AI That Pays: Lessons from Revenue Cycle — Nathan Wan, Ensemble Health

Channel: aiDotEngineer

Published at: 2025-07-24

YouTube video id: TquUsN1QsWs

Source: https://www.youtube.com/watch?v=TquUsN1QsWs

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So great to be here today. U excited to
talk to you about a little bit of the
healthare system that often gets
overlooked. It's part of the healthare
system. A system that actually continues
to grow in multiple dimensions. Over the
past couple of decades, its size, cost,
and complexity outpace many other
benchmarks. That's because right now 40%
of hospitals operate at a negative
margin.
Let me put it another way. Almost half
the hospitals in the country are losing
money. It's uh and it's not because of
the clinical costs. It's because of the
broken and manual processes around the
revenue cycle. There's delays, denials,
a lot of rework, a lot of lost revenue.
My name is Nathan. I'm the head of AI at
Ensemble Health Partners and we work
with hundreds of hospitals and health
systems in the US to manage the revenue
cycle. We're about 14,000 people and
we've been a leader on the quality side
within the industry.
uh as an end-to-end solution that means
we support the entire uh process every
stage and gives us a really unique lens
into uh all the problems and
inefficiencies that occur throughout the
entire process and also an opportunity
to stop them um before they happen.
Revenue cycle management or RCM refers
to the financial process uh of the
patient's journey uh within the
healthcare system
and it's traditionally thought of as a
series of steps uh that go that goes
from one to the next
and just a little bit about how I I got
here. Uh I started my career in tech
working at Google building operational
software for operational teams and then
working on speech recognition and
language modeling.
Back in my day which is now like over 10
years ago we were comparing language
models that took you know traditional
models with Google scale data and
compared them to deep learning models
trying to make them compete with each
other and see which one worked better.
But one of the really interesting
projects that we worked on was called
what is now called ambient. Um this is
one of the things that um where people
are trying to use the technology to
improve the
administrative burden for um for for for
doctors.
Um that's because oftent times doctors
will spend hours after they see a
patient writing, documenting and
creating notes for themselves.
Um while we weren't successful back back
then uh today there's multiple projects
and multiple uh multiple groups
launching this uh and making it
commercially viable and so it's been
really successful and really exciting to
see that change.
Then I spent a little time uh in the
world of startups in the world of
biotech. uh I changed both the scale and
the domain that I worked in and it was
very exciting. I was really excited to
work on a very strong mission a really
exciting mission where we had a a big
opportunity to make a big impact. I
started first in diagnostics. Uh this is
where I built models uh that and and
built teams to detect cancer from blood.
Um the goal was to give early insight
into whether or not a patient had cancer
or not. And we use machine learning to
look at the blood and look for
biomarkers and look across multiple data
sets and patients to identify you know
where might be the signal for cancer.
After spending some time there I and
seen the company grow from 30 people to
over 300 people. I ended up at a even
smaller therapeutic startup. uh we
worked on novel data sets looking for
unique interactions in complex
microbiome communities to try to
identify compounds that could be unique
and uh really valuable in uh drug
discovery
and that's the thing when most people
think about AI in healthcare these are
these are the things that most people
think of right there's diagnostics
there's imaging there's other ways to
improve uh documentation
and uh decision-m for clinicians and
drug discovery and these really
impactful and really important problems.
Uh, you know, I I I really enjoyed
working on them and but there are some
of the toughest and hardest to solve.
Um, but that's also because some of the
benefits are going to be massive as we
continue to see groups and organization
crack parts of it and make make headway.
Um, we're seeing a lot of change
already. But there is another part of
the system, the healthare system that's
not nearly as flashy, not doesn't nearly
have the same attention on research or
in media. Um that uh that also has a
huge measurable real impact and is also
ripe for AI disruption.
And that's the healthare the financial
side of healthcare. Uh right now we
estimate that 20% of the GDP uh is uh is
is attributed to the healthare system. A
large proportion of that is the
administration of healthcare.
Uh this is billing, insurance related
things, but it starts from like the very
beginning through to the very end of the
patient's journey uh with any healthcare
provider starting with eligibility
checks, registration, there's
documentation, medical coding, denial
management, so on and so forth.
Um often times these aren't things that
you'll see when you visit a PA doctor's
office. uh not unless you're you
encounter like a really challenging
situation or you lack coverage or um you
face some complication with with your
care. Um but this process is very
complex because it is very manual. It is
very rules driven and very inconsistent.
Um and in over the past three decades uh
this healthcare administration in
general has the number of people working
in healthcare administration has
increased 30fold. Um but in the same
time period the number of clinicians
clinic clinicians excuse me has barely
