Latest / Becker’s Healthcare Digital Health + Health IT / How CVS Health Is Using Agentic AI Twins to Transform Patient Experience

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How CVS Health Is Using Agentic AI Twins to Transform Patient Experience · Becker’s Healthcare Digital Health + Health IT

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  Everyone. This is Jacob Emerson with the Becker's
• 0:02
  Health IT podcast.
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  Thrilled today to be joined by CVS Health's
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  vice president of enterprise customer experience and insights.
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  Sri, thank you so much for taking the
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  time to be with me on the podcast
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  today. Pat, thanks, Jacob. Appreciate the time. Excited
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  to be here with you. Likewise. And before
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  we dive into everything we wanna talk with
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  you about, some interesting new tech that CVS
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  has been rolling out, can you first tell
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  our audience a little bit more about yourself,
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  your background in health care, and a little
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  bit about your role at CVS today?
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  Yeah. So,
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  as you mentioned, I lead, customer experience and
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  insights for CVS Health. I I started my
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  career actually as an economist.
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  So,
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  looking more at, you know, markets and evaluating
• 0:43
  them for competitive dynamics and
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  defending mergers and and acquisitions to department of
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  justice.
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  And, you know, that's where I kinda first
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  got exposed to health care. I used to
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  look at things like hospital mergers and and,
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  you know, medical device mergers, and I got
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  really fascinated by health care. And then my
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  career kinda took a a bit of a
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  winding path. I worked at, you know, GE,
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  including GE Healthcare,
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  Did spend some time at Wells Fargo,
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  you know, in the banking industry, so got
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  a little bit more exposure to the to
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  the regulated banking industry
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  before joining here, at CVS Health. And my
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  role at CVS Health
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  is I look at all of the consumer
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  feedback we get, whether that's through surveys
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  or,
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  social media data, digital session data,
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  call transcripts, whatever we can get our hands
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  on
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  to understand,
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  what are some of the patient client, you
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  know, member pain points that we have across
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  our entire enterprise, so retail at
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  health care delivery, Caremark.
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  And how do we improve the experiences for
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  those stakeholders and make sure that we're delivering
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  best in class experiences
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  so that those people can get the health
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  care they need.
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  When I when I kinda give my team
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  a tagline, I say our job is to
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  learn from the past, improve the present, and
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  predict the future,
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  particularly with respect for our members who we
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  serve as the internal advocate for
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  across all of our our internal teams, whether
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  that's in technology or the business or elsewhere.
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  Understood. So your background is more in the
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  business and finance sector. Now you're here at
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  CVS,
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  modeling what patients and and customers
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  are doing. And and you're here with us
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  today, Shree, to talk a little bit about,
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  something really interesting that we I I haven't
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  necessarily heard a lot from other health care
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  organizations, at least publicly.
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  CVS, since late last year, has been rolling
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  out Agentic AI twin simulations.
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  So talk to our audience about what that
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  is. When you talk about
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  agentic twins,
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  what does that actually mean, and and what
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  does that actually look like in practice
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  for CVS specifically?
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  Yeah. And I bet on this podcast, you
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  talked to a lot of leaders who talk
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  about being consumer centric. Right? And I think
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  that's something that
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  everyone says. You probably see it in every
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  single 10 k
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  out there, every CEO,
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  dialogue. I I think what's hard is being
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  consumer centric is really difficult. Getting the voice
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  of that customer into all of the aspects
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  of what you do
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  is challenging because it's hard to get to
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  customers. It's hard to talk to them. It's
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  hard for you to test things with them
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  or and learn from them.
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  You're limited in terms of how you can
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  outreach these folks. You know, you can leverage
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  surveys or maybe the scale.
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  You know, you have to do pilots.
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  And
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  it's being consumer centric is challenging. You have
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  that challenge simultaneously
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  with
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  the advent of AI and all the advancements
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  you're having in AI.
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  And what we realized in in working with
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  these, you know, company from Stanford founded by
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  a bunch of PhDs and computer science from
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  Stanford
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  is that we can actually build
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  agentic twins of our customers or or patients
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  or clients
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  where we can actually
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  build the way they make decisions.
