Hello, everyone. And thank you for joining
today's webinar
Interco Auto Review,
Optimizing um with automation
and A I.
My name is Heather Volmer with Optum and
I will be your host today.
Before we begin, please note the
following housekeeping items
at the bottom of your audience console
are multiple application widgets. You
can use to customize your viewing experience.
If you have any questions during the webcast,
you can click on the Q and A widget at the bottom
of your screen to submit a question.
If you experience any technical difficulty,
please click on the help widget. It covers
common technical issues.
You can expand your slide area by
clicking on the maximize icon on
the top right of the slide window
or by dragging the bottom right
corner of the slide window.
There is a survey widget which you can use
at the end of the webcast to provide us
with feedback on today's presentation.
Additionally, this presentation uses streaming
audio. You may listen to the audio
through your computer speakers or headphones
to ensure the best possible system
performance. Please be sure to shut
down any VPN connections
and connect directly to the internet
and with that. I will hand it over to
Laura mcintyre.
Thank you, Heather
and good afternoon everyone.
My name is Laura mcintyre. I
lead our strate strategic client
teams for inter qual. Um
I've worked for change health care for many
years, um which is now part of Optum.
I am a registered nurse with a Bachelor of Science
degree in nursing and a master's in health
care administration and I've been in the industry
for more years than I'd like to count.
Um But it's a pleasure to be here today
to share with you how Interpol auto
review can be used to help optimize
your um program. And to share a little
bit about how some of our customers
who are using auto review have realized
success in their um transformation
journey with this capability. So I'm
excited to be here.
So to start off, I wanted to share a little
bit about some of what we're all seeing and feeling
in the industry because regardless
of what type of organization you work
for, we're all facing challenging
times across the health care industry
globally, right? There's a global
nursing shortage. Um As
a result of the pandemic, we've seen
this thing that we're dubbing the great resignation
across health care, which really
further compounds this problem that's been
building for years. And honestly,
there's really no clear path to how,
how we're gonna really reduce that nursing
um shortage, how we're gonna really reduce
that gap in in nursing resources
to compound things. We've got an aging
population that brings with
it more complex care needs
and those numbers are just continuing to grow.
We also know that access to behavior,
health and mental health services is
incredibly challenging right now.
And compared to other industries,
we have um really
outdated technologies and a
tremendous amount of inefficiency in our
systems.
Our processes are all highly manual,
they're tedious, they're fraught with error
and authorizations are really
time consuming
to add on to that. The back and forth
between hospitals and and health
plans is incredibly burdensome
for anyone that's involved in that process.
And as we all know, costs are still continuing
to rise. So since the onset of COVID,
we're actually seeing denial rates rising,
they've gone up about 11% nationally.
And we know that the entire appeals process
is a challenge, a challenge that's resource
intensive and burden burdensome.
We also are seeing that success rates
of appeals are declining. So increasing
the lack of resources to prevent and
work those denials, which means we need to be
diligent
about getting the right level of care from
the start.
So we're really at a tipping point more
now than ever. And we're really needing
to look at what opportunities and capabilities
exist out there to help reduce the burden,
to help offset the impact of the nursing
shortage, to help ensure that we're
doing things accurately and appropriately
from the start. And that we're doing the best
we can to manage costs um
in the most efficient way possible.
So we talk a little bit about,
you know, how we can think about
automation. It's not all doom and gloom. Right.
We, we've got a lot that's going on out
there in the market. If you've been thinking about
how to transform your UN program.
Um There's a lot of things to be thinking
about and a lot of capabilities to look at
out there um at change healthcare.
