
co-founder & ceo @concurrencehq
New York City
Joined December 2013
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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
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.
Are all the neolabs converging on the same thing?
@togethercompute started with inference and is now offering sandboxes and RL training
@FireworksAI_HQ AI started with inference and is now offering RL training
@PrimeIntellect started with RL training and is now offering inference and sandboxes
@modal started with cloud compute and sandboxes and is now offering inference and RL training
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.
Ali Khokhar retweeted
when it comes to scaling, there is really no theoretical wall
but we have to start thinking more carefully about the properties we build on the models we train
when the data foundation doesn't provide sufficient means to produce casual inference, the models will learn representations that are potentially bullshit
which is actually fine, for many use-cases
but for healthcare, where a single decision can alter the trajectory of life or death, a smooth experience or suffering, it is actually extremely important that we carefully design the data foundation the creates the space of possibilities for the models to experiment with and learn their internal representation
we often under-estimate how fast models can discover good representations given a proper search environment and proper verifiers
we often overestimate how well they’ll do when those environments are improper and verification is ill-defined or difficult.
Let the healthtech talent wars begin
great turnout at our sf tech week event last night. the best questions came from engineers who have never touched healthcare, bullish
shoutout @meetshahdev and @nonlogic, excellent panelists
anyone building this pls dm me will fund. (Robot must run away within 30 mins of starting its shift or its not realistic)
Will AI replace healthcare professionals?
Here is an AI robot on its first day working in a hospital after finding out that:
• the EHR needs 17 clicks to order one test
• the medication list is different in three systems
• interoperability sometimes means making a phone call
• and fax machines are still critical infrastructure
What else?
The video is obviously AI-made.
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
Trend I'm following: Teams using AI "simulations" to quickly test product ideas and flow tweaks.
Products like @simile_ai, @primitivelabsai, @syntheticusers, trytenera.ai, Seldon.com, and others.
If you've tried something like this, how'd it go?
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…
I'm sitting down with @databricks and @nvidia tonight for a fireside chat on building at the intersection of AI and life sciences!
New York has the largest concentration of biomedical research institutions in the world and the country's biggest biotech workforce. It's also home to one of the deepest AI ecosystems anywhere. Come hang out if you're building or investing in the middle of that venn diagram :)
RSVP via Luma: luma.com/nyc-bgcx
🤝
Ali Khokhar retweeted
i made a personal site 👉👈
i didn't want it to feel like a resume or a blog (i already have those things) so i made a place instead
claireuhm.com/
Excited to announce that @concurrencehq is partnering with @databricks to build Health World Models that predict patient outcomes.
Together, we give healthcare organizations a unified data foundation that supplies full patient context to clinicians and AI, deployed natively on Databricks with no migration required.
For those not yet on Databricks, Concurrence provides the data foundation and AI applications as a single deployment.
Thank you to the Databricks healthcare and life sciences team for their partnership!
Read the full announcement here: concurrence.com/blog/concurr…
Read more about how to work with us here: concurrence.com/partners/dat…