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  So not just what they did in the
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  past, but how do they actually make decisions?
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  How do they actually,
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  handle trade offs? What are the behaviors that
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  they they would exhibit in a given situation?
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  And then we can generalize that to new
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  scenarios and new situations. So if you do
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  that at scale, and in our case, something
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  like a 100,000
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  plus
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  Agenctic Twins,
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  you will have a set of customers with
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  you all the time
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  that are, you know, effectively amplifying the consumer
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  voice.
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  So they are with you from when you
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  develop the product, when you wanna test the
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  outcome, if you wanna test some operational change.
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  And they it creates that real consumer centricity
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  because
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  that representation of the consumer, how they think,
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  how they behave is with you throughout the
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  entire process.
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  So, you know, it's the way we've gone
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  about doing that is we ingest a lot
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  of different information. Of course, all consumer consented,
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  but we take things like a a short
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  behavioral interview, behavioral signals,
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  spend data, things like that where we can
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  build a twin
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  of each individual consumer. And, again, you do
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  that at scale
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  where you actually have their thought process with
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  you, and this agentic twin of the customer
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  lives on and is always on,
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  for you to investigate and challenge new concepts,
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  new ideas, and to actually even perform dress
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  rehearsals of new things you're trying to launch
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  out. And it's really helped our decision making
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  our understanding of the consumer throughout the whole
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  product life cycle,
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  and and really just it's brought them to
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  life in a way that I don't think
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  was possible in the past.
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  And now, we really we really do. It's
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  kinda putting your money where your mouth is
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  in terms of centricity.
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  You're having these customers with you across the
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  entire journey.
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  Wow. I mean, this is fascinating. You you
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  literally copy and pasted
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  your customer base, and and now you can
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  see how they interact with all your different
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  businesses.
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  So
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  back backing up here, Sri, why did CVS
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  wanna go down this route? How why do
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  you wanna leverage this kind of technology?
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  And what does it actually do problem wise
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  for your patients and for your customers?
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  How does it solve issues for them on
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  the ground?
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  Yeah. So I think the reason you would
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  wanna we wanted to undertake this is we
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  really do take consumer centricity seriously. I mean,
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  we wanna build products and services
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  that help people access health care in an
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  effective way
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  and really solve the big challenges with The
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  US health care system around, you know, simplicity,
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  you know, ease of access, all those things
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  that you hear about constantly from from patients.
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  And I think where we we're we're landing
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  is we have the ability to get to
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  these folks via traditional methods, whether that's panel
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  research or surveys
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  or even to a degree looking at operational
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  data. But each one of those things had
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  challenges. Right? Either it was
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  speed, you know, or fidelity or any of
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  those things that
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  that challenge you in terms of getting to
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  that group. So when we heard about this
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  methodology and this approach, it was really exciting
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  because
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  it would it would really unlock what we've
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  been trying to do, which is get the
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  patient
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  front and center on the way we build
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  things. And I think if you if you
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  look at what we've seen so far, the
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  four main things we've gotten out of these
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  twins,
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  just in terms of the value of how
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  we build, is one speed.
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  You go from weeks to months to generate
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  insights. So if you have to go get
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  a panel and test something, whether that's messaging
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  or some product or service,
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  it takes time. You gotta go find those
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  patients. You have to go interview them. You
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  know, you have to you have to distill
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  those insights.
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  Now we're talking about fifteen to thirty minutes.
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  And, you know, I'll tell you honestly, Jacob,
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  the first handful of times we did this,
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  I was stunned. Like, things that would take
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  us eight weeks, we were doing in fifteen
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  to twenty minutes.
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  The second is fidelity.
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  You know, you you can only test so
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  many things with a human being. Right? There's
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  only so many
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  scenarios you can put in front of them
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  before their brain sort of breaks. They'll get
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  fatigue. You know, the signal quality reduces.
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  They're not able to answer. With these AI
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  representations,
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  you can test thousands of real world scenarios,
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  and you're not extrapolating out what they're gonna
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  what they're gonna do in a given situation.
