Hello, everyone. This is Erica Spicer Mason with
Becker's Healthcare. Thank you so much for tuning
in to the Becker's Healthcare podcast series.
So today, we're going to discuss AI and
the payer landscape and how to balance innovation,
ethics, and regulatory
change.
And joining me for this discussion is Nitu
Rajpal,
the CEO of Lilac Software.
Nitu, welcome to the podcast. Thank you so
much for making time for us today. Thank
you, Erica. It's really great to be here.
Oh, we're so thrilled to have you. And
before we get into our discussion, wanted to
see if you'd like to share a little
bit more about yourself, your work in health
care, your organization,
whatever feels pertinent to listeners.
Oh, thank you. So I'm Neetu Rajpal, and
I am the
CEO and cofounder of Lilac Software.
With Lilac Software, we're very focused on making
sure good data gets to all the practitioners
that are currently in health care. We're building
a fully managed
solution for data analytics,
insights, AI
for payers and providers.
Before my work at Lilac,
I was the CTO of Oscar Health. I
appreciated and loved being at Oscar Health.
I oversaw work for all of the tech
stack that's used to run the insurance company,
everything from
from claims processing to enrollment, billing, brokers, member
engagement. And going through that journey really, like,
inspired what I am doing at EyeLac now.
And and overall, my career has all been
building software. So I've been building software for
multiple decades,
and enterprise data analytics is is my passion
to get make sure the right data gets
to the right people to make good decisions
and to help them with, with agents.
Yeah. Me too. I really appreciate the background
information. And it it sounds like you have
the perfect blend of experience, you know, coming
from a a major payer organization and all
of your decades of experience in software development
and building.
Sounds like you are the perfect person to
talk to us about
AI and the payer landscape
today. So
I wanted to start us off by
just kind of acknowledging a trend that I
know I've seen and our audience has seen
in health care coverage lately, and that's the
fact that Agentic AI is really gaining momentum.
So can you provide a brief overview of
what this technology is and maybe even share
a practical example of how it's being used
right now?
Yeah. I think Adjentic AI is one of
the most exciting advancements in in in AI.
I
I know I say that in addition to
the fact that there was already chat TPT
and there's machine learning.
But just to give a bit of a
primer, machine learning in is
is colloquially
more about, like, finding the right set of
patterns and data and understanding data.
Large language models use that information
to then understand and get context and and
to forecast
build new documents or build new conversations or
build new things.
Agentic
AI is this exciting
advancement
where
Agentic AI systems can, like, really be in
the world and understand it and observe it
and learn from it,
and then reason about it and build a
plan to take action, and then go ahead
and take an action. So that all sounds
very much like like what a human AI
assistant would do, and and I think that's
a pretty good way of thinking about agentic
systems and agentic AI,
assistants
or independently
operating folks who can start doing more and
more things.
And as the assistants get more familiar with
the work that they're doing, they get more
and more independent, and then I think that's
also a good corollary.
In in terms of, like, health care world,
a a simple simple example that I can
come up with is something like, think about,
like, appeals and grievances or grievances against claims.
And it's somebody files a grievance about a
denied claim.
AI, in this case, can step in and
streamline the process of, like, working through that
grievance as in they can cross reference all
of the information that's available about the member,
about the policy and the plan, about identifying
what the discrepancy is, if there's any missing
information.
AI can gather all of this information behind
the scenes without a person being involved, And
then they can, like, understand the context and
then
generate
a personalized response for this particular
claim grievance.
And that, I think, is the most exciting
part. Then they bring a plan and an
execution plan perhaps
to to a human who can approve it,
but with all of the supporting information included,
and then go execute on that plan. And
this this, like, super dedicated problem solver
doesn't actually have a scale requirement.
A person can totally do it, and a
person will likely do it in most complicated
scenarios for a long time. But the simpler
scenarios can actually be handled by an agentic
system in this case. So that I think
is a pretty practical example, and I know
of places that are already starting to implement
this.
