Latest / Becker’s Healthcare Podcast / Turning Healthcare’s Toughest Operational Challenges into Breakthrough Solutions: Part 2

Transcript

Turning Healthcare’s Toughest Operational Challenges into Breakthrough Solutions: Part 2 · 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
• 0:04
  the Becker's Healthcare podcast series. It's great to
• 0:07
  have you.
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  Today, I'm joined again by Ben America's president
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  at UiPath and Jason Buie, who leads the
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  health care industry practice at UiPath
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  as well.
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  I want to make sure you know about
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  our first podcast episode together where we discuss
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  some of the ways health care organizations are
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  already applying AI to solve those operational challenges.
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  Make sure you don't miss that episode. It's
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  fantastic. This,
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  however,
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  is part two
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  of that conversation where we focus on what
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  leaders are learning from those deployments and what
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  it actually takes
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  to scale these capabilities
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  across the organization.
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  Ben and Jason, thanks again for being here
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  today. It's so great to have you. Happy
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  to be here, and love your pronunciation of
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  my last name and Bowie's last name for
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  that matter. There you go. It's that I
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  e that I e that gets people. Yeah.
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  I before e except after c as I
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  tell too many people when I'm checking in
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  hotels. But There you go. There you go.
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  Well, I wanna hop into the conversation here.
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  We touched on some of the operational pieces
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  in our first episode too.
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  And, Jason, I'll start off with you. When
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  we talk about this aspect of real operational
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  change, right,
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  again, I'd love to have you touch on
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  what that actually looks like inside a health
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  care organizations
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  from your perspective
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  right now?
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  Yeah. Thanks for the question, and thanks for
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  having us back here, Lucas.
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  You know, when we say real operational change,
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  the way that we talk about this with
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  our customers
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  is that it needs to reduce
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  tasks that weren't meant for humans in the
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  first place. Right? There's a lot that we've
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  let grow and permeate throughout the health care
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  over the years. And
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  we're we're finding that we were living with
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  it because we have to and that change
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  can be hard and is hard.
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  And
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  now with agentic
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  AI combined with generative AI, we're seeing
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  that we can now eliminate those tasks in
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  in a clean way
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  and then elevate the the human in the
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  loop to be more of a decision maker,
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  more of an oversight role,
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  and then practice what they were originally meant
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  to do, especially in the clinical space.
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  And we're seeing that when you combine and
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  coordinate
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  agents, robots, and humans together,
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  you're achieving a new level of of efficiency
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  that health care never had. And so
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  these are aspects that I consider when we
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  see real operational change happening within the payer
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  and provider organizations we partner closely with.
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  Now, Ben, one of the things that we
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  didn't touch on in our first part of
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  our conversation of our podcast here is we
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  didn't talk financials.
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  We didn't talk we didn't talk ROI when
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  it comes to
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  AI.
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  And, Ben, I'd love to know how you
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  think about ROI differently
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  when moving from these isolated AI solutions that
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  we're seeing
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  to then really
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  the scale deployment piece.
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  Yeah. I think, you know, a lot of
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  organizations look at ROI,
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  in kinda simplistic measures, I'd say, like, where
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  they introduced it, is it time saving, reviewing
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  a chart, or pulling to the right documentation.
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  But that's those are, like, shavings,
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  that certainly make up a pile, but that's
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  probably not where organizations are losing a ton
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  of money. And if you look more at
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  claims or denial management,
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  I should say, you see some of these
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  cases sitting for days or bouncing between teams
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  or getting reworked because something upstream didn't line
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  up. And that's that's where the real ROI
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  is. Right? Where you're missing filing windows, delayed
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  payments, write offs.
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  So it's back to Jason's point, like, how
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  do you not just look at the one
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  step in the process or the simple task,
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  but looking at the end end process or
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  the end end workflow,
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  to just make sure cases are moving faster,
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  things are not getting stuck, there's less rework.
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  So it's not, did I save time pulling
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  that chart? Right? It's did the workflow move
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  faster throughout end to end or the process
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  end to end,
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  seeding significant improvements and better outcomes.
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  And I think once we've seen that from
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  some of our top clients, and that that
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  has really transformed their organization to say, hey.
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  This is something that really works. Let's push
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  it to scale. Right?
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  And scalability
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  or scale has almost become this buzzword. Right?
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  Scalability is so crucial.
