July 10, 2025 • 49 mins
“We truly see ourselves as an operating system for payers,” says David Pierre, CEO of Machinify. Pierre joins Bloomberg Intelligence analyst Jonathan Palmer to unpack how Machinify is using AI to reimagine payment integrity, turning a manual process into a proactive, tech-enabled platform. They discuss Machinify’s formation from four legacy and technology companies, its AI-first claims strategy and how the team is driving real-time adjudication and measurable cost savings. Pierre also shares what he’s learned from Cerner and Signify, and where Machinify is heading next.
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Episode Transcript
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Speaker 1 (00:18):
Welcome to another episode of Bloomberg Intelligence's Vanguards of Healthcare podcast,
where we speak with the leaders at the forefront of
change in the healthcare industry. My name is Jonathan Palmer,
and I'm a healthcare analyst at Bloomberg Intelligence, the in
house research arm of Bloomberg. I'm very happy to welcome
today's guest, David Pierre. He's the CEO of Machinify, a
new reformed payments company sponsored by New Mountain. Before that,
(00:41):
he was the CEO of Signify, and earlier in his
career spent some time at Cerner. Welcome to the podcast, David.
I'm excited to hear about Machinify and this new company
and where you guys are going.
Speaker 2 (00:51):
Great.
Speaker 3 (00:51):
Thanks Jonathan, happy to be here and happy to share
our story and where we're going as a company.
Speaker 1 (00:56):
Excellent. So why don't we Why don't we dive in,
you know, as I understan and it machinifies a combination
of a couple legacy assets. Can we talk about those
companies and what the vision is for the combined entity.
Speaker 2 (01:09):
Yeah, happy to do it.
Speaker 3 (01:11):
So at our heart, I mean, we're a healthcare intelligence company.
Machineify is and we're focus on really transforming payment integrity
within kind of this healthcare ecosystem, and we worked with
a number of the leading health plans across the US,
and we're taking an AI first approach, which I think
is very different than a lot of our competitors and
(01:33):
how we approach some of the problems within the industry,
and we use AI to really simplify and streamline the
payment process, so reducing friction and ultimately reducing costs. When
we went about forming Machinify, it was really a combination
of four different companies that came together, and it was
(01:54):
over a year ago now, but we took kind of
the best in class across companies across payment and take
one being the Rollings Company, which pretty much invented the
subrogation and cob coordination and benefits businesses, really family ran
business that was extremely well ran and doing some really
good work. We combined that with Varius, which was another
(02:17):
family led organization that was kind of best in class
on coding validation, really looking at codes and making sure
that they're documented appropriately within healthcare and looking at a
lot of claims, and then we combined it with two
technology assets, the first one being a Pixeo Payment Integrity,
which was really one of the first companies to offer
(02:39):
a SaaS platform, so using technology to really look at
complex payments, complex audits and really help payers streamline those operations.
And then we combined that with the leading edge AI
company and Machinify how to Palo Alto, and that was
really the the final asset for us.
Speaker 2 (03:01):
That we've brought together.
Speaker 3 (03:02):
That really brought everything together in terms of the platform
and the technology in order to truly transform payments and
payment integrity. So that's a journey we've been on with
New Mountain Capital as our sponsor over the last year
or so year and a half and really excited about
what the future might bring here.
Speaker 1 (03:19):
No, it's a great overview, maybe just for the way
people in our audience or I'm thinking maybe my mother
is going to listen to this, could you maybe dive
a little deeper into what each one of those categories is,
you know, whether it's payment integrity or the claims processing.
Speaker 2 (03:36):
Yeah, yeah, happy to do it.
Speaker 3 (03:38):
So just a little bit about payment integrity, I mean
it's really the processes, the systems and strategies that are
really working together to sure accurate and appropriate payments. So
when a healthcare claim comes in from a provider, it
goes in you know, through a clearinghouse basically a way
to get that claim over to a payer. Where we
(03:59):
come in is being more proactive about it. We're actually
looking at it before that payment's made. So you hear
a lot in the industry about retrospective claims analysis versus prospective.
We're actually looking at that claim, trying to determine if
it's appropriate, if it's price appropriate, that there's that the
right codes are present, and then ultimately determining if it
(04:24):
should be paid and how much should be paid. So
what we do is really ensure that you know, consumers
and the providers, the providers get paid appropriately at the onset,
and then that consumers are ultimately paying their their portion
appropriately and that our clients, which our payers, are actually
(04:44):
paying the right the right amount for the services that
we're rendered. So you can think about it from a
number of different means, the first being is the right
payer actually responsible, So our clients will look at it
and a lot of times there's primary or secondary cupverage
for a member and you know, this benefits again the
consumer at the end of the day because you're ensuring
(05:06):
that the right coverage is applied from a number of
insurance If you think about, you know, someone who may
have been in an auto accident, for instance, we have
a ton of algorithms and machine learning, and then our
subject matter experts, which could be attorneys, that could be analysts,
that could be clinical specialists. They're actually looking at the
claim and determining should they health insurance be the primary
(05:29):
payer in this or should it be their auto carrier
perhaps if it's an auto accident that the member was
involved in, ensuring that you know, ultimately those those coverages
are documented appropriately and then ultimately pay the responsibility to
save the consumer dollars at the end of the day
and ultimately.
Speaker 2 (05:49):
Get the provider paid appropriately.
Speaker 3 (05:51):
And then our customers obviously are ensuring that they're paying
their portion we're appropriate.
Speaker 2 (05:58):
Got it?
Speaker 1 (05:58):
So, maybe going back to one of the things you
said earlier about bringing the technology assets to bear, can
we can we talk about how that coalesques with the
legacy businesses and does it does it improve the productivity
of those legacy businesses or create new opportunities? Uh, you know,
by combining these these various functions, you know, with the
technology overway.
Speaker 2 (06:21):
Yeah, it's a great question.
Speaker 3 (06:22):
So you can think of our artificial intelligence throughout the
entire platform that we have through across across those four
companies that we brought together, which is really operating as
one company, one platform today, and how we do that
is through pairing that artificial intelligence with the technology our people,
deep industry, obviously domain expertise that enhances the speed, the quality,
(06:47):
and performance of all of our solutions. So if you
look across you know we're using it in all of
our businesses today. The most basic example that I could
give and which should resonate is using AI to enhance
the scoring in our scoring models to eliminate any non
positive type investigations that we may do. The last thing
(07:10):
that we want to do is create more friction within
the healthcare ecosystem. So AI is a great way to
synthesize a tremendous amount of data, increasing our rate at
which we find findings, but ultimately reducing the false positives
that you may see in some legacy businesses. You know,
(07:32):
what we do a lot of time requires a medical record.
The last thing that we want to do is request
a medical record from providers if there really is any finding.
So it's increasing our productivity but then also reducing the
friction within healthcare unless back and forth. What AI ultimately
does it has the ability to summarize massive amounts of
(07:53):
data very quickly, and that ultimately increases our experts, our
clinicians or code or others, their efficiency and accuracy and
what they do. I'm happy to give you a few
examples if that would be helpful.
Speaker 1 (08:06):
Yeah, let's dive into it.
Speaker 3 (08:08):
So one of our flagship products is our Machinify audit tool.
So you can think of this as looking at coding
or deep clinical expertise that may be needed to look
at a medical record and determine if the charges are
actually appropriate for what was either documented or what was
actually delivered by the provider. And so we use our
(08:29):
AI assistant model and machine learning algorithms to find any
suspicious billing patterns. You know, we look at erroneous claim
issues and pricing anomalies that may be in there for
the purpose of performing these complex audits and these complex reviews,
and ultimately that determines which claims we should select for
(08:51):
a manual audit. And those manual audits are then you know,
benefit from machine learning and be able to synthesize a
vast amount of inform from the claim, through the medical record,
through the payment policy, through the contract that the member
may have with their health plan, and ultimately, what we've
seen is it really generates a two x return versus
(09:13):
what we're able to do and two acts the results
from other technology that's in this space. So ultimately that
technology is used to not only synthesize information, but make
our experts really a lot more productive and what they
do on a day to day basis.
Speaker 1 (09:32):
Maybe help me understand a little bit better because we've
we've had another payment integrity company on the podcast, But
my understanding is the landscape is really filled with a
lot of technology debt on the part of a lot
of different players in the ecosystem. Is that the right
way to think about it, that we're bringing this into
the modern the modern era?
Speaker 2 (09:51):
Yeah, exactly.
Speaker 3 (09:52):
I mean, there is a tremendous amount of debt, and
if you think of some of our clients, the struggle
that they've had on the payer side, they have, you know,
a tremendous amount of legacy technology that has a lot
of technology debt associated with it. What we're doing and
if you could think about the claims adjudication systems that
are actually going through taking the claim, making sure some
(10:14):
basic edits on it, and make sure that it comes
out of it with the correct price to pay and
the estimation and benefits that then goes back to the provider.
A lot of those systems are, you know, ten, fifteen,
twenty years old. A lot of them are on premise technologies.
What we have is pure cloud technology that then sits
(10:35):
on top of those systems and is able to get
to the claim before even those adjudication systems come into play,
so ultimately saving the amount of back and forth between
payers and providers. And you know, when you think about
our technology, it's really we're viewed as more of a.
Speaker 2 (10:54):
Secondary adjudication system.
Speaker 3 (10:57):
So we're able to price it in milliseconds, able to
get the claim through our technology and determine should we
pay this claim or not. And our clients are actually
pushing that up against their core adjudication system and saying,
you know, this may not be your adjudication system, but
we're able to look at this and determine if any
errors that we should pause this claim from being paid
(11:18):
appropriately right now, should we go back and take a
further look at this and that really helps our clients
who are on the clock. You know, they have thirty
days to get claims paid. The ability to do this
in milliseconds versus having individuals look at it and legacy
technology really makes a huge difference and controlling costs within
(11:40):
the system, but then ensuring in accuracy as well.
Speaker 2 (11:43):
Got it.
Speaker 1 (11:44):
You know, as I think about the customer, you know,
you had these four assets that you've put together under
one umbro. How has it changed, I guess the go
to market strategy with your customers. I mean, are you
going as one one brand to your to your customers.
Speaker 2 (12:02):
Yeah, we're one brand now.