Now, often we've been looking at technology
to help drive efficiencies
in the process as the workflow processes
of today for more than a decade. Um
It's incredibly hard complex
work and to be blunt, there's no
silver bullet out there, there's no silver
bullet solution that's complete or perfect
um capabilities like um artificial
intelligence and robotic process automation
are evolving. Uh We know
that the industry standards think fire,
you hear, you hear fire anywhere, any
in any article or any uh podcast
you're listening to related to health care, industry
standards like that are also continuing
to evolve. So we're not quite there yet
in terms of having an end to end
all encompassing perfect solution,
but we don't need to wait until technology
is perfect. There's still a tremendous amount
of value to be gained by
taking advantage of capabilities that are
in the market today. Transform in
your program with these automation capabilities
really can help reduce administrative
burden we know that they can help
drive accuracy and also
foster collaboration uh between
payers and providers really trying to build
trust because we all know that's lacking
today. Um So anything
we can do
to employ machines to
do what they're good at, can free
up reach sources, those precious clinical resources
to do what they're good at. Right.
So with staffing challenges that I think
we all have today, we can really
take some of those routine tasks
or basic tasks that are high volume
out of the equation, freeing up
clinical staff and allowing them to focus
on more complex patients and thus being
able to get to a state where we
can actually begin to manage
by exception.
So I think it's a, it's a place we all wanna be.
I'm gonna talk a lot today about the Interpol auto review
capability. I'll jump into some case
scenarios and do a couple of demonstrations
for you, but just to talk a little bit about
it. Um But before we jump
in and start, um at Interpol, we've
had a team of clinical consultants who have done thousands
of audits chart audits, case audits to
really help our customers opti optimize
the use of um Interpol
in our in our capability solutions. And
in that audit process, we have found
somewhere around one out of three cases,
have some sort of error or some sort
of inaccuracy or some
sort of um process that
they didn't package up the case in a
way that made clinical sense to a health plan
and therefore getting denied or, or they're challenged
with it. So whether it's the right subset
being selected, whether it's driving
to the right level of care or
whether it's finding criteria
that's validated by data
that exists in the E hr it could be
any of that.
And we know that auto review Interpol auto
review can help solve for a lot of that.
It really can drive to accuracy because
we're populating a medical necessity review
with data from the
source. The source of truth that E hr
and we're automatically creating that
ro review without having a human
even intervening it. So that
drives um trust and
sensibility
little bit about inter Q auto review and just
give you a little background if you're not familiar with
it. This is a capability, it's a cloud
based solution that automates
the medical necessity reviews
at the point of admission decision
using real time data from your
E hr
this robotic process automation
capability direct directly integrates
into your workflow which is critically
important. And I'll talk a little bit more about that as we
move forward.
But with the admission order serving
as the trigger to begin the automated
process, the process kicks
off when that um admission order
hits the record, it creates
a medical necessity review. It
populates that medical necessity review
with the data in the E hr
and completes that review. So it extracts
that data structured data and
unstructured data from the E
hr and transmits that review
with the embedded data back into
your um workflow.
OK. This is where your staff can then
access that review
for the ones that are fully completed. They
can validate that that's appropriate
and correct and send it off to the the
health plan the way they normally would
or if it's only partially completed,
they can edit it or finish the review
if you will. And it will also
provide a notification back to the E hr
So really exciting to think
about the possibilities here and to think about
the cases that we maybe can take off
the workload of the care manager today,
as I mentioned, it integrates directly into
the workflow and from a workflow standpoint,
just to give you a sense for where inter qual
auto review comes into play, you
may have a patient that presents to the er
or the EV uh they're triaged
and there's some sort of clinical assessment process
that occurs. And once the clinician
or physician makes a determination
that the patient needs to be admitted to the hospital,
regardless of whether or not it's observation
status or an inpatient status,
the auto review capability is going to trigger
and that auto review will trigger
and continue to run for the 1st
24 hours. It'll collect
data and it will populate that review
for the 1st 24 hours
um automating that um episode
day one review for you.
So really saving a lot of time
for the care manager and in trying to
gather that information and
do that work from the start.
