Dr. Michael Gao

So as an example, on the smarter DX side,

The average patient visit that we analyze has about 30,000 data points.

There's over 100,000 possible ICD codes.

Okay.

And your average patient has, you know, kind of 10 to 15 codes.

And so if you're looking for a missing code out of 100,000 possibilities and 30,000 data points,

It's a lot like finding a... Sounds like a pretty easy to miss.

It's like looking for a missing person in a stadium of people.

Yeah, that's a good analogy.

And so this is where we use AI to first identify what it thinks is missing, and then sort of the human intelligence, that clinical experience, is still used to validate each and every finding.

But it kind of changes the nature of the workflow into kind of a...

AI does the work and humans do the supervision type approach.

Yeah.

So I'm, of course, a pretty big nerd, as you might imagine.

So as you had mentioned, I did residency at New York Presbyterian.

And before residency, I had built FindCare, which was kind of a

a version of ZocDoc, but targeted toward patients without health insurance.

So learned a good amount of, you know, kind of programming in that context.

And before that, did a lot of applied math.

So I was leading a lot of AI initiatives for New York Presbyterian.