Fascinating. And I think you hit on an
area that certainly is kind of the center
of a lot of conversations when it comes
to efficiency in health care organizations,
whether it's on the provider or payer side.
You know, claims is is such an area
where folks are really trying to identify those
opportunities to streamline things. So
really appreciate that example. And I was actually
planning to ask you also how how payers
can leverage Genentech AI maybe to even help
address quality or care delivery challenges. Could you
shed some light on that and maybe elaborate
on any improvements you've seen in those use
cases?
Absolutely.
As of right now, like, the the STARS
program and the and the HEDIS gap closure
and just generally performing well on STARS is
very near and dear to my heart.
So I'll speak a pick an example from
there. So
most
folks in in pair organizations that I've talked
to try very, very hard to, quote, unquote,
close every gap as in identifying all of
the members that need a particular service
and help them get that service. It could
be it could be as simple as ensuring
that all the right members who should be
getting a breast cancer screening are, in fact,
getting a breast cancer screening. The way this
happens today
is is
some person generates some list, and then some
other person takes that list, and then some
other person or the same person makes a
phone call and and and then tries to
make another phone call to encourage the member
to to go schedule an appointment with a
provider
and then go to the appointment and and
and, like, have have a mammogram.
All of this is ripe for an agentic
system to do everything behind the scenes as
in identify the members that need a gap
closed,
figure out the right time and way that
that gap needs to be closed,
making a phone call to the member,
identifying that the barrier is actually lack of
scheduling, then maybe just, like, going ahead and
scheduling an appointment.
And then also maybe recognizing there are some,
like, nonmedical
determinants of,
detractors of health here. So maybe the member
can't get to the to their appointment on
time. So, like, booking an Uber as well.
So everything to do from
knowing who needs help,
identifying
how they need help, identifying the the blockers
for them to close that gap, and then
executing on all of those pieces
behind the scenes to just make it easy
for the member to actually go get their
mammogram.
What I think is, like, super exciting and
where it, like, really transforms things
is it is not necessarily that you can
do this for a group of members or
a subset of members. You can, in fact,
do this for every member because you're no
longer limited. Your scale is no longer limited
by the number of people who can make
phone calls or the amount of time a
person
you can scale horizontally
to compute.
You can have lots of agents doing lots
of things all at the same time, and
you can personalize care. You can personalize outreach.
You can personalize
the the the plan the member needs to
go close the care gap. So I'm, like,
super excited about super excited about the possibilities
here where
personalization
of health care is is that is is
available now.
Yeah. Me too. That that example
of the care gap list really hits home
for me personally. I had a past life
working as a health educator at a health
plan, and I remember manually working off of
those lists and doing the phone call outreach
and
talk about time consuming. So to your point
about being able to scale that, not just
to a small subset of members, but potentially
all members at a health plan, it's it's
a really exciting idea.
Yeah. I'm very excited for our future here.
Yeah.
Yeah. And
so a follow-up question for you too. So
it sounds like how health plans are are
certainly
experimenting with AI, piloting it, and even using
it in these ways as you've mentioned.
And as they're as they're innovating here, how
can they also ensure that these AI tools
are ethical and unbiased and also patient focused,
you know, especially when we're talking about Medicare
or Medicare Advantage population?
Yeah. Really, really excellent question. We we all
know that your AI models are only going
to be as good as the data that
they've been used to train get trained on,
and any bias in that data will come
through in the models themselves. So I think
the very least, it it needs to it
needs to be monitored
as to which model you're using to implement
your solution and maybe trying a few different
ones and and having lots of spot checks
in the middle.
At least for a while until these systems,
like, you we can we can eliminate,
known biases from these models. Or
so I think the the mitigating bias
based on the data that the models have
been trained on and mitigating
that risk by using multiple models is is
is the first one.
Secondly, I I would encourage that, like, when
we try things, we actually keep the member
at the center of all of it and,
ensure that, like, the real world needs are
met.
So we're not we're not building tech ahead
of its ability to be used in productive
ways.
