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Leveraging AI and Predictive Models to Improve Member Experience: Insights from Humana and Everise · Becker’s Payer Issues Podcast

• 0:00
  Hi, everyone. This is Brian Zimmerman with Becker's
• 0:02
  Healthcare. Thank you so much for tuning in
• 0:04
  to the Becker's Healthcare podcast.
• 0:06
  Today, we're on-site at Becker's payer roundtable. We're
• 0:08
  going to talk about the future of member
• 0:10
  experience, engagement, efficiency, and outcomes.
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  Joining me for today's discussion is Sadie Rizzuto,
• 0:15
  vice president at enterprise technology solution implementation with
• 0:19
  Humana, and Stuart Langley, senior vice president of
• 0:22
  client services with Evervise.
• 0:24
  Sadie and Stuart, thank you so much for
• 0:25
  being here. To begin, I'm gonna tap on
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  each of you to introduce yourselves and share
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  a few pertinent details about your your your
• 0:32
  professional background and your organization.
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  Stuart, why don't you kick things off?
• 0:36
  Sure. So my name's Stuart Langley.
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  I am SVP of client services at EverRise.
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  And,
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  EverRise, we are a a full services BPO
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  providing contact center,
• 0:49
  back office, and technology services.
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  And we have a specialism in the health
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  care space working with wonderful clients such as
• 0:57
  Humana. Excellent. Well, with that, that's a perfect
• 0:59
  transition over to Sadie then.
• 1:02
  Sadie Rizzuto, vice president for technology enablement and
• 1:05
  readiness within our insurance operation space at Humana.
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  Within Humana, we focus on our Medicare and
• 1:12
  Medicaid
• 1:13
  population and how we improve our health outcomes
• 1:16
  within those members.
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  I am responsible for several product teams, including
• 1:21
  our enterprise CRM and knowledge management populations, and
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  I'm also responsible
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  for our,
• 1:28
  artificial intelligence,
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  program for insurance operations.
• 1:32
  Excellent. And and, Sadie, we could begin with
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  you with this next question.
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  So,
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  when we think about predictive models and and
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  leveraging those to deeper deepen member engagement, you
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  know, it's not just about sending more messages,
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  but driving meaningful action. Can you talk about
• 1:46
  how you're thinking about that at Humana?
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  One of the key things with our predictive
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  models is understanding how to meet members where
• 1:53
  they're at and when they are there and
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  how we make those interactions count. And it's
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  really around quality over quantity,
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  how we guide towards the right programs
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  based on what we know about those members.
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  And the key to that is how we
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  use the data that we have. We use,
• 2:10
  our predictive models to understand their engagement levels
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  and their styles.
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  One of the key examples we have of
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  that is for example, medication adherence,
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  medication adherence is one of the key
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  stars scores as a lot of payers know
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  that's one of the three weighted stars measures.
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  And some of the key examples around med
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  adherence is around social determinations of health, transportation
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  challenges, financial challenges,
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  the data that we have around our members
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  helps us understand what are some of those
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  key examples of those that what are the
• 2:43
  members running into challenges
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  with and how can we bring information forward.
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  And when we're talking to those members that
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  could help them change those outcomes sooner rather
• 2:53
  than later.
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  Those behavior patterns are really critical, and the
• 2:57
  predictive models help us understand which members we
• 2:59
  need to change up the conversations with. Yeah.
• 3:02
  Thank you, Sadie. It and Stuart, what does
• 3:04
  what is what Sadie just shared, how does
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  that sort of align with what you're seeing
• 3:08
  among EverRise's other partners, and what what are
• 3:10
  your thoughts here?
• 3:11
  Yeah. Absolutely. Across the different,
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  pairs that we're working with,
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  mainly, they're in different stages, but everybody's working
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  to to the same set of ideas of
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  trying to build these big predictive models and
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  and trying to really
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  get, proactive and get ahead of the need
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  instead of waiting for members to call in,
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  which helps
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  smooth out the staffing that's needed to things,
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  but
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  also makes it more frictionless for the member.
• 3:35
  Mhmm. And that that friction that you that
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  you're speaking to, that claims friction is a
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  top pain point for for members as well.
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  Stuart, can you expand on even how you've
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  seen,
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  automation or AI materially improve that experience?
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  What what what have you seen out there?