doubled. So it just goes to show how
much faster how much more complex how
much how how much how much more quickly
this area grows compared to the clinical
side of healthcare. And just another
note about terminology I'll keep using
these words patients providers and
payers. Patients I think we all can
understand those are the people who
receive care and providers are the ones
who deliver it. people like the
hospitals, the specialty offices, the
specialists, nurses and doctors that
actually conduct and provide care
medical care to the patients. And then
payers are are those who provide the
funding. So largely insurance companies,
which would be private payers, but also
other government institutions like
Medicare and Medicaid.
And just to make um and just to help
make that more concrete, right, the the
the cost and complexity of healthcare is
really related uh is really correlated,
we estimate a large amount of the the
cost associated with healthcare is
actually related to friction. And in his
case, friction means the inefficiencies
around communicating back and forth
between payers and providers and
patients. And often a lot of that
actually results in um uh in the same
situ same outcome, right? Either the
claim gets paid or it doesn't get paid.
Uh one of the things we'll talk about is
denials because that's one of the
biggest components of friction. And
that's because it's both time and money
for the provider to manage appeal and
work through that process. And then with
again very slim margins uh for these
hospital systems um any any any impact
or any change in the appeal uh denial
rate can have a huge impact
and ultimately AI has a big opportunity
to shift resources from this bureaucracy
this this friction towards hopefully
something else right something more
productive we might agree would be like
the clinical care or anything else that
we've uh we've described
so just to make it concrete for you
guys. This is an example of what a claim
might look like and and uh also how much
conversation occurs between the patient
well largely the providers and the
payers you know before during and after
a patient visit or patient encounter. In
this case this claim was denied four
separate times and appealed and uh
appealed four separate times as well. uh
the the p the provider had to send uh
documentation multiple times through
multiple interfaces and probably through
multiple different uh manual processes
and for the provider they didn't receive
payment for you know 200 days until
after the uh procedure occurred
and
and with AI happening you know across
the field you know providers aren't the
only one looking to AI to make an
improve improve their process. Pairs are
also thinking about it as well. They've
also increased their denial rates. They
they are leveraging AI as well for
increasing the volume that u they're
able to adjudicate and to identify uh
issues for denials um making this entire
process more more strenuous and and
creating a much larger backlog for all
these providers.
And the thing is that most of these
denials aren't necessarily don't
necessarily require a better appeal
system. It's not like they need a a
smarter appeal system. They just need a
way to avoid errors that cause these in
the first place. That is most of the
time they're not necessarily medical
agreements. They're just technical
errors in registration or missing data
that if we were to put them together in
the right way the first time around, we
could avoid a lot of this friction.
So how at ensemble are we hoping to be
able to solve this problem? Uh we think
because we are a end-to-end uh RCM
organization full service provider we
have an opportunity to see the
longitudinal data connect the dots
between from the very beginning of the
process to the very end of the process
and and and really make uh make a change
before the error occurs. One of the
first situations I'll talk about is is
prior off. Um this is uh this is an
issue that affects the entire industry
and prior arts occur because the prov um
payers have required providers to
um to ask for permission for certain
procedures. Um but it's really
challenging because it's often not clear
when a prior author is required. You
sometimes have to go to the payer portal
and say you know is this procedure um is
this does this procedure require prior
authorization? And even when you do,
sometimes they still might deny it
because it was incorrect or uh uh it it
just the policy had changed.
And I think this is where we really
think we have an opportunity to change
uh to to basically correct the error
before it happens because we can see
that data from you know see all the
historical data from the beginning part
where prior offs are requested um and
acquired um to the end of the process
where we see the final denials.
uh where AI can help uh not only can we
try to predict the denial, we can also
try to identify and correct the the
dial. So an example is like if we if we
see certain procedures and we know that
often another denial reason is that uh
the procedure was missing from the
original document. And so we can try to
flag that early and say if you're
looking at these procedures, you
actually might actually want this other
one. And finally, even the actual
process of acquiring prior authorization
is a big opportunity. There's a it's a
manual process. It requires
documentation from different parts of
the system to be put together by someone
and and sent off to the payers um to to
to make that uh request um for for power
authorization.
But sometimes we we were not as
successful. Uh sometimes it's still it
still may be the case that um uh denials
may still occur. Um, we can't always
avoid denials, but we're we're really
but we're excited because Genai really
has an opportunity to help us accelerate
