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to learn more today. Hello, everyone, and welcome
to the Becker's Healthcare Podcast. I'm Scott King
joined by a very, very special guest today,
Ratnakar Lavu, chief digital information officer with Elevent's
Health. Ratnakar, thanks so much for joining us.
Really excited to have you. How are you
doing?
Good, Scott. Thank you for having me here
today.
Really excited to talk through things with you.
Yeah. We have we have some big things
to get into with, you know, on in
the payer
forefront and in health care and technology. But
before we dive in there, can you please
just tell us a little bit about yourself
and your background?
Yeah. So,
I've
my name is Raka Karlavu. I'm the CDIO,
here at Alvan's Health, and I've always been
within technology and really focused on how we
can leverage technology, data, and AI,
to simplify actually,
consumer experiences and member experiences.
And here,
I'm really focused on simplifying
health care to improve outcomes, leveraging technology, data,
and AI. We're embedding AI responsibly
across all our operations,
not just as an experiment, but we're doing
it at scale
to make care more affordable,
accessible,
and personalized, Scott. So that's what's actually really
exciting about the work that, we do here
at Eleventh Health.
Yeah. Yeah. I I feel like in health
care, we're hearing just more and more about
how vital, you know, especially on a payer
side, data is and then across the board
in the industry,
AI and the the uses there, and and
we're just tapping into that. But what do
you have
your eye on right now as far as
opportunities go?
Well, Scott, you know, health care is very
complex. Right?
And, every one of us, who interact with
health care,
see some of this complexity, whether it is
trying to understand the benefit,
trying to connect with the provider
to get care,
and really focused on kind of, you know,
the health care outcomes itself or the clinical
outcome. So in, Eleventh Health, we're really focused
on three things.
One is simplifying
kind of the member experiences itself. We wanna
create personalized and more seamless experiences.
We wanna actually simplify the,
care provider experiences also, the interoperability.
We wanna empower care providers to improve health
outcomes.
And then finally, we wanna simplify
how we work to actually better serve our
customers and members. And I'll give you a
couple of examples.
Let me start with,
the member or the customer,
example itself.
So we have something called,
proactive member engagement.
And,
what this is is it's actually an AI
driven tool
wherein we identify members who could benefit from
an outreach
to proactively address care gaps. So we study
the members, their clinical data. We understand
who could actually benefit from closing care gap.
We do an outreach for to them
to explain to them what would be beneficial,
as they think about going to their care
provider,
what will be beneficial for them to address
so that they can actually have that meaningful
interaction with their care provider,
with a lot more information. And we do
millions of these outreaches
today.
And it's the AI, the in data,
and then the kind of the intelligence that
we build that enables that to happen. The
other example is when members actually call in
to our call center,
we provide our call center agents with the
intelligence
to be able to actually,
address the member issues. So we we correlate
all the information,
the clinical data,
you know, kind of their,
claims data,
their benefits data, and automate the things,
for our, call center agent.
Not only that, but once they finish the
call, we actually do a post call wrap
up,
which is we use AI to understand how
the call went.
How did our members feel?
What were their issues?
And we do millions of these, post call
wrap ups,
every month. And the reason for that is
we actually understand the sentiment of the member
so that we can proactively
address that.
The reason these examples are important is because
we're very focused and obsessed about the member
experience, the member interactions itself. And I'll then
talk a little bit about the care providers.
So in the care providers, we wanna make
the interaction between us and the care providers
easy. So within
UMAI, where they actually,
kind of submit their prior on, we actually
look at what they've submitted,
analyze it with the data that we have,
and our entire goal and objective
is to approve things as fast as we
can, for the care provider.
And,
we use a lot of AI to look
at medical record, to look at their prior
auth submission,
to look at the member's benefits data, and
orchestrate that approval
in a more seamless way for the care
provider.
And another thing is the care providers actually
submit a lot of claims. Our providers submit
a lot of claim. Sometimes those claims are
not complete.
And so we have at applied AI
to look at those claims and see where,
the information is not complete. And the AI
actually help in
completing that information so we can process the
claims on behalf of the providers. Why is
this important? Because a lot of care providers
spend a lot of time in administrative work,
and we wanna take away that or simplify
that administrative work for the for them so
that they can really take care of our
members and,
close those care gaps that are extremely important
for our members.
Now let me come to how we are
simplifying
the work itself for our associates.
So we have we're deploying a lot of
AI productivity tools so that they we can
automate a lot of their workflows
so they really can actually service our members
and providers.
So we have over 52,000
associates today.
We will that are leveraging some sort of
AI capabilities,
within their workflows. But we're also rolling out
now,
chat GPT across the enterprise.
So not only is it for that that
they can do their work more easily,
but they can also kind of understand leveraging
in these chat GPT and other capabilities
to be able to serve our members effectively.
And then the not only are we rolling
out these tools,
we're actually going to take them through AI
certification, working with OpenAI.