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  Absolutely. So so to your point,
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  when people
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  are going through a claims process,
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  typically, they're they're already in a high stress
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  situation. Right. They're dealing with a health care
• 4:00
  issue or maybe an emergency.
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  So
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  anything that you put in the way of
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  them getting to care is just adding to
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  that frustration, adding to that that that stress.
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  Something as simple
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  as through the app,
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  not just when someone's looking for care, having
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  it list up a set of providers that
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  can provide that care, but allowing them through
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  that app to directly reach out and schedule
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  that care
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  just takes a step out of the process
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  for them, makes it a little bit easier,
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  makes it a little bit more intuitive,
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  and and eases that friction for the member.
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  It's such a good point that they're going
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  through a challenging experience because people can get
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  frustrated just when they're calling, you know, their
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  their bank about an automated thing versus
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  calling somebody about their own health. The stakes
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  get higher. People get a little bit more
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  tense, understandably so. Right?
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  Yeah. Definitely. I, for me,
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  grew up outside
• 4:52
  US and and and moving here a few
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  years ago and and and trying to navigate
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  the health care system, it's not the most
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  straightforward
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  process.
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  Anything that we can do to to remove
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  some of that confusion, make it it it
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  more straightforward and ensure that people are getting
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  the right outcomes and the most cost effective
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  outcomes
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  is a huge benefit.
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  Yep. Yep. Sadie, can you expand on this,
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  how how you think about,
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  leveraging AI,
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  automation to to really improve this experience?
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  One of the ways that we're looking at
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  that is also, you know, to Stuart's point,
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  recognizing
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  that especially at that claims interaction point is
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  when the members tend to be at one
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  of a difficult moments in their life is
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  how we also smooth it out from the
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  provider side
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  and how the providers interacting with the members
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  using intelligent data automation to reduce the friction
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  with between payers and providers
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  from an accounts receivable receivables
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  perspective is gonna help the providers be there
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  for the members as well.
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  In 2023,
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  there was identified that there was an opportunity
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  of over $800,000,000
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  in payments and over a billion dollars in
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  remit advice.
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  If providers were able to get to a
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  full digital adoption for reconciliation.
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  So there's huge opportunity out there for payers
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  to work with providers to smooth out those
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  friction points that that only brings value down
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  to smooth out those points for the members.
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  So as we continue
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  to create those interaction points between the providers
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  and payers, it's just gonna bring that down
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  to the members as well. Yeah. And and
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  can we talk about speaking to sort of,
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  some of Stewart's points, just the navigating,
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  some of this stuff is really hard. And
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  claims education,
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  is another thing that's really challenging and very
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  complex,
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  but very important to do, especially to help
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  folks understand their benefits, the care they need,
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  and make sure they're getting the care. Also,
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  understanding the right side of care. Like, there's
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  just so much that goes into to education
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  here. Can you talk about,
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  Sadie, any any tools you've seen that have
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  actually simplified,
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  education member education,
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  specifically as it
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  relates to sort of,
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  open enrollment seems like a a a specifically
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  challenging moment for folks. So in anything you
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  can share there. Yeah. That plan education with
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  members is something that we are continuously looking
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  at how do we shift that as far
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  left as possible
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  in working with members and beneficiaries.
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  Understanding you know, very similar to where we're
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  talking about meeting members where they're at,
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  and using the data that we have with
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  members, either with their current plans or even
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  their previous payers,
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  understanding how they wanna be engaged with and
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  using predictive models to understand where they're going
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  to engage more effectively, whether that's with inbound
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  engagements, outbound engagements, or even digital engagements.
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  That's gonna help us understand
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  how do we get in front of potential
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  health
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  gaps? How do we help them
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  understand
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  what are those
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  plan opportunities that they may not know about
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  like transportation benefits that could help them get
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  ahead of things that whether it's medication adherence,
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  like we were talking about a minute ago,
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  whether it's other services that they don't know
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  that they have an opportunity that they can
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  tap into sometimes even before their plan effective
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  date.
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  So by shifting those conversations further left and
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  in a way that the member is going
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  to engage with us in a way that
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  maybe they didn't know about and ways that
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  we know that they're gonna step up sooner
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  because that's how they are more apt to
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  engage with us is one of our big
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  focuses. And we get that to that by
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  using the data in a more proactive manner.
• 8:32
  Yeah. Big big component of education here is
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  letting them know what they've got. Yeah. Because
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  sometimes they've got they've got coverage for things
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  that they didn't realize, and I imagine finding
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  out about that as a positive experience as
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  well. Stuart, what would you add to this?
