Subscribe
Share
Share
Embed
Where we share our weekly news debriefs and discussions with industry experts. These are lo-fi recordings aimed at giving our readers more opportunities to engage with our analysis and a view into some of the conversations that shape it.
Martin: So congratulations on the round.
Thank you.
Big milestone.
Joyful Health.
Like I said, $17 million,
$22 million in funding raise.
I thought the framing in the press
release was really interesting.
So you're talking about helping
practices manage finances.
Rev cycle is, it seems like all
anyone can talk about today.
And when we talked before, when we did
our pre-interview, you talked about a
sort of more expansive view and in the
press release you made a similar point.
You said, you know, we, we went
around two dozens of practices and
spent time as a fractional CFO.
Can you talk about the underlying
issues practices have here and
what you saw as a fractional CFO?
Eliana: Yeah, absolutely.
And I really don't have a background
in finance, so it must have been
a pretty dire situation to, to get
me into our CM in the first place.
So.
Uh, yeah.
I got into this space because my
family ran a behavioral health
practice as I was growing up.
So we had spent a lot of our nights
and weekends dealing with all the
administrative complexity that
comes with running a practice.
But because my background's
an early stage product, I was
at Charlie Health before this.
I really wanted to
experience it for myself.
So I actually went into about a dozen
practices actually in very open-minded
at first as like a fractional consultant
and kept getting pulled into finance
and I would say, Hey, I'm here to do
whatever you need me to do, whatever
the biggest hair on fire problem is.
And across the board, the challenge
was always, I have no idea
what I'm supposed to get paid.
I have no clue who's gonna pay
me on what timeline if I made
what I'm supposed to make.
So I got to work with a whole bunch of
spreadsheets trying to figure it out,
and I realized that I was spending
probably 80% of my time just trying to
track down all these payments across
seven systems that don't talk to each
other across the EHR and the billing
system, and the clearing house, and the
payer portals, and the bank account.
And that made it pretty clear to
me that the root cause issue of all
of this missing revenue is actually
that there's really no financial
source of truth in healthcare.
So providers have to play financial
detective to piece together all these
different systems, uh, before they
can even go track down what's missing.
They first had to figure out where
did it go in the first place.
So that's kind of what pulled me
into this problem space originally.
Kevin: One of the things I was, um,
particularly interested in, in the
Modern Healthcare article that was
talking about the funding round, was this
notion of AI services versus AI agents.
Martin and I were just talking
this morning about AI services with
anthropics, new consulting venture
that they just announced this morning.
I, I'd be curious, um, when you think
about that distinction of AI services
agents, how are you thinking about
that distinction organizationally
at joyful, and how are your.
Customers thinking about your
organization, like do they know you
are an AI first centric organization?
Is that part of the pitch?
Where does that come to play and, and
how you think about the narrative?
Eliana: Yeah, it's a great question and
one of my favorite things to talk about.
So I think in healthcare, oftentimes
people just want you to own the outcome.
And originally when I went to sell
our, uh, like the first version
of our solution, I actually was
selling it as a software to help
practices recover revenue internally.
And pretty quickly I started
to get the feedback of, I'm
actually really overwhelmed.
There's so much money that
I'm leaving on the table.
Why are you showing it to me and
you're not doing something about it?
So I realized pretty quickly that
actually what they were really
looking for was for us to go in and
own the outcome for them end to end.
And currently, uh, you know, denials and
AR is one of the most labor intensive
part of the revenue cycle that requires
a very significant amount of expertise.
It's, it's not rules based.
In fact, our head of RCM opt-in says
that there's actually more anti-patterns
than there are patterns when it
comes to working denials because
there are just so many permutations
of, of how they can show up.
And with thousands of payers all having
completely different payer rules, like
the rules are changing very frequently.
So.
AI agents do really well when there are
very clear if then statements, and some
of that exists actually more so on the
front end of revenue cycle where you
can build more of a rules-based engine.
But with denials and ar, it's just, it,
you know, you can't really get there yet.
I think it, there, there's a very clear
path to that, but there's a lot of
expertise that you still need in the loop.
So really the only way for us to do
that was to go and, uh, really own
the outcome and to automate the pieces
that we can automate, which we call
kind of like the, the science of RCM.
And then, uh, go and bring in
experts on kind of the art of RCM.
And the way that this really
shows up in the sales process,
actually, interestingly, Kevin, I
also thought, you know, maybe we
shouldn't really lead with the ai.
If you look at our website, it doesn't
really talk about AI much at all.
