Hi, everyone. This is Lucas Voss with Becker's
Healthcare. Thanks so much for tuning in to
the Becker's Healthcare podcast series. Today, we're talking
about the next evolution of prior authorization. And
joining me for today's discussion, very excited to
have her, is Elizabeth Crowley. She's the vice
president for clinical and care management solutions at
EXL.
Liz has more than thirty years of industry
expertise in health care and various leadership and
executive roles in the payer consulting and delegated
services market. Market. She's focused on leading health
care operations and business process optimization in highly
complex and matrixed organizations
to bridge the gap between clinical technology and
business objectives. Liz, thanks so much for being
here today.
Lucas, thank you so much for having me
on your podcast. I'm really excited today
to dig into
our topic and,
have a great conversation.
Yes. We do have a lot to dig
into. There's a lot to talk about here,
specifically with prior authorizations.
Again, it's been traditionally framed sort of as
this administrative burden,
but then we're seeing that shift to increasingly
being discussed as part of the member experience,
provider relations,
and certainly compliance issues, and it it's happening
all at once. Right?
From your perspective,
what's fundamentally broken into today's prior auth model,
and what's been missing most specifically from these
conversations
around how to fix all of this?
Yeah. You know, fundamentally,
the prior authorization
model, it's totally viewed as a gatekeeping, right,
or limiting access
to
care by the provider community and by members.
However,
you know, prior auth was instituted
as part of the larger managed care program
evolution over the last, you know, twenty, twenty
five years.
So over time,
the the process has been burdened
administratively
due to the increasing in regulatory
pressures,
compliance and quality requirements,
and just the scope of services,
new therapies, new medications
that have, you know, become part of mainstream
clinical care delivery,
has just made prior auth very, very complex.
There's lots of
different things the payer community has been trying
to do to make this prior authorization process
easier,
but, traditionally,
they've been kind of like gold carding programs.
Right? So giving certain providers certain services a
free pass, right, for so many services or
for certain types of members or
other
rule based,
solutions where,
you know, low,
cost and,
low abused services, for a lack of a
better word, were kinda given an auto approval
pass. You know? But that didn't really remove
the burden of prior authorization. Right? The provider
still had to go through the motions of
submitting
prior authorization and things of that nature. So,
you know, fundamentally,
that's
that's the problem. Right? So but over the
past twelve to twenty four months, there's been
this heightened attention, right, to the whole end
to end experience in prior authorization and
how to make it better.
Uncovering the root causes needs to be the
top priority, so not just making it easier
to follow the process,
but is the process itself broken? So things
like the FHIR interoperability
standards of electronic
data sharing, that has kinda spring boarded us
into
more robust automation,
AI forward automation, you know, to now that
we have the data connection, how do we
leverage that and really make the experience more
beneficial?
You mentioned the end to end aspect of
this, which is really crucial as you just
highlighted.
And there is a very growing shift toward
AI first work flow, specifically this year. There
was some last year, but certainly in 2026.
How do you distinguish between
basic automation and these AI agents that are
coming up that change how clinical and care
management teams work
day to day?
Yeah. So,
you know, automation
has typically been characterized as, like, RPA. Right?
Robotic process automation and workflow business rules.
These two approaches, they're heavily defined but with
scripts and logic. Right? And that logic and
those scripts have to constantly be updated
with change in program execution,
change in criteria sets, or change in requirements,
regulatory requirements.
With agentic
forward
automation,
the agentic AI agents actually
interpret requirements
and interpret guidelines
as a human would.
So instead of having to,
constantly maintain
scripts and algorithms,
the agentic agent reads source
materials and source guidance each and every time
the agent is engaged
to support a a prior auth process.
So so this is a couple of things.
Right? It
significantly reduces
the administrative burden to the payer of having
to maintain scripts and logic. Right? So they
can then turn their attention in looking at
the outcomes of the AI, looking at the
data that is being
harnessed through the end to end workflow.
It creates the opportunity
to continuously
capture and audit and evaluate
the accuracy of decisions
and the time required to process each step
of the workflow.
Right? So this creates more information for leaders
to manage and process
in real time.
