Hello, everyone. This is Erica Spicer Mason with
Becker's Healthcare. Thank you so much for tuning
into the Becker's Healthcare podcast series.
So today, we're going to talk about how
small domain specific language models are transforming healthcare,
unlocking new opportunities for innovation,
operational efficiency, and data driven applications
across the payer and provider ecosystem.
And joining me for this conversation, we have
with us Gene German, the chief technology officer
at Lyric.
Gene, welcome to the podcast. It's so great
to have you with us today.
Hi. Thanks for having me. I'm Gene. I'm
the chief technology officer for Lyric. I've been
with Lyric for approximately three years.
Prior to,
joining the company, it's been in health care
for another, seven years focused on the consumer
tech.
When I joined Lyric, the company already
had decades
of service to our clients and has built
trust,
over these decades through delivering of value,
to those clients,
you know, through our products.
And so, we already had, like, a really
good place to start at,
But there was a challenge, that we faced,
and that challenge was the bulk of our
software
was hosted on prem,
in the customer's,
data centers.
And not only is it difficult to deliver
AI solutions,
when your software
is hosted on prem, but also any other
capabilities,
that we wanted to give to our customers,
because, typically, upgrade cycles,
happen every two to three years, for our
software in the past.
And so we've built our platform, Lyric 42,
that gave us kind of the pipes and
the capability,
to deliver,
multiple products,
across a whole spectrum of payment integrity,
solutions, not just, in primary,
but also our secondary content.
We've acquired a company called,
ClaimShark,
with Replay and,
Virtusa,
replay platform and Virtusa products on top of
it
as well,
which allowed us to deliver manual reviews. None
of that will be possible with our without
our Lyric 42 platform.
Well, Jean, that's great to learn more about
you and also Lyric's platform.
Great context setting for this conversation because we
are gonna go,
a little bit deeper on some of the
trends that we're seeing here with small language
models across the industry.
We know that SLMs are enabling
really this new wave of purpose built domain
specific applications
across industries and including health care. So from
your perspective, what makes health care uniquely positioned
to apply these tools effectively right now?
Well,
AI generally
can be very useful in working
with,
very large datasets.
And AI can certainly drive innovation. And in
health care,
there's plenty of that. There's,
datasets
of claims,
member data, provider data, eligibility,
and so on and so forth. And that
data comes from various different sources.
It varies in formats. It varies in levels
of completion
and many other attributes.
And so
we see that LLMs are, very good at
dealing with this,
high textual
complexity.
We also see that the small language models,
which Lyric tends to use,
are at least as good or better than
large,
language models
in,
solving problems when those,
small language models are grounded in specific domains.
We see them being cheaper,
which is as important as being faster or
more accurate when grounding them in the specific
domain knowledge.
Jean, thanks so much. It's been really helpful
getting your perspective on
what makes health care kind of unique in
this space as they're apply as leaders are
applying tools.
At the same time, what unique challenges do
these technologies introduce for health IT systems?
Sure.
Well, being in the health care industry for
the past,
decade or so,
we typically see,
health care IT comp
groups,
focus on deterministic,
type of algorithms. So typically, for a given
set of inputs,
there's a very predictable way to see a
given set of outputs.
And LLMs,
by definition,
they're much more nondeterministic.
And so
it is very important as you think of
the use cases to which you're gonna be
applying AI that those use cases kind of
combine this concept of determinism and, nondeterminism,
and they deal with a very high cost
in health care of failure.
And, typically, that involves having humans in the
middle,
having well defined workflows in which the models
are trained and given feedback as well as
validated,
and so on and so forth.
Mhmm.
Yeah. And I know another important piece of
driving innovation
or at least driving it successfully is data.
So how should organizations think about leveraging their
own data to build differentiated applications on top
of emerging SLM platforms?
That's a great question. Data is indeed very
critical in driving innovation, and so it is
very important, first and foremost,
the the design tracking and usage of data
is not an afterthought.
There's many different kinds of data in health
care as I mentioned,
but also, you know, some data, like you
said, is generated
by the health health care organization
itself,
but some data is received from,
members or from providers.
And so all of that data can be
used to drive,
improve areas such as efficiency, quality,
outcomes, and speed.
The key here is to make sure that
you focus on the goals,
upfront, and you define the use cases
around those goals to make sure that everything
from data rights
to data pipelines,
is, thought through clearly in the beginning. And
you bring in your, you know, clients,
along with you around the value that is
being driven and the usage,
of data.
And it's true for your own data as
well.
When you combine AI technology and people together,
you can achieve very many different goals leveraging
this new tech.
Yeah. And, Gina, I've heard you emphasize a
few times now the importance of getting really
clear on those use cases. So I appreciate
you,
underscoring that as a consideration for our listeners.
And I know at Lyric, you focused on
payment integrity and claims analytics, which you mentioned
in some of your introductory remarks.
Where are you seeing the most meaningful efficiency
gains today across the payer provider ecosystem in
this realm?
Yes.
For us,
because we've
deployed Lyric 42, and there's a very high
adoption from our client base,
across the client base of our platform,
it opens up a whole new set of
avenues. So we could have done a lot
of different things, but we've decided
to focus on four key areas,
for us. So one is the
ideation building and validate validating the payment integrity,
content. So this is our bread and butter,
both primary and secondary content.