So um to recap,
it's integrated the workflow, it automates
the review by triggering from the point
of admission order. It continues
to collect data for that case for the 1st
24 hours and populates the
criteria, the inter quality criteria,
not only with the checkmark indicating that
that criteria point is met or not
met, but it also embeds
the data, the EMR data and
its source date and time stamps
and brings it right into your um system
under the covers. What we've done is tag
the words within Interpol to
standard nomenclature databases.
And we have a rigorous process for mapping
the data to make it machine readable.
And we perform a technical and
clinical validation process to ensure
accuracy
and auto review is gonna select the appropriate
subset based on the admission diagnosis.
It's going to select the appropriate level
of care and the corresponding
criteria point based on that
patient severity, the interventions
that have been delivered and it's gonna
be defensible and clear because it's gonna
have that actual data coming from the
record embedded as that source
of truth
today. This cap
capability is available
in the market. We are able
to automate over 80% of
condition um focused
reviews for the um admission
day. So that first episode day,
while we're not yet able to auto
populate all data points, we are
able to fully complete about 15%
of reviews to a level of care.
And for those that were not able to fully complete,
we can populate about 70
75% of the criteria needed
to complete that review. So there's
a tremendous amount of value in those that
we can partially complete as well. Um
I don't wanna discount those because I think that piece
is incredibly important. So for example,
if you're doing a medical necessity review,
and there are four criteria that you need
to collect four data points that you need
to collect to complete that review,
we can capture and populate three
of those. So if we're not able to fully get
all of them, we can get at least at least three
of those four. So that means the
user simply needs to open up that automated
review, find the single criterion
we were not able to collect or not able
to verify or interpret
and they can simply check that record to see
if that criterion can be manually completed.
So the amount of time savings
can be substantial, there's always
gonna be some criteria that are challenging
uh to make machine readable such
as um things like the word greater
than baseline. Um These are things
that are gonna require a clinical interpretation.
They're a little bit harder to get at a little
bit more challenging for a machine to
read and accurately interpret
it. Because if we can't ensure accuracy
is there, we're not gonna have that auto
populate. We're gonna still rely on uh care
managers or utilization managers to go in and verify
those criteria points, but lots
of value still to be had, whether it's fully
met to a level of care or even just
partially met to that level of care.
And because auto review is a cloud based technology,
we also include along with that a really
robust reporting platform.
Um This is where uh you can monitor
how many auto reviews are generated, you can
track user adoption um
as well as productivity measures such
as the number of reviews that users are completing.
Um our reporting platform
has interactive drill downs.
It really allows you to focus on the data
that's pertinent to whatever issue
is at hand. So we have our level
data that allows you to look at
um um behaviors on a case by
case level. Um We have performance
visualizations that allow you at a glance
information. So you can see how the auto
review capability is performing.
We have productivity reports
by user by facility
by subset lot lots of ways
that you can drill down and
figure out, you know how things are going
or where you might have opportunities to improve.
We even have observation versus inpatient
data along with estimated roys.
Um And many, many many more things
um our platform is also somewhat customizable.
So if we don't have a report that you're looking for
information on, we can work with you to figure
that out.
But our ru customers uh today
are using these reports really
to advantage, trying to their advantage, trying
to, you know, keep their finger on the pulse
of what's happening across their department
and improving the results by taking
action based on them, whether it's you
individual user education, whether
it's process changes or process
improvements or maybe even system
updates. So I'll talk a little bit more about
that when we get into the results.
Ok. So let's jump in. Um
Before I jump into the demonstration, I let me walk
through and set the stage with a patient case
scenario. So I'll take you through
this and then I'm gonna, I'm gonna move over into
our, our, our application to be able to show
you what on review would look like.
Um So in this case scenario, we
have an 89 year old patient
that presents to the Ed with a three
day history of shortness of breath,
cough, weakness
and fever that started that morning.
Um In the Ed, he had
02 sacs that ranged from
85 to 87% on
room air. Uh looks like he was
placed on three liters of nasal cannula.
And then his 02 sacs came up to somewhere
around 93 to 97.
He had orders to be admitted for community
acquired pneumonia.
