Latest / Vanguards of Healthcare by Bloomberg Intelligence / Smarter Technologies’ Blueprint for AI-Driven Revenue Cycle Management

Transcript

Smarter Technologies’ Blueprint for AI-Driven Revenue Cycle Management · Vanguards of Healthcare by Bloomberg Intelligence

• 0:21
  Speaker 1: Welcome to another episode of Bloomberg Intelligence's Vanguards of Healthcare podcast,
• 0:25
  Speaker 1: where we speak with the leaders at the forefront of
• 0:27
  Speaker 1: change in the healthcare industry. My name is Jonathan Palmer,
• 0:30
  Speaker 1: and I'm a healthcare analyst at Bloomberg Intelligence, the in
• 0:32
  Speaker 1: house research arm of Bloomberg. I'm very thrilled to welcome
• 0:35
  Speaker 1: today's guest, doctor Michael Gaugh, the CEO of Smarter Technologies.
• 0:39
  Speaker 1: Prior to assuming the CEO role, he was the co
• 0:41
  Speaker 1: founder and CEO of Smarter DX, one of the three
• 0:44
  Speaker 1: predecessor companies that were combined by the financial sponsor New
• 0:47
  Speaker 1: Mountain Capital to form this new automation and insights platform.
• 0:52
  Speaker 1: Doctor Goo completed his medical degree at the University of
• 0:54
  Speaker 1: Michigan and his residency and fellowship at New York Presbyterian,
• 0:59
  Speaker 1: where he was also a medical director focused on transformation
• 1:02
  Speaker 1: before his foray as an entrepreneur. Welcome to the podcast, Michael.
• 1:05
  Speaker 2: Thanks Jonathan, it's great to be here.
• 1:07
  Speaker 1: So why don't we start with a thirty thousand foot
• 1:08
  Speaker 1: view of what Smarter does and where you fit into
• 1:11
  Speaker 1: the ecosystem.
• 1:12
  Speaker 2: Yeah. Absolutely, you know, I think to understand what Smarter does,
• 1:16
  Speaker 2: one has to understand a little bit about what healthcare
• 1:18
  Speaker 2: revenue cycle even is, and I promise it's not going
• 1:22
  Speaker 2: to be an arcane two our sort of sort of
• 1:25
  Speaker 2: sort of pre ramble on this. You know, fundamentally, what
• 1:29
  Speaker 2: revenue cycle is is that for hospitals to get paid
• 1:34
  Speaker 2: for the care that they deliver, they have to first
• 1:38
  Speaker 2: take that care and generate a receipt for the care.
• 1:42
  Speaker 2: So that's the process of forming the claim and understanding
• 1:45
  Speaker 2: what the clinical care all corresponded to, and then they
• 1:48
  Speaker 2: have to jump through the you know, fifty or so
• 1:51
  Speaker 2: hoops for the payers to actually you know, deliver payment
• 1:56
  Speaker 2: for that care. And so smarter technologies is about solving
• 2:02
  Speaker 2: both of those challenges with you know kind of AI
• 2:07
  Speaker 2: and scale delivery. And so the first component of that,
• 2:11
  Speaker 2: which is really generating an accurate receipt for the care,
• 2:15
  Speaker 2: is principally about clinical intelligence and being able to ingust
• 2:20
  Speaker 2: care data and understand what that means and then you know,
• 2:24
  Speaker 2: use that to either help facilitate documentation or you know,
• 2:29
  Speaker 2: understand if there's misses in the receipt. And then the
• 2:33
  Speaker 2: second is you know, really the jumping through the fifty
• 2:37
  Speaker 2: hoops process, and that might be submitting things through a
• 2:42
  Speaker 2: specific website, following specific rules and guidelines and things like that,
• 2:47
  Speaker 2: and so on that side, it's really about sort of
• 2:50
  Speaker 2: the scale needed to understand those patterns and then the
• 2:55
  Speaker 2: automation needed to do that in a low cost manner.
• 2:58
  Speaker 2: So at the end of the day, it's just about
• 3:00
  Speaker 2: helping hospitals make sure the receipt is correct and then
• 3:03
  Speaker 2: decreasing the cost of collecting on the sort of payment
• 3:08
  Speaker 2: for that care.
• 3:09
  Speaker 1: No, well said, So maybe thinking about this as as
• 3:11
  Speaker 1: kind of a journey. If we think about the evolution
• 3:14
  Speaker 1: of our CM you know, we have the pre E
• 3:16
  Speaker 1: M R days, which I would think of as paper
• 3:19
  Speaker 1: post the High Tech Act, and now we're kind of
• 3:22
  Speaker 1: in this AI evolution. So how has it changed along
• 3:26
  Speaker 1: that longitudinal journey? And I guess when did you get
• 3:29
  Speaker 1: the idea or the white bulb to tackle this problem?
• 3:32
  Speaker 1: Maybe that's the ending point.
• 3:34
  Speaker 2: Yeah, yeah, yeah, So it's a really fascinating framework. Actually,
• 3:38
  Speaker 2: So you know, the reason why we have claims or
• 3:45
  Speaker 2: you know, if you've heard of ICD codes or CPT
• 3:47
  Speaker 2: codes in the first place, is