Latest / Becker’s Healthcare Podcast / From Detection to Prevention: AI’s Role in Payment Integrity

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From Detection to Prevention: AI’s Role in Payment Integrity · Becker’s Healthcare Podcast

• 0:00
  Hi, everyone. This is Lucas Voss with Becker's
• 0:02
  Healthcare. Thanks so much for tuning in to
• 0:03
  the Becker's Healthcare podcast series, and welcome to
• 0:06
  today's episode from detection to prevention,
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  AI's role in payment
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  integrity. And I'm very excited
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  to welcome Steve Southern. He's the senior vice
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  president information systems at Cirrus
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  for our conversation today. Steve has close to
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  thirty years
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  of experience in IT and joined Cirrus in
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  1996.
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  He's been passionate
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  and very involved in proactively building efficiencies and
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  leveraging technology to keep health care organizations ahead
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  of the curve. Steve, I did not call
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  you old. Welcome to the podcast.
• 0:38
  Thank you. Thank you, Lucas. Thank you very
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  much for the introduction. Yeah. Thirty years, time
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  flies when you're having fun as they say.
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  Right? Exactly. I'm so excited to have you
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  just because of all of the experience that
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  you bring to our conversation today. And and
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  I wanna start off with
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  putting things into perspective
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  for our audience.
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  As we're looking
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  at our topic today, how do you define
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  payment integrity today, and how is that definition
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  evolved
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  as AI has become more embedded in payer
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  operations?
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  Sure. That's a great question.
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  In today's environment, I would say payment integrity
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  really spans the entire claim life cycle. So
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  end to end set of processes from claim
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  submittal
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  all the way to final payment for in
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  a prepayment setting
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  as well as the traditional
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  post payment reviews.
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  Along the way, there are many tools, analytics,
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  technologies out there used to ensure the health
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  care payments are getting paid or getting adjudicated
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  paid accurately compliant
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  and timely.
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  AI is obviously at the forefront of all
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  of that. Just about every organization's
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  strategy and road map
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  are intertwined, wrapped around that, if you will,
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  these days. So I feel we're really just
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  starting to scratch the surface with the impacts
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  that AI can and will have down the
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  road.
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  We're seeing we're already seeing some major impacts
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  with process improvements,
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  automation, obviously.
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  However, I do think that some organizations
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  might be taking a slightly more conservative approach
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  or slower pace, let's say, to
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  to roll some of these things out just
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  due to governance, regulatory,
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  data use challenges, just making sure those those
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  boxes are checked.
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  And you've seen
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  so many trends evolve over your time in
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  health care IT, and there's certainly
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  a lot of change that's happening right now,
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  especially 2025 and then now obviously in 2026.
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  From your work with these organizations across the
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  country,
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  how has the integration of AI and machine
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  learning
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  changed the strategic approach then to payment integrity
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  for leaders? And is there anything that surprises
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  you about that process?
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  Yeah. Sure. I think the biggest change over
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  the last year or so has been the
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  shift from a traditional post payment or a
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  pay and chase model
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  to,
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  prioritization
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  of of prepayment solutions.
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  So as I mentioned previously,
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  audits that are happening during the payment cycle
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  as opposed to after,
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  AI has really helped fuel this due to
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  the acceleration of development tools and the speed
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  at which some of these solutions sophisticated solutions,
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  if you will, can be deployed.
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  I would say I'm most surprised by the
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  rapid adoption rate that we've already experienced.
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  Everybody is using these tools. They're everywhere. You're
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  hearing about them
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  constantly.
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  Also,
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  on the flip side of that really is
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  the significant amount of resources and effort required
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  for oversight and governance.
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  This is an area that sometimes can lag
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  behind the development cycle,
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  but it really cannot be minimized or ignored.
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  You know, the question today is not is
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  no longer are you using AI, it's how
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  are you using AI and what are the
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  guardrails and framework that you have established around
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  it.
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  Yeah. And you mentioned the the fast adoption
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  rates and everybody sort of jumping
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  to or on that trend, so to speak,
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  but that doesn't necessarily mean there is an
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  impact right away for a lot of organizations.
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  Where are you seeing the most meaningful impact
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  for AI today? And is there an example
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  that sticks out to you
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  where AI really surfaced
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  patterns that traditional rule based systems
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  that we've touched on likely would have missed?
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  Definitely.
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  The most meaningful impact I'm seeing today is
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  the ability to convert
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  and process very large,
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  complicated,
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  unstructured
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  documents and and even datasets into
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  useful structured,
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  easily machine readable data, which can be processed
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  much more efficiently and accurately.