Um He started on antibiotics.
He got some IV fluids, he
got it. He had a chest X ray that showed
right lower lobe infiltrate and
had a bunch of uh standard
blood work urinalysis, cultures,
things of that nature.
You can see in his vitals that his blood pressure
was low on arrival. His temperature
was low grade.
He had a couple of abnormal um lab
values there. B un elevated
at 42 sodium was
1 32. His U A was negative
COVID was negative definitely has
a white count that's elevated and and
those blood cultures are still pending.
So let me move over. I'm gonna
uh move over to
the application,
share that screen with you.
Now when we integrate
inter qual auto review, we
integrate it into your
um workflow application
and and run against the E hr
so we don't have access to that. So what we
here have here is our uh
acne care manager fig tool
that we've created to be able to demonstrate and
show you the capability. So
when you, when you have auto review integrated
and you go in and you access
the case and you click on it just like
I did, it's gonna bring you right into that medical
necessity review and you can see that
the medical necessity review has already
been completed for me. I don't have
to actually do anything with this case
other than take a peek and say I'm gonna send
it off to the health plan
and there's a couple of things I'll point out to you,
you can see that the acute level of care has
been met
and you can see we have some gray text to the
left of it here with some arrows. E
hr updates complete. This
means that the auto review capability
has run for 24 hours or
more. Once it runs for 24
hours or more, you'll see that it's complete.
And if it was still running, uh
meaning it was less than 24 hours,
you would see that circle, these
arrows would be turning in a circle
and it would say E hr updates are,
are being gathered, right? So it's continuously
gathering data. So as additional
data comes into the E hr maybe it's
imaging results or lab tests
or, or other orders, it's
gonna continue to collect that information
until the 24 hour
I don't mark,
but initial of care was met and somebody
decided to complete the review and that's gonna stop
the auto review from running. And in this
case, acute level of care has been
met. When I click on acute
level of care and expand,
I can see that it says pneumonia confirmed
by imaging and it's a both rule.
And when I expand that I'll see the findings
and the intervention. So I'm just gonna expand
a little bit more so I can show you here,
what we're looking at for the obs level
of care.
And if you look at pneumonia confirmed by imaging,
you're gonna see a file folder next to it with
some um little dots in it that's
called an ellipsis. You'll also see
other uh criteria points have
folders next to it with a plus sign
that that is a visual cue or
an indicator to let you know that there is e
hr data that has been collected
relative to that criteria point.
And if I were to click on this one next
to pneumonia confirmed by imaging, it's gonna
pull up for me the imaging results.
So this is the imaging test, imaging
narrative. I can see that the chest CT
angiogram was done. I have
the ability to click on these little plus signs
next to it and you're gonna be able to see
the uh the narrative summary.
You're gonna see some text that might be highlighted
that the auto review capability is focused
on. In this case, it's saying bilateral
multifocal ground glass opacities
and nodules likely reflecting
multifocal pneumonia. If
there's multiple results, you're gonna see multiple
results in there. And so it
is confirming that there is likely
a pneumonia by imaging.
So that one was checked, you're gonna see
that some criteria are outlined
but they are not filled in. Uh That
means that the auto review capability found
data, but it did not, it either
did not support the criterion
or we were unable to make a
full interpretation and the user
needs to come in and take a look at that.
I can take a look at these two criterion
and see what's happening in those folders
if I want to. But I don't even
have to do that for this case because
this patient is meeting at the acute level
of care because they have a pneumonia confirmed by imaging.
They are meeting the curve 65
criterion and they are meeting the imaging
component. Um Sorry, the intervention
component, excuse me,
if I were to look at the curve 65 criteria,
we can see that the system auto selected
four components.
There was no confusion that we mentioned
in our clinical scenario,
but we did mention that he had a B UN of
42. So that meets this criterion.
And if I were to click on that file folder,
it's going to tell me uh what
the code name and description are.