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  In addition to to the area Sadie was
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  covering, another important part for us, you know,
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  we we we do build, in our Ever
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  AI labs. We're working with our clients to
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  build
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  technologies,
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  AI that can directly outreach to the members.
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  But another important area for us is we've
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  got a
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  large contingent
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  of advocates in our call centers.
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  And due to the seasonality, you're onboarding people
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  for the season each year. So building tools
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  that can help those advocates get to the
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  right information,
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  maybe by doing predictive intent modeling and understanding
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  what a member might be calling about before
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  they call in or just,
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  improved
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  AI back tools to support knowledge bases,
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  to improve the training process for the advocates
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  allows them to be more informed
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  and allows them to provide a much better
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  level of experience to the member, getting to
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  the information quicker,
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  resolving in a single call, something that that
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  previously might have taken a few calls to
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  call in. All of these different areas come
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  together and improve that member experience. Yep. And
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  what you're laying out here, there's a deep
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  need for it.
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  It's very exciting opportunity to be able to
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  give this to members.
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  But, of course, we wouldn't be talking about
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  health care if we weren't talking about hurdles
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  and potential challenges. So,
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  we should we should honor those and talk
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  a bit about those. Stuart, we can start
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  with you. What compliance or regulatory hurdles are
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  gonna prove most challenging here, and how can
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  folks navigate those without losing momentum and and
• 10:14
  really still being able to take advantage of
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  this really excellent opportunity?
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  One of the areas, funnily enough,
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  that I've seen as a challenge
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  is
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  you've got a bit of a a friction
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  between
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  marketing groups at companies such as ours who
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  wanna plaster AI,
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  on everything that they're doing in the marketing
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  material and
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  technology
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  groups who rightly
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  are looking at risk
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  and have concerns around
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  how AI is being used.
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  AI is an umbrella term. It means a
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  lot of different things to different people, and
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  it's it's very important to be very precise
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  in what we're talking about
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  because,
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  you may have a solution
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  that is truly
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  generative AI interacting
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  data set that includes member information that really
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  does need to go through,
• 11:08
  full regulatory
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  body, and and understanding what the implications are
• 11:12
  before it gets deployed. But there may be
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  another technology
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  that's kind of AI or has very controlled
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  AI use in a completely ring fenced,
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  setup that doesn't need to go through that
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  full full setup. And you can have a
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  situation where you've got a great tool that
• 11:29
  you want to deploy that's gonna deliver fantastic
• 11:31
  either cost reductions or improvements or an experience.
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  And it sits in the back of the
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  queue waiting to go through that full regulatory
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  review that probably could go through a faster,
• 11:40
  smaller setup. So so for me, that's one
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  of the areas that's important is
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  education,
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  education within our business to understand what we're
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  really talking about and being a little bit
• 11:51
  more precise as we define what these tools
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  and technologies are and what they do. Yeah.
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  Cutting through the the umbrella term. Right?
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  So that people don't just think AI. We
• 12:00
  gotta we gotta check this out. This is
• 12:02
  gonna take time, and they can they can
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  then be more, like you said, intentional with
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  how you're educating about certain solutions.
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  Sadie, what would you add here?
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  You know, I think to Stewart's point, one
• 12:13
  of our biggest challenges
• 12:14
  is actually our internal governance.
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  And that's part of why, you know, not
• 12:20
  only AI, but technologies
• 12:22
  moving faster than ever before,
• 12:25
  in our lifetime right now. And you need
• 12:28
  to have really strong dynamic partnerships
• 12:31
  within legal risk compliance and your technology organizations
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  internally
• 12:37
  to keep up with those changes.
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  And they have to be ready to evolve
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  and look at from a multifaceted
• 12:46
  perspective
• 12:47
  what are all of those things that are
• 12:48
  changing. And you have to look beyond your
• 12:50
  current
• 12:51
  industry and understand what else is changing and
• 12:54
  start looking ahead.
• 12:55
  You have to start looking at what else
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  is happening across,
• 13:00
  the technology
• 13:01
  across even across in industries that maybe you
• 13:04
  wouldn't anticipate
• 13:06
  outside of health care and how that could
• 13:08
  be what's coming up in the next six
• 13:10
  months, nine months, a year ahead, so that
• 13:12
  you can be ready for that to start
• 13:14
  influencing the healthcare industry.