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  You can present them with tons and thousands
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  of different scenarios or situations
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  and see how they'll actually behave. And that
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  actually also solves a big problem in research
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  where you have what's called stated intent.
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  So what someone tells you and what they'll
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  actually do. And here with this scenario testing,
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  just because you've generalized how someone makes a
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  decision or how they think,
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  you can actually get to the actual behavior
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  and even understand that gap between stated intent
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  and actual behavior.
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  The scale here, you have all these folks
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  on
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  all the time, so you don't have to
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  generalize the small pilots.
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  You know, you have thousands,
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  tens of thousands, and so some cases for
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  the populations we looked at, agentic twins available
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  to us at all times. So that scale
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  of the consumer voice is is tremendous
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  and now available to us to access,
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  you know, as we go forward. And then
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  the final component is the reach. Like, these
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  hard to reach populations
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  that we often have,
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  you know, whether that's particular disease states or
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  conditions where it's just difficult to find those
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  people or that can be really costly.
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  Now we only have to really find them
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  once because then we can create this twin
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  of that person, and then they'll be with
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  us the whole time. And it goes back
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  to, I think,
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  amplifying that consumer voice with that hard to
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  reach population particularly because
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  that's a group that's, you know, often
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  marginalized because we can't get to that group,
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  to understand what they're experiencing.
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  So if you if you wrap all that
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  up, we're getting away from reacting
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  to small groups
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  and with long timelines to get their feedback,
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  to predicting behavior, acting almost in near real
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  time, and having these consumers with us along
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  the entire journey,
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  it's transformative. It's it's changing the way we
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  deliver care because we're able to understand
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  what's gonna actually impact our consumers in a
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  real way while we're building these products
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  and not having to do things like, you
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  know, really narrow pilots and constant testing.
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  We can go out with a lot more
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  confidence in what we're doing
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  because we've been able to dress, rehearse it
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  with, you know, tens of thousands of customers
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  before we actually hit the market, and and
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  anyone has to actually experience what we're building.
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  Absolutely. I mean, listening to you talk about
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  this is incredible. It it it sounds transformative.
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  I I I've done marketing initiatives myself in
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  terms of doing those panels like you said
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  that take eight weeks and boiling that down
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  to fifteen minutes is that's incredible.
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  Not to mention what to hook on the
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  last part of your answer, the
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  the equity of it all of being able
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  to reach your entire customer base and know
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  how they all respond,
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  and not having to put everybody into pockets.
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  It's it's amazing. I I can't imagine what's
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  gonna be coming from this. And so I
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  think that's that's the natural follow-up question here,
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  Sri, is how does this change the way
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  that CVS
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  makes decisions compared to those traditional approaches you
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  just laid out for us even just from
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  a marketing perspective. But maybe you could expand
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  a bit on in terms of clinical decisions,
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  other areas of the pharmacist of the of
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  the industry that the company reaches.
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  How does this change the day to day
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  for CVS and and the business overall?
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  Yeah. And I I can give you a
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  couple of just concrete examples of that we've
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  that we've tried so far, that we've seen
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  success with. I think one, you know, one
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  classic one for us in the pharmacy side
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  is adherence.
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  That's an area we we've got a tremendous
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  amount of focus. Obviously, we want people to
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  be adherent to their medication. We want them
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  to stay healthy.
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  Accessing medications
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  is really important to
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  us. That can be a a difficult group
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  to talk to, particularly nonadherent patients. There's a
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  lot of reasons people don't, you know, adhere
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  to medication. Some of them related to stigma,
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  fear.
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  And you're in a sense, if you're going
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  to talk to that group,
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  you're making them relive whatever that was the
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  challenge that they had. And that can be
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  really difficult that you it can lead to
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  false signals. It can lead to, you know,
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  different types of challenges in terms of getting
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  to the population,
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  fatigue, lack of sample, those sorts of things
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  because they're just a difficult group to reach.
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  What we were able to do with the
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  twins is we were able to actually go
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  through and and investigate in detail adherence issues.
