Chris, welcome.
Tell me, if you would, how is AI impacting patient engagement?
It's kind of an exciting time, right?
Because AI is helping the patient both when they're on
their own but also when they're with their provider.
Let's say I'm overdue for a colorectal cancer screening.
If there's an AI algorithm running on the EMR in the
background, the doctor not only knows the patient needs the
screening but knows, oh, I see that you've done screenings at
home before,
I see that Cologuard is covered by your insurance,
therefore, let's do a home screening test instead of, say,
schedule a colonoscopy—and all that can be powered by AI to
make the experience better.
Where do you see the greatest opportunities for AI in this
space
but also what are the challenges in reaching that?
I mean, right, those are two different things.
Let's start with the second one first.
So the challenge in health care—what's the number one rule?
Do no harm, right?
So do not harm the patient.
AI is an exciting thing.
Companies like pharma want to do more with AI to help patients
and providers, but they're very scared.
What if it were to go and do something that I wasn't
expecting?
You know, typically, they have an MLR—medical, legal,
regulatory—team approving things very tightly.
But AI isn't built to do that.
So that's important.
And the way to solve it is to have what I'd call medical-grade
AI.
So this is LLMs designed with guardrails specific to health
care, specific to pharma, specific to medical, legal,
regulatory.
It does things like make sure that the AI doesn't speak off
label, it doesn't speak off guideline, it doesn't provide
treatment recommendations.
It captures AEs and adverse events in the background
automatically, sends them off to where they need to occur.
That's how you build in kind of the safety guardrails to allow
people like pharma to be innovative in AI.
What other trends do you see shaping this year ahead?
So I think one of the things—it's, it's not
necessarily going to change the world in 2025, but it's starting
to happen pretty quickly—is AI usage in the EMR.
One of the leading use cases we're finding that providers are
loving is that AI can capture the dialogue between the patient
and the provider and automatically put that into
structured notes.
Instead of the doctor having to sit there and type and not look
at you, the patient, the doctor can now actually pay attention,
have a dialogue, and let the computer capture all those notes.
And honestly, it does a better job than they could at
structuring them in a way that we can then use them,
we can bill appropriately.
So that's one big use case. But I think the next big wave is
having all the clinical algorithms become smart behind
the EMR.
So if you think, maybe a simple example, hey, instead of just
saying, well, you're overdue for a shingles shot because you're
50 years old and you haven't had one, what if it says you're
overdue for a shingles shot, but I see you've never taken any
vaccination that I prescribed.
I'm not going to bother the doctor with that because we
don't want more alert fatigue.
If the doctor wants to address it, they can.
We're going to instead focus on something else,
but that's one algorithm that's easy to do.
What if you have thousands of algorithms running across all of
patient care?
Now you need a systemic system, and only AI can have all those
algorithms be dynamic.
And the last piece that I would say is, we can go beyond just
clinical and talk about economics.
So how do we pull in value-based care contracts into that AI
algorithm so that when, say, they're doing really well on
stars, on vaccinations, but not on screenings,
it intelligently knows how to push forward and put more
information at the tips of the fingers of the doctor that
drives the contract.
Chris, thank you so much for your time.
Thank you.