Because one of the things we find is
it's not just about the tool, it's our
associates need to understand how to use those
tools, what what it means to leverage AI
to actually do their work better, and that's
where we're we're focused on quite a bit,
with our associate.
So, again, to recap,
we want to actually create a more personalized
and more seamless experience for our members. We
wanna empower our care providers to improve health
outcomes,
and then we wanna simplify
how we work to better serve our
customers and members itself.
Incredibly fascinating. A lot of so much great
information in there right in the car. Let
let me ask you, I guess, to play
and to play devil's advocate a little bit,
you know, even the most practical uses of
AI the average person uses, there's still some
some trial and error. So just curious with
something like the the, AI member outreach that
the elephants has been doing and even those
summaries that it provides, what what's kind of
some of the, I guess, the errors that
there might be, and are they kind of
fixed through the AI self learning? How's how's
that working? How's it getting kinda past any
any hiccups there?
Yeah. Actually, I I wouldn't say it's a
lot, about the hiccups. What we have learned
actually is we need to obsess about the
journeys themselves.
So we are really, kind of focused on
the member journeys,
overall or the provider journey. So I'll give
you an example. In the UMAI
case,
when, a prior auth is submitted,
our entire focus is to how to get
to real time prior authorization. Right? So when
we focus on that journey,
what we have found is when we deploy
AI solutions,
we our our own learning has been
don't just deploy it in in a part
of the workflow,
really think about the entire workflow. So when
we deployed it in the case of prior
auth,
we were only looking at it as the
kind of the
approval for the prior auth. In in fact,
there's a lot more in the process that
we'll have to think through. And so we
went and pivoted.
Instead of just a portion of the workflow,
we pivoted to actually looking at the entire
workflow.
And that has been the big learning for
us, is when you think about AI, just
don't think about it in a silo of
a workflow.
Think about the entire workflow,
and
we
reimagine that workflow
as you think about,
building solutions and deploying AI solution. And that's
how actually you'll make the entire workflow simple.
We we we're re and then we'll also
get them to be more,
kind of connected
as we think about the interoperability
between provider and payer.
Right. It's a journey. So focus on that
journey. I I I think that makes complete
sense.
In in terms of, you know, the potential
maybe that people or payers have even tapped
into
for AI, what are some of the things
that Evolence is kind of looking to in
terms of focus for the future?
Well, I see tremendous amount of potential,
in terms of the focus for the future.
Again, we're deep rooted into creating personalized and
more seamless experiences.
So I'll give you an example. We we
have deployed a virtual
assistant, in our Sydney app
for our members to use that. It's like
a chat GPT experience. So they can actually
ask questions. Is is this particular procedure covered
in my benefits?
Right? And then we we kind of the
AI has intelligence to bring that information back
to the member.
And then not only that, it act it
can then connect that member
to the right care provider
so who can drive quality outcomes at a
at an affordable price. So we have built
that intelligence where the AI understands what the
member is asking for and how to connect
them to get the care and
the clinical outcomes
that,
we we actually want to guide the member
through. And so what I'm excited about is
this creating this personalized and more seamless experiences
at scale
now leveraging AI. The other thing that I'm
really kind of,
excited about, as I mentioned,
is improving this interoperability
between here and provider because we wanna streamline
and make it simple
for the,
providers to actually engage with us. And I
think there's tremendous amount of potential there. And
finally,
I'm really,
kind of excited about how we can transform,
internally our company
so that we can kind of automate a
lot of the mundane tasks,
that are there, within a large company like
ours with, leveraging AI, and really focus on
on the member and the provider and the
high value added task because that's where we
need to be spending a lot more time
to simplify health care.
Yeah. And for anyone listening who wants to
to get more information or or read about
this virtual assistant Ovens is working on, Retina
Carr did a great interview with Becker's, looks
like the end of of October. And,
let let me ask you. How's how's it
how are the
the total number of members that'll have access
to the virtual assistant? How's that tracking as
we're here almost, you know, at the 2025?
Actually, it's tracking really well. So we're getting
a lot more adoption,
you know, as we roll this out to,
kind of,
a large set of our members, we're getting
a lot of adoption,
for the virtual assistant.
What we are seeing is the members are
highly engaged. They wanna understand their benefit. And
the first for the first time, we made
it actually extremely simple for them to understand
the benefit.
Not only that, it is actually making them
easier also to connect with the right provider
because the intelligence that we have built through
this
actually connects them with the right provider who
can drive high quality of care at an
affordable price. And so we're seeing a lot
of engagement. What actually it's helping us do
is the member is able to,
kind of do self-service
instead of calling and trying to understand their
benefits or calling to actually get to the
right provider. Now all of it is moving
towards self-service, and that's what we're really excited
about. Because we really wanna empower our members,
to kind of navigate this complex health care
system, and we wanna make it easy and
simple for them to do that.