• 13:16
  And that's how we're gonna start being able
• 13:18
  to both understand and partner within governance or
• 13:21
  government policy makers to start planning ahead and
• 13:25
  making sure we're getting the right outcomes for
• 13:26
  our beneficiaries.
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  I think that's also how we make sure
• 13:30
  we're staying focused on the problem at hand
• 13:32
  and not necessarily focusing on the shiny object,
• 13:35
  which is helps us get ahead of some
• 13:37
  of those advertising techniques. Yep. Focusing on the
• 13:41
  actual needs,
• 13:42
  of your members and not just the the
• 13:44
  the technology in of itself. Technology for technology's
• 13:47
  sake. Yeah.
• 13:49
  It was a real pleasure getting to speak
• 13:50
  with both of you today. And before we
• 13:52
  sign off, I'll I'll open it up to
• 13:54
  each of you to share any closing thoughts
• 13:55
  with listeners. Sadie, you can go ahead. Go
• 13:57
  first. You know, we were just talking about,
• 13:59
  you know, making sure you stay focused on
• 14:01
  the problem at hand even if it's not
• 14:03
  the sexy one. I think it's staying in
• 14:05
  on that same topic.
• 14:06
  You need to be, you know, we talk
• 14:08
  about, you know, across the industry, folks are
• 14:10
  talking about being agile and failing fast, but
• 14:13
  you really have to live that and be
• 14:15
  willing to pull the plug. If you're not
• 14:17
  seeing the outcomes that's expected.
• 14:19
  We're seeing that a lot with AI that
• 14:22
  you have to be willing to say, if
• 14:23
  you're not seeing the outcomes that you were
• 14:25
  expecting
• 14:26
  and make the changes that you need to
• 14:28
  see,
• 14:29
  that's really critical to get to the outcomes
• 14:32
  that are expected. You know, there's a lot
• 14:33
  of big promises out there. If you really
• 14:35
  wanna see the outcomes, you have to be
• 14:37
  able to make the tweaks and pull those
• 14:39
  triggers as you go, and that's really critical
• 14:42
  and and be comfortable making those big changes.
• 14:44
  Yeah. Yeah. Stuart, what would you add?
• 14:47
  It's been a fantastic conference here,
• 14:49
  hearing a lot of speakers up on the
• 14:51
  stage talking about similar
• 14:53
  areas around the technologies.
• 14:57
  Obviously, it's a it's an exciting time. It's
• 14:59
  a it's a challenging time, but I think
• 15:02
  another area that's important with with such a
• 15:05
  rapidly
• 15:06
  developing set of technologies,
• 15:08
  it is easy to get distracted
• 15:10
  by by the the marketing spin that's being
• 15:13
  put out there and and for everybody to
• 15:15
  try and jump on the next shiny object.
• 15:16
  So it's easy for companies to to get
• 15:19
  kind of a fractured strategy where they're getting
• 15:21
  all these promises. There's so many companies reaching
• 15:24
  out, and they're they're jumping from solution to
• 15:26
  solution and and and kind of over indexing
• 15:28
  on on trying a bit of everything.
• 15:30
  And and maybe the best approach
• 15:32
  is to pick a couple of key partners
• 15:35
  that you can work with, that you have
• 15:37
  trust with, that you have experience with, that
• 15:39
  understand your business needs to your point so
• 15:42
  that you can partner on a strategy.
• 15:44
  And understanding these technologies, they're evolving so rapidly
• 15:47
  that they're gonna leapfrog each other in terms
• 15:49
  of the the the immediate deliverables.
• 15:52
  So having that slightly longer term view can
• 15:55
  get you to better outcomes instead of just
• 15:57
  getting drawn in a a thousand directions and
• 15:59
  getting nowhere. And you need a partner that's
• 16:01
  willing to take that long term view with
• 16:03
  you. Yep. And invest with you, I mean,
• 16:05
  and and partner. Excellent. Well,
• 16:08
  Sadie, Stuart, it's been a pleasure speaking with
• 16:10
  you today. I wanna thank you both for
• 16:12
  coming on the podcast. I wanna thank EverRise,
• 16:14
  of of course, for for sponsoring this episode.
• 16:17
  You can tune in to more podcasts from
• 16:18
  Becker's Healthcare by visiting our podcast page at
• 16:21
  beckerspodcast.com.