Hi, everyone. This is Brian Zerman with Becker's
Healthcare. Thank you so much for tuning into
the Becker's Healthcare podcast.
Today, we're going to talk about how AI
is transforming the payer strategy.
Joining me for today's discussion live in Chicago
at Becker's Payer Issues Roundtable is doctor Mira
Atkins, chief medical officer at Lyric. Doctor Erica
Atkins, thank you so much for being here.
Thanks for having me. So to get us
started here, can you just share a bit
more about yourself, the work you're doing at
Lyric, just so listeners can sort of appreciate
where you're coming from as we move through
the conversation today? Sure. So I'm actually a
board certified OB GYN, and I practiced for
several years prior to transitioning out of practice.
And I've been in the payer space for
the last ten years, but recently joined Lyric
as their chief medical officer,
where my focus is really to scale clinically
informed AI for payment accuracy.
And we're really moving towards prepayment intelligent that
just helps to preserve the provider trust.
Excellent. I appreciate that background. So here at
at at Becker's Parish's roundtable,
you've discussed with other leaders how AI and
advanced analytics are are helping payers move from
reactive to proactive care and payment models.
So from your perspective, how is the shift
showing up in areas like payment integrity and
clinical validation, which you just flagged as really
important to the to the work you're doing?
And, really, I guess, in that context, if
you could
unpack what the word proactive actually means. I
think we hear that word a lot, but
put some,
a concrete definition for folks out there. Sure.
So for decades, you know, claims review has
been really a reactive
adversarial
exercise
where
we are looking at either post payment and
audits, and it's just been a really
blunt force.
I think the future is really we're moving
towards the left where clinical AI provides a
lot more transparency,
and it drives more appropriate payments,
where it reduces provider friction, and it really
focuses
people on the outliers. Those cases that really
matter where you're getting the the high hit
rates, I guess.
Yeah. And that moving away from that adversarial
sort of nature,
do you think that AI
is really an opportunity for that finally to
happen? Do you really think that this technology
is kind of might be the the the
solution to some of the that sort of
administrative
gridlock, I guess. Yes. Absolutely. And I think
it takes, like, true partnership and trust in
order for to make that happen.
So I think that the technology
is out there. I think we just the
people
have to have the trust
and
the willingness
to do something a little bit different. Yeah.
And and coming to that trust, you really
need sort of that,
clinical validation. It's gotta be clinically sound. So
how do you do that? How how can
peer organizations really integrate these tools in a
way that
is equitable, transparent?
And thinking from your experience as a as
a clinician and also someone who who leads
digital strategy,
what are sort of your guiding principles or
safeguards
that are are essential to achieving that balance
to making sure that
AI is clinically sound and then you're coming
to to that place of trust together?
I think the first
step
is that intentionality.
I think you have to be really thoughtful
and intentional
to ensure that we're embedding
our human values
of being patient centric. Right? Member centric.
So
at Lyric,
our governance model is really a clinical governance
first
model.
So we have multidisciplinary
reviews. So we have clinicians. We have data
scientists. We have legal. We have compliance. We
really have just the whole gamut of people
to give their input
on
what
AI
should be deployable
before it's actually deployed and during deployment
as well.
And then we have continuous monitoring
for equity
and safety. Right? We hold our AI to
really rigorous safety standards,
and
we require
that we have this, like,
subgroup performance checks,
in real time governance to detect
drift because the models can actually shift based
on the data that's being,
inputted.
So and then finally, it's like that transparency.
You can't build trust without the transparency.
So the automated decisions, they have to be
explainable,
to clinicians and providers,
to the people.
So,
you have to be able to back them
up. Yeah. And that that transparency is a
continuous act too. Just like as you're tweaking
the models, there's no, like, set it and
it's done. You know what I mean? Like,
it's it's it's always constant work. It is
constant work. Right? Because things are constantly changing,
constantly evolving.
I mean, look at medical evidence. When we
talk about clinical, the medical evidence is constantly
changing and evolving, so there is no set
it and forget it.