Speaker 3 (12:04):
We took the name of Machinify, which was the most
recent acquisition that we did, and really that was to
signify to the market a new way of doing things
with these uh, you know, four companies coming together and
what we'd like to think about. We have, you know,
more than forty years of experience across our rallyings business,
(12:25):
but we've come at it now differently from the standpoint
of using technology. First, we're able to lower costs and
ensure outcomes, which is a different approach, and we're able
to keep the speed of a Palouto kind of Silicon
Valley company, which is really important to me as CEO,
ensuring that we keep the expertise that we have in
(12:45):
house from our legacy businesses. But we're moving and adapting
as quick as a Silicon Valley kind of VC back company,
almost even though we have the capital of a private
equity you know back company that's you know, profitable and
doing extremely well and growing. So from a go to
market standpoint, you know, what we've been able to see
is a tremendous amount of cross sell when you have
(13:07):
those great companies that come together. You know, they may
be working with us on our coordination and benefits solution,
they may be working with us on subrogation, and then
we're able to come in with that same claim speed
that we get from the client and say, hey, let
us take a look at complex audit, let us look
at you know, some of the data mining solutions that
(13:29):
you may be using today, and let us show you
what technology can really do. And the difference for us
is we're able to meet clients where they are, which
is historic. You know, the industry is built on pure
outsourcing and you see some of the legacy providers only
offering outsourced solutions. What we're able to do with this
technology is say.
Speaker 2 (13:50):
Hey, large payer, why don't you We'll meet you where
you are. If you would like to outsource everything to us,
that's great, we'll ensure we'll ensure certain outcomes for you.
But if you'd like to bring it in house, that's
also great.
Speaker 3 (14:02):
Why don't you use our technology which is the best
at you know, finding errors, which is the best at
you know, reducing fraud, waste and abuse within the industry.
And we'll train you to use our technology and continue
to enhance those large language models.
Speaker 2 (14:18):
That we that we have in the industry.
Speaker 3 (14:21):
And so it's kind of a hybrid approach opinding on
where our clients are.
Speaker 2 (14:24):
And that's really resonating in the market as well.
Speaker 1 (14:27):
There's a lot to unpacked there. I'm fascinated by this,
you know, as we think about those two different models.
Are the economics different for machinify whether they bring it
in house or use you as the provider I guess
as a software as a service, or how does it
how does it differ?
Speaker 3 (14:43):
Yeah, I mean you could think of it as you know,
you hear a lot of buzzwords in the industry, but
service as software.
Speaker 2 (14:49):
So typically what we're charging is.
Speaker 3 (14:51):
A platform fee associated with some form of outcome and
some outcome based pricing. You know, we're we're fully aligned
with the our clients in terms of, you know, when
when they save money, when they reduce their administrative costs,
when they reduce their costs internally, we save in that savings.
(15:12):
We generate savings as well. But in terms of the
business model, it takes all different types and approaches. As
you may imagine, some are simply playing the SaaS feed
depending on what they're using from us.
Speaker 2 (15:25):
Some are pure contingency based.
Speaker 3 (15:28):
So again alignment from a client standpoint, what we're seeing
more and more of is what you see across the
industry and technology businesses, really a platform fee and then
some form of outcome based results. And that's really resonated
and I love the alignment with our clients. I will say,
you know, we're spending a lot more time thinking about
(15:49):
how do you not only save money and make sure
that you know, our clients are saving money, but ultimately
making sure that every claim is paid correctly and in
order to get to you really need to be you know,
further left in the payment stream, and so using our content,
the smarts, the AI, the technology, and our subject matter experts,
(16:10):
we see a real opportunity to continue moving upstream in
order to make sure that all claims are paid appropriately
versus just looking at them from either a prospective or
a retrospective review.
Speaker 1 (16:22):
No, that's well said. So as I think about the
journey that you've been on as a combined entity, what
did you have to do I guess in the last
year or so to put everything together in a holistic way.
You know, was there any real heavy lifting that that
had to occur to get everything to work together?
Speaker 2 (16:38):
Yeah?
Speaker 3 (16:38):
I mean I think anytime you're pulling together companies, it's
about culture, right and do you have the right culture?
And I was very fortunate to acquire, you know, and
bring together four different companies that have very strong leadership.
So if you looked across kind of our product and
innovation component, which is Machinify, the founder and CEO of
(17:00):
machine Ifies and now our chief product officer prisona Geniesean,
and he's done a great job, you know, really bringing
that level of enthusiasm and technology expertise to bear across
our entire platform. And then when you look across the
other businesses you know we have we're set up in
two different business units today are right payer solutions, So
(17:22):
you can think of that as, you know, as the
right primary and secondary coverage in place. Are we making
sure that it's being built appropriately to that coverage? And
you know, is there anything like a subrogated claim that
may be in place? And that's being ran by Rolling
CEO who's now the president of that business Unity. And
(17:43):
then we took the EPIXEO former Claim Logic CEO and
he's now running Tom Ignata who's now running our complex
payment solution business. So the culture has really come together
and I think that's a first phase and I learned
that early on in my career, you know, working as
a cerner. You mentioned that at the beginning, having the
right culture makes all the difference, and bringing the cultures
(18:06):
together that's really focused on innovation and results for our clients.
Speaker 2 (18:10):
It has stayed true.
Speaker 3 (18:12):
The other thing that I would say, it's culture and
then it's platforms as well, and we've been able to
make quick work of platforms and ensuring that we're taking
that AI platform and putting it across all of our businesses,
which is something that traditionally can take years to typically do.
We're already doing it within the first six months of
(18:32):
bringing Machinify in.
Speaker 2 (18:34):
You know, we've only closed on that.
Speaker 3 (18:36):
Acquisition about four or five months ago now, and so
the ability to take best in class technology and put
it across an environment and really make that top priority,
along with ensuring the culture and employees that are being successful,
has really.
Speaker 2 (18:51):
Been what we've been focused on.
Speaker 3 (18:54):
And it's the great results in terms of clients seeing
the results and the US seeing some of those results,
and then ultimately our employees really being engaged and the
vision that we have which is truly to lower the
cost of health care, and you can't do that by
you know, adding additional friction or processes within the mix.
(19:15):
You really need to streamline things. And our employees are
really bought into this, this vision that we have, which
is you know, frictionalists healthcare system where quality is of
the utmost important and importance, and you know, we're all
working to ensure that the cost of healthcare is affordable
for everyone.
Speaker 1 (19:35):
Interesting you brought up Cerner, and I was thinking about
your background as you were speaking, and you know, you
also had some time and signify you know, as a COO,
and that was a combination of businesses in a New
Mountain play as well. You know, how much of that
playbook did you take from maybe the Signify and I
know the problems are very different, but maybe the vision
(19:56):
and and how you're going to run the company from
a processes perspective? Is it very similar? Am I thinking
about that in the right way?
Speaker 2 (20:03):
Yeah?
Speaker 3 (20:04):
I think I've it's kind of a culmination of both
work experiences.
Speaker 2 (20:09):
You know, I spent ten years at Cerner.
Speaker 3 (20:10):
I was, think of it as chief of staff for
the chairman and CEO of UH and founder of Cerner,
and then I was chief operating officer of Signified for
almost eight years, and then part of the CBS for
a year post acquisition. And I would say there's a
tremendous amount of you know, I kind of cut my
teeth at Cerner in terms of learning what the opportunity
(20:32):
was when you digitized healthcare, you know, and I was
there from early stages of VMR through at you know,
meaningful Use, which obviously accelerated the industry greatly, but at
Cerner we always said, you know, the first order effects
of digitizing medical record were obviously care related and they
did some great things to do that, But what we're
(20:53):
the most excited about was truly a second, third, fourth
order effects. So you can think of it as once
it's digitized, is how can you improve care through algorithms, right,
just having all the lab results, the clinical documentation within
that record and doing things like generating sepsis algorithms that
can save people's lives.
Speaker 2 (21:13):
Right. And then third order effects of once you digitize.
Speaker 3 (21:17):
It and you're using it in day to day practice,
how can you then use it for other use cases
and problems, which is exactly what we're doing at Machinify,
which is using a lot of the data. One of
the inputs is medical records to be able to determine
if care was ultimately appropriate, is it priced appropriately, those
(21:38):
sorts of things, and you're able to do that now
that it's digitized. So I think that was a kind
of lesson learned at Cerner.
Speaker 2 (21:46):
And then at Signify.
Speaker 3 (21:48):
We did bring two companies together and it made some
additional acquisitions along the way, But what we did there,
which was different than the industry, is really take a services.
Speaker 2 (21:58):
Business and make it into it technology business.
Speaker 3 (22:01):
And that's largely what you see as doing a machineified
taking a tech enabled services business and make it into
a true technology business. With some of the expertise, with
the experts that we have on our team, and now
with the addition of large language models generative AI, we're
able to do it a much faster clip. So there
(22:23):
were a tremendous amount of lessons learned at Signify, and
obviously having the backing of New Mountain and both machinifying Signify,
it is a huge advantage because you understand the strategic
expertise that New Mountain brings to the table, the capital
expertise that they bring to the table.
Speaker 2 (22:40):
But I was really.
Speaker 3 (22:40):
Focused on how do you automate as much as possible
within the healthcare system at Signify and make it so
that your clinicians are the most productive generating the results
for the members, being able to find care management programs
that those members are eligible for and should be enrolled in,
(23:02):
and really focus their time and attention on what's best
for the patient through automation. And that's what we're doing
here is differences. Instead of clinicians that are front of members,
we're actually looking at how can we make our our attorneys,
how can we make our clinicians, our coders the most
productive possible, so they can actually use their expertise to
(23:24):
actually find the most complex errors and be able to
ultimately train our larger language models and others to really
make it more consistent, less variability, and less friction within
the healthcare system. So I think those are the big
lessons learned, and you know which really helps me in
this in this role of CEO of Machinify.
Speaker 1 (23:43):
You know, one of the things you mentioned a bunch
of times was just the technology that you're applying, you know,
to the problem. You know where you sit in this
intersection of technology and healthcare. You know how challenging is
it for organizing, for an organization like yours to actually build.
Speaker 2 (23:59):
Upon this vision.
Speaker 1 (24:01):
I mean, is there constraints in terms of how fast
you can move is it? Are there constraints around hiring
the right people? I mean, I have to imagine there's
a war for talent in the world of AI right now.
Speaker 2 (24:13):
Yeah.