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  So get because you don't have to face
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  that and it's you know, you don't have
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  that same sort of challenge
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  with the Gentic twins because they're AI representations,
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  we were able to get way deeper than
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  I think we could have in a standard
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  qualitative or even quantitative interview.
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  What we're we were able to figure out
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  is a lot of the things about adherence
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  were the same that you would expect that
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  we've always seen.
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  You know, it's,
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  you know, fear of side effects and fear
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  of, you know, you know, not being able
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  to talk to a clinician and all those
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  things that we've been solving,
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  over the, you know, course of the past
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  few years.
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  But a new one's emerged, and it was
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  really around
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  the fear of getting a refill, which sounds
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  really you know, it sounds a little bit
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  strange. It wasn't what we expected, but we
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  were able to see, like, you know, a
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  lot of what we were observing was people
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  were saying, okay. Now once I start a
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  medication, how do I make sure I'm always
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  gonna get it? What happens if I can't
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  get that medication?
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  You know, does that is that gonna have
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  outcomes for me? And all those things that
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  we were able to see that that were
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  clear in the data.
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  And I think what what it pointed to
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  is that we have a lot of best
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  in class digital tools we're building and can
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  have built
• 13:30
  that are making accessing medications
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  much easier. So it gave us a lot
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  more confidence
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  in those tools we're building. And in addition,
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  we're able to actually look at how do
• 13:39
  we optimize some of those tools to ensure
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  that,
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  you know, we're we are making it as
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  easy as possible on the refill.
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  And I I think what it really pointed
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  out is
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  this anxiety, and it's patient anxiety that you
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  were able
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  that you were able to see in in
• 13:54
  the agentic twins around all the process related
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  to the prescription. So it wasn't just the
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  medication.
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  It was the process
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  and the experience
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  that led to adherence. And, you know, we
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  were able to back that up with a
• 14:06
  lot of the actual data we were able
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  to we were collecting from the customers.
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  When I say actual, I mean, operational data
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  that we were collecting from the customers.
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  It it it was a it's a it
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  was a way for us to unlock something
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  that we didn't we didn't see in the
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  past.
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  And I think what's particularly unique about it
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  is we're talking about effectively about robots here,
• 14:26
  Jacob. We build robots that represent customers,
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  but they were unlocking
• 14:31
  something that's as that's superhuman
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  and anxiety.
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  And so we were able to get to
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  the emotion through these agentic twins, which I
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  think is a really fascinating
• 14:42
  use case for us.
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  And then just a a quick related one
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  just because you mentioned marketing. Another one was
• 14:48
  related to our PetRx.
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  I'm gonna just tell a story really quickly.
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  You know, PetRx is our our you know,
• 14:54
  we're looking to to move into pet care,
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  and we're looking at how do we message
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  to those to those, pet parents, if you
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  will. I'm a pet parent. I've got a
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  dog.
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  And, you know, there was a lot of
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  the data we were expecting, like emotional data
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  that people care a lot about their pets.
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  They become, like, family members.
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  One of the more interesting things we saw
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  from the Agenctic twins was
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  people were saying, you know, that even giving
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  medication to a pet was an act of
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  devotion, not necessarily a chore.
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  So any messaging appealing to the emotions was
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  gonna be successful.
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  But what the the agentic twins allowed us
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  to do is present scenarios
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  beyond that. And what we found was it
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  wasn't enough
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  to just be
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  connecting emotionally. You had to show that we
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  had the capability to actually get the the
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  scripts from the veterinarians,
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  have an understanding what those scripts are, how
• 15:44
  the side effects work. That was really important
• 15:46
  to the pet parents. So
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  by being able to drill in and get
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  to the kind of stated versus actual actual
• 15:52
  behavior using the twins, we were able to
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  uncover
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  that the messaging needed to have both. It
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  wasn't sufficient to just have messaging and marketing
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  that was appealing to emotion. We had to
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  appeal to the tactical side too, because pet
• 16:05
  owners are incredibly pragmatic despite this emotional connection
• 16:09
  that I think we all observe and see.
• 16:11
  So, anyway, those are two kinda quick examples
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  of where we've we've seen early wins.