It'll tell me what the recorded values
are. And if there's multiple values
those would be listed in here. Um
This is just a demonstration. So the numbers
might not be accurate here. We this is,
you know, just sort of pulled together for demonstration
purposes, but it will show you all
of the results for A B
UN. It would show you all of the
results for the respiratory rate. So any
recorded values date and time stamp
for what this individual's respiratory
rate was um you know what the value
was where it was found in the record
and the timing of it.
So we know that he was um tit
it, we know that the blood pressure was on
the low side and we know that this individual
was elderly. So from the facts
of the case, uh looking at what was found
in the E hr this is exactly how it
would work. It would auto select the criteria
points and we know that this patient was
also receiving anti-infective. And again,
we could pull this up, we could see that he's on
set Axon 1 g, um,
and 50 mL piggyback. Um
And how many times it was administered
at the time the data was collected, date and time
stamp. So it's gonna pull all the information,
whether it comes from the MA R, whether
it comes from vital signs, whether it's a diagnosis.
Um It's really gonna pull all the data that it can
to be able to satisfy that review.
So that's an example of a case
that's fully met. And the user would simply
take a look at this and say, yep, this all seems great.
I'm done. I don't need to do anything. I don't
need to hunt in the E hr I don't need to find
any data points to pull together.
Um For this review, I'm free to send
this off and, and move along to my next case.
So I'm gonna close this one.
I'm gonna come back into our, our
um homemade um application
here and I'm gonna open up another case that
was only partially met. So I wanna
show you what it would look like if the review
was only partially met.
Um In this case, you'll see it says partial.
So it did not fully meet the level of care
and I can open up any of these levels
and see what data it was able to
gather. So if the request is for the patient
to be inpatient, we'll start here
again and again, it's
got pneumonia confirmed by imaging. It
has some of the finding criteria
and it has that intervention for anti-infective.
So it did meet for the intervention,
it did meet for the uh uh curb
65 criterion. And
then there's a couple of other things that maybe couldn't
be confirmed.
We can look at the imaging study for pneumonia
and we can see that it is able to
pull some of that data and be able to read
what that imaging narrative is. And we
can see that it does say right, lower lobe pneumonia
infiltrate as with underlying pneumonia.
So it's telling us that it is believing
that this is pneumonia.
And for this case example, it wasn't
able to fully read that or read it
with enough accuracy and confidence to be
able to select criteria point. But
as the user, I can go ahead and select
that and say yes, I want
to select that criteria point because I am
verifying that that is accurate. And it is true
based on that imaging study. And I don't
even have to go out to the record to find that.
So it's, it's, it's able to pull some of
that same thing with this one. Here. You can see
for two lobes, there is a file folder
and I can pull that open and take a look
at what it says. And it doesn't say that
there's two lobes, it says it's a right lower lobe.
So this criteria point is not met.
Um But it is providing me that E
hr data in there to go along with
my case and help me paint that picture.
So that's how it would work. If it was a
partially met case, I would come in,
I would look at the case. I would see what was
met, see where we maybe weren't able
to find criteria or
maybe where we weren't able to satisfy
it enough that we could check that criteria point
and I can finish the review myself.
But I don't have to go hunt for the anti-infective.
I didn't have to go hunt for the curve 65.
I didn't have to go hunt for the imaging narrative.
It's all been pulled in here for me and I
just need to verify it and complete it on my
own.
So fairly straightforward, fairly simple
um that those are just two examples
of how the auto review capability works
and how it looks. So let me
close out of this
and I will come,
I will stop sharing that screen
and come back on over to
the review here. So those are
our two examples to walk you through around
how the auto review capability would work.
Um Depending on what system you're in.
Like I said, um we're able
to do some things um a little
bit more robustly and epic than we are in Cerner,
but we certainly can provide information
in Cerner that helps you to complete that review.
Um But we're still working on evolving
the capability.