Speaker 3 (24:14):
Absolutely, I think there's a number of things. It's been
a great question. So there is a tremendous war for talent,
which is where I feel that we have a leg
up in terms of the competition and others that are
in the industry. We're really focused on things that matter,
and what we see is Silicon Valley and engineers, data
(24:36):
scientists wanting to work for a company that has a
bigger vision, bigger mission than what may be and just
general high technology today. So what we provide is some
of the best technologists in the industry in pallelto being
able to be able to recruit retain sort of the
(24:57):
best talent within the industry. It's very impressive to me
to see what we've been able to do in terms
of retaining talent and then focusing them. I've always said
that the biggest thing that people want is they want
to work for a company that has a larger vision.
They want to be challenged, but also work on things
that matter. And so we're able to do that from
a technology standpoint that others in the industry like a
(25:18):
Meta or others may not be able to do, may not.
Speaker 2 (25:21):
Be able to provide them.
Speaker 3 (25:22):
The other thing I would say is, you know, there's
also a tremendous war for talent in terms of clinicians,
people that understand true payer operations and healthcare. We've been
able to recruit some of the best talent in the industry.
For instance, we pride ourselves on seeing kind of.
Speaker 2 (25:41):
The full vision.
Speaker 3 (25:42):
We've hired the our CTO, Brian Gambs, came from R one,
so really understanding payer revenue cycle solutions, which you don't
typically see payment integrity, which is more focused on payer side.
Speaker 2 (25:55):
And then clinicians.
Speaker 3 (25:56):
You know, that's probably the lifeblood of generating content, being
able to look at content and find where there may
be errors and really avoid some of the fraud, waste
and abuse because at the end of the day, what
we're finding our errors driving accountability, improving cost control, other things,
and you need clinicians side to side with engineers in
(26:18):
order to really make that magic happen. So, you know,
we find ourselves being able to really retain and you know,
being able to recruit some of the best talent in
the industry.
Speaker 1 (26:31):
So maybe just switching gears a little bit. You know,
we've talked about that, We've talked about the journey and
around the talent. You know, if we think about the
vision over the next three to five years, you know,
what are you telling your employees to kind of inspire
them where you're going to be in a couple of
years and I guess maybe how are you spending your
time a little bit differently than maybe standing up the
(26:52):
business over the last year.
Speaker 3 (26:53):
And to put it all together, yeah, I mean, we
truly see ourself as an operating system for payers.
Speaker 2 (27:00):
And if you think about.
Speaker 3 (27:01):
That, you know, there is a tremendous amount of technology
debt within the industry. What we bring is a fresh
new approach to it. So when I talk to our employees,
when I talk to you know, our capital partners, others,
we're reimagining what's possible in terms of moving from true
payment integrity, which is ensuring that things are accurate and
(27:22):
paid appropriately, to actually ensuring that all claims are correct
and being that operating system that reduces a lot of
the admin functions that are frankly driving the cost of
health care much higher than it needs to be.
Speaker 2 (27:36):
I mean, if you.
Speaker 3 (27:36):
Look at the spend within healthcare today, whether it's you know,
four to five trillion dollars, you know, the estimates are
close to you know, anywhere from you know, five hundred
to five hundred billion to a trillion dollars in terms
of fraud, waste, abuse, and complexities that are adding to
the cost of health care and Ultimately, our vision is
(27:59):
to transform form that and really transform that healthcare ecosystem
with an AI first platform where rapid, accurate payment decisions
and real administrative tasks are being done faster, more affordable,
and driving better care.
Speaker 2 (28:15):
For everyone across the ecosystem.
Speaker 3 (28:17):
So we see ourselves as eventually, you know, moving beyond
even traditional payment integrity. Anything that's going to lower the
cost and improve the quality within healthcare through technology and
our expertise is where we're headed as a company.
Speaker 1 (28:32):
Could you maybe give us one one example without you know,
maybe tipping your cap too much.
Speaker 3 (28:38):
You know, I think in terms of just payment integrity
is probably the best examples to use because that's where
we are today. But taking it one step further, looking
at the payments, you know, we're not processing payments today,
but we are generating you know, a tremendous amount of
data that can be used for that those payments. And
(29:00):
as I mentioned earlier, the core adutication systems that are
in place, we're able to do a lot of that
same work because the inputs are the same. If you
think about the inputs communication system, it's claims, it's eligibility,
it's medical records, you know, those sorts of data inputs.
Speaker 2 (29:17):
What we're able to do is do it.
Speaker 3 (29:18):
A much faster and much more accurate pace to be
able to you know, generate what should be paid, those
estimates of benefits, those sorts of things, and you know,
do it in an accurate, you know, ninety nine percent
accurate you know way where we're not.
Speaker 2 (29:35):
Seeing that today.
Speaker 3 (29:36):
So I think you'll continue to see us move further
left in terms of I mentioned it earlier from retrospective.
We're doing primarily pre payment today, really focused before the
actual payment's made. But then how can we even get
further upstream to ensure that we're working with those provider groups.
You know, we're not a black box, which is additional
(29:58):
something additional for us that we see from our competitors.
They're making edits and changes, but they're not sharing that
with providers. We think it's better for the industry if
we actually provide that to providers, what those errors are,
how it's leading to some of the challenges within the industry,
and then ultimately ensuring that you know, what they're submitting
(30:19):
doesn't happen again, right because you see that and we
don't think it's right for an industry to be focused
on just finding errors.
Speaker 2 (30:27):
We want to eliminate those errors before they occur.
Speaker 1 (30:29):
So if I sit back and think about the operational
side of the business, you know, I think we've chatted
a lot about how the nuts and bolts all fit together.
Is there anything on the regulatory front or the policy
front that is a catalyst for the industry or your
business or is there anything you're paying attention to that
you know could potentially be aheadwind.
Speaker 3 (30:49):
Yeah, I mean we're paying very close attention to what
CMS is doing. Obviously, we have commercial clients and commercial
payers as clients. We have Medicare A Vantage clients, we
have Medicaid you know, Medicaid MCOs as clients. But we
see a tremendous opportunity in the government space in terms
of what they're doing, how they're looking at things, and
(31:12):
we think that will then come over to commercial and
some of the other lines of business that we operate in.
You know, I think from a government perspective, they're focused
on the right things, right, They're focused on fraud, waste
and abuse. If you look at some of the numbers
you know out of Medicare feed for service, you know
they anticipate are they estimated around an eight percent error
(31:32):
rate and some of the payments that they make, so
thirty two billion and errors that are made and according
to CMS just last year alone in terms of errors
and payments, and that can be due to a number
of issues.
Speaker 2 (31:47):
But where we see it going is, you.
Speaker 3 (31:49):
Know, those changes that CMS is making will flow down
to commercial payers, that will flow down to our clients today,
and we'd love to be right there provided in our
ex ertise of what we're seeing in our clients and
able to provide that back to the government, you know
how and when that takes effect you know, maybe seen
(32:10):
in different timelines and other things, but certainly focus on fraud,
waste and views. So focus on audits and ensuring that
they're auditing their member.
Speaker 2 (32:20):
Or their providers appropriately.
Speaker 3 (32:23):
I think is something that they can take best practices
from what we're doing and bring that back into the
government space. So yes, we're tracking it very closely and
we think there's a tremendous opportunity. And you know, we
also see the government much more focused on program and
payment integrity than they've been in the past, which we
think is ultimately a good thing.
Speaker 2 (32:42):
Got it.
Speaker 1 (32:43):
We talked a little bit about the funding model or
the way the business was combined. I mean, can you
talk a little bit about your funding needs for the business?
You know, are you profitable now? Any KPIs you could
share public g.
Speaker 3 (32:57):
Yeah, I mean we don't share financial information publicly. But
what I can say is, you know, we're profitable and
we're able to fund you know, extreme innovation, which couldn't
have been done at each of these individuals individual companies standalone.
So we're investing a lot in innovation and technology, I think,
much more than our competition, much more than any of
(33:19):
the standalone organizations would have been able to do on
their own.
Speaker 2 (33:24):
I think what's interesting about that is, you know.
Speaker 3 (33:26):
It's not a one time you build a large language
model and you put it to work against your platform.
Speaker 2 (33:32):
You know, it takes continuous honing.
Speaker 3 (33:34):
It takes learning from not only what your internal agents
and employees are doing, but also as we put our
technology in the hands of our clients.
Speaker 2 (33:44):
You know, we need to validate the work that our
clients are doing on the platform because that goes into
our learning model or modules that ultimately determine how successful
our technology is. So, you know, while it's saving us
a tremendous amount of dollars. From pro activity standpoint, there
is a lot of investment that needs to occur in
(34:04):
order to keep training the models and ensure that you know,
our quality is top notch and you know, and we're
we're going to continue to invest in technology here and
we'll continue to look at M and A as well
where we're appropriate. I think we have a great technology
in the platform today, but there may be areas that
we want to expand into that we're not doing today.
(34:27):
When you look across a full payment integrity stack or
you look at admin functions within our payer clients, they've
asked us to.
Speaker 3 (34:36):
Do a number of things that we're not doing today
because they've seen the results that we're able to generate.
And so we'll continue to be inquisitive and we'll continue
to invest in technology as we move forward.
Speaker 2 (34:46):
Here.
Speaker 1 (34:47):
You beat me to the question. I was going to
ask you if there was a preference between the internal
and inorganic investments, So thanks.
Speaker 2 (34:54):
For covering that.
Speaker 1 (34:56):
As you think about that, I mean, what are the
what are the key herd in your mind, whether it's
from a strategic or financial perspective to doing a deal,
I mean what buckets, does it what checks does it
have to check on the checklist?
Speaker 3 (35:10):
Well, I think whenever we look at a company from
an M and A standpoint, it needs to make sense
in terms of synergies with our business and does it
fit the overall vision of where we're going.
Speaker 2 (35:21):
So those are the two.
Speaker 3 (35:22):
Things that I spend most of my time on as CEO.
Is does a business further get us closer to our
vision where we want to go, which is again to
reduce the overall cost of healthcare and sure that providers
and payers are working together in concert to actually deliver
high quality results at the lowest cost possible. So the
(35:44):
strategic fit in the vision of the companies are very
important to me. And then synergies with our business. Is
it a business that we think we can add our
technology to that can generate much better results? Is it
you know, exactly what we did at Signify, exactly what
we've done so far at Machinify is a business that
is still pretty manual that we can, you know, eliminate.
Speaker 2 (36:08):
The technology stack that they're using and put.
Speaker 3 (36:10):
In our technology to automate to use our AI platform
to generate better results, make their workforce much more productive.