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  Jason, are there specific
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  operating models or or governance approaches that you
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  were seeing that actually enable scale?
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  Yeah. Definitely.
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  I think a lot of the
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  health care organizations we work with are still
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  exploring which model will support scale. And,
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  again, there's not a one size fits all.
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  But the common thread between the ones that
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  are successful, I think, are the ones that
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  are combining their AI teams and practices
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  along with their automation centers of excellences
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  and combining their strategies.
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  And so those two teams working together
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  really accelerate the adoption of these new capabilities
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  that are available
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  and then also enable scale,
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  at a big, big level.
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  And then I think what's critical is to
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  have intelligent orchestration
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  along with real observability and the controls that
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  we need in health care. Without that, it's
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  extremely tough to scale any of these new
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  AI capabilities.
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  In health care in general, we're very risk
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  averse, rightfully so.
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  And we need to vet that thoroughly
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  so that we can, springboard
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  from that capability to attack all the
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  largest and most complex problems in the ecosystem.
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  And then also, I I find that decision
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  committees that are willing to explore and experiment
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  in a controlled manner
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  help the scalable
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  story.
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  If they're
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  willing to get after it, then
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  the organization can rally behind them rather quickly.
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  And then we start to see the successes
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  and it just compounds. The momentum builds.
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  But if decision committees
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  are taking a long time
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  and really slowing down the process, then the
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  momentum,
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  declines along with it. So those are just
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  some of the things that I'm seeing on
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  the marketplace today.
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  Jason, in our first episode, which, by the
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  way, I highly recommend everybody listen to, you
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  provided so many great examples of where AI
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  today exists across the health care ecosystem.
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  Can you walk us through an example where
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  a work flow crosses
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  those clinical and administrative
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  boundaries
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  and where AI either succeeded or failed because
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  of that crossing?
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  Yeah. Absolutely.
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  You know, I talk a lot about prior
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  auth in the marketplace
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  because it's the place that
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  both payers and providers are trying to optimize
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  over the last, call it, you know, three
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  to five years.
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  And now with Agenca capabilities,
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  they're truly able to span end to end
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  and improve
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  the quality of submission and the quality of
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  decisioning and turning it around quicker
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  so that the patient gets the care more
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  timely.
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  And the successes we've seen
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  start small.
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  So for an example,
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  let's say the prior auth comes through and
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  there's a stack of medical records
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  and you have to analyze, make sure that
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  you're not missing anything to go through the
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  review process.
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  Agents can now look at that and ensure
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  that everything is in place before it moves
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  to the next step so that it's not
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  found later in the process and have to
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  revert back to square one and then again
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  elongating the timelines and turnaround times.
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  And
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  AI has been most effective
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  that I've seen so far in summarizing medical
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  records. Right? Summarizing it to the persona
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  that cares.
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  So a prior auth intake coordinator has a
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  different view that they look at as opposed
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  to the clinician who's rendering a decision on
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  whether or not to approve this care.
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  And so having it tailored to that persona
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  has really impacted,
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  the ability to make a decision faster.
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  And we're seeing customers leverage this capability and
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  experience
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  50 plus percent reduction in their overall turnaround
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  times and 75%
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  plus
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  in just reducing the review time of medical
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  records.
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  Another great example of where AI is impacting
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  the clinical space
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  is the ability
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  to create care plans, generate them,
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  and have them be tailored
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  to the actual patient's experience
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  and continuum of care.
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  Historically, we kinda grouped folks into tiers and
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  categories
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  and sent out boilerplate
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  messages or care plans.
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  Now agents can really assess
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  their historical claims information,
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  other metadata they found in medical records
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  to create again that very pointed care plan
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  that's gonna be most effective for them. And
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  then once that care plan is created,
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  coordinating
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  the care afterwards,
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  is now possible,
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  to do at scale. Right? You can refer
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  them to the right programs.
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  You can follow-up with them through all of
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  the various channels that patients engage with their
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  healthcare administrators and providers with
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  and really impact,
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  their ability to adhere to the care plan.
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  So a lot of innovative stuff is happening
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  in the space, and we expect it to
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  grow over the next few years here significantly.
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  Ben, Jason touched on something that he called
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  orchestration,
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  right, as an as an enabler as a
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  key enabler for all of this these processes
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  that we've touched on. Right?
