch. After what happened with my mom, I made it my mission to make sure no one else has to go through this We're building Health World Models that provide a unified foundation for clinical intelligence across clinicians and AI Every act of care compounding across the entire network If this work matters to you, DM me Big announcement coming soon