Speaker 2 (36:19):
And then ultimately deliver greater results for their clients. So
that's really the lens that we look at is are
there synergies with our business in terms of are we
able to make them more productive generate greater results, And
we think with our technology there's a lot of businesses
that we could do that with and then ultimately is
a strategic fit in terms of the direction that we're
looking and moving as a company, and there are organizations
(36:42):
that are out there. It's a matter of how much
organic growth we've been.
Speaker 3 (36:47):
We've proved our ability to grow organically, not only through
cross selling but developing new products. So if you look
at our Machinify pay solution, that was you know, really
the thought was working with a key strategic national account
and developing a data mining solution that you know, historically
has been a black box as I mentioned earlier for clients,
(37:09):
and then it's ultimately been a black box in terms
of what gets shared with the providers and what we said.
We went into it and said, we want to develop
our own solution that ultimately won't be a black box.
We can put it in the hands and let our
clients run it and it will move from just looking
at claims retrospectively and detecting anomalies and other things to
(37:31):
actually doing it prospectively more like an editor would and
finding some of those errors and looking at anomalies with
technology that we can put in the hands of our clients.
And that's been growing at you know, high double digits,
and you know, it's just a great testament to the
fact that we can develop organically solutions and products that
(37:51):
we can bring to market and cross sell.
Speaker 2 (37:54):
But we also may see.
Speaker 3 (37:55):
An opportunity where it can accelerate through acquisition of a
truly different sector. Then we may not be in today
and be able to bring those to our client base.
When you look across our client base, we have you know,
the large majority of the major payers as clients.
Speaker 2 (38:11):
We have over seventy clients today.
Speaker 3 (38:14):
We're processing a tremendous amount of data, so claims that
are coming through our system. If you think, you know,
four point eight trillion is being spent on healthcare today,
we have close to trillion dollars of claims flowing through
our systems. Think of it as twenty to twenty five.
Speaker 2 (38:29):
Percent depending on the numbers use.
Speaker 3 (38:32):
And so you know what that allows us to do
is we have the data, we have the technology expertise,
we have the clinical expertise, and we're able to point
those at problems that you know, ultimately are causing you know,
everybody in the industry to say that costs are too
high within the ecosystem. So hopefully I explained a little
(38:53):
bit about our you know, acquisition to market as well
as our organic growth strategies.
Speaker 1 (38:58):
Well, one are the things you mentioned was just the
client coming to you. I mean, how often do you
have those conversations where one of your customers is very
key in on a specific pain point and you develop
those sorts of solutions. Is that is that a common
iteration of new products and services?
Speaker 3 (39:15):
It is, I mean it goes back all the way
to kind of our early days and some of these
businesses have been in business.
Speaker 2 (39:20):
For forty years.
Speaker 3 (39:22):
And we developed and basically developed the manual around subrogation, right,
and then we took that and moved into technology from
a machinify standpoint, and you know, there's a better way
to do this than how it's been done in the past.
And you know, we have some very strategic clients that
work very closely with us. You know, we have strong
(39:43):
commercial clients. We have strong Medicare advantage clients. We have
strong Medicaid you know MCOs that work with us. So
what we find best is to work with the experts
in the industry and our clients really have a development
partnership on what their challenges are. And it could be
anything as taking COB coordination benefits. It's been in the
(40:07):
industry for a long time and you know, at the
most basic level, coordination benefits is looking at a tremendous
amount of data, not just claims, data of external information,
external data and looking at things like when employees are retiring,
you know, are they going to a different plan, looking
at data that's publicly available, also eligibility data from different clients,
(40:32):
and pulling that all together. We're able to work with
our clients to develop prepay COB, which has historically been
done retrospectively, so after it's been paid, after the claim's
been paid, you're going through and saying, well, actually, Medicare
maybe was primary or secondary, depending on the scenario. What
(40:53):
we've been able to do with technology is bring what
was historically done manually in terms of looking at external
dat in the base, hitting websites, other things, we're able
to have our AI agents look through that information and
summarize it for our experts, so we work with our
clients to do then. Typically how innovation starts is we
have a technology suite and the products, but ultimately they're
(41:17):
having an issue and seeing things above and beyond what
we're delivering today, and they come to us and we're
able to innovate very closely with our clients. And it's
one of the things that I enjoy most about the
role is having extremely great clients that we're able to
innovate with and develop new technology and new solutions and
impact overall healthcare. So really satisfying from that front.
Speaker 2 (41:39):
Got it.
Speaker 1 (41:40):
You know, as we kind of get to the end here,
you know, I think about your career and you've been
across a couple of different sectors and you know, very
big companies. You know, as you sit here today, you
know with that technology and healthcare had on and that intersection,
you know, going through machinify, what are you most excited
about from a technology perspective in healthcare the industry? And
(42:00):
I don't necessarily mean from like maybe a payments perspective,
but as you look down the pipe pipeline. You know,
five ten years from now, what do you think is
going to surprise us the most.
Speaker 2 (42:10):
Well, I think the data will become.
Speaker 3 (42:13):
Much more ubiquitous in terms of where it is, how
it's used, and I mentioned that, you know, in terms
of my earlier career in terms of digitizing medical records
and just making it available. I think you see some
unique companies like data Van, which is also a new
amountain capital company that's able to really use privacy controls
tokenization of healthcare data to make it more available. And
(42:37):
I think ultimately that data is going to be able
to be used to drive greater transparency, reduce some of
those high admin costs, you know, and ultimately help consumers
who are struggling with navigating some of the complex systems
and high financial responsibilities that are out there in the industry.
So I think there's two things, really, the data and
(42:59):
just having dat much more available. And I think CMS
is really focused on this some of the new administration
making data much more available, whether it's on the medicaid
side or on the Medicare advantage side. Obviously with controls
in place, the right level of security, other things which
I think is going to be a key component to
actually making healthcare much better, much more affordable if we
(43:24):
can have medical records as opposed to having to request
them and wait and cause friction with providers other things.
Speaker 2 (43:32):
The more that we can have medical records available for
us to be able.
Speaker 3 (43:35):
To run our technology and our human expertise against them,
to be able to look at it and understand what
challenges there may be in that record will ultimately drive
down the cost of healthcare and ultimately improve care as well.
So I think that's number one in terms of just
having data much more available. And I think the quality
(43:55):
of data, I would also say, is becoming much more
readily available, and you know, we're able to trust the
data a lot more so things like virtual scribes and
using AI. I think it's going to make a tremendous
more amount of data available. Now you're going to need
technology that actually looks through it and says, is this
(44:15):
clinically relevant, does it actually fit the criteria for this
member in this patient, and how can that ultimately use
to improve care. So that's where I'm the most excited
just around the availability of data, the availility to use
it to drive down and be in cost. But ultimately
to improve care as well. And we see, you know,
(44:37):
ultimately machinify in our technology being able to be used
in that regards and take in a tremendous amount of data,
whether we're generating it or taking it in from you know,
a multitude of other systems. So I think we have
a lot to look forward to in terms of, you know,
improving quality and lowering the cost of healthcare across the board.
Speaker 1 (44:57):
I'm right there with you. I'm so interested to see
what's going to happen, you know, not just on the
maybe the back end, but on the clinical side as well. So, David,
I like to wrap these conversations up by focusing on
a wife lesson that the guest has maybe learned in
their personal or professional life that drives them. Is there
one thing that you think about when you think about
your day to day that that kind of you see
(45:18):
informing your mission in life?
Speaker 2 (45:21):
Yeah, yeah, no, it's a great question. You know.
Speaker 3 (45:24):
I grew up in a family. There are six kids.
I'm the youngest of six kids. My dad was a periodontist.
All six of us are in healthcare in some form
of fashion. It's interesting that three are actual providers ones
a physician, ones, a PA another, there's an end adonis
(45:44):
and then you have three on the business side, right,
And so that's impact of me because I'm running by
my brother who's a physician every day and you know,
what we're doing in the industry isn't making his life easier?
Is it causing complexity in his life? And I think
really humanizing the element of health care and you can't
lose sight of that.
Speaker 2 (46:04):
I learned that early on, right, is that we're.
Speaker 3 (46:07):
All consumers of healthcare and if you really personalize it
and from everyone from our clients to our employees, it
allows everyone to get the vision on where you're going.
And it's something that you know, I've taken throughout my career,
really focusing on a vision where we're going and who's
really going to impact from what.
Speaker 2 (46:25):
You're doing on a day to day basis.
Speaker 3 (46:27):
So that's number one, and then I think number two
would be I learned early on in my career from
one of my mentors, Neil Patterson, who is a founder
of Cerner, really to use there's a lot.
Speaker 2 (46:40):
Of things that we can go focus on.
Speaker 3 (46:42):
So if we talk about admin costs within healthcare, there's
a tremendous amount of areas that we can go focus
on and you can sometimes get lost. So it goes
back to the vision where you're going, but then able
to really use a laser in terms of your time,
energy resources, in terms of really focusing on those things
that you can impact. So I've used that throughout my career,
(47:04):
really being purposeful on you know, the challenges in front
of us, but focus on those things that we can
impact the greatest.
Speaker 2 (47:11):
And that's what you're seeing Machinify right now. We're focused on.
Speaker 3 (47:15):
Being the best platform and technology and results within payment integrity.
But as we continue to generate those results, you'll continue
to see us move into other areas and we'll be
very thoughtful on those areas and really make sure that
we're focused on the right things.
Speaker 2 (47:28):
Going back to the vision of reducing costs within the
system today, those.
Speaker 1 (47:32):
Were two great lessons. Thank you so much for sharing.
I'm so curious about the holiday discussions around the table
with three versus three.
Speaker 2 (47:42):
Yeah, it's not really the three versus three, you know,
it's more of alignment and truly understanding one another, right,
because you come at it from different angles, and you know,
i'd say it's probably better than speaking politics at the
dinner table, and I think, but yeah, it's great to
(48:03):
be able to bounce things off. I have a brother
who's a physician at Kaiser, and you know, it's great
to understand some of the work that we're doing, how
it could potentially impact him, how could impact our clients,
and just really fun things to go focus on in
terms of challenges that we can solve together.
Speaker 1 (48:20):
That's great. Thank you David for sharing all that. I
think we'll wrap up here. David, it's been great learning
more about machinifying your career. Thanks for joining us.
Speaker 2 (48:30):
Yeah, I appreciate it. Jonathan, thanks so much.
Speaker 1 (48:32):
And so that's David Pierre, the CEO of Machinify. Thank
you so much for joining us on our latest episode.
