Hi, everyone. This is Lucas Voss with Becker's
Healthcare. Thanks so much for tuning in to
the Becker's Healthcare podcast series, and welcome to
today's episode from detection to prevention,
AI's role in payment
integrity. And I'm very excited
to welcome Steve Southern. He's the senior vice
president information systems at Cirrus
for our conversation today. Steve has close to
thirty years
of experience in IT and joined Cirrus in
1996.
He's been passionate
and very involved in proactively building efficiencies and
leveraging technology to keep health care organizations ahead
of the curve. Steve, I did not call
you old. Welcome to the podcast.
Thank you. Thank you, Lucas. Thank you very
much for the introduction. Yeah. Thirty years, time
flies when you're having fun as they say.
Right? Exactly. I'm so excited to have you
just because of all of the experience that
you bring to our conversation today. And and
I wanna start off with
putting things into perspective
for our audience.
As we're looking
at our topic today, how do you define
payment integrity today, and how is that definition
evolved
as AI has become more embedded in payer
operations?
Sure. That's a great question.
In today's environment, I would say payment integrity
really spans the entire claim life cycle. So
end to end set of processes from claim
submittal
all the way to final payment for in
a prepayment setting
as well as the traditional
post payment reviews.
Along the way, there are many tools, analytics,
technologies out there used to ensure the health
care payments are getting paid or getting adjudicated
paid accurately compliant
and timely.
AI is obviously at the forefront of all
of that. Just about every organization's
strategy and road map
are intertwined, wrapped around that, if you will,
these days. So I feel we're really just
starting to scratch the surface with the impacts
that AI can and will have down the
road.
We're seeing we're already seeing some major impacts
with process improvements,
automation, obviously.
However, I do think that some organizations
might be taking a slightly more conservative approach
or slower pace, let's say, to
to roll some of these things out just
due to governance, regulatory,
data use challenges, just making sure those those
boxes are checked.
And you've seen
so many trends evolve over your time in
health care IT, and there's certainly
a lot of change that's happening right now,
especially 2025 and then now obviously in 2026.
From your work with these organizations across the
country,
how has the integration of AI and machine
learning
changed the strategic approach then to payment integrity
for leaders? And is there anything that surprises
you about that process?
Yeah. Sure. I think the biggest change over
the last year or so has been the
shift from a traditional post payment or a
pay and chase model
to,
prioritization
of of prepayment solutions.
So as I mentioned previously,
audits that are happening during the payment cycle
as opposed to after,
AI has really helped fuel this due to
the acceleration of development tools and the speed
at which some of these solutions sophisticated solutions,
if you will, can be deployed.
I would say I'm most surprised by the
rapid adoption rate that we've already experienced.
Everybody is using these tools. They're everywhere. You're
hearing about them
constantly.
Also,
on the flip side of that really is
the significant amount of resources and effort required
for oversight and governance.
This is an area that sometimes can lag
behind the development cycle,
but it really cannot be minimized or ignored.
You know, the question today is not is
no longer are you using AI, it's how
are you using AI and what are the
guardrails and framework that you have established around
it.
Yeah. And you mentioned the the fast adoption
rates and everybody sort of jumping
to or on that trend, so to speak,
but that doesn't necessarily mean there is an
impact right away for a lot of organizations.
Where are you seeing the most meaningful impact
for AI today? And is there an example
that sticks out to you
where AI really surfaced
patterns that traditional rule based systems
that we've touched on likely would have missed?
Definitely.
The most meaningful impact I'm seeing today is
the ability to convert
and process very large,
complicated,
unstructured
documents and and even datasets into
useful structured,
easily machine readable data, which can be processed
much more efficiently and accurately.
So I guess
an example I would say is so traditional
rules based systems
usually only detect specific patterns. Right? Programmatic,
deterministic
algorithms that, you know, humans have have developed
as where on the flip side, where machine
learning and large language models are able to
analyze ambiguous notes, narrative patterns across these very
large datasets that those traditional rules would have
missed.
A great example of this is medical records.
So these documents are typically very large,
many, many pages, sometimes hundreds, if not thousands
of pages
in these documents, very cumbersome to handle,
challenging to process, and for, you know, humans
to to review.
So using a model to summarize that information,
extract key data points can really assist clinical
auditors
by increasing their efficiency, accuracy, consistency
to go through this information.
Yeah. And we're seeing already seeing that impact
on organizations,
I think. I I wanted to come back
to the governance piece that you've mentioned and
sort of a couple of related topics in
regards to governance because I think this is
a very important topic.
Again, you've talked about it. Automation can improve
speed and consistency. It's very crucial. But there
is concern around fairness, false positives, provider trust.
Again, we're sort of getting into that governance
territory here. How can organizations
balance efficiency
with that clinical accuracy and credibility at scale,
really?
Yeah. I I think finding this balance is
really one of the most urgent challenges that
we face today in the payment integrity space.
So just as with the development tools and
the technology,
the appropriate resources and prioritization
must be dedicated
specifically to these areas
these areas as in, you know, the governance,
security,
those things. So
building a strong governance program,
transparent
controls,
always keeping humans in the loop,
and continuous
modeling,
not all not only model monitoring, but, you
know, solution monitoring to make sure, you know,
of model drift and the things that are
of concern are are at the forefront.
I would say those are some of the
keys the the main keys, I would say,
to ensure trust and credibility in this space.
Now turning some of these topics that you've
mentioned into strategic elements. Right?
What should payer IT and operations leaders be
doing right now
to prepare for that next phase of AI
driven payment integrity?
Sure. I can share a couple of, at
least, points that I think are important here.
So
IT leaders should be assessing and improving their
teams,
teams from a staffing standpoint,
experience,
technology,
governance, and data infrastructure,
as we just talked about a little bit.
AI success really hinges on high quality, accessible,
interoperable data. So data is key here.
So having a clear, concise, consolidated data foundation,
again, is the most important thing when it
comes to AI.
Preparing to support the shift to prepaid solutions
by proactively considering some of the core systems,
the foundational
applications and and data and workflows
that would support that.
Considering
integrated ecosystems
and prioritizing
API integrations with partners over, you know,
more traditional fragmented point to point solutions.
And finally, building equity and fairness testing into
model governance,
always maintaining a human in the loop,
creating clear and transparent documentation along with defensible
policies and procedures to back everything up.
The future of AI driven payment integrity isn't
really just about more automation. It's about trusted
transparent
human aligned intelligence that's embedded across the entire
claim life cycle.
Three decades of experience
in fifteen minutes.
Steve, thanks so much for for being here.
Thanks so much for your time. It's so
great to have you.
You bet. Hey. It's it's truly exciting time
to be working in this industry and at
the intersection of payment integrity and technology.
We're watching AI reshape not just health care,
but nearly every industry,
and the pace of innovation is just accelerating
daily.
So the work we're doing now will define
the next generation of accuracy, fairness, efficiency in
the health care space. And I'm just grateful
to be a part of the transformation and
working with the Saris team and our partners.
I also appreciate the opportunity to collaborate with
Becker's and share these conversations, so I appreciate
you having me.
Absolutely. It's great to have you. Thanks for
your time. And we also want to thank
our podcast sponsor, Cerus. You can tune in
to more podcasts from Becker's Healthcare by visiting
our podcast page at beckershospitalreview.com.