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  So I guess
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  an example I would say is so traditional
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  rules based systems
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  usually only detect specific patterns. Right? Programmatic,
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  deterministic
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  algorithms that, you know, humans have have developed
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  as where on the flip side, where machine
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  learning and large language models are able to
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  analyze ambiguous notes, narrative patterns across these very
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  large datasets that those traditional rules would have
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  missed.
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  A great example of this is medical records.
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  So these documents are typically very large,
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  many, many pages, sometimes hundreds, if not thousands
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  of pages
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  in these documents, very cumbersome to handle,
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  challenging to process, and for, you know, humans
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  to to review.
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  So using a model to summarize that information,
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  extract key data points can really assist clinical
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  auditors
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  by increasing their efficiency, accuracy, consistency
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  to go through this information.
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  Yeah. And we're seeing already seeing that impact
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  on organizations,
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  I think. I I wanted to come back
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  to the governance piece that you've mentioned and
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  sort of a couple of related topics in
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  regards to governance because I think this is
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  a very important topic.
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  Again, you've talked about it. Automation can improve
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  speed and consistency. It's very crucial. But there
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  is concern around fairness, false positives, provider trust.
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  Again, we're sort of getting into that governance
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  territory here. How can organizations
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  balance efficiency
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  with that clinical accuracy and credibility at scale,
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  really?
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  Yeah. I I think finding this balance is
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  really one of the most urgent challenges that
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  we face today in the payment integrity space.
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  So just as with the development tools and
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  the technology,
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  the appropriate resources and prioritization
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  must be dedicated
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  specifically to these areas
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  these areas as in, you know, the governance,
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  security,
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  those things. So
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  building a strong governance program,
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  transparent
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  controls,
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  always keeping humans in the loop,
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  and continuous
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  modeling,
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  not all not only model monitoring, but, you
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  know, solution monitoring to make sure, you know,
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  of model drift and the things that are
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  of concern are are at the forefront.
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  I would say those are some of the
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  keys the the main keys, I would say,
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  to ensure trust and credibility in this space.
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  Now turning some of these topics that you've
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  mentioned into strategic elements. Right?
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  What should payer IT and operations leaders be
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  doing right now
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  to prepare for that next phase of AI
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  driven payment integrity?
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  Sure. I can share a couple of, at
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  least, points that I think are important here.
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  So
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  IT leaders should be assessing and improving their
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  teams,
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  teams from a staffing standpoint,
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  experience,
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  technology,
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  governance, and data infrastructure,
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  as we just talked about a little bit.
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  AI success really hinges on high quality, accessible,
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  interoperable data. So data is key here.
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  So having a clear, concise, consolidated data foundation,
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  again, is the most important thing when it
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  comes to AI.
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  Preparing to support the shift to prepaid solutions
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  by proactively considering some of the core systems,
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  the foundational
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  applications and and data and workflows
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  that would support that.
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  Considering
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  integrated ecosystems
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  and prioritizing
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  API integrations with partners over, you know,
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  more traditional fragmented point to point solutions.
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  And finally, building equity and fairness testing into
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  model governance,
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  always maintaining a human in the loop,
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  creating clear and transparent documentation along with defensible
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  policies and procedures to back everything up.
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  The future of AI driven payment integrity isn't
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  really just about more automation. It's about trusted
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  transparent
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  human aligned intelligence that's embedded across the entire
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  claim life cycle.
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  Three decades of experience
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  in fifteen minutes.
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  Steve, thanks so much for for being here.
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  Thanks so much for your time. It's so
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  great to have you.
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  You bet. Hey. It's it's truly exciting time
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  to be working in this industry and at
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  the intersection of payment integrity and technology.
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  We're watching AI reshape not just health care,
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  but nearly every industry,
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  and the pace of innovation is just accelerating
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  daily.
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  So the work we're doing now will define
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  the next generation of accuracy, fairness, efficiency in
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  the health care space. And I'm just grateful
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  to be a part of the transformation and
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  working with the Saris team and our partners.
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  I also appreciate the opportunity to collaborate with
• 9:19
  Becker's and share these conversations, so I appreciate
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  you having me.
• 9:23
  Absolutely. It's great to have you. Thanks for
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  your time. And we also want to thank
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  our podcast sponsor, Cerus. You can tune in
• 9:28
  to more podcasts from Becker's Healthcare by visiting
• 9:31
  our podcast page at beckershospitalreview.com.