Second is the adoption of that content. Once
that content is built and we've seen significant
increases in how quickly and accurately,
we can deliver that content leveraging,
AI technology,
it's the adoption and deciding how to prioritize
it
with our clients and figuring out which content
will cause more abrasion, less abrasion, and, you
know, focusing on the holistic,
health care system.
Three, it is, like I mentioned,
a replay product,
which is a manual review product integrated with
Lyric 42, our platform.
Now that we're able to,
roll it out much quicker to our customers,
this new product,
We are leveraging AI,
for prioritization automation
and
the summary
and,
search of very complex information that's required,
for those reviews, whether it's
information related to eligibility, like, for coordination of
benefits or itemized bill review or pulling a
chart.
AI is definitely helpful in putting all those
tools into the fingerprint
fingertips
of people that are leveraging our software.
And then fourth,
last but not least is,
leveraging our Lyric 42 pipes, and the learnings
that we get, to
create new products and test those products for
our customers because our our clients are constantly
asking us,
for new, products that improved,
their claims flow.
Yeah, Jean. Again, thank you for for that
helpful overview. And I'm curious as you're helping
organizations
roll out some of these tools
in the payment integrity and claims space, have
there been any outcomes that have surprised you
or maybe even key lessons that you think
leaders should take away from that work?
Yes. You know, there are certain areas we
expected,
to improve such as
speed, accuracy, and the outcomes and the value
prop our technology is driving.
What one area that we're kind of surprised
is how much how much quality,
we can drive with AI,
even on top of already, like, high quality
products. So not only are we accelerating the
throughput,
but we're also
accelerating the way that we deliver quality at
a level that would be just not possible
without this technology.
Yeah. So good to know, Jean. Thank you.
And as we kind of close our conversations
today, something I wanted to make sure we
touched on was sustainability
with some of these tools. So at Becker
as we hear about leaders
implementing
new tools
such as what we've touched on today with
SLMs,
but I think the struggle is in maintaining
those those tools, making sure that they're driving
continuous improvements and continuous outcomes that they're aiming
for and really ensuring that they're going from
pilot to real scale.
So as more developer tools come to market,
for you, what does a practical road map
look like for for leaders who are aiming
to balance innovation, trust, and measurable outcomes?
Yes. That's a great question,
and I I think it's a challenge for
everyone.
It's really important not to get distracted on
every shiny thing that comes to the market.
As engineers,
We tend to do that,
more often than not. So, it is also
important to understand that AI is not necessarily
an answer for everything. It is a very
powerful set of tooling.
But whether it's new models or a new
set of tools, the key here is to
focus on the problem you're solving versus the
tool itself.
So start by cleanly outlining the objectives and
important KPIs. You know, for us, those were
the four areas,
that I mentioned. But for a different company,
maybe a different set of priorities. And,
these KPIs will help you measure along the
way and not, you know, get you distracted
from what success looks like. And a good
road map would look like,
especially in AI,
where you laser focus on your key use
cases, where you believe that AI is the
right solution based on the attributes of those,
use cases. And road maps we typically see
is that are successful
is where you start small.
You prove it out. We started,
in kind of a single space state Medicaid
that was extremely difficult,
to run-in quality,
without these tools, and we were extremely successful
in that. And we've expanded those results,
to other areas. And then sometimes, you know,
you may fail. You wanna abandon it and
not kind of continue
doing the same thing,
if you're not seeing a progress there. And
if it works, then the idea is you
scale it by kind of building the foundations
that are needed, then you expand either to
other populations or to larger sizes of data
or to kind of adjacent,
use cases.
But if you're
kind of responsible looking at the roadmap that
way, then more often than not, you're gonna
be successful.
Such sound advice, Jean. Thank you. And I
know we've covered a lot of ground today,
but wanted to just check-in to see if
there was anything that we haven't covered that
you think is important for health IT leaders
to understand right now.
Yes. We talk a lot about the value
that AI can drive in certain circumstances.
We haven't talked,
much around the foundation
that's needed,
to be able to build
AI successfully.
Unfortunately,
AI is expensive, and it's kind of very
hard to do it at the side of
your desk to do it at a scale,
especially in health care. You need to make
investments into your governance programs.
You need to be able to make investments
into technology,
tracking both data and model drifts,
automatically
and be able to monitor and alert on
those, when they happen to make sure that
your models
change successfully over time.
And, setting the AI controls to prevent things
like data poisoning, dealing with edge cases,
building, like, the right
user experience to be able to provide,
feedback and train your AI algorithms and then
have kind of human in the middle.
And, you know, that differentiates going from a
POC to production ready
solutions. It's somewhat maybe kind of some of
the boring stuff, not as exciting as the
ROI. But without
that boring stuff, you're not gonna be able
to scale.
Yeah, Gina. It's a really great point. I've
I've noticed this through line throughout our conversation
that going back to the drawing board, really
establishing the use cases you're looking for, getting
clear on those, getting clear on KPIs, but
also going back to the foundational systems that
are in place.
Those steps have to be done before success
can be expected.
Absolutely.
Well, Jean, it's been great having you on
the podcast today. I wanna thank you again
for your time and your insights,
and we'd also like to thank our podcast
sponsor for today, Lyric.
Listeners, be sure to tune in to more
podcasts from Becker's Healthcare by visiting our podcast
page at beckershospitalreview.com.