Now you've touched on the advantages just now
too. Is there one specific piece of this,
one specific workflow
that comes to mind for you where this
just is a no brainer, where this is
illustrated perfectly?
Yeah. Yeah. So when you think about
how clinical decision support or medical necessity reviews
are done,
rule based automation,
for that step of the prior authorization process
requires that there needs to be a documented
definition
every single step of that criteria statement. What
does it mean to meet that criteria? What
does it mean not to meet that criteria?
Aside from that, then there's the complexity of
all of the following must be demonstrated
or any
two or more of the following need to
be demonstrated.
And then even more complicated is if there
are time sequence parameters
throughout the medical evidence: six months' worth of
therapy,
two failed courses of treatment of a medication,
makes it very difficult to quantify that in
logic and definition.
The AI agent, as I I shared before,
reads the medical policy guidance or the CMS
criteria bulletin, whatever is being used as that
source of
medical necessity
criteria, and then reads the evidence submitted by
the provider and then makes those matches
just as a human would.
But the AI agent does a little bit
more than what a human typically does. A
human will find what is necessary to move
down the algorithm.
Right? One of the following. As soon as
one is found, typically,
a human reviewer will go to the next
step, and and, Agentic AI Forward workflow will
find every piece of evidence. So it makes
the review more robust. It makes the review
definitely more member centric and more appropriate to
that member in that situation.
And then because the agent reads from the
source,
if there's a change to a policy, if
CMS updates a a policy bulletin,
the agent is reading that real time. So
the decision tree that was in place at
the last iteration of that workflow
is validated by that agent reading that source
document again and updating it if necessary if
there's a change to criteria.
Now
with the technology itself, we're having a lot
of conversations about governance,
about checks and balances. Right? You just talked
about the human in the loop, which is
certainly really, really crucial. Right? And certainly one
concern for payer leaders specifically is how to
balance that, right, with speed that you just
mentioned really, really quickly, really, really accurately
with clinical rigor when AI is involved in
that decision support. Right?
What have you seen work well in maintaining
that clinician trust, that that oversight, and certainly
also
accountability
while still, again, improving the efficiencies that you've
just talked about?
Yeah. I think whenever an AI forward workflow
is
being implemented,
it's really important to
be very focused and purposeful
about where you want that,
agentic solution to start. So it's really a
a a small segment of the overall prior
offline. So maybe it's a single service type
or
pie you know, putting it in place with
one provider group and all of their requests.
This allows clinicians and leaders to evaluate the
accuracy and the effectiveness
of the AI workflow outcome
in a more timely manner. Right? So you
could imagine a payer that gets thousands of
transaction
possibly
quality
review the AI output of every single one
of those transactions
in early implementation
to gain that confidence and trust, right, that
the AI agents are delivering
clinically solid, consistent
recommendations.
So starting small allows there to be almost
a side by side or one to one
and watching that AI evidence.
Then
it's able to be scaled. Once the clinicians
have trust, the AI,
agent is accurate, consistent, and
is saving them time,
then we scale. However,
most importantly, though, is the oversight doesn't stop.
The program monitoring and the analytics to monitor
the consistency and accuracy has to stay in
place,
and there's reason for that. Right? Brand new
policies like anything, whether it's a a group
of clinicians
reviewing against a new policy
for the first time,
or an AI agent, a Genentech agent reviewing
against a policy for the first time. It
needs deep oversight.
So
that, component needs to be consistent. And I
I think sometimes that is a misstep,
that as soon as we see a process
or a technology driven solution work,
there's sometimes too much trust in the technology,
so the oversight is critical.
Also,
this is all evidence based output. Right? So
the decisions and the recommendations
are being captured by that agentic workflow,
and this really creates opportunity
for
evidence based rigor and decision making
in the workflow and can really help reduce
and educate human bias.
So to be able to enable that then,
right,
looking ahead,
what are those things from an investment perspective
and from a focus perspective
that payers should really ensure is actually there
and is met for the future? What are
those things that need to be in place
right now? Yeah. So I'm really not even
gonna talk about the technology
components, right, because
those are defined. I think, one of the
biggest things that
I found to be a miss,
and that causes delay and a a lot
of uptime in trying to get these, agentic
workflows in place is,
a defined change management and oversight process. Right?