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  And, you know, we're operating this at scale,
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  so there's more to come, I think, here
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  as we keep moving forward.
• 16:22
  Oh, absolutely. I I can imagine so. And
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  for pretty much any company listening in right
• 16:26
  now, I would imagine they are taking notes
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  on how to replicate something like this, Sri,
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  because the the possibilities seem endless just listening
• 16:34
  to even those those two examples you shared,
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  and I really like how you framed it
• 16:38
  of unlocking something that is human. You're you're
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  unlocking something that the humans on the other
• 16:44
  side maybe hadn't been able to before, which
• 16:46
  is really fascinating.
• 16:48
  It's a really unique AI use case. You
• 16:50
  know? It's not it's in a sense, it's,
• 16:53
  it's not one of the ones that I
• 16:54
  think are traditional,
• 16:56
  and it's it's using AI to understand human
• 16:58
  behavior,
• 16:59
  which, again, sounds a little counterintuitive.
• 17:01
  But I think No. It makes sense. Yeah.
• 17:03
  Yeah. It's really creating this always on approach
• 17:06
  with our customers. Definitely. Definitely. Is there anything
• 17:09
  you'd add for for the health care audience
• 17:11
  that's listening in terms of the value prop
• 17:14
  here for the wider health care system?
• 17:17
  Yeah. I think, you know, if you if
• 17:20
  I were thinking about more broadly,
• 17:22
  a EO, even things like patient experience
• 17:24
  and and I'll use the experience tag because
• 17:26
  that's that's where I, you know, tend to
• 17:28
  focus.
• 17:29
  It it's
• 17:31
  ability to test messaging. Like, how do we
• 17:34
  change in particular in health care is really
• 17:36
  painful. That's observed everywhere.
• 17:38
  You can see that across multiple populations when
• 17:41
  you change people's
• 17:42
  medications, when you change formularies, when you change
• 17:45
  the way they access care, it's painful.
• 17:48
  But what this allows you to do is
• 17:49
  test different messaging and different approaches to see
• 17:52
  how you how do you smooth that, how
• 17:54
  do you make that easier on the patient.
• 17:56
  And you're able to do that here in
• 17:58
  a controlled, safe environment
• 18:00
  that you you are able to see the
• 18:02
  results before you actually have to go into
• 18:04
  the market and test that. I think that's
• 18:05
  a really big unlock for health care.
• 18:08
  The the other one, I think, is understanding
• 18:10
  journeys. Health care journeys are incredibly complex,
• 18:13
  and, you know, it's it can be difficult
• 18:15
  to actually get patient feedback along every aspect
• 18:18
  of that journey.
• 18:19
  But here, you can you can do that,
• 18:21
  and you can see how people are experiencing
• 18:24
  every aspect of where they engage with you.
• 18:27
  And I think understanding quickly where you have
• 18:29
  pain points and where you may need to
• 18:31
  do some work and may where you're excelling
• 18:33
  and maybe where you need to double down.
• 18:35
  Health care in particular to me,
• 18:38
  this is a way to
• 18:40
  test and learn
• 18:42
  quickly,
• 18:43
  with those audiences.
• 18:45
  And I think it's it's a major unlock
• 18:48
  because I I think that's where and I
• 18:49
  know I keep using the word unlock because
• 18:51
  I think in in these populations,
• 18:54
  traditionally, it's really difficult to get the feedback,
• 18:56
  and they're also really difficult to pilot with.
• 18:59
  Because if you're running tests, you're actually running
• 19:01
  tests on someone that's experiencing something,
• 19:04
  or trying to access care. And that's not
• 19:06
  always easy, and that's not always right, and
• 19:08
  it's it can be difficult.
• 19:09
  Here, you're able to do those tests in
• 19:11
  this controlled environment without having to worry about
• 19:13
  the patient risk or outcome. It makes it
• 19:16
  safer, and it and it makes it more
• 19:17
  controlled.
• 19:19
  And I think, you know, the follow on
• 19:21
  effect is you're gonna deliver better care,
• 19:24
  for those patients and for those stakeholders.
• 19:26
  Sure. Let me ask you a a bit
• 19:28
  of a prodding question.