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  I'd love to know what orchestration
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  means in this context in a health care
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  AI context,
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  and what's the risk if organizations
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  continue to deploy AI
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  in silos without set orchestration?
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  It's a great question. And I think if
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  you look at health care in general, right,
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  and health care workflow, specifically Jason explained out
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  that the challenges in the steps, it's it's
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  how things connect and how things are interoperable,
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  right, and
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  move across and then process. And we hear
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  from a lot of clients.
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  Right? Like, between tokens, models, constant change, it's
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  it's really hard for them to make AI
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  feel real and also have predictable costs. And
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  I think our orchestration
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  story and being more vendor agnostic and sitting
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  on top of a lot of these hard
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  coded tools across health care and our ability
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  to to offer a lot of the the
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  the prepackaged out of the box solutions, if
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  you will, that are designed for payers and
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  providers,
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  has proven wildly successful. Right? And I think
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  when you think about orchestration,
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  this is more, as we used to say,
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  like, the Switzerland of this. Like, we're not
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  stuck into one in any any specific technology.
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  Right? We help sit on top of all
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  of this to help connect the workflow end
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  to end, which allows you to make sure
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  that decisions are happening in the right place,
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  things are staying consistent, governable,
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  safe, right, and and really driving the consistency
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  that's so incredibly important,
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  in an industry like health care that's so
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  highly regulated and and so important to get
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  right on the first time to drive better
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  patient outcomes.
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  Absolutely. I think, again, we come back to
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  it. That's really what matters is making sure
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  that the patient gets the care that they
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  need at the right time at the right
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  place, and that's an enabler
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  force that process.
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  Jason,
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  I wanna close our conversation today with something
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  actionable for the folks that are listening right.
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  What metrics or signals truly indicate
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  that an organization
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  is truly
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  ready to scale
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  today? What are those signals that leaders need
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  to watch out for, need to know about?
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  And being on the ground level with a
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  number of health care organizations,
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  I'm seeing, you know, one common thread, which
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  is
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  the willingness not only at the leadership level,
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  but all the way down to the operators
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  and the clinicians that are operating on the
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  front lines of health care.
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  You know, and that requires
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  a lot of education,
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  a lot of explanation,
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  and a lot of back and forth feedback.
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  And so if there's the willingness that's there,
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  the the scalability will happen.
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  We also find that a number of health
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  care organizations who have a strong foundation of
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  automation already in place,
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  tend to be the ones to scale first.
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  Right? They understand what automation can do. And
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  now that AgenTek AI is available and
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  scalable,
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  they're simply evolving their automation programs to take
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  on larger, more complex problems and, you know,
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  finalizing
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  steps in the process that only humans could
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  do prior.
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  And then being intentional about governance and observability
• 12:43
  over these autonomous capabilities is extremely crucial.
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  Right? So if that is mature and
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  the governance programs
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  are in place,
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  the scalability will come along with it.
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  And lastly,
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  I think early signals would be having those
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  quick wins,
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  tackling some of the smaller use cases and
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  then gradually
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  progressing from there
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  so that we can build trust within our
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  workforce.
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  And then
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  have our teams realize that this isn't just
• 13:14
  an operational expense play.
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  This is more about cost avoidance
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  as well as improving the quality of data
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  that flows throughout health care.
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  Ben and Jason, thank you so much for
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  sharing your insights with us. So great to
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  have you, and thank you to UiPath for
• 13:31
  bringing us together for this conversation.
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  We've covered so much ground over our two
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  episodes here. And if you missed episode one
• 13:37
  on turning health care's toughest operational challenges into
• 13:40
  breakthrough solutions, make sure to listen in by
• 13:42
  visiting our podcast page at peckershospitalreview.com.
• 13:46
  And as Ben and Jason shared, the organization's
• 13:49
  making progress aren't just introducing it into parts
• 13:51
  of the process. They're rethinking how work moves
• 13:54
  across the system so those workflows actually run
• 13:57
  end to end. And if you're interested
• 13:59
  in having a deeper
• 14:01
  conversation about those processes and how AI and
• 14:04
  automation can help automate and orchestrates some of
• 14:06
  these workflows worth touched on, UiPath will be
• 14:09
  attending the Becker's annual meeting April
• 14:13
  in Chicago. Make sure to stop by and
• 14:16
  say hello.
• 14:17
  We're excited to see you there.