Yeah. It sounds like the rollout has been
going great.
As organizations
continue to expand and implement AI driven solutions,
how do they do so in a responsible
and ethical way?
It's a great question, Scott. So, by the
way, we begin with
responsible AI. We begin with transparency.
We begin with explainability.
So we actually have
an entire responsible AI
governance framework within our company. So every AI
solution has to go through that governance
framework where we evaluate,
the AI solution
for,
responsible
AI, transparency, and explainability,
and how the solution is actually designed and
being implemented.
Only then will we actually approve that AI
solution
to go into production.
And then we monitor that,
you know, once it's in production
to ensure that all the transparency and explainability
is are
has been implemented
effectively once it goes into production. So you
need to both have a framework in terms
of the governance framework.
That governance framework has to start from the
beginning of the solution when it's actually designed,
not after the fact. And so it has
to be part of the process.
And then once you actually implement the solution,
you actually have to also monitor it for
its effectiveness.
And that's the way we have actually done
that, and we've been very successful
in doing that,
across all our AI solutions.
In terms of all the uses Elvents has
had for AI, a lot of the things
you've discussed already,
has Elvents been able to pinpoint,
like, you know, just a a number for
efficiency in regards to hours saved between
payers and and and members?
Yes. We do. And so one of the
things we constantly watch is,
is it improving
the outcomes that we we have kind of,
outlined,
you know, in terms of the member efficiency
to actually self serve. Right?
That's a huge efficiency because the members,
are effectively
getting all the information that they had to
go to multiple places in the past. So
self-service.
And then self-service, not only just self-service, how
satisfied are they about with the self-service, so
NPS scores and other thing. So we monitor
all of that. We also look at in
the peers,
in the peer provider interaction,
we also looked at kind of the effectiveness
of our interaction overall. How,
satisfied
are the providers,
both in terms of how we
kind of set them up to begin with,
and then how do they,
actually how do we process
their kind of, prior auths, claims,
and and any other administrative thing. We actually
look at how effective those are, how are
we reducing the time of processing those,
administrative
tasks, and then how satisfied are the providers
there. And then in the associate population, we
do a lot of,
self assessment.
How satisfied are are
our our associates
in actually
looking at, the tools that we've deployed, and
is it actually helping them to do their
job better? So you we monitor a lot
of KPIs
across
the entire ecosystem.
And, again, I come back to
these
these the solutions that we're deploying,
the KPIs that we're monitoring
actually yield to a better and more simplified
experience
for all. And and and I think that's
a way to actually simplify health care,
for everyone.
Yeah. It really seems that for all these
uses, you're you're mentioning that Evans goes to
great lengths to get member feedback. How crucial
is that feedback kinda during the early stages
of some of these rollouts?
It's actually Scott, it's
I'm glad you brought that point up because
it's actually extremely important
to get this feedback, but both from members
and providers.
And we
we actually study so even before rolling out
a solution like the virtual assistant,
we've done a lot of market analysis. So
we brought in members to say, will this
even be
valuable to you?
Is this going to simplify
in how you kind of look at your
benefits, how you can connect your provider?
So we do a lot of market research
and assessment.
And then when we deploy it, we get
a lot of this feedback,
and our teams are really obsessed about member
feedback and provider feedback.
Because we wanna make sure
that we are constantly listening to that feedback
to simplify these experiences.
Because that's the way we actually learn, and
we continue to evolve the solution
because we're in service of them. And and
we wanna make that, very easy
and simple
for our members and providers. And so this
feedbacks,
loop is actually very important for us in
how we evolve our solutions.
Last question I have for you, Radhnikar.
What do you think 2026
will bring for digital technology and AI?
Yeah.
I I think,
you know, as AI is evolving,
as we are evolving
kind of our thinking about data,
our understanding,
better about our members and providers and how
they interact with the tools and capabilities
that we're deploying,
we see tremendous amount of potential
to actually
streamline
and create
more personal,
a more connected and seamless experiences for our
members.
We also,
kind of are bullish on
simplifying the interoperability
with our providers so that we can empower
care providers
to improve health outcomes.
And finally, we really want to accelerate
a lot of the tool adoption
internally
and the capabilities internally
so that we can simplify our workflows
so that we can better serve our members
and providers. And I I'm actually very excited
about that because we've laid the foundation, and
now we believe we can accelerate
this transformation.
A 100%. It's so great to hear how
Elevent is kind of, you know, stepping on
a gas in terms of AI and leveraging
it for the member experience and then also
constantly gauging that member feedback, which is crucial.
The Randekar, this is a great conversation. You
know, we're ending now. I feel like I
could've talked to you for two hours about
this. So thanks so much, and really looking
forward to working with you again soon. Thank
you so much, Scott. It it was really
nice. Really excited about, our journey, and I'm
glad, to share that with everyone. Thanks a
lot.