Hi, everyone. This is Lucas Voss with Becker's
Healthcare. Thanks so much for tuning in to
the Becker's Healthcare podcast series. It's great to
have you. Today, we're talking about how health
plans can build scalable high performance infrastructure to
support
growing
complexity, enable AI, and stay ahead in the
certainly rapidly evolving landscape that we're all facing
right now. And joining me for today's discussion,
very excited to have him is Rob Duffy.
He's the chief technology officer at HealthEdge. Rob,
thanks so much for being here today. It's
great to have you. Yeah. Welcome, to Ben's
point. Thank you very much. I really, really
appreciate the opportunity to talk to you today.
Yeah. Absolutely. I do wanna start with introductions
here really quickly before we hop into the
meat of this conversation.
If you just wanna briefly introduce yourself, who
you are, what you do at HealthEdge,
and, what's going on with you.
Amazing. So I'm Rob Duffy. I'm the chief
technology officer at HealthEdge.
My role is really to execute on it
on the product road map in the most
efficient and the high velocity way. And what
we do is we, you know, as an
engineering team, we really think about how we
can leverage AI, how we can leverage cloud
native infrastructure, how we can leverage all the
things that are out there and available to
us to really scale our operations and meet
our customers
through their scaling journey and make sure that
they're getting the most leverage out of, the
underlying technology and then also AI and anything
that we build, from an AI perspective. So
HealthEdge, for those those of you that aren't
familiar with us, we are a, initially, we
were a core admin platform, that does claims
processing. We're a company called HealthEdge at the
time.
We acquired three other companies, GuidingCare, utilization management,
and care management platform,
Source, a,
payment integrity platform,
and then Wellframe, a member engagement platform.
And then recently, we merged with Healthproof,
who are a business process as a service,
organization. And together, we have become HealthEdge's combined
entity.
And what that enables us to do is
provide claims processing as a commodity to our
customers. And what we can do because of
our scale and because of our technology and
because of our labor arbitrage,
we can actually guarantee
outcomes, guarantee the same claims processing outcomes
for a a discounted rate,
than the the ability for our our plans
to actually process on their own. So, you
know, on a per member per month basis,
we are able to
guarantee the same outcomes from claims processing to
our customers,
and do that at a discount.
And the large portion of that, obviously, which
we'll talk about in a little bit here
is not only scale, but also,
our ability to leverage AI in order to
make bring more and more efficiency to the
claims processing and claims operations. Yeah. I was
gonna say we'll we'll walk through the way
this works here in a little bit detail,
but you mentioned something very important, which is
high velocity.
It's it's a high velocity market. Certainly, when
we talk about, again, that rapidly evolving landscape,
I'd love to touch on this a little
bit more because, again, we know that health
plans are dealing with a lot of pressure,
right, growing membership, complex care models, and then
you add regulatory demands onto that, I I
think it's really crucial.
From where you're sitting right now,
what does it actually look like when a
health plan's claims processing infrastructure
starts
to buckle under all of this weight that
we're experiencing right now? I mean, you look
at at any health plan right now, you
know, medical loss ratio shooting through the roof
either through, you know,
very expensive drugs, more utilization,
you know, people just getting, an aging population
or even people getting smarter about their health
care. I mean, the the impact of AI
and chat GPT has meant that people are
actually now going and sort of self diagnosing
and seeing doctors where they where they wouldn't
have seen before. So utilization is increasing. You
know, cost of drugs is increasing. And then
you have this interesting dynamic where on the
revenue cycle management side of things,
providers are using AI to code,
their claims more effectively, and that's causing an
increase in the in the the medical loss
ratio. So you you think about that bucket
of, you know, cost.
There's really increasing
in in terms of, the the MLR for
our for our payers. And And then the
other bucket of cost is the administrative loss
ratio, which is like, how much does it
just cost to own and operate the plan,
process claims, do everything?
And
that bucket, I think, is just absolutely ripe
for for disruption from AI tools, from automation,
from scalable modern platforms.
And that's really what we wanna bring to
health care plans is a is a platform
for scalability and a platform for innovation. And
one where we can innovate on behalf of
our customers and invent things using,
all of the tools that are available us
today to us today from an automation perspective,
from an AI perspective, and just continually drive
that ALR cost down so that our our
plans, our customers
can, you know, redivert some of those savings
back to, you know, better health care outcomes,
better customer, better, you know, patient experience,
and really maintain their business and maintain their
margins in the face of increasing it a
lot.
You mentioned innovation, which is certainly really crucial
work from where you're sitting. You're innovating every
single day. That's part of your job.