And please make sure to click the follow button on
your favorite podcast app or website so you never miss
a discussion with the leaders in healthcare innovation. I'm Jonathan Palmer,
and you've been listening to the Vanguards of Healthcare podcast
by Bloomberg Intelligence. Until next time, take care.
Speaker 4 (49:06):
Muss aasure uses a ton of bases
Welcome to another episode of Bloomberg Intelligence's Vanguards of Healthcare podcast,
where we speak with the leaders at the forefront of
change in the healthcare industry. My name is Jonathan Palmer,
and I'm a healthcare analyst at Bloomberg Intelligence, the in
house research arm of Bloomberg. I'm very happy to welcome
today's guest, David Pierre. He's the CEO of Machinify, a
new reformed payments company sponsored by New Mountain. Before that,
(00:41):
he was the CEO of Signify, and earlier in his
career spent some time at Cerner. Welcome to the podcast, David.
I'm excited to hear about Machinify and this new company
and where you guys are going.
Speaker 2 (00:51):
Great.
Speaker 3 (00:51):
Thanks Jonathan, happy to be here and happy to share
our story and where we're going as a company.
Speaker 1 (00:56):
Excellent. So why don't we Why don't we dive in,
you know, as I understan and it machinifies a combination
of a couple legacy assets. Can we talk about those
companies and what the vision is for the combined entity.
Speaker 2 (01:09):
Yeah, happy to do it.
Speaker 3 (01:11):
So at our heart, I mean, we're a healthcare intelligence company.
Machineify is and we're focus on really transforming payment integrity
within kind of this healthcare ecosystem, and we worked with
a number of the leading health plans across the US,
and we're taking an AI first approach, which I think
is very different than a lot of our competitors and
(01:33):
how we approach some of the problems within the industry,
and we use AI to really simplify and streamline the
payment process, so reducing friction and ultimately reducing costs. When
we went about forming Machinify, it was really a combination
of four different companies that came together, and it was
(01:54):
over a year ago now, but we took kind of
the best in class across companies across payment and take
one being the Rollings Company, which pretty much invented the
subrogation and cob coordination and benefits businesses, really family ran
business that was extremely well ran and doing some really
good work. We combined that with Varius, which was another
(02:17):
family led organization that was kind of best in class
on coding validation, really looking at codes and making sure
that they're documented appropriately within healthcare and looking at a
lot of claims, and then we combined it with two
technology assets, the first one being a Pixeo Payment Integrity,
which was really one of the first companies to offer
(02:39):
a SaaS platform, so using technology to really look at
complex payments, complex audits and really help payers streamline those operations.
And then we combined that with the leading edge AI
company and Machinify how to Palo Alto, and that was
really the the final asset for us.
Speaker 2 (03:01):
That we've brought together.
Speaker 3 (03:02):
That really brought everything together in terms of the platform
and the technology in order to truly transform payments and
payment integrity. So that's a journey we've been on with
New Mountain Capital as our sponsor over the last year
or so year and a half and really excited about
what the future might bring here.
Speaker 1 (03:19):
No, it's a great overview, maybe just for the way
people in our audience or I'm thinking maybe my mother
is going to listen to this, could you maybe dive
a little deeper into what each one of those categories is,
you know, whether it's payment integrity or the claims processing.
Speaker 2 (03:36):
Yeah, yeah, happy to do it.
Speaker 3 (03:38):
So just a little bit about payment integrity, I mean
it's really the processes, the systems and strategies that are
really working together to sure accurate and appropriate payments. So
when a healthcare claim comes in from a provider, it
goes in you know, through a clearinghouse basically a way
to get that claim over to a payer. Where we
(03:59):
come in is being more proactive about it. We're actually
looking at it before that payment's made. So you hear
a lot in the industry about retrospective claims analysis versus prospective.
We're actually looking at that claim, trying to determine if
it's appropriate, if it's price appropriate, that there's that the
right codes are present, and then ultimately determining if it
(04:24):
should be paid and how much should be paid. So
what we do is really ensure that you know, consumers
and the providers, the providers get paid appropriately at the onset,
and then that consumers are ultimately paying their their portion
appropriately and that our clients, which our payers, are actually
(04:44):
paying the right the right amount for the services that
we're rendered. So you can think about it from a
number of different means, the first being is the right
payer actually responsible, So our clients will look at it
and a lot of times there's primary or secondary cupverage
for a member and you know, this benefits again the
consumer at the end of the day because you're ensuring
(05:06):
that the right coverage is applied from a number of
insurance If you think about, you know, someone who may
have been in an auto accident, for instance, we have
a ton of algorithms and machine learning, and then our
subject matter experts, which could be attorneys, that could be analysts,
that could be clinical specialists. They're actually looking at the
claim and determining should they health insurance be the primary
(05:29):
payer in this or should it be their auto carrier
perhaps if it's an auto accident that the member was
involved in, ensuring that you know, ultimately those those coverages
are documented appropriately and then ultimately pay the responsibility to
save the consumer dollars at the end of the day
and ultimately.
Speaker 2 (05:49):
Get the provider paid appropriately.
Speaker 3 (05:51):
And then our customers obviously are ensuring that they're paying
their portion we're appropriate.
Speaker 2 (05:58):
Got it?
Speaker 1 (05:58):
So, maybe going back to one of the things you
said earlier about bringing the technology assets to bear, can
we can we talk about how that coalesques with the
legacy businesses and does it does it improve the productivity
of those legacy businesses or create new opportunities? Uh, you know,
by combining these these various functions, you know, with the
technology overway.
Speaker 2 (06:21):
Yeah, it's a great question.
Speaker 3 (06:22):
So you can think of our artificial intelligence throughout the
entire platform that we have through across across those four
companies that we brought together, which is really operating as
one company, one platform today, and how we do that
is through pairing that artificial intelligence with the technology our people,
deep industry, obviously domain expertise that enhances the speed, the quality,
(06:47):
and performance of all of our solutions. So if you
look across you know we're using it in all of
our businesses today. The most basic example that I could
give and which should resonate is using AI to enhance
the scoring in our scoring models to eliminate any non
positive type investigations that we may do. The last thing
(07:10):
that we want to do is create more friction within
the healthcare ecosystem. So AI is a great way to
synthesize a tremendous amount of data, increasing our rate at
which we find findings, but ultimately reducing the false positives
that you may see in some legacy businesses. You know,
(07:32):
what we do a lot of time requires a medical record.
The last thing that we want to do is request
a medical record from providers if there really is any finding.
So it's increasing our productivity but then also reducing the
friction within healthcare unless back and forth. What AI ultimately
does it has the ability to summarize massive amounts of
(07:53):
data very quickly, and that ultimately increases our experts, our
clinicians or code or others, their efficiency and accuracy and
what they do. I'm happy to give you a few
examples if that would be helpful.
Speaker 1 (08:06):
Yeah, let's dive into it.
Speaker 3 (08:08):
So one of our flagship products is our Machinify audit tool.
So you can think of this as looking at coding
or deep clinical expertise that may be needed to look
at a medical record and determine if the charges are
actually appropriate for what was either documented or what was
actually delivered by the provider. And so we use our
(08:29):
AI assistant model and machine learning algorithms to find any
suspicious billing patterns. You know, we look at erroneous claim
issues and pricing anomalies that may be in there for
the purpose of performing these complex audits and these complex reviews,
and ultimately that determines which claims we should select for
(08:51):
a manual audit. And those manual audits are then you know,
benefit from machine learning and be able to synthesize a
vast amount of inform from the claim, through the medical record,
through the payment policy, through the contract that the member
may have with their health plan, and ultimately, what we've
seen is it really generates a two x return versus
(09:13):
what we're able to do and two acts the results
from other technology that's in this space. So ultimately that
technology is used to not only synthesize information, but make
our experts really a lot more productive and what they
do on a day to day basis.
Speaker 1 (09:32):
Maybe help me understand a little bit better because we've
we've had another payment integrity company on the podcast, But
my understanding is the landscape is really filled with a
lot of technology debt on the part of a lot
of different players in the ecosystem. Is that the right
way to think about it, that we're bringing this into
the modern the modern era?
Speaker 2 (09:51):
Yeah, exactly.
Speaker 3 (09:52):
I mean, there is a tremendous amount of debt, and
if you think of some of our clients, the struggle
that they've had on the payer side, they have, you know,
a tremendous amount of legacy technology that has a lot
of technology debt associated with it. What we're doing and
if you could think about the claims adjudication systems that
are actually going through taking the claim, making sure some
(10:14):
basic edits on it, and make sure that it comes
out of it with the correct price to pay and
the estimation and benefits that then goes back to the provider.
A lot of those systems are, you know, ten, fifteen,
twenty years old. A lot of them are on premise technologies.
What we have is pure cloud technology that then sits
(10:35):
on top of those systems and is able to get
to the claim before even those adjudication systems come into play,
so ultimately saving the amount of back and forth between
payers and providers. And you know, when you think about
our technology, it's really we're viewed as more of a.
Speaker 2 (10:54):
Secondary adjudication system.
Speaker 3 (10:57):
So we're able to price it in milliseconds, able to
get the claim through our technology and determine should we
pay this claim or not. And our clients are actually
pushing that up against their core adjudication system and saying,
you know, this may not be your adjudication system, but
we're able to look at this and determine if any
errors that we should pause this claim from being paid
(11:18):
appropriately right now, should we go back and take a
further look at this and that really helps our clients
who are on the clock. You know, they have thirty
days to get claims paid. The ability to do this
in milliseconds versus having individuals look at it and legacy
technology really makes a huge difference and controlling costs within
(11:40):
the system, but then ensuring in accuracy as well.
Speaker 2 (11:43):
Got it.
Speaker 1 (11:44):
You know, as I think about the customer, you know,
you had these four assets that you've put together under
one umbro. How has it changed, I guess the go
to market strategy with your customers. I mean, are you
going as one one brand to your to your customers.
Speaker 2 (12:02):
Yeah, we're one brand now.
