Hi, everyone. This is Erica Spicer Mason with
the Becker's Healthcare podcast series. Thank you so
much for tuning in today. I'm thrilled to
be joined by Steve Sutherland, the senior vice
president of information systems at Saris,
who will talk to us about AI and
machine learning in the payment integrity space.
Steve, welcome to the podcast. Thank you so
much for joining us today.
Hello, Erica. Thank you very much for having
me. I appreciate the opportunity.
We're thrilled to have you here to talk
about this topic today. And before we really
get into it, I wanted to see if
you'd like to share just a little bit
more about yourself,
your role, your organization,
whatever feels top of mind for you.
Sure. Sure. Well, I'm a native Texan. I
was born and raised here in in the
Dallas Fort Worth area, and I have been
with,
the Saris Organization now for going on 30
years, actually. I think I'm I think I'll
celebrate my 29th anniversary
in the fall or early
early next year. And I've had I've served
in
a various different roles within the IT spectrum
here with the company, but I really just
have seen the company,
grow exponentially and and really grown along with
it from a very small
organization into, you know, what it is today,
which, you know, we've got almost a 1000
folks within the within the Cirrus
part of the company,
and and we're, you know, a part of
a bigger company, CorVel Corporation. So
Wonderful. Thanks so much, Steve. Appreciate you sharing
a little bit more about your background. It's
incredible. You've been with Cerus for about 30
years.
So I'm sure you've seen so much change
in in those decades.
So I'm really looking forward to the perspective
that you'll have here with
technology and payment integrity. So
I think we can kind of get into
it from there.
So I'm wondering if you can share with
our audience what you see as the role
of artificial intelligence
and machine learning in addressing payment integrity pain
points.
And in your experience working with payers, are
there specific benefits that partners have realized in
applying those tools to claims processes?
Would love to know any success stories or
case studies that might come to mind.
Sure. No. That's a it's a great question
and really a relative and and hot topic
today in today's industry.
And and those technologies are really
helping improve
accuracy and consistency and efficiencies and in payment
processing in general. I mean, they're applicable all
across all sorts of other industries, obviously, as
well. But in payment processing,
you know, payers have a very specific time
frame
within
which they have to make payments to providers
based on contractual
and regulatory
requirements. So they not only have a short
window
to do claim adjudication, but also,
validate and apply various types of
reviews and business logic and and all sorts
of things that happen within the process while,
you know, adhering to those very strict time
time frame. So efficiency really is key. They've
there's a lot to be done in a
short amount of time frame, and it needs
to be done accurately. So
artificial intelligence and machine learning
specifically can really help improve these processes by,
you know, automation. So automating certain
very manual and sometimes inefficient steps.
Claim identification.
So helping
identify and tag or flag problematic
claims or trends that they might see that
it can be taken off of the
conveyor belt, if you will, or out of
the process and looked at by a by
an expert or some some other type of
of audit reviewer. So,
those are just a few of those of,
you know, examples of how it can be
used.
Very general, but we we have several specific
use cases within our workflow
where we've taken very manual processes
like like data capture, you know, keying data
from an image or something and improving that
dramatically by applying this type of technology.
Yeah. I appreciate the example, Steve, and I
think everything you outlined really speaks to what
an impact
efficiency in this process
can play when it comes to compliance, timely
payments, adhering to contractual agreements.
There's a lot kind of on the line
in that in that window of time as
you mentioned.
And I know you also mentioned how payment
integrity plays kind of this key role in
health care where it ensures that claims adjudication
is accurate and that health care organizations are
appropriately reimbursed for the care that they provide.
So as health care billing processes become more
complex,
what key challenges are there when it comes
to the widespread adoption of AI in the
PI space?
And aside from complexity, are there other factors
that are kind of driving those issues?
Sure. Yeah.
There's always challenges, you know, with everything, especially
anything new and and new technologies. So, you
know, applying this
complex technology
to complex billing processes in general and then
also health care data in itself can be
a challenge.
It's
not always
consistent. It's not always standardized based on your
billing practices and those sorts of things. So
the data working with the data in itself
can be
very complicated
and challenging.
So there are certain
pitfall
or many pitfalls you want to try to
avoid. A very important one is to exercise
caution and and be diligent when it comes
to data privacy and security,
as well as transparency and defensibility.
So those all kind of, you know, go
along together,
regulatory
challenges in some cases.
So really relying on your compliance and security
teams to provide
a sound governance and policy,
when you go into these
deploying these types of solutions. Really, you need
to need to look at that upfront and
really have that established.
There can also be a steep learning curve
with staff and finding,
expertise
and resources
to be able to to build these types
of solutions and have the knowledge and and
expertise. So really just have patience and be
persistent as you try to recruit talent and
expertise and and also train current staff on
learning these technologies.
There can also be some gaps
with,
the output of some of these solutions and
tools.
So as you deploy these things,
you know, what the analysis showed or what
the research
showed might not necessarily
come to fruition
when you put it in in an operational
setting and the actual results might be off.
So really just having,
a continuous improvement and validation process so that
you're always making sure that, you know, the
output is what it should be and that
it's valid and continually improving
whether it's a a machine learning model or,
know, an
AI, solution
in itself.
