A New Operating System for Physicians with OpenEvidence Founder Daniel Nadler
episode
No Priors: Artificial Intelligence | Technology | Startups
44 min
1 speaker
7 chapters
transcribed 1 month ago
Transcript
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What makes OpenEvidence the operating system for 40% of U.S. physicians?
Daniel, thanks for doing this.
Happy to be here.
So uh give us a sense of this incredibly viral sensation that has been open evidence, uh, in terms of what type of Um coverage it has of American doctors today.
As much as We would like to think that it's going especially well for us. I I would sort of say as a qualifying point that um in all of the sub-industries of AI, you you're seeing an acceleration and compression, right? So the the adoption cycles, even outside of open evidence before we get to open evidence, in other fields of knowledge work and coding and so on are hyper-compressed, right? It used to take You know, half a decade or a decade for something to become standard, and now it seems to happen in two years or a year. So the same things happened with open evidence. In about 18 months, it's become the operating system for clinical knowledge in the United States. Uh, it is used something like 20 times more than the next most used platform of any kind in our specific segment, which is high-stakes clinical decision support for doctors.
So high-stakes. Clinical decision report for doctors is a specific category of medicine. It's distinct from say paperwork or it's distinct from scribing. Um, those things are you know part of the workflow of being a doctor, uh, but the stakes and the consequences uh are different. Um, if you get it wrong, you can go back and do it again. Uh, that's not the case with a patient. Uh, you have to get it right, you have one shot to get it right, and so clinical decision making. uh of which clinical decision support uh is in service of is unquestionably the highest stakes area of medicine. We're probably the only company working at the tip of that spear. Most people have self-selected themselves out of the problem of high stakes clinical decision making, uh certainly through an AI lens, um, because they view it as ambitious.
And could you explain it, Orda or Lance? Because I think fundamentally it's about picking information and then translating that into specific either recommendations or diagnosis for a patient. Can you tell us more about how that works? Yes.
One way to sort of simplify it down is at its foundation it's a search problem, but it's a very semantic search problem. So most search traditionally works with keywords, right? So like you know, flights to Barcelona or hotels in Barcelona, most of the, you know, most of the keywords there can be captured in like a couple of words and certainly in a sentence. And that's sort of traditional Google search. Even if you were to think about clinical decision support as a A search problem, simply describing your search query, if you want to think about it that way, usually takes many sentences. So an example I like to give is you have a 44-year-old female patient. She has moderate to severe psoriasis, that's the red stuff on your skin.
You know, you're a dermatologist, that's so far so simple, you would just prescribe one of the many creams you see commercials for on television, except uh she has um MS. Uh so now it gets interesting because you want to treat her psoriasis, um, but you don't want to make the MS worse. And you are not a neurologist, you're a dermatologist, so neurology is not your specialty. Um, but you don't want to go refer her to a neurologist because you want to treat her psoriasis. And if you just keep referring people in circles, medicine never happens. From the ether, you might have heard as a dermatologist that the new classes of psoriasis treatments, um, which are biologics, they're IL-17 inhibitors and IL-23 inhibitors, might have some interactivity uh with the neurological dimension of a patient's condition.
That's about all you know. Um, you didn't learn this in medical. School because IL23s were FD approved in 2019, right? So one of the great themes of open evidence is that the sort of golden age of biotechnology is sort of the dark ages of physician burnout because it's just impossible to keep up with all the new drugs and all the new mechanisms of action and so on.
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Chapters
7 chapters
1
What makes OpenEvidence the operating system for 40% of U.S. physicians?
0:05–8:13
2
How did OpenEvidence achieve viral adoption in just 18 months?
8:13–11:49
3
What is the semantic‑search problem in medicine and how does OpenEvidence solve it?
11:49–16:18
4
Why does treating doctors as consumers unlock rapid growth?
16:18–22:25
5
How does OpenEvidence keep patients in the loop while giving doctors autonomy?
22:25–31:04
6
What future impact could AI‑driven clinical decision support have on medicine?
31:04–37:58
7
How will medical education change when knowledge updates every 73 days?
37:58–44:47
Speakers
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