uh and improve that process as well. Uh,
the the case study I'll talk about right
now is called clinical denials and this
occurs when the payer and the provider
disagree about what was medically
necessary um to to care for the patient.
And when when this happens, uh the
provider has uh in order for them to
appeal, they have to go through a
process where they have to build the um
build the entire appeal packet. Um
they'll need to look through the patient
record. They'll look through guidelines
or standard guidelines of care to
identify what um uh what what care
should have been provided to the
patient. They look through payer
policies to see what which would or
would not be covered. And this is all a
very timeconuming process. Some of these
EMRs or electronic medical records are
hundreds of pages long. Uh they have um
they have all types of data in them.
Text, images, labs, notes, tables.
The clinical guidelines themselves have
you know hundreds of clinical
guidelines. And for different
situations, we'll you'll need different
different um different guidelines.
And all this is done under tight
deadlines to make sure that you respond
in time to the payer after the denial.
And all this means that there's a
there's a very real and limiting factor
of, you know, how many expert clinicians
can we get um into the process to help
us build and generate these ns appeals,
excuse me.
Uh you you as you might expect, you
know, Genai can actually uh uh generate
an appeal letter for you. an
off-the-shelf one can if you prompt it
will give you some appeal letter. Um but
unfortunately that alone wasn't
sufficient. We found that when we worked
directly with our clinical experts that
uh off-the-shelf models alone wasn't
sufficient. And so we really worked hand
inand to develop uh a model and a
pipeline that allows uh the not only the
the letter to be to meet the standard
the quality standard um that we have as
an organization
uh but also to allow the clinical expert
to make the final decision on whether or
not uh the letter meets the meets the
standard of quality before it gets
submitted to the to the payer.
And this is important because there
there's also complexity around the
clinical appeal process. There's
different service lines, different uh
different clients and all that all that
gets put together in our Genai system to
make sure that we can deliver these
appeals uh more quickly and more
consistently.
We've seen already that we're uh
increasing the speed of the process, a
40% reduction in time uh sometimes even
more. Um we've also seen higher quality.
We've been able to measure quality in
terms of the overturn rate. How often
are we seeing uh uh appeals being over
denials being overturned and we have
also seen the as a result the volume
grow. But one thing I really want to
point out here is that as as part of
this operational and service team, we're
able to really measure the the ROI
directly. It's not just like a hand wavy
this is value. We're tracking it very
specifically and measuring it um uh very
concretely. And that's one of the really
exciting things about uh bringing
bringing uh bringing AI to this RCM
process.
So I know AI won't I won't propo I won't
purport to say that AI will be able to
solve all of the problems overnight.
This is an industry that's been reliant
on a lot of processes um for a long
time. You know, as I said, there's lots
of rules. They're inconsistent. It's
unstructured. Um you'll see data
scattered across a wide range of
systems. And this is one of the things
that makes it really challenging to to
bring together as a cohesive or um and
and unified process. Um Ensemble has
spent invested a long a lot of uh uh a
lot already in building up a single
consistent uh infrastructure to be able
to do this. And one of the reasons we
have been successful has been because of
the the platform that we call EIQ where
we bring together multiple formats,
multiple data formats um within within a
single platform. Um, but obviously
there's uh still great opportunity to to
be able to do that. You'll see that EMRs
have many different format types and it
will challenge any uh multimodal LLM to
to parse correctly.
We're excited because we already see AI
deliver value as you saw with the
clinopial case. Um, but also uh in the
prior authorization case, we're built
continuing to build agents for all
aspects of the revenue cycle process.
But we know automation alone isn't going
to be enough. There's complexity and
revenue cycle. Uh that you know clicking
buttons and pushing things uh faster,
pushing pieces through faster might not
be the only way to do it, right? Like
there's really an aspect of reasoning
and connectivity that that we think
about when we think about, you know, how
to take errors that occur at the end of
the process like the appeal process or
the denial process and try to fix them
upstream and early early on. And what
we're really hoping for and what we're
really excited about is being able to
not just build better tooling, but also
a smarter, more coordinated system that
allows us to reduce and uh reduce waste
in the overall revenue cycle process.
So that's why I'm really excited to be
on at Ensemble. I think we have a unique
position to lead this transformation. We
have the right data set. We've been
building uh the the right team as well
to bring all the experts from multiple
disciplines to achieve this goal and we
have the full scope of the RCM process
to not only collect the data but also
intervene and act on behalf of our
clients.
I thank you. I thank you all so much for
your attention. Uh I hope this gives you
a new way to think about AI and
healthcare. And if if you have a chance,
please find me and uh connect with me
afterwards. Thanks so much.
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