As I mentioned earlier, there is
no perfect capability that exists
out there in the market. These technologies
with A I and robotic process automation
are continuing to evolve. So
we keep growing our road map and keep
building functionality and capability
to help evolve things. Um A as
time goes on
today, we've got um somewhere
around 100 and 50 hospitals or so using
auto review in the market. And
when it comes to um
program transformation with
automation, there's a tremendous
amount of lessons that
we've learned. Uh We've got lots of
hospitals in various stages of
implement implementation. And um as
we continue to grow, we're seeing over 25,000
automated reviews occurring on a monthly
basis and that number is just continuing
to tick up. But as we implement
and integrate our customers. We're
learning a tremendous amount about how
we can tweak and massage things
to make them work better, how we can help
our customers to do things different or better.
So we're learning from our users, we're continuously
improving this capability and
evolving our solution as the industry
standards evolve and change as well.
So, really exciting to think about
what we can do and and to see some of
the successes that we have within the market.
So when it comes to automating
with a capability like Interpol auto
review, um with those 150
or so hospitals that we have, we've
learned that when it comes to
um program transformation with
automation, it's really a three
legged stool to reach success
and value. And the, the three
legs of that stool are really, you
know, looking critically at your systems,
really looking at your processes
and looking at your people. And
we're finding that if you're not focusing
time and attention on any one of those
um stool legs, if you will,
um it's really hard to see the success
and the value that you want to get.
So let me talk a little bit about
that because when I say clinical systems,
I'm referring to your current
technology infrastructure.
And this really, this really has to be the starting point
because if your technology isn't ready for you
on transformation,
the processes in the people, those legs of the
stool really, really, really don't matter right, you, you've
got to have technology that is ready
and it's ripe for, for receiving
the auto review and being able to drive
those efficient. So some questions
to ask yourself is has your E hr
been upgraded in the last 18
months or so with fire servers
and web service availability,
those kinds of things, we really need to make
sure you're on um a current
E hr um version
or if you are thinking about upgrading,
keeping that in your mindset as you,
as you head up on this journey, in terms
of the phases and stages of getting to automation,
also your um or cm workflow
tool. Are you on the latest version? Um
Is it compatible for integration
and will it allow for for this type of automation
capability?
So lots to lots to think about
there for sure.
And then
when it comes to processes,
it's really important to step back and understand
the impact that the automation is gonna have
on your processes and your workflow.
Um You know, really thinking about what
you want your future state program to
look like because now is the time
to be able to make some
changes and maybe tweak some processes
to help optimize how the capability
is gonna work.
And then the la the last piece uh last but
not least is the people, right? Um
When it comes to people, they're really gonna be
your, your key to their success. Um
We've learned that uh change is hard
and change comes hard to some folks, especially
where we've got, you know, an aging
uh nursing workforce and
we often don't spend enough time
or in invest enough energy in
preparing our
for this type of change.
So I think it's critically important
um to ensure that you've got
a really good go live readiness plan
that you're thinking about allocating
your resources in the right way and
that you're really clear with your goals
and your, your, your key performance indicators
or metrics of measure what metrics matter
to you
and how you're going to measure those to define
your success and bring your people
along on some of that journey with you.
Um You can have the most advanced technology
on the market out there, but if you don't
have the people that are willing to embrace it,
um And really, you know, stepping
up to be champions to, you know,
envision this future state can really
make or break your process.
It's also a really good time to
be thinking about staff competency and what
else you can do to um bolster people
and get support um around moving
towards automation and technology.
Um People get afraid that when you bring in a technology
or capable a capability, it's,
it's gonna diminish their value or
potentially reduce um you
know, what is needed from them. And, and that's not
the case. We, we're just not finding that to be
the case we, we often talk
with our customers or, or our customers
that are thinking about the auto review capability
in terms of the efficiencies and
the time savings, which can translate
to a lot when it comes to um FTEs
or, or resources.