So when we're bringing AI into the workflow,
that is materially
changing
the day in the life
of all the humans in the loop, whether
they are support staff, nurses,
physicians. It it doesn't matter. So
the the change management aspect is critical,
to ensure that there is adoption,
process adherence, and that being able to evaluate
process effectiveness.
So so to to me, that is
quintessentially
important.
It also allows
for the human in the loop to more
effectively
gain trust when they understand
what their new day in the life is
gonna be like, why it is important to
embrace it, and demonstrating for them the value
that bringing technology and agentic support into their
workflow will give them.
And as with anything else, the the end
users can then start to rely on the
data that is being presented to them
and kind of change the way they
execute their responsibilities
instead of having to build the case example
and then making the recommendation.
They can re case example and then validate
a recommended
result and modify that as necessary.
Now the end goal, obviously,
if and when this is all happening is
the fact that this needs to be scalable
across an enterprise. This needs to be scalable
for a large number of people, for multiple
business units, etcetera, whoever is working in those
processes. Right? However,
I like to say we've been living in
the year of point solutions in 2024 and
2025.
Yep. Many organizations
struggle to move beyond those point solutions in
prior auth, specifically,
especially when there is legacy platforms evolved, etcetera.
Right?
What does it take to really scale those
AI driven decision supports
across an enterprise?
Yeah. So I think it's really
it all lies in
the fundamental development architecture
of that AI forward workflow.
So when we think about
how to bring AI capabilities across multiple utilization
management workflows.
It's really the agentic agent approach is the
most nimble and the most flexible. So, for
example,
you can create a single
AI agent
that handles
a prior auth workflow end to end that
can be integrated in and around any
product or platform workflow. Right? But it's still
a single agent.
So that agent only knows that workflow. It's
trained on that work flow. But if you
think about deconstructing
the end to end of prior auth, let's
talk about things like a medical record agent
or a clinical policy agent and a decision
support agent. So when we think about those
three key steps,
the agent is trained to read medical policies
with extreme accuracy.
Another agent is trained to extract
the key relevant clinical data in submitted
clinical records
to align
to that medical policy, and that's what that
decision support agent does is orchestrating that decision
tree.
Because they're independent agents finely tuned on a
specific skill or step,
anywhere in the organization that that same function,
occurs, that agent can be deployed. So when
you think of utilization management,
it can pry off we've been talking about.
Appeals and grievances,
same set of agents, but just doing a
different type
of medical necessity review. But the steps of
reviewing a record, looking at criteria,
and supporting that decision
are there.
Medical claim review. Right? When claims pen for
medical necessity review and clinical is needed, same
set of steps.
So by investing
in foundational
agents
that are,
tied to a single executable task.
That agent can be deployed across multiple systems
and multiple workflows within the organization.
Maybe we finally reached the year of the
agent at this point in time. That might
be it. Liz, it's so great to have
you on. So many great nuggets in this
conversation. Anything else that you'd like to touch
on that that we haven't mentioned for our
audience?
You know, I think the only other thing
I'd like to talk about is we've talked
a lot about work flows and AI in
the workflow,
but we really have been spent time on
what is truly required to allow
any AI forward workflow to execute accurately, and
that's data. Right? So analyzing the accessibility
of the data
and an organization
to having a data, what we call AI
ready,
is a critical first step, right, to any
AI forward
workflow effort. So
being able to convert structured and unstructured data
means faster retrieval for analytics and operational integration.
So really the first step. You know, we
technology can do anything, but that that foundational
information, which is data. It won't execute as
effectively as reliably
and produce the outcomes necessary to be worthwhile.
You heard it here first. Next podcast with
Liz on data. You'll have to make sure
to tune in. Liz, thank you so much
for being here. It's great to have you.
Thanks so much, Lucas.
We also want to thank our podcast sponsor,
eXol. You can tune in to more podcasts
from Becker's Health Care by visiting our podcast
page at beckershospitalreview.com.