Speaker 3 (12:04):
We took the name of Machinify, which was the most
recent acquisition that we did, and really that was to
signify to the market a new way of doing things
with these uh, you know, four companies coming together and
what we'd like to think about. We have, you know,
more than forty years of experience across our rallyings business,
(12:25):
but we've come at it now differently from the standpoint
of using technology. First, we're able to lower costs and
ensure outcomes, which is a different approach, and we're able
to keep the speed of a Palouto kind of Silicon
Valley company, which is really important to me as CEO,
ensuring that we keep the expertise that we have in
(12:45):
house from our legacy businesses. But we're moving and adapting
as quick as a Silicon Valley kind of VC back company,
almost even though we have the capital of a private
equity you know back company that's you know, profitable and
doing extremely well and growing. So from a go to
market standpoint, you know, what we've been able to see
is a tremendous amount of cross sell when you have
(13:07):
those great companies that come together. You know, they may
be working with us on our coordination and benefits solution,
they may be working with us on subrogation, and then
we're able to come in with that same claim speed
that we get from the client and say, hey, let
us take a look at complex audit, let us look
at you know, some of the data mining solutions that
(13:29):
you may be using today, and let us show you
what technology can really do. And the difference for us
is we're able to meet clients where they are, which
is historic. You know, the industry is built on pure
outsourcing and you see some of the legacy providers only
offering outsourced solutions. What we're able to do with this
technology is say.
Speaker 2 (13:50):
Hey, large payer, why don't you We'll meet you where
you are. If you would like to outsource everything to us,
that's great, we'll ensure we'll ensure certain outcomes for you.
But if you'd like to bring it in house, that's
also great.
Speaker 3 (14:02):
Why don't you use our technology which is the best
at you know, finding errors, which is the best at
you know, reducing fraud, waste and abuse within the industry.
And we'll train you to use our technology and continue
to enhance those large language models.
Speaker 2 (14:18):
That we that we have in the industry.
Speaker 3 (14:21):
And so it's kind of a hybrid approach opinding on
where our clients are.
Speaker 2 (14:24):
And that's really resonating in the market as well.
Speaker 1 (14:27):
There's a lot to unpacked there. I'm fascinated by this,
you know, as we think about those two different models.
Are the economics different for machinify whether they bring it
in house or use you as the provider I guess
as a software as a service, or how does it
how does it differ?
Speaker 3 (14:43):
Yeah, I mean you could think of it as you know,
you hear a lot of buzzwords in the industry, but
service as software.
Speaker 2 (14:49):
So typically what we're charging is.
Speaker 3 (14:51):
A platform fee associated with some form of outcome and
some outcome based pricing. You know, we're we're fully aligned
with the our clients in terms of, you know, when
when they save money, when they reduce their administrative costs,
when they reduce their costs internally, we save in that savings.
(15:12):
We generate savings as well. But in terms of the
business model, it takes all different types and approaches. As
you may imagine, some are simply playing the SaaS feed
depending on what they're using from us.
Speaker 2 (15:25):
Some are pure contingency based.
Speaker 3 (15:28):
So again alignment from a client standpoint, what we're seeing
more and more of is what you see across the
industry and technology businesses, really a platform fee and then
some form of outcome based results. And that's really resonated
and I love the alignment with our clients. I will say,
you know, we're spending a lot more time thinking about
(15:49):
how do you not only save money and make sure
that you know, our clients are saving money, but ultimately
making sure that every claim is paid correctly and in
order to get to you really need to be you know,
further left in the payment stream, and so using our content,
the smarts, the AI, the technology, and our subject matter experts,
(16:10):
we see a real opportunity to continue moving upstream in
order to make sure that all claims are paid appropriately
versus just looking at them from either a prospective or
a retrospective review.
Speaker 1 (16:22):
No, that's well said. So as I think about the
journey that you've been on as a combined entity, what
did you have to do I guess in the last
year or so to put everything together in a holistic way.
You know, was there any real heavy lifting that that
had to occur to get everything to work together?
Speaker 2 (16:38):
Yeah?
Speaker 3 (16:38):
I mean I think anytime you're pulling together companies, it's
about culture, right and do you have the right culture?
And I was very fortunate to acquire, you know, and
bring together four different companies that have very strong leadership.
So if you looked across kind of our product and
innovation component, which is Machinify, the founder and CEO of
(17:00):
machine Ifies and now our chief product officer prisona Geniesean,
and he's done a great job, you know, really bringing
that level of enthusiasm and technology expertise to bear across
our entire platform. And then when you look across the
other businesses you know we have we're set up in
two different business units today are right payer solutions, So
(17:22):
you can think of that as, you know, as the
right primary and secondary coverage in place. Are we making
sure that it's being built appropriately to that coverage? And
you know, is there anything like a subrogated claim that
may be in place? And that's being ran by Rolling
CEO who's now the president of that business Unity. And
(17:43):
then we took the EPIXEO former Claim Logic CEO and
he's now running Tom Ignata who's now running our complex
payment solution business. So the culture has really come together
and I think that's a first phase and I learned
that early on in my career, you know, working as
a cerner. You mentioned that at the beginning, having the
right culture makes all the difference, and bringing the cultures
(18:06):
together that's really focused on innovation and results for our clients.
Speaker 2 (18:10):
It has stayed true.
Speaker 3 (18:12):
The other thing that I would say, it's culture and
then it's platforms as well, and we've been able to
make quick work of platforms and ensuring that we're taking
that AI platform and putting it across all of our businesses,
which is something that traditionally can take years to typically do.
We're already doing it within the first six months of
(18:32):
bringing Machinify in.
Speaker 2 (18:34):
You know, we've only closed on that.
Speaker 3 (18:36):
Acquisition about four or five months ago now, and so
the ability to take best in class technology and put
it across an environment and really make that top priority,
along with ensuring the culture and employees that are being successful,
has really.
Speaker 2 (18:51):
Been what we've been focused on.
Speaker 3 (18:54):
And it's the great results in terms of clients seeing
the results and the US seeing some of those results,
and then ultimately our employees really being engaged and the
vision that we have which is truly to lower the
cost of health care, and you can't do that by
you know, adding additional friction or processes within the mix.
(19:15):
You really need to streamline things. And our employees are
really bought into this, this vision that we have, which
is you know, frictionalists healthcare system where quality is of
the utmost important and importance, and you know, we're all
working to ensure that the cost of healthcare is affordable
for everyone.
Speaker 1 (19:35):
Interesting you brought up Cerner, and I was thinking about
your background as you were speaking, and you know, you
also had some time and signify you know, as a COO,
and that was a combination of businesses in a New
Mountain play as well. You know, how much of that
playbook did you take from maybe the Signify and I
know the problems are very different, but maybe the vision
(19:56):
and and how you're going to run the company from
a processes perspective? Is it very similar? Am I thinking
about that in the right way?
Speaker 2 (20:03):
Yeah?
Speaker 3 (20:04):
I think I've it's kind of a culmination of both
work experiences.
Speaker 2 (20:09):
You know, I spent ten years at Cerner.
Speaker 3 (20:10):
I was, think of it as chief of staff for
the chairman and CEO of UH and founder of Cerner,
and then I was chief operating officer of Signified for
almost eight years, and then part of the CBS for
a year post acquisition. And I would say there's a
tremendous amount of you know, I kind of cut my
teeth at Cerner in terms of learning what the opportunity
(20:32):
was when you digitized healthcare, you know, and I was
there from early stages of VMR through at you know,
meaningful Use, which obviously accelerated the industry greatly, but at
Cerner we always said, you know, the first order effects
of digitizing medical record were obviously care related and they
did some great things to do that, But what we're
(20:53):
the most excited about was truly a second, third, fourth
order effects. So you can think of it as once
it's digitized, is how can you improve care through algorithms, right,
just having all the lab results, the clinical documentation within
that record and doing things like generating sepsis algorithms that
can save people's lives.
Speaker 2 (21:13):
Right. And then third order effects of once you digitize.
Speaker 3 (21:17):
It and you're using it in day to day practice,
how can you then use it for other use cases
and problems, which is exactly what we're doing at Machinify,
which is using a lot of the data. One of
the inputs is medical records to be able to determine
if care was ultimately appropriate, is it priced appropriately, those
(21:38):
sorts of things, and you're able to do that now
that it's digitized. So I think that was a kind
of lesson learned at Cerner.
Speaker 2 (21:46):
And then at Signify.
Speaker 3 (21:48):
We did bring two companies together and it made some
additional acquisitions along the way, But what we did there,
which was different than the industry, is really take a services.
Speaker 2 (21:58):
Business and make it into it technology business.
Speaker 3 (22:01):
And that's largely what you see as doing a machineified
taking a tech enabled services business and make it into
a true technology business. With some of the expertise, with
the experts that we have on our team, and now
with the addition of large language models generative AI, we're
able to do it a much faster clip. So there
(22:23):
were a tremendous amount of lessons learned at Signify, and
obviously having the backing of New Mountain and both machinifying Signify,
it is a huge advantage because you understand the strategic
expertise that New Mountain brings to the table, the capital
expertise that they bring to the table.
Speaker 2 (22:40):
But I was really.
Speaker 3 (22:40):
Focused on how do you automate as much as possible
within the healthcare system at Signify and make it so
that your clinicians are the most productive generating the results
for the members, being able to find care management programs
that those members are eligible for and should be enrolled in,
(23:02):
and really focus their time and attention on what's best
for the patient through automation. And that's what we're doing
here is differences. Instead of clinicians that are front of members,
we're actually looking at how can we make our our attorneys,
how can we make our clinicians, our coders the most
productive possible, so they can actually use their expertise to
(23:24):
actually find the most complex errors and be able to
ultimately train our larger language models and others to really
make it more consistent, less variability, and less friction within
the healthcare system. So I think those are the big
lessons learned, and you know which really helps me in
this in this role of CEO of Machinify.
Speaker 1 (23:43):
You know, one of the things you mentioned a bunch
of times was just the technology that you're applying, you know,
to the problem. You know where you sit in this
intersection of technology and healthcare. You know how challenging is
it for organizing, for an organization like yours to actually build.
Speaker 2 (23:59):
Upon this vision.
Speaker 1 (24:01):
I mean, is there constraints in terms of how fast
you can move is it? Are there constraints around hiring
the right people? I mean, I have to imagine there's
a war for talent in the world of AI right now.
Speaker 2 (24:13):
Yeah.
Speaker 3 (24:14):
Absolutely, I think there's a number of things. It's been
a great question. So there is a tremendous war for talent,
which is where I feel that we have a leg
up in terms of the competition and others that are
in the industry. We're really focused on things that matter,
and what we see is Silicon Valley and engineers, data
(24:36):
scientists wanting to work for a company that has a
bigger vision, bigger mission than what may be and just
general high technology today. So what we provide is some
of the best technologists in the industry in pallelto being
able to be able to recruit retain sort of the
(24:57):
best talent within the industry. It's very impressive to me
to see what we've been able to do in terms
of retaining talent and then focusing them. I've always said
that the biggest thing that people want is they want
to work for a company that has a larger vision.