I can tell you though, we've never encountered
a customer that's decided to cut their
resources because they've been able to
employ a capability like this. I
think we all can appreciate
that, you know, with staffing shortages
and challenges, we don't have enough people
to do the things that we wanna do and it's
very infrequent that we encounter
a hospital that's able to do all of the medical
necessity, reviews or the due diligence
when it comes to um other functions
for um or CM or discharge
planning or other programs
that you're looking to do because you don't
have the resources to do it. So we
definitely uh are not seeing the there's
a resource attrition, but we're
seeing that people are able to reallocate resources
in places or to support
programs where they can really have an impact
and make a difference. So super
super exciting to um to
think about the potential, right? If
you, if you think about taking some
of the routine rote basic
cases off the plate, really the the
impact can be transformational to free
up your resources.
So let me share a little bit. Um
This is a compilation of some
of the real world results that our customers
are achieving and seeing today with
the Interpol
a auto review capability,
uh we have an organization that's seen
um a 76% decrease
in medical necessity denials over
one year. Now, is that 100%
related to Interpol auto review and what
that can deliver? No, probably
not. But it's a combination
of utilizing the Interpol auto review capability
and them in
some clinical and technical workflow
improvements. So bringing our capability
in really gave them some support
and guidance for really digging
in and rethinking some of their processes
in ways that they could do things different,
do it better and do it with more accuracy.
And those results are translating to real
real world dollars for them.
Uh We've seen an organization decrease
their uh condition code 44
by 56%.
Um And that the automation is really
what allowed them for, for them to be able
to do uh more reviews earlier
on in the process. Uh And therefore
patients, patients were being placed in the
right level of care the first time
around or cases were being
escalated to a physician advisor
with partial information earlier on
in the process. So if you're getting it right
from the outset, you don't have to go back
and make changes after the fact. So
reducing condition code 40 fours
by driving to a more accurate level of
care from the outset.
Uh We have an organization that has
um had 6.2
FTEs and it translates to
somewhere around a million dollars in annual
savings. And again, not that they were
cutting staff, but they are able
to increase their staff productivity
with review volume, enough volume
that it's equating to somewhere in the order
of 6.2 FTEs um
from their old manual processes. So
6.2 FTEs that they
don't have to hire, uh, doing
more with less, uh, it's translating
to over 40,000 reviews more
a year that they're able to do. Um
saving them some again, real dollars
and being able to take their staff
and stretch them further. So, really
exciting results we're seeing with that customer.
Uh We have another customer that um
has had 78% of reviews
being automated. Um This customer
is really exciting because when we say
automated, we mean automated, either
fully completed or partially completed.
But they, they achieve this result
by rolling out auto review, recognizing
that the results that they were getting are hoping
for were not coming to fruition.
And when we were able to dig in with them and
help them understand what the root
cause was around that
they needed to change a single process
that they had with their physician documentation
and help them optimize or really
ignite what was being able
to be automated. So they started out
with 7% of reviews being automated.
And when they made this change to their process,
they went from 7% to 78%.
So really impactful changes
just by digging in and looking at the data saying,
why the heck aren't we getting more automated
reviews here? So, really um
super excited to see where that customer
has been able to take um auto review
and really help them on their transformation journey.
We have a customer that has had uh somewhere
between 30 40% decrease
in the time to complete a medical necessity
review. Um This is really what we're
seeing on average with our customers. We
can really cut the time to do a review down.
So if you think about those partial reviews
that I I mentioned where we're able to get
some of the data but not all of it, they
were able to complete that review and significantly
less time than, than what they were doing previously.
And then the last statistic I have on
here um is that we have
a customer that has seen a 13%
overall decrease in the time it takes
to conduct that first review. And
this is really exciting because if you come in
to start your work day as a un nurse
and auto review has been running
and you're looking at your work list or
your work cue and many of those
reviews are already fully completed for
you and others are partially completed
for you.
Well, you can just see how your work day can be much
easier and how you can get to more reviews,
quicker, reduce that lag time
and really be able to increase your productivity.
