Hi, everyone. This is Erica Carbajal with Becker's
Healthcare.
Thank you so much for tuning in to
this episode of the Becker's Healthcare podcast series.
Today, we're gonna be talking about scaling smarter,
how automation is redefining revenue for surgical centers.
And joining me for today's discussion is Aman
Magoon,
cofounder and chief of product at Adonis.
Aman, thanks so much for being on. Excited
to have you. Thank you. Thank you for
having me. Excited for the conversation. Yeah. Really
relevant topic. But before we get into it,
do you mind just sharing a little bit
about yourself, your work in health care, and
Adonis? Yeah. Absolutely. So as you mentioned, my
name is Aman. I'm the chief product officer
at Adonis. I'm also one of the cofounders
alongside
my younger brother, Akash. Him and I are
second time entrepreneurs in the health tech space.
So the first company we started,
in 2019 was helping
solve a different challenge at the intersection of
insurance and technology,
picking your benefits during open enrollment if you're
an employee of a large company.
And so our software
in our first business, which was called NAYYA,
n a y y a, was really helping
everyday American employees pick their insurance plans during
open enrollment in in a way that was
very personalized to their financial needs and their
physical health needs.
And so that company building experience was really
transformative in a number of ways, primarily in
helping us understand the different challenges that those
that interface insurance,
are
encountering on a daily basis,
particularly the provider challenge of revenue cycle. And
so over the course of our first business,
which we spent about three years building before
we exited,
we learned a ton about
revenue cycle and and the very intricate
challenges in the nature of revenue cycle issues,
between the payer and the provider, and that
was really the inspiration for starting Adonis. So
we've been building this company for about three
years now as well. We've raised about $50,000,000
across a number of different rounds of funding,
and we're headquartered in in New York City.
The
central premise behind starting the company is really
helping initially helping revenue cycle teams pinpoint
root cause issues,
that told the story of what was breaking
across their revenue cycle organization on a daily
basis,
similar to how you might have fraud detection
if you use a credit card or you
might have
threat detection if you have an alarm system
in your house. You wanna know when a
intruder's on the way. And so that was
really our core focus was specifying,
with a very
distinct
amount of detail the issues that teams are
facing on a daily basis so that they
could then
affect those, issues.
And more recently, given the proliferation of AI,
we're not only identifying threats, we're actually mitigating
them through a number of different modalities.
Yeah. Thanks for sharing all that. It's interesting
of the comparison
you make of of kind of how it
works with the credit card and the proactive
nature of this, of really
pinpointing those root cause issues, and imagine it's
fun working with your brother.
Yeah. It's a delight. We we get along
really well. Yeah. Well, administrative costs already came
up, continue to be a financial burden for
organizations and and a potential threat to patient
care in some cases. So
what are the most time intensive or error
prone RCM workflows that AI agents can actually
automate today? And how should leaders prioritize
what does get automated?
Yeah. It's a great question and one that
we think about quite often.
I like to call it the standard operating
procedure for revenue cycle really consists of four
or five core steps.
The first step is usually
fetching information from one spot and moving it
into another spot. Let's say if you're adding
a modifier to a claim, but you don't
know about a payer policy change, maybe you're
doing a little bit of research on a
on a payer policy and then
making an update to a claim. So you're
moving information from point a to point b
as, like, job number one. Job number two
is fetching information from,
the payer. So it might be through a
phone call or a portal based intervention that
you might have to make if you're on
a revenue cycle team as, like, job number
two, fetching information through the phone or through
the portal.
The third is document transmission. So, like, taking
medical records and moving them from an EHR
to a payer portal, for example.
And the fourth is typically some sort of
patient outreach, whether it's previsit to get eligibility
or prior auth related details
or postvisit to inform them on their patient
responsibility or any other follow ups. And so
if you think about those four core steps,
you can assemble them in a number of
different ways to recreate basically any standard operating
procedure, business procedure within a revenue cycle.