They want to be challenged, but also work on things
that matter. And so we're able to do that from
a technology standpoint that others in the industry like a
(25:18):
Meta or others may not be able to do, may not.
Speaker 2 (25:21):
Be able to provide them.
Speaker 3 (25:22):
The other thing I would say is, you know, there's
also a tremendous war for talent in terms of clinicians,
people that understand true payer operations and healthcare. We've been
able to recruit some of the best talent in the industry.
For instance, we pride ourselves on seeing kind of.
Speaker 2 (25:41):
The full vision.
Speaker 3 (25:42):
We've hired the our CTO, Brian Gambs, came from R one,
so really understanding payer revenue cycle solutions, which you don't
typically see payment integrity, which is more focused on payer side.
Speaker 2 (25:55):
And then clinicians.
Speaker 3 (25:56):
You know, that's probably the lifeblood of generating content, being
able to look at content and find where there may
be errors and really avoid some of the fraud, waste
and abuse because at the end of the day, what
we're finding our errors driving accountability, improving cost control, other things,
and you need clinicians side to side with engineers in
(26:18):
order to really make that magic happen. So, you know,
we find ourselves being able to really retain and you know,
being able to recruit some of the best talent in
the industry.
Speaker 1 (26:31):
So maybe just switching gears a little bit. You know,
we've talked about that, We've talked about the journey and
around the talent. You know, if we think about the
vision over the next three to five years, you know,
what are you telling your employees to kind of inspire
them where you're going to be in a couple of
years and I guess maybe how are you spending your
time a little bit differently than maybe standing up the
(26:52):
business over the last year.
Speaker 3 (26:53):
And to put it all together, yeah, I mean, we
truly see ourself as an operating system for payers.
Speaker 2 (27:00):
And if you think about.
Speaker 3 (27:01):
That, you know, there is a tremendous amount of technology
debt within the industry. What we bring is a fresh
new approach to it. So when I talk to our employees,
when I talk to you know, our capital partners, others,
we're reimagining what's possible in terms of moving from true
payment integrity, which is ensuring that things are accurate and
(27:22):
paid appropriately, to actually ensuring that all claims are correct
and being that operating system that reduces a lot of
the admin functions that are frankly driving the cost of
health care much higher than it needs to be.
Speaker 2 (27:36):
I mean, if you.
Speaker 3 (27:36):
Look at the spend within healthcare today, whether it's you know,
four to five trillion dollars, you know, the estimates are
close to you know, anywhere from you know, five hundred
to five hundred billion to a trillion dollars in terms
of fraud, waste, abuse, and complexities that are adding to
the cost of health care and Ultimately, our vision is
(27:59):
to transform form that and really transform that healthcare ecosystem
with an AI first platform where rapid, accurate payment decisions
and real administrative tasks are being done faster, more affordable,
and driving better care.
Speaker 2 (28:15):
For everyone across the ecosystem.
Speaker 3 (28:17):
So we see ourselves as eventually, you know, moving beyond
even traditional payment integrity. Anything that's going to lower the
cost and improve the quality within healthcare through technology and
our expertise is where we're headed as a company.
Speaker 1 (28:32):
Could you maybe give us one one example without you know,
maybe tipping your cap too much.
Speaker 3 (28:38):
You know, I think in terms of just payment integrity
is probably the best examples to use because that's where
we are today. But taking it one step further, looking
at the payments, you know, we're not processing payments today,
but we are generating you know, a tremendous amount of
data that can be used for that those payments. And
(29:00):
as I mentioned earlier, the core adutication systems that are
in place, we're able to do a lot of that
same work because the inputs are the same. If you
think about the inputs communication system, it's claims, it's eligibility,
it's medical records, you know, those sorts of data inputs.
Speaker 2 (29:17):
What we're able to do is do it.
Speaker 3 (29:18):
A much faster and much more accurate pace to be
able to you know, generate what should be paid, those
estimates of benefits, those sorts of things, and you know,
do it in an accurate, you know, ninety nine percent
accurate you know way where we're not.
Speaker 2 (29:35):
Seeing that today.
Speaker 3 (29:36):
So I think you'll continue to see us move further
left in terms of I mentioned it earlier from retrospective.
We're doing primarily pre payment today, really focused before the
actual payment's made. But then how can we even get
further upstream to ensure that we're working with those provider groups.
You know, we're not a black box, which is additional
(29:58):
something additional for us that we see from our competitors.
They're making edits and changes, but they're not sharing that
with providers. We think it's better for the industry if
we actually provide that to providers, what those errors are,
how it's leading to some of the challenges within the industry,
and then ultimately ensuring that you know, what they're submitting
(30:19):
doesn't happen again, right because you see that and we
don't think it's right for an industry to be focused
on just finding errors.
Speaker 2 (30:27):
We want to eliminate those errors before they occur.
Speaker 1 (30:29):
So if I sit back and think about the operational
side of the business, you know, I think we've chatted
a lot about how the nuts and bolts all fit together.
Is there anything on the regulatory front or the policy
front that is a catalyst for the industry or your
business or is there anything you're paying attention to that
you know could potentially be aheadwind.
Speaker 3 (30:49):
Yeah, I mean we're paying very close attention to what
CMS is doing. Obviously, we have commercial clients and commercial
payers as clients. We have Medicare A Vantage clients, we
have Medicaid you know, Medicaid MCOs as clients. But we
see a tremendous opportunity in the government space in terms
of what they're doing, how they're looking at things, and
(31:12):
we think that will then come over to commercial and
some of the other lines of business that we operate in.
You know, I think from a government perspective, they're focused
on the right things, right, They're focused on fraud, waste
and abuse. If you look at some of the numbers
you know out of Medicare feed for service, you know
they anticipate are they estimated around an eight percent error
(31:32):
rate and some of the payments that they make, so
thirty two billion and errors that are made and according
to CMS just last year alone in terms of errors
and payments, and that can be due to a number
of issues.
Speaker 2 (31:47):
But where we see it going is, you.
Speaker 3 (31:49):
Know, those changes that CMS is making will flow down
to commercial payers, that will flow down to our clients today,
and we'd love to be right there provided in our
ex ertise of what we're seeing in our clients and
able to provide that back to the government, you know
how and when that takes effect you know, maybe seen
(32:10):
in different timelines and other things, but certainly focus on fraud,
waste and views. So focus on audits and ensuring that
they're auditing their member.
Speaker 2 (32:20):
Or their providers appropriately.
Speaker 3 (32:23):
I think is something that they can take best practices
from what we're doing and bring that back into the
government space. So yes, we're tracking it very closely and
we think there's a tremendous opportunity. And you know, we
also see the government much more focused on program and
payment integrity than they've been in the past, which we
think is ultimately a good thing.
Speaker 2 (32:42):
Got it.
Speaker 1 (32:43):
We talked a little bit about the funding model or
the way the business was combined. I mean, can you
talk a little bit about your funding needs for the business?
You know, are you profitable now? Any KPIs you could
share public g.
Speaker 3 (32:57):
Yeah, I mean we don't share financial information publicly. But
what I can say is, you know, we're profitable and
we're able to fund you know, extreme innovation, which couldn't
have been done at each of these individuals individual companies standalone.
So we're investing a lot in innovation and technology, I think,
much more than our competition, much more than any of
(33:19):
the standalone organizations would have been able to do on
their own.
Speaker 2 (33:24):
I think what's interesting about that is, you know.
Speaker 3 (33:26):
It's not a one time you build a large language
model and you put it to work against your platform.
Speaker 2 (33:32):
You know, it takes continuous honing.
Speaker 3 (33:34):
It takes learning from not only what your internal agents
and employees are doing, but also as we put our
technology in the hands of our clients.
Speaker 2 (33:44):
You know, we need to validate the work that our
clients are doing on the platform because that goes into
our learning model or modules that ultimately determine how successful
our technology is. So, you know, while it's saving us
a tremendous amount of dollars. From pro activity standpoint, there
is a lot of investment that needs to occur in
(34:04):
order to keep training the models and ensure that you know,
our quality is top notch and you know, and we're
we're going to continue to invest in technology here and
we'll continue to look at M and A as well
where we're appropriate. I think we have a great technology
in the platform today, but there may be areas that
we want to expand into that we're not doing today.
(34:27):
When you look across a full payment integrity stack or
you look at admin functions within our payer clients, they've
asked us to.
Speaker 3 (34:36):
Do a number of things that we're not doing today
because they've seen the results that we're able to generate.
And so we'll continue to be inquisitive and we'll continue
to invest in technology as we move forward.
Speaker 2 (34:46):
Here.
Speaker 1 (34:47):
You beat me to the question. I was going to
ask you if there was a preference between the internal
and inorganic investments, So thanks.
Speaker 2 (34:54):
For covering that.
Speaker 1 (34:56):
As you think about that, I mean, what are the
what are the key herd in your mind, whether it's
from a strategic or financial perspective to doing a deal,
I mean what buckets, does it what checks does it
have to check on the checklist?
Speaker 3 (35:10):
Well, I think whenever we look at a company from
an M and A standpoint, it needs to make sense
in terms of synergies with our business and does it
fit the overall vision of where we're going.
Speaker 2 (35:21):
So those are the two.
Speaker 3 (35:22):
Things that I spend most of my time on as CEO.
Is does a business further get us closer to our
vision where we want to go, which is again to
reduce the overall cost of healthcare and sure that providers
and payers are working together in concert to actually deliver
high quality results at the lowest cost possible. So the
(35:44):
strategic fit in the vision of the companies are very
important to me. And then synergies with our business. Is
it a business that we think we can add our
technology to that can generate much better results? Is it
you know, exactly what we did at Signify, exactly what
we've done so far at Machinify is a business that
is still pretty manual that we can, you know, eliminate.
Speaker 2 (36:08):
The technology stack that they're using and put.
Speaker 3 (36:10):
In our technology to automate to use our AI platform
to generate better results, make their workforce much more productive.
Speaker 2 (36:19):
And then ultimately deliver greater results for their clients. So
that's really the lens that we look at is are
there synergies with our business in terms of are we
able to make them more productive generate greater results, And
we think with our technology there's a lot of businesses
that we could do that with and then ultimately is
a strategic fit in terms of the direction that we're
looking and moving as a company, and there are organizations
(36:42):
that are out there. It's a matter of how much
organic growth we've been.