You at HealthEdge and you certainly your team
as well, you've reached an incredible milestone specifically
with AWS, which most of our listeners are
obviously familiar with, a big player in the
space,
in a single instance handling the equivalent of
40,000,000 members. And, again, you cut that latency
by more than half at that same time
while also going through that process. Can you
just walk us through what this meant for
you?
And, again, because you improved scale and speed
at the same time,
which is really, really hard to do.
Yeah. And, you know, we made a decision
to go all in with AWS,
about eighteen months ago now. We said, hey.
There's no point I was pursuing
a multi cloud strategy. It's just diluting not
only our purchasing power, but also the partnership
opportunity we have with our cloud provider. And
part of that was an acknowledgment that
going all in with a with a single
provider would also enable us to get really,
really
familiar with everything that would that provide that,
cloud provider was able to offer us and
really develop deep expertise and partnership with them
in order to bring these outcomes to our
customers. And we very early on realized that,
you know, we've been pushing the boundaries of
scalability
using a physical hardware
mindset. Right? And then we can we were
previously in a private cloud and a data
center virtualized there. And whenever we thought about
scalability, it was like, you know, let's buy
some more machines, long lead time, larger machines,
whatever. Right? We we we would scale to
the point where we could afford the infrastructure
and and, you know, it's expensive infrastructure when
it's in in a data center. So we
would so we're thinking about it in a
in a very non high performance compute way
and then more of a sort of, like,
horizontally scaling traditional data center way. When we
moved to the cloud, all of a sudden
we were able to take advantage of some
new, you know, AWS silicon, the Graviton chips,
the high performance compute instances, and we're able
to dynamically scale them to meet the demands
of our application and drive where we were
able to scale to way beyond where we
had previously been able to. And I think
that's just a
a a a a great indication of how
deeply partnered we are with AWS and then
also the the ability for that partnership to
bring true scalability and and true outcomes for
us from a technology perspective. And you mentioned
the latency there. I mean, really,
you know, AWS
is a a
has deep expertise
in how to get their infrastructure to talk
to each other and how to, you know,
continuously fight against the speed of light
to to to bring latency down between their
their applications and between their services. And that
really shows when you look at things like
the network IO, the latency, the disk.
You know, we had our our database administered
getting a bit technical, but we had our
database administered
administrator,
incredible lady called Manu.
And she was, like, having a little bit
concerned about the IO. And then after we
routed, the the ability to write information to
the disk, and then after we moved to
the cloud, she was like, oh, wow. We're
gonna be fine. This is amazing. It's it's
much better than we're able to achieve. So
I I think, you know, maintaining your own
infrastructure,
you can do well. I think moving to
a a cloud hyperscaler and really leaning into
one cloud hyperscaler, you can do amazing and
great things with them because they will also
invest in you, and you can invest in
that partnership together.
Applying this to
the use case in and of itself, just
as a quick follow-up here. Right?
Why does the single instance that you just
described make such a difference
from a day to day operations perspective? What
difference does it make for day to day
operations?
So traditionally,
for a health plan, you would have different
lines of businesses, maybe different regions on multiple
different core admin platforms.
So you have the the
the same system, sometimes a different system, sometimes
the same system, but a different version of
the same system
running in multiple places across across the organization.
And for us, when we're trying to bring
down the cost of operations and we're trying
to commoditize claims processing and really sort of
continue to lower the cost of operations,
having multiple different systems and multiple different tools
and multiple different instances
really means that you can't get economies of
scale or leverage from the investment that you
have in that technology. And the same is
true for for any plan. You know, if
you look at a plan that, has been
around for any length of time, there's gonna
be many, many different systems, and they're gonna
be thinking about this consolidation into one.
When you can consolidate
all these different things into one, it brings
a tremendous amount of efficiency,
and it brings a, you know, it it
brings lower cost in the long run because
you're not maintaining multiple different systems. You're not
maintaining multiple different workforces that have to integrate
with these different systems. You can get a
single view of your data. You can start
to talk about events across these, diff, you
know, the different customer life cycles that are
triggering things on your data platform. You don't
have to worry about integrations. Like, each of
these systems has to be integrated with up
to 120
different inside internal tools. Right? So now all
of a sudden, if you have 10 systems,
you have 1,200
integrations that you have to maintain. You're shuffling
data everywhere from different things. So it's really
a a a grand simplification
of the internal architecture
of the health plan
and the and the claims,
operating system. And that enables you to do
much more interesting things with AI, with data,
with your visibility into, you know, how your
business is performing with the latest BI tools
that you can query with natural language. And
it removes this sort of diaspora of systems
that are very hard to integrate and and
gather intelligence out of and and provides you
really with an edge to continuously optimize your
business and continuously,
you know, reduce the cost of outcomes.