Mhmm. And probably the last thing I'd mention
is just is just to avoid becoming too
reliant on technology,
on this specific technology or any really in
general
over, you know, human knowledge and expertise. So
finding that good balance between, you know, how
the technology can be used and what parts
of processes
we can make more efficient and take and
take the human element out of it. But
then, you know, finding that balance of there's
always going to
be some of that that that really does
need a human touch.
Yeah. That balanced approach sounds essential, and I
appreciate what you said at the beginning of
your response, Steve, that applying complex technology to
already complex billing processes
bound to have some challenges and barriers.
So I appreciate you outlining some of those
kind of pitfalls that organizations can avoid and
acknowledging there will be a steep learning curve.
The continuous improvement and validation is key.
So, yeah, thank you again for for outlining
all of that. And I think that leads
me into the next question that I had
for you, which
is, you know, just kind of acknowledging this
idea of change management and demonstrating value anytime
leaders are
kind of encouraging their teams embrace new technology.
We hear from leaders all the time that
change management is one of the the most
challenging aspects of introducing new tech.
So wondering if you have best practices or
resources that you'd recommend to leaders who are
planning to or have even newly adopted AI
and machine learning for payment integrity.
And maybe you can say a few words
on how they can prepare for future advancements,
and other changes ahead.
Definitely. I can share some thoughts and and
and some of my experiences in in that
space. So
I I always say
and and have found based on our experience,
identifying the right use cases upfront.
These technologies can be used to do
a plethora of things. There's a there's a
1,000,000 use cases. Right? But really identifying what
is the right use case to start with.
So if you're just getting into this, how
do I how do I get my foot
in the door? What can I do? What
you don't want to do is
bite off the most complex
project that that's out there that's on your
radar.
Keep it simple.
Find a very
simple,
easy use case to start with. Don't try
to tackle that most complex solution first, but
keep it simple. And then that way, you
can get, you know, get your foot in
the door, get your feet wet. You can
you can get some experience and have some
some quick wins without, you know, having to
put in all of the time and effort
before you're able to see sort of the
fruits of your labor. Right?
So the and then building proof of concepts,
it's kind of the same it's kind of
the same thought process. Right? So let's build
a proof of concept
on this use case, prove it out,
get your feet wet, you know, make sure
that it that it proves out what your
goal was to begin with and that and
then you can lower your risks and and
increase your success by having those proof of
concepts because you're gonna have some failures. Right?
So if you build a proof of concept
and it doesn't quite work the way you
thought it would,
then, you know, you sort of back up,
make some adjustments, and and start again. So
we we always like to do proof of
concepts
on especially
complex projects like this or or with the
potential to be really complex.
One of the other things is to get
early buy in from key stakeholders. I mean,
this may sound like a a no brainer,
but you really gotta make sure that you've
got your business strategy and your and your
technology initiatives aligned so that everybody is on
the same page as far as, you know,
what the goals are and what the expectations
are.
Like a lot of
current technologies out there, data really is the
key.
So having good sound data government management
around that data and then the architecture.
The old saying,
garbage in and garbage out definitely applies in
this scenario. You've gotta have good data, and
you've gotta have a good strategy and management
around that data.
And I guess the last thing I would
say is just to educate,
your staff and and your business units about
these technologies, about data science,
explaining these use cases and proof of concepts
and, you know, getting folks to buy in
and and get excited about these things.
Training always helps with the learning curve. But,
I mean, having some positive outcomes and realizing
value really will increase your excitement and building
trust in these these new tools and technologies
and the processes.
Thanks so much, Steve. I think this is
really great advice, and I especially appreciate that
point that you said about keeping it simple,
especially
in the beginning of deploying this technology, perhaps
an organization is doing it for the first
time and,
keeping it simple can help help them get
those quick wins, which I imagine would go
a long way in demonstrating value upfront and
kind of justifying the investment. So,
again, great advice for our listeners. And
before we end our time together today, is
there anything else you wanted to share that
maybe we didn't cover already or any final
takeaways that you'd like our audience to know?
No. Maybe maybe just reiterate, you know, kinda
what you just said, you know, making sure
that your strategy and technology
initiatives are aligned, getting that early buy in,
and building bonds with your key stakeholders.
Keep it simple. Keep it flexible.
You know, the right use cases,
proof of concepts, those quick wins, build some
momentum gradually.
The quicker you can demonstrate
value, the quicker you're gonna get not only
management,
buy in, and confidence, but also your end
users, the folks who are actually going to
be using these solutions,
and show that value within the organization.
And just just to kinda wrap it up,
I mean, you know, Saris I've been, you
know, in the business for, like I said,
for 30 years with Saris. I mean, we
we are a technology leader in the PI
space, and we've been doing this for a
long time. So I would just, you know,
extend the offer to reach out if if
there are any questions or anything that, you
know, I could assist with, and,
my door is always open.
Fantastic.
Thank you so much, Steve. Really appreciate the
time and all of the insights today. It's
been a great discussion.
Thank you very much, Eric. I've I've enjoyed
it, and, it's a it's been a pleasure.
Well, thank you again, Steve. And we'd also
like to thank Saris for sponsoring today's episode.
You can tune into our podcast from Becker's
Healthcare by visiting our podcast page at beckershospitalreview.com.