• 19:30
  Just as this technology
• 19:32
  continues to develop, we'll continue to see it
• 19:34
  across many companies,
• 19:36
  different industries.
• 19:38
  How are you thinking about how CVS wants
• 19:41
  to
• 19:41
  ensure responsible use
• 19:44
  of of agentic twins at enterprise scale? You
• 19:46
  know, you're able to now test an an
• 19:49
  external pressure
• 19:50
  on on your customer
• 19:52
  base. How do you balance,
• 19:55
  some of the potential negatives of that or
• 19:57
  or ensure that there's gonna be human judgment,
• 19:59
  trust,
• 20:00
  good governance of this technology as it begins
• 20:03
  to as already has started to influence,
• 20:06
  real health care on the ground?
• 20:08
  Yeah. It's a really important question.
• 20:10
  I I think one, we do. We have
• 20:12
  very strong controls around this, and there's a
• 20:14
  couple different ways we do it.
• 20:16
  We continuously test
• 20:18
  these twins,
• 20:19
  against not only our own data,
• 20:22
  but third party studies
• 20:24
  to ensure that we have the highest quality
• 20:26
  information.
• 20:27
  And, you know, one of the things that
• 20:28
  we're doing that these twins do as well,
• 20:31
  is they actually live in the world, so
• 20:32
  they ingest current events.
• 20:34
  You know, if you are a, if you
• 20:36
  have a a twin that's a democrat, they'll
• 20:38
  read democratic news organizations. If you have one
• 20:41
  that's a republican, they'll read republican news organizations.
• 20:44
  So you're able to actually keep them up
• 20:45
  to speed,
• 20:47
  you know, even on current events. But
• 20:49
  regardless of that, we're always back testing. We're
• 20:51
  always making sure that they're meeting our standards
• 20:54
  of of
• 20:55
  matching known,
• 20:57
  human results,
• 20:59
  that we have. The second is, you know,
• 21:01
  right now, we have my team of researchers.
• 21:04
  I have a team of,
• 21:05
  you know, analysts
• 21:07
  and market researchers that we have organizationally that
• 21:10
  are are the ones who are administering this
• 21:12
  test because they represent human behavior.
• 21:14
  So you can lead the witness. So if
• 21:16
  you don't have someone, you know, at the
• 21:18
  controls who's engaging with effectively the way it
• 21:21
  works is an agentic chat
• 21:23
  and you're not asking the questions appropriately, you
• 21:26
  can get results that are misleading. So right
• 21:28
  now, it's my team of researchers
• 21:30
  that are are looking at that information and
• 21:32
  validating it, and making sure it's meeting the
• 21:35
  standards that we would have for human studies.
• 21:38
  But one of the things I point out
• 21:39
  there, though, is not to live in an
• 21:40
  echo chamber. On occasion,
• 21:42
  the Agenent twins will teach us something new
• 21:44
  we didn't know, and we shouldn't just be
• 21:45
  sitting there saying,
• 21:47
  oh, wait. It it didn't match exactly what
• 21:49
  we thought in the past, so, therefore, it's
• 21:50
  wrong. What we need to do is investigate
• 21:53
  it further. So we make sure we have
• 21:54
  that process,
• 21:56
  built out.
• 21:58
  And then I think the third thing and
• 21:59
  probably most importantly, I feel like everyone always
• 22:01
  frames AI as replacement.
• 22:04
  And as like, what is this gonna replace?
• 22:05
  It's always the first question you get when
• 22:07
  you're deploying an AI solution. I don't think
• 22:09
  this is replacing talking to our customers.
• 22:12
  It's augmenting our understanding.
• 22:14
  It's amplifying their voice, as I've said.
• 22:17
  But, ultimately, we're gonna have to always talk
• 22:19
  to our customers because that's important, and that's
• 22:21
  what good companies do. You need to listen
• 22:23
  to people, and you need to understand what
• 22:24
  their friction and their pains are. And then,
• 22:26
  also, it's important to do that to potentially
• 22:28
  catch emerging issues and things like that. So
• 22:31
  I view this as additive to what we're
• 22:33
  doing with our human populations.