And that may not always mean just everything
all tech. It may mean a blended version
of
tech and process. And and one of the
last things I'd say here is
picking
the amount of automation you want here.
I think there
tends to be a lot
of intention to
automate everything.
I have been doing tech for a very,
very long time, and I think
the first
seventy percent is usually the most exciting and
the fastest piece to get to. The next
10 is a little bit harder, and then
the last 20 is really, really hard.
And preciseness
and and accuracy
in health care specifically matters a lot. So
I actually think it's perfectly fine to go
with, like, aim for 80% as long as
the accuracy and and completeness are really high
on the required list of things.
And given how, like, stretched thin almost everybody
in a health plan is, I think 80%
is pretty awesome.
So mitigating bias in the data that's used
by using multiple models,
embedding
patient
or member at the center of whatever value
that we're bringing. And finally, like,
I think it's it's worth worth the question
to ask if %
is really should be the goal. Maybe it
should just be 80% for for a while
until we're sure.
I really appreciate how practical those tips are.
Me too. So thank you for that. And
I think that 80%
metric that you mentioned, it's definitely something interesting
for leaders to keep in mind as they
move forward with innovation.
And similarly,
you know, we're talking about ethics here with
AI and technology implementation.
I I know that we're also seeing in
the news some headlines about
AI regulations
kind of going away or at least being
much less restrictive,
and that's, you know, rapidly changing and evolving
in 2025.
So how do you foresee the role of
AI in health care changing, especially shaping the
future of value based care and Medicare programs?
Yeah. I hope
that we will build systems that are built
on top of systems that are currently subject
to HIPAA and
security and compliance and privacy
and
security
rules around there. And I think a really
good way would be to assume your own
AI system is also subject to the same
rules. So data privacy, especially when it comes
to health care, seems really, really valuable data
to protect
even if the regulations
lag in time. Because I think where there
is a there is a
really
different
regulations and the advancements in AI are moving
at. And and I think
the people's privacy and people's privacy on their
health care data is is paramount, and I
and I really encourage the
plans using AI to impose the same requirement
on themselves and while the regulation is still
being worked out.
Yeah.
I I really appreciate how you put that
need to, you know, how there's kind of
this discrepancy in the velocity of the speed
of innovation and and the regulations
behind it. So
really appreciate all of these insights that you've
shared with me and our listeners today. Is
there anything else that you wanted to highlight
or maybe anything we didn't touch on but
you wanna mention?
Yeah. Thank you for the opportunity. I,
actually wanna just talk. Like, I've I am
a technologist, and I've been a technologist pretty
much my whole career.
One of the
things that I find the most fascinating about
doing things in health care is it is
a very integrated environment,
I think.
So
being able
to bring technology
to a plan, but fully recognizing
the current state of the world is going
to be really helpful. It's a it's a
bit of a a weird way to say
it, but I think bringing everybody along is
actually going to be the fastest way to
get this technology adopted. It's like you how
you need trust,
you need adoption,
and you need tech that works. And you
only know if your agentic system works and
is actually solving the problem of the workflow
and the practitioners
if those practitioners are using it and are
telling you what is working and what is
not working, which requires a lot of trust
and it requires a lot of adoption. So
just as much investment in tech, I think,
also needs investment in just change management with
the organization.
I'm very excited about our future here. I
I think there is real opportunity here to
personalize care and outreach and and help lots
of people in the ways they like to
be helped.
Yeah. No.
I can hear the excitement in your voice,
Me too, and it's always it's a pleasure
to talk to people who are really passionate
about the work that they're doing. And and,
I really appreciate also those last three pillars
you shared with our listeners. You know, what
will it take looking ahead? It'll take trust,
adoption, and tech that works.
Such a fantastic discussion. Thank you so much
again, Mitu, for making the time for Becker's
today.
Thank you very much, and thank you very
much for the opportunity. Really excited to be
here.
And we also wanna thank our podcast sponsor
for today, Lilac Software.
You can tune into more podcasts from Becker's
Healthcare by visiting our podcast page at beckershospitalreview.com.