A lot of leaders are pointing to
that piece specifically, the integration
part and the infrastructure piece as a crucial
enabler for health care as we move into
2027
and beyond. If that's not the case,
then organizations are going to have an issue
moving forward. And I wanna stick with that
theme a little bit because, again, I feel
like especially with AI, and you've talked about
the AI native enterprise, so to speak. Right?
How does that infrastructure work then lay the
groundwork
for organizations
to have
AI driven automation in place?
And, again, just touching back on on what
we've just talked about, what's the risk of
Healthfence that just try to bolt on, and
they're not establishing that infrastructure that we've just
talked about.
Yeah. There's a lot of really great demos
out there for, you know, AI startups that
will bolt something on top of the system
of record. You know? And they'll they'll get
to a really good, yeah, you know, demo
maybe in a couple of weeks, and they'll
show you it. And I'll be like, this
is proof concept, and you'll get really excited
about the fact that that that this could
actually scale out across, you know, the entire
the the entire set of use cases that
you want to deploy this tool in. And
the reality is
it that it's not gonna it's not gonna
scale out because, ultimately, you have the system
of record. And in order for you to
expand all the use cases, you would have
to, you know, take the data that's in
the system of record and march out. And
it's like it it becomes it becomes a
nightmare anything beyond the first one or two
use cases. And, you know, we believe that
by deeply integrating AI into our platform and
by providing a platform that is
equipping AI tooling on top of the system
record and inside the system record, you can
get much
more efficiency out of that, and you can
get a much higher return on your investment
from AI. And you can do it a
lot faster than if you're just still marshaling
all these different tools and you're trying to,
you know, add bolt ons on top of
the system of record. So our belief is
that we can move incredibly quickly to your
point earlier on about velocity
in deploying AI capabilities
to, you know, reduce the amount of dependence
we have on on humans and human effort
clicking through UIs and and moving information left
and right, then we can very, very rapidly
bring a truly integrated
AI toolkit that is deeply connected to the
system of record. And again, that that single,
platform and single instance enables us to do
that. We can build, you know, these these
tools that enable AIs to consume information, enable
AI agents to act within the within the
system of record, and work in in concert
with each other and with all of the
other systems in the ecosystem
rather than being something that's sort of bolted
on from the outside.
Realistically,
how much time do we have then to
rethink these legacy payer platforms before
we hit that hard ceiling. Right? And from
your perspective, from the conversations you were having
with health system leaders across the country,
what are the questions
that leaders need to ask
right now that they need to ask their
technology partners to avoid running into some of
the issues that we've highlighted?
You know, I might I tie it back
to the the performance of the company. Right?
Like, when you think about and if everything
that we do in technology,
it's really
about reducing the cost of operations. Right? Like,
it's how we continually driving down the operating
costs. And if I was a, you know,
a business leader at a plan,
the real question I would be asking is,
are we going to be able to accelerate
the savings that we want to realize
in our administrative
loss ratio
fast enough
to keep up with the competition?
And are we gonna be able to invest
enough in it
to be able to deliver true outcomes and
true value,
or should we partner with someone who's gonna
do that and who's gonna guarantee that that
cost is gonna come back and who's gonna
continue to invest with the best and brightest
and get, you know, huge leverage out of
partnerships with AWS much broader than individual plans
would be able to and and hitch onto
that wagon. And I think, like, that that's
the way I'd be thinking about it. So
can I internally develop the skill set and
rapidly ship and, you know, put all the
pieces in place
to guarantee that my business is gonna stay
healthy and that my administrative spot for ratio
is gonna continue to decline,
or
why not partner with someone who's gonna guarantee
that? And then I can focus on things
like member engagement,
health care outcomes,
marketing,
you know, taking care of the population.
Because if you do that, then you're not
going to run out of runway in 2026
and 2027.
We're just very crucial, especially in health care.
Rob, it's so great to have you on.
I just wanna give the floor to you
too. Is there anything else that you wanna
touch on? I know we've we've hit on
a lot of numbers here.
Again, I feel like this is about confidence
for health plans, but just wanna hear any
final thoughts that you have for our listeners.
No. I I think, you know, it's always
a pleasure to come on the on the
Beckett's podcast and and talk about the the
future and the future of AI. And it's
a very exciting but also scary time. You
know, there's excitement in the possibility,
but there is the fear of not moving
fast enough. And, you know, what we do
is help our plans and help our customers
feel like they're moving fast enough. And I
think that brings an enormous amount of excitement
and energy to to our customers and ourselves.
Velocity
is the word of the day for us
today. Rob, so great to have you. Thanks
so much for your time. Thank you. Take
care.
We also want to thank our podcast sponsor,
HealthEdge. You can tune in to more podcasts
from Becker's Healthcare by visiting our podcast page
at beckershospitalreview.com.