• 22:36
  And I think if you you combine those
• 22:37
  three things,
• 22:39
  you know, the the the back testing,
• 22:42
  the researcher at the wheel, and then always
• 22:44
  talking to the human populations as well,
• 22:46
  it's a major it's a it's a set
• 22:48
  of governance that gives you a lot more
• 22:50
  confidence in how the twins are operating.
• 22:53
  And it also helps you amplify the impact,
• 22:56
  because when you're going out to the various
• 22:57
  stakeholders
• 22:58
  and you're doing the change management of saying,
• 23:00
  hey. Listen to listen to these agentic twins.
• 23:02
  They're telling us, you know, what to do.
• 23:05
  The people you're working with have more confidence
• 23:07
  in you because they know that you're taking
• 23:08
  the the care
• 23:10
  and you're doing the work to make sure
• 23:12
  these things are accurate.
• 23:13
  Absolutely. And like you said, Shari, I mean,
• 23:15
  this is it sounds like this is unlocking
• 23:17
  things that your customers were already feeling and
• 23:19
  thinking.
• 23:20
  Anyway, this is now just another way for
• 23:23
  you to for you to reach to reach
• 23:24
  that. So really, really fascinating.
• 23:27
  Anything else we're missing here? Any final thoughts
• 23:30
  you wanna share with our audience?
• 23:33
  Yeah. I think, you know, at at CVS
• 23:35
  Health, it's not just that we're experimenting with
• 23:38
  this because I think a lot of people
• 23:39
  will frame this as it it's it is
• 23:41
  new. And I I get it's an emerging
• 23:43
  technology, but we were at the forefront here.
• 23:45
  And I think it's gone beyond experimentation.
• 23:48
  You know, we're we're at this point. We're
• 23:50
  at operating at scale. As I mentioned,
• 23:52
  a 100,000
• 23:54
  twins. You know, my hope is to get
• 23:56
  us to 300,000
• 23:57
  or more in the in the future, but
• 23:59
  we're using this responsibly at that scale to
• 24:02
  improve people's lives.
• 24:03
  And this is giving our teams unprecedented
• 24:06
  clarity
• 24:07
  and foresight
• 24:08
  into, you know, what's happening in the industry,
• 24:10
  what's happening with our consumers, how can we
• 24:13
  better serve them, how can we build
• 24:15
  better experiences? How can we learn faster,
• 24:18
  and design better?
• 24:20
  It's it's helping us it's a step change
• 24:23
  for us in terms of how we actually
• 24:25
  deliver,
• 24:26
  health care.
• 24:27
  And I think in the end, it's gonna
• 24:29
  help us deliver better health and support for
• 24:31
  every customer member,
• 24:33
  that we serve, and we're gonna do it
• 24:35
  with more confidence, empathy, and understanding.
• 24:37
  And it's gonna help us achieve our goal
• 24:39
  of being the most consumer centric company in
• 24:41
  health care. So,
• 24:43
  yeah, I I hope everyone listening out there,
• 24:45
  particularly those in health care,
• 24:48
  understand that
• 24:49
  this is a,
• 24:50
  this is a a a tool
• 24:53
  that I think can really give your customers
• 24:55
  an unprecedented voice in the way you operate.
• 24:59
  100%. I mean, this does seem to be
• 25:01
  the one of the largest
• 25:03
  commercial rollouts we've seen of this technology or
• 25:05
  that we've been, we've heard about in health
• 25:07
  care on our end. So, Sri, really appreciate
• 25:10
  you taking the time to chat with us
• 25:12
  about this, and then please keep us updated
• 25:15
  as as this all evolves. We'd love to
• 25:17
  hear more about it. Yeah. Always a pleasure,
• 25:19
  talking about this, and it was a pleasure
• 25:21
  talking with you. So excited to come back
• 25:23
  and tell you all the great things we're
• 25:24
  doing in the future.
• 25:25
  And to our audience, if you'd like to
• 25:27
  listen to more podcasts from Becker's Healthcare, you
• 25:29
  can visit beckershospitalreview.com.