I think over time now that
providers have become a lot more
familiar with AI and uh, folks are
actually more open to adopting it.
What we've actually been doing is that
we've been very, very clear in our
demos, in our sales conversations around
where the AI fits in, and we'll actually
demo the AI that we're using behind the
scenes and we show very clearly like,
Hey, here's where the drop off is.
Here's where we pull in an expert.
And I think that level of transparency is
very helpful because otherwise it can feel
kind of opaque to know, you know, what
exactly is it that we're doing and how are
we using the technology on the backend.
Martin: One of the things I find
so fascinating about this space is
it feels like there's this tug of
war between payers and providers.
It's playing out in earnings calls in
sort of very subtle jabs at one another
where they'll say, oh, the, the payers
will say, okay, well the providers are
upcoding, and the providers will say,
we're getting increased claim denials.
You're kind of sitting in
between those two at the moment
working with these practices.
I'm curious.
If, if providers are seeing a revenue
uplift from, from working with you, how
durable is that given that payers are
now, I think, you know, it's a little bit
of the empire strikes back where they're
like starting to, to, to use AI tools
to try and, and, and tamp that down.
Eliana: Great question.
I think it's inevitable that everybody
will be using AI on both sides.
Uh, and we build joyful to work within the
way that the current structure is built.
You know, we didn't come in and say,
Hey, we're gonna redo the entire thing.
We're gonna rebuild the whole relationship
with Paris providers on the ground up.
Uh, we are working within the
existing pathways that providers
already use to get their claims paid.
My hope though is that we're actually
making it more efficient over time.
I mean, we're not, you know, we're called
joyful health written goals really to.
Create a better ecosystem
for all involved.
And I think payers are spending
an inordinate amount of money on,
uh, kind of this back and forth
between providers and our hope's
actually to make that more efficient.
So for example, we'll often actually
work with payer reps to go batch
reprocess claims without having to go
and overturn every claim one by one.
So the longer term hope is really to
help kind of build the relationship
between payers and providers and
maybe eliminate some of that, um,
administrative burden on both sides.
But regardless, I think we're kind of
built for an environment where we know
nothing will materially change between
this dynamic and, and the hope is just
to kind of make it easier on both sides.
But we're certainly seeing AI already.
I think we're seeing AI on the payer
side for sure, in terms of who's
picking up the phone and, and all
of that changes pretty frequently.
So, uh, I think longer term,
you know, there's gonna be a
lot of AI on, on both sides.
Kevin: Juliana, I wanted to pick up
on that topic of anti-patterns and,
and what you're seeing in practice and
see if we could talk about, like for
folks who aren't up to their eyeballs
in this every day, like how do you,
what actually happens from a claims
denial perspective and what's changing?
Like, is there a good example of what
changes for a practice and where they're
seeing that revenue lift when they see it?
Eliana: Yeah, I mean, I think the
biggest part of it is that it's
just super labor intensive today.
So I'd say probably like 70 to
80% of actually working a denial
ends up being detective work.
So currently a provider sends a
claim out the door and then they
receive a what's called like a ERA,
an electronic remittance advice,
uh, back, uh, from, from the payer.
It, it might actually be on paper if
they're not registered electronically.
That ERA covers dozens if not hundreds of
visits, and it doesn't cleanly map back to
the original claim that the provider sent.
And the way that the payer will
communicate a denial is through
a very vague denial code.
So it'll say something like CO 16, which
in practice actually means billing error.
There are thousands of reasons why there
might be a billing error, and every payer
will use it in a different manner, and
they can change that up at any given time.
So what the provider then has to do at
that point is essentially put on their
detective hat and look at pieces of
data across seven disparate systems.
So they'll look at their EHR
to see what was billed and
what the clinical notes said.
They'll look at the clearing house to
see what information went out the door.
They'll read the payer policy manuals,
they'll probably log into the payer
portal to check the claim status.
They might even make a call to understand.
Okay.
What billing error, like
what exactly did I do wrong?
And all these steps take hours and hours
per claim to the point where sometimes
it's actually not even profitable to go
and put all this effort in because the
amount of money that you spend trying
to fight that denial might actually
be more than the revenue that you're
gonna get back at the end of the day.
So oftentimes I think
what we're seeing is.
A significant amount of denials
are just never overturned in the
first place because nobody really
has the bandwidth to go after them.
And with large provider groups, that
really compounds over time to the point
where they're leaving somewhere between
10 to 20% of their revenue just on
the table and end up playing catch up.