Yeah. And you could make the argument too
that clinical evidence and medical evidence is just
is gonna start evolving faster than what we've
seen in the past. Absolutely.
I mean, I think there's,
a lot of evidence out there that says
and I
think someone quoted to me in 1950,
the rate of doubling of medical literature is
fifty years. And I think in 2024,
it's something like sixty two days.
So it's astonishing.
So we have
to be conscious of that and really intentional,
about our models. Yeah. I I heard it
put to me too. That's a I mean,
that's that's a wild stat. Yeah. I also
heard it put to me once that it's,
you know, technology advancement innovation has never moved
as fast as it's moving now, and it'll
never move this slow again. Right. Yeah. I
I have heard the same thing. Right? Like,
this is the worst that it's gonna be.
Yeah. So
I don't know. That could be a little
scary. Yeah. Yeah. That's a lot. It's a
lot.
But but let's let's come back to sort
of thinking about your expertise and and what
you're doing at Lyric.
How does all this sort of fit into,
I guess, helping health plans accelerate value based
care goals,
which are crucial to to getting to that
payer provider,
equanimity, I guess, to where things are sort
of in harmony. Value based care is gonna
have to flourish for that to happen. Right?
Yeah. You know, I mean, that partnership is
so important.
I think that
you can use AI, like, from that predictive
modeling perspective,
and you'll get better risk stratification or identification
of at risk patients early.
So you can really direct your clinical efforts
to members who are gonna benefit the most
and drive those better outcomes.
I think from an administrative
perspective,
it can really decrease the administrative burden,
which ultimately means
providers, doctors, clinicians
are gonna be spending more time with the
patients. Right? And that's really what I think
we want.
So,
automating those activities, it really frees,
clinicians up to work top of license.
And then
shared measure measurement. Right? There's always been
just that source of truth kind of friction,
who has the right source of truth. And
I think AI can really help align all
the stakeholders,
payers, providers, patients, by really integrating the data
sources so you have one source of truth
in a comprehensive
view of outcomes.
So data has always been really difficult to
do with value based care, but I think
there's a real opportunity here with AI to
make the data far more accessible than it
has been in the past,
and transparent.
So and then trust. Again, trust is at
the center of all of it. That that
that's where where my head went to. It
comes back to, like, trust and transparency.
You have the best technology in the world,
but if you don't have trust and transparency
sort of around it, it's not gonna not
gonna do what it's supposed to do. Right?
Right. Nobody's gonna use it. Right. Right. So
So for folks listening out there who want
to sort of, I guess, perhaps
accelerate their their value based care goals and
and put AI sort of at the center
of that strategy, what's one step they could
take? What advice would you give? You
know, that's a tough one.
But
I would say,
look at what's actually actionable. Right? Take something
do a pilot on it. Partner with someone
just on, you know, one aspect.
You know, for example,
here at Lyric, you know, we look at
all sorts of different types of claims,
look at a high cost inpatient DRG
or coordination of benefits,
Measure the hit rate. Look at provider appeals.
Look at time to, you know, resolution.
Grab some KPIs with just a short pilot
and see if it works, and then iterate
on that. Right? It's not gonna work the
first time. It's just those multiple iterations and
learning off the iterations,
and I think that you'll find it really
successful.
That's, I think wonderful advice.
You started at you said it was a
tough question, but I think you landed the
plane pretty good there.
Mara, is there anything we didn't touch on
or final thoughts you'd like to share? Perhaps
it's something you wanna reemphasize for listeners. I'll
leave it to you how how you wanna
close out the podcast.
Yeah. I think when we build AI with
clinical intelligence,
the governance, and the transparency,
we move from a system that has a
lot of friction in it to much better
alignment across the system.
It can help clinicians
be clinicians.
So it's a tool that we can use
to improve outcomes for patients while
really
decreasing that administrative burden.
Wonderful. Mara, thank you so much for being
on the podcast. Thanks for having me. We
also wanna thank our podcast sponsor, Lyric. Listeners,
you can tune to more podcasts from Becker's
Healthcare by visiting our podcast page at beckerspodcast.com.