And what's interesting is that each of those
four
jobs to be done can almost effectively be
replaced by
artificial intelligence now. The challenge really is how
do you stitch together the right actions and
orchestrate the right combination of those four things
in a way that actually recreates that standard
operating procedure. And that's really where our technology
specializes in the orchestration of AI and is
less focused on the actual
verb, if you will, the phone call or
the portal upload or the patient outreach. Like,
those are really exciting
verbs. But in order to tell a complete
story, in order to speak a full sentence,
you'd you can't just use a verb. You
have to be able to specify a subject
and a noun and a predicate or whatever
it is. I'm I didn't really do that
well in English. But, yeah, that's really what
our technology is meant to do is take
those actions, but turn them into actual business
processes. Yeah. It makes sense. And kind of
like along the continuum versus kind of this
the siloed actions with AI can help streamline
the whole process. Can you share an example
of what this kind of looks like in
practice? What you just mentioned about those those
core steps and how the AI can help.
Totally. So one of the costliest things within
revenue cycle is working aged AR. You know,
if you're a revenue cycle team,
you're almost always tasked or you have someone
on your team that's working that ninety day
bucket and is prioritizing,
let's say, claims with a higher dollar amount
relative to what's average. Right? And so you're
working through a a stack ranking of maybe
thousands of claims depending on the size of
your organization
that are sitting in this age bucket.
And typically, the first step
to to recovering those those proceeds or those
funds is to do some sort of forensic
analysis on what the actual status of that
claim might be.
In the old world,
statusing each of those
thousand several thousand claims would require
taking the claim level details, going into the
payer portal, searching for that claim ID, hopefully
getting a response back, and getting enough granular
detail
on the outstanding status of the claim so
that you can then resubmit it or take
some sort of action to get that revenue
recovered.
But that forensic analysis or that research was
highly costly. It would take, you know, ten
to fifteen minutes to get into the portal,
and then it would take maybe ten to
fifteen minutes to actually make sense of the
information in the portal.
And should that information not be comprehensive enough,
you're then having to pick up the phone
and call the payer. And so one example
of how AI
specifically deployed through Adonis Intelligence
is intervening and actually creating economies of scale
is
basically replacing that entire forensic process by using
the payer phone call as the first line
of defense.
We all know that the phone call to
the payer is oftentimes the most comprehensive
way of getting information, but it used to
be the most expensive.
And what AI has allowed us to do
is take that most comprehensive method, the phone
call, and actually make it the least expensive.
So it's becoming the first resort,
you know, in in how we think about
working AR, where
in kind of yesteryear, the status quo is
is usually the last and final resort. So
that's one example of how this technology is
really
changing the paradigm of how revenue cycle
can operate and certainly changing the the paradigm
around
the efficiency associated with collecting aged AR.
Yeah. And it's it's interesting how the it
kind of flips the process on its head
and, as you said, like, goes straight to
calling the payer as the first line of
defense.
On the topic of of payers, the requirements,
documentation standards, they're complex. They continue to evolve,
something we hear from all health system leaders
that we we talk to.
What role can AI agents play in improving
claims accuracy and overall compliance?
And what are some of the common misconceptions
that you hear from leaders that they may
have about using AI specifically in those areas?
Sure. So
I think there's a number of best practices
that AI can now
help accelerate for organizations
as it relates to payer compliance and just
staying on top
of payer integrity, I'll call it. And I
think number one is staying apprised of changes
changes to payer policies.
If you think about how payer policies were,
you know, very sneakily modified by payers in
the past,
it was almost designed to make it hard
for humans to detect. And therefore, it wasn't
until there's a large kind of backlog of
AR that indicated a payer policy change that
organizations were actually picking up on relevant changes
to policies.
What our technology is doing, through Adonis Intelligence
is act is we're actually scanning on a
daily basis
all of the relevant
pair policies and almost doing a a comparison
from,
you know, what the policy looked like last
week relative today.
And from there, looking at those relevant changes
and identifying which of those changes might be
consequential to the organization that we're working with.