So super exciting results and
these numbers are just gonna continue to grow as
we evolve our capability and, and
learn from our customers. But that
robust reporting capability that we have
is really what's helped us to help
our customers dig in and
really be thoughtful about making meaningful
changes, whether it's to their systems,
whether it's to their processes or whether
it's with their people. Um It could
be any one of those. And again, that's the three legged
stool that we've seen to really help
ensure and drive success.
OK.
So
just in summary, uh what I
shared with you today around auto
review, really auto review
is designed and intended to help
you reduce, you know, administrative
task burden by driving
efficiency. Uh really reducing
the burden of that manual review process
by integrating directly into the workflow,
right? So you're not going outside of a medical
necessity review um to be
able to find this information just pulling right
into your workflow. So really
um having that integration in there is really
gonna eliminate the need to toggle in and
out of disparate systems and bring
efficiencies.
We also are driving to accuracy.
Um Interpol
on review is not a black box capability.
There's many black box capabilities on
the market with, you know, promises of
A I uh and, and promises
of, of having you uh eliminate
the need to be able to do a medical assess
the review overall. Um We don't
believe that that's the the route to go,
you know, at the end of the day, Interpol
and auto review is founded on
the gold standard evidence-based
Interpol criteria that many of you have been
using for a long time. It really
creates transparency and
leaves no guess work for health plans
as to the validity um of
the need for care at whatever
level is being requested. So
when we compare results
with of auto review, with manual case
audits, we know that there, the automation
really brings um accuracy
in the form of getting to the right subset,
right? We're looking at the codified
admission diagnosis, the driver,
the reason for being there and we're getting
to the right subset. We're driving
to the right level of care
and the right criterion um
being selected and supported with the source
of truth really to ensure accuracy.
And then lastly, this capability
really um is also designed to help
foster collaboration and trust.
We know that there is still uh
a lot of challenge between payers
and providers in the market. We write
Interpol and sell Interpol to both
sides. Uh We don't write it for
one side or the other. We really try to
toe the line and write the criteria based
on the evidence. And our, our goal
and ideal state is to help foster
collaboration by providing a capability
that both sides of the equation can use
and can trust. And I know
that when we're out talking to health plans,
they have a AAA real
interest and intent to automate
their off decisions based on data
that comes from the source of truth
that they can trust
that's not been manipulated by a human.
So I think, I think, you know, our future
and where we're headed from a strategic
trajectory is really exciting
to think about those cases that
we can just take off the plate. I mean,
no one's arguing if a stemi
needs to be in a hospital, inpatient
level of care, whether you're the hospital
or the health plan, we're sort of all
in agreement with that. I mean, these patients
are sick and they're complex. Um
And yet we're still doing medical
necessity reviews on the hospital side, on the health
plan side, there's a lot of back and forth
and, you know, looking for clinical information
to validate and support it. If we can take those
things out of the equation, we're really
gonna be able to make some significant
strides. And um really
the automating on the front end has such a tremendous
capability to help transform
how we operate in the future. And it's
really just the tip of the iceberg when it
comes to meaningful
transformative change. So
I hope you found this helpful. Um
I think we're gonna, we're gonna pause and
maybe take a couple of questions here, but I hope
you found this helpful Interpol um
is here to
help you along that journey and
for our customers that do choose
to license Interpol auto review. We
don't integrate this capability and then leave
you on your own.
We do provide consultative services
to ensure that you're optimizing
the use of it and that we're really helping
you be thoughtful about your, your
p your processes
and your systems and making
small adjustments to make sure that these kinds
of capabilities can really help you
and really drive me
efficiency. So um would love
to see you uh consider um Interpol auto
review.
So, Heather with that, I think we're gonna
um pause
and, and take a few questions.
Yes. Thank you so much, Laura.
Before we get started with the Q and a portion
of today's webcast, we'd like you to take
a moment to remind you of the survey located
beneath the slide deck area within your audience
console. We'd love it.