Speaker 3 (36:47):
We've proved our ability to grow organically, not only through
cross selling but developing new products. So if you look
at our Machinify pay solution, that was you know, really
the thought was working with a key strategic national account
and developing a data mining solution that you know, historically
has been a black box as I mentioned earlier for clients,
(37:09):
and then it's ultimately been a black box in terms
of what gets shared with the providers and what we said.
We went into it and said, we want to develop
our own solution that ultimately won't be a black box.
We can put it in the hands and let our
clients run it and it will move from just looking
at claims retrospectively and detecting anomalies and other things to
(37:31):
actually doing it prospectively more like an editor would and
finding some of those errors and looking at anomalies with
technology that we can put in the hands of our clients.
And that's been growing at you know, high double digits,
and you know, it's just a great testament to the
fact that we can develop organically solutions and products that
(37:51):
we can bring to market and cross sell.
Speaker 2 (37:54):
But we also may see.
Speaker 3 (37:55):
An opportunity where it can accelerate through acquisition of a
truly different sector. Then we may not be in today
and be able to bring those to our client base.
When you look across our client base, we have you know,
the large majority of the major payers as clients.
Speaker 2 (38:11):
We have over seventy clients today.
Speaker 3 (38:14):
We're processing a tremendous amount of data, so claims that
are coming through our system. If you think, you know,
four point eight trillion is being spent on healthcare today,
we have close to trillion dollars of claims flowing through
our systems. Think of it as twenty to twenty five.
Speaker 2 (38:29):
Percent depending on the numbers use.
Speaker 3 (38:32):
And so you know what that allows us to do
is we have the data, we have the technology expertise,
we have the clinical expertise, and we're able to point
those at problems that you know, ultimately are causing you know,
everybody in the industry to say that costs are too
high within the ecosystem. So hopefully I explained a little
(38:53):
bit about our you know, acquisition to market as well
as our organic growth strategies.
Speaker 1 (38:58):
Well, one are the things you mentioned was just the
client coming to you. I mean, how often do you
have those conversations where one of your customers is very
key in on a specific pain point and you develop
those sorts of solutions. Is that is that a common
iteration of new products and services?
Speaker 3 (39:15):
It is, I mean it goes back all the way
to kind of our early days and some of these
businesses have been in business.
Speaker 2 (39:20):
For forty years.
Speaker 3 (39:22):
And we developed and basically developed the manual around subrogation, right,
and then we took that and moved into technology from
a machinify standpoint, and you know, there's a better way
to do this than how it's been done in the past.
And you know, we have some very strategic clients that
work very closely with us. You know, we have strong
(39:43):
commercial clients. We have strong Medicare advantage clients. We have
strong Medicaid you know MCOs that work with us. So
what we find best is to work with the experts
in the industry and our clients really have a development
partnership on what their challenges are. And it could be
anything as taking COB coordination benefits. It's been in the
(40:07):
industry for a long time and you know, at the
most basic level, coordination benefits is looking at a tremendous
amount of data, not just claims, data of external information,
external data and looking at things like when employees are retiring,
you know, are they going to a different plan, looking
at data that's publicly available, also eligibility data from different clients,
(40:32):
and pulling that all together. We're able to work with
our clients to develop prepay COB, which has historically been
done retrospectively, so after it's been paid, after the claim's
been paid, you're going through and saying, well, actually, Medicare
maybe was primary or secondary, depending on the scenario. What
(40:53):
we've been able to do with technology is bring what
was historically done manually in terms of looking at external
dat in the base, hitting websites, other things, we're able
to have our AI agents look through that information and
summarize it for our experts, so we work with our
clients to do then. Typically how innovation starts is we
have a technology suite and the products, but ultimately they're
(41:17):
having an issue and seeing things above and beyond what
we're delivering today, and they come to us and we're
able to innovate very closely with our clients. And it's
one of the things that I enjoy most about the
role is having extremely great clients that we're able to
innovate with and develop new technology and new solutions and
impact overall healthcare. So really satisfying from that front.
Speaker 2 (41:39):
Got it.
Speaker 1 (41:40):
You know, as we kind of get to the end here,
you know, I think about your career and you've been
across a couple of different sectors and you know, very
big companies. You know, as you sit here today, you
know with that technology and healthcare had on and that intersection,
you know, going through machinify, what are you most excited
about from a technology perspective in healthcare the industry? And
(42:00):
I don't necessarily mean from like maybe a payments perspective,
but as you look down the pipe pipeline. You know,
five ten years from now, what do you think is
going to surprise us the most.
Speaker 2 (42:10):
Well, I think the data will become.
Speaker 3 (42:13):
Much more ubiquitous in terms of where it is, how
it's used, and I mentioned that, you know, in terms
of my earlier career in terms of digitizing medical records
and just making it available. I think you see some
unique companies like data Van, which is also a new
amountain capital company that's able to really use privacy controls
tokenization of healthcare data to make it more available. And
(42:37):
I think ultimately that data is going to be able
to be used to drive greater transparency, reduce some of
those high admin costs, you know, and ultimately help consumers
who are struggling with navigating some of the complex systems
and high financial responsibilities that are out there in the industry.
So I think there's two things, really, the data and
(42:59):
just having dat much more available. And I think CMS
is really focused on this some of the new administration
making data much more available, whether it's on the medicaid
side or on the Medicare advantage side. Obviously with controls
in place, the right level of security, other things which
I think is going to be a key component to
actually making healthcare much better, much more affordable if we
(43:24):
can have medical records as opposed to having to request
them and wait and cause friction with providers other things.
Speaker 2 (43:32):
The more that we can have medical records available for
us to be able.
Speaker 3 (43:35):
To run our technology and our human expertise against them,
to be able to look at it and understand what
challenges there may be in that record will ultimately drive
down the cost of healthcare and ultimately improve care as well.
So I think that's number one in terms of just
having data much more available. And I think the quality
(43:55):
of data, I would also say, is becoming much more
readily available, and you know, we're able to trust the
data a lot more so things like virtual scribes and
using AI. I think it's going to make a tremendous
more amount of data available. Now you're going to need
technology that actually looks through it and says, is this
(44:15):
clinically relevant, does it actually fit the criteria for this
member in this patient, and how can that ultimately use
to improve care. So that's where I'm the most excited
just around the availability of data, the availility to use
it to drive down and be in cost. But ultimately
to improve care as well. And we see, you know,
(44:37):
ultimately machinify in our technology being able to be used
in that regards and take in a tremendous amount of data,
whether we're generating it or taking it in from you know,
a multitude of other systems. So I think we have
a lot to look forward to in terms of, you know,
improving quality and lowering the cost of healthcare across the board.
Speaker 1 (44:57):
I'm right there with you. I'm so interested to see
what's going to happen, you know, not just on the
maybe the back end, but on the clinical side as well. So, David,
I like to wrap these conversations up by focusing on
a wife lesson that the guest has maybe learned in
their personal or professional life that drives them. Is there
one thing that you think about when you think about
your day to day that that kind of you see
(45:18):
informing your mission in life?
Speaker 2 (45:21):
Yeah, yeah, no, it's a great question. You know.
Speaker 3 (45:24):
I grew up in a family. There are six kids.
I'm the youngest of six kids. My dad was a periodontist.
All six of us are in healthcare in some form
of fashion. It's interesting that three are actual providers ones
a physician, ones, a PA another, there's an end adonis
(45:44):
and then you have three on the business side, right,
And so that's impact of me because I'm running by
my brother who's a physician every day and you know,
what we're doing in the industry isn't making his life easier?
Is it causing complexity in his life? And I think
really humanizing the element of health care and you can't
lose sight of that.
Speaker 2 (46:04):
I learned that early on, right, is that we're.
Speaker 3 (46:07):
All consumers of healthcare and if you really personalize it
and from everyone from our clients to our employees, it
allows everyone to get the vision on where you're going.
And it's something that you know, I've taken throughout my career,
really focusing on a vision where we're going and who's
really going to impact from what.
Speaker 2 (46:25):
You're doing on a day to day basis.
Speaker 3 (46:27):
So that's number one, and then I think number two
would be I learned early on in my career from
one of my mentors, Neil Patterson, who is a founder
of Cerner, really to use there's a lot.
Speaker 2 (46:40):
Of things that we can go focus on.
Speaker 3 (46:42):
So if we talk about admin costs within healthcare, there's
a tremendous amount of areas that we can go focus
on and you can sometimes get lost. So it goes
back to the vision where you're going, but then able
to really use a laser in terms of your time,
energy resources, in terms of really focusing on those things
that you can impact. So I've used that throughout my career,
(47:04):
really being purposeful on you know, the challenges in front
of us, but focus on those things that we can
impact the greatest.
Speaker 2 (47:11):
And that's what you're seeing Machinify right now. We're focused on.
Speaker 3 (47:15):
Being the best platform and technology and results within payment integrity.
But as we continue to generate those results, you'll continue
to see us move into other areas and we'll be
very thoughtful on those areas and really make sure that
we're focused on the right things.
Speaker 2 (47:28):
Going back to the vision of reducing costs within the
system today, those.
Speaker 1 (47:32):
Were two great lessons. Thank you so much for sharing.
I'm so curious about the holiday discussions around the table
with three versus three.
Speaker 2 (47:42):
Yeah, it's not really the three versus three, you know,
it's more of alignment and truly understanding one another, right,
because you come at it from different angles, and you know,
i'd say it's probably better than speaking politics at the
dinner table, and I think, but yeah, it's great to
(48:03):
be able to bounce things off. I have a brother
who's a physician at Kaiser, and you know, it's great
to understand some of the work that we're doing, how
it could potentially impact him, how could impact our clients,
and just really fun things to go focus on in
terms of challenges that we can solve together.
Speaker 1 (48:20):
That's great. Thank you David for sharing all that. I
think we'll wrap up here. David, it's been great learning
more about machinifying your career. Thanks for joining us.
Speaker 2 (48:30):
Yeah, I appreciate it. Jonathan, thanks so much.
Speaker 1 (48:32):
And so that's David Pierre, the CEO of Machinify. Thank
you so much for joining us on our latest episode.
And please make sure to click the follow button on
your favorite podcast app or website so you never miss
a discussion with the leaders in healthcare innovation. I'm Jonathan Palmer,
and you've been listening to the Vanguards of Healthcare podcast
by Bloomberg Intelligence. Until next time, take care.
Speaker 4 (49:06):
Muss aasure uses a ton of bases
Host
Jonathan Palmer
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