So within the orthopedic space, you know, there's
a lot of changes to prior auth rules.
There's a lot of changes around,
the amount of documentation that needs to be
required, submitted should there be an appeal situation.
And so being able to accelerate
the awareness around payer policy changes is a
is a huge, I I think, way we're
enabling teams to stay on top of
payer integrity or or,
payer compliance.
Furthermore, I
think many revenue cycle teams will tell you
that documentation
uploads or, documentation
requests from the payer are
usually
amongst the most costly things that they have
to do. And the challenge is not that
the providers don't know which claims are gonna
require record requests.
It's more so having to wait for the
denial to come back before they can actually
take action against,
those known claims that are gonna get denied.
And to so through our technology, what we're
able to do is not only identify which
claims are highly likely to get denied for
lacking
records, medical records, but actually proactively take the
records and and submit them to the payer
before the denial ever even needs to be
contended with. And so, you know, being proactive,
I guess, is the is the second best
practice. I would say number one is staying
on top of what the payer is doing
and how they're changing their policies. But number
two, implementing as much as many proactive measures
as you possibly can to avoid
pain that is avoidable.
Yeah. It really sounds like so much of
this is about going upstream. And as you
mentioned with the staying on top of payer
policies, like, how do we help those orgs
try to almost have a sense of that
in real time and and work and move
accordingly.
Looking ahead, how do you see AI and
automation
affecting the ASC space in the next three
to five years, let's say? What are the
some of the innovations that you're really excited
about? Yeah. And I'll I'll actually touch on
the question you asked in in the previous
question as I respond. So, you know, one
of the biggest common misconceptions is that automation
needs to be a zero sum game.
And what I mean by that is it
does not need to be true that
a piece of automation needs to eliminate the
human from the loop entirely,
especially so in in kind of the world
that we're moving into where there are certain
actions that AI is really good at performing
on behalf of teams, and there's other actions
that required decision making or judgment or some
sort of taste, if you will, or analysis
that maybe the AI is not fully equipped
to perform quite yet.
And so as it relates
to revenue cycle within ASC specifically,
I think the shift we're gonna see is,
not in the elimination of of teams, but
really the upscaling of teams where
revenue cycle personnel specifically will be tasked with
working what we like to call exceptions, where
the AI is not
suited or does not have an expertise in
a particular subject matter. And therefore,
they're doing the,
let's say, the first three steps of the
five step process and pushing the last two
steps
to, you know, a smaller group of people
that are highly specialized and can take action,
but take action in a way where the
technology is assisting them or enabling them and
allowing them to avoid doing the monotonous, tedious,
and more routine
work that ordinarily they'd have to do soup
to nuts. Mhmm. Yeah. Makes sense.
Aman, is there anything else that you wanna
share or leave listeners with on the topic?
Absolutely. I think
the
winners and losers in in in this space
or or let's say
fast forwarding
five years into the future,
the groups
that accept and adopt that AI is is
here to really
elevate
the entire health care landscape,
and
embrace that change rather than view it as
a as a risk,
I think we'll really see a lot of
benefit in in accelerating their ability to push,
I think, a lot of value back to
their patients and their providers.
I think anytime you're able to do
more with less or more with a constant
amount of inputs, or or resourcing, anytime you're
able to do that, you're fundamentally becoming more
efficient as a business and therefore can either
shift those proceeds to your consumers or
to those that make your business work. And
I think in both cases,
the patients and providers will be the ultimate
winners. And
I think the revenue cycle teams that,
you know, ordinarily would be
consequential to these processes will find that their
jobs are also much easier and and more
intellectually stimulating as well. Yeah. Certainly.
Oman, thank you so much for your time
and insights today. It was a pleasure having
you on. Likewise. Thank you.
And we also wanna thank our podcast sponsor,
Adonis, and listeners, you can tune in to
more episodes
of the Becker's Healthcare podcast by visiting the
podcast page on our website at beckershospitalreview.com.
Thanks, everyone.