So, uh, I think the biggest
revenue lift that folks see when
working with us is that we don't
leave a single stone unturned.
We are staffed to take on every single
claim, no matter the dollar amount,
and we're able to keep up with the
volume as, as the practice grows
because of the technology that we
have behind the scenes to actually
make this sustainable over time.
So a lot of it's just kind of trudging
through all of the manual work and then
also using the pattern recognition that
we have across all of our customers
and billions of dollars of, uh,
claims transaction data to be able
to say, Hey, we've actually seen this
denial before and here's how we've
solved it successfully in the past.
Martin: Like I mentioned in the top, it
feels like uh uh, there's a lot of startup
attention on the rev cycle process.
When we talked before, you mentioned
having a sort of more expansive view of
provider finances beyond just rev cycle.
I'm curious in terms of go to
market, how are things going?
Can you talk a little bit about what
it's like selling into to groups and
where you're finding some traction today?
Eliana: Yeah.
Yeah, I think that, um, I think that
there's, there's clearly a moment around
this right now because there's just so
much administrative waste happening.
So, and it's also one of the biggest
tear on fire problems for providers.
So I think it makes so much sense
that there's a lot of startup
activity concentrated in this space.
I think what's different about our
take is that rather than coming in
and saying, Hey, we're gonna go and
automate revenue cycle, we actually
started in a very different place.
We started at the root cause
of the problem, which is
really the data fragmentation.
So we spent two years not selling
anything, just kind of staying behind
the scenes, trying to figure out can we
build a platform that is system agnostic?
So can we go directly to the practice
and plug into their existing tech
stack without any migration needed
and recover missing revenue?
Since we figured out how to do that, the
sales cycles end up being actually pretty
quick because the pitch is like, Hey, we
plug into the systems you already have.
We work alongside your existing
team and we find you money
that's being left on the table.
You don't need to go and recomplete
all your EDI enrollments.
You don't need to move to
a whole new billing system.
And there's no training costs or
time associated with the transition.
So the switching costs
are actually pretty low.
So as a result, we're able to, uh,
get, go live with a clinic in like,
you know, a matter of like four to
eight weeks, really, depending on their
size and just plug in immediately.
So I think that's, that's, that's
what's different about our approach.
And we're not building a brand new
billing system that requires migration.
We just fit really nicely
alongside their existing stack.
And the longer term vision is because
we're so tightly integrated with all
their data, we can start to help out
on other areas where providers are
leaving revenue on the table, and then
longer term act more as their financial
insight layer to really make sure that
they are capturing all the dollars that.
Uh, they've builded and that they
have full end-to-end financial
visibility because we've integrated
across like their full tech stack.
Kevin: I'd be curious if, um, do
you think about going a step further
than that to the, the negotiation
layer between the payer and provider?
Like I, I would imagine there's,
there's solving the data
infrastructure inside the provider.
There's also one step back of that,
of solving it with it through that
contractual relationship with payers
and understanding what's going on.
Do you see that as part of
future state in these models too?
Eliana: It might be.
Yeah, I think that that provider contracts
are a, a very important data source
that for we already, uh, use today,
and I think there's a lot more that we
can be doing with it moving forward.
I think the goal really is to be fully
integrated from, uh, contract all
the way through to the bank account,
and that's where you can really
start to do more of this interesting
kind of, uh, financial analysis.
I'm, I'm a lot more interested in that
than, you know, going to like the front
end of revenue cycle where I think
that there's a lot of really wonderful
solutions today that are very specialty
specific and, and laser focused on that.
I think there's a lot of opportunity in
sitting across all of these disparate
data sources and then using that to
power various workflows downstream.
So absolutely, there's a lot of very
important information in the payer
contracts, both the rates and also how
the rates are applied, which currently
is like mostly in, uh, you know, dozens
of pages of free text and are not stored
in any sort of accessible manner today.
Kevin: Yep.
Makes sense.
Martin: It's been a pleasure chatting.
I know we have to let you go.
If folks have questions about Joyful Their
Practice and they wanna reach out, where's
the best place for them to either reach
out to you or find joyful on the internet?
Eliana: Yeah, joyful health.com.
Uh, email is eliana joyful health.io.
Would love to hear from you.
Um, thank you so much for having me.
Big fan of Health Tech nerd.
So, uh, always a pleasure.
Martin: Thanks so much.
You have a nice rest of your day.
Eliana: Thank you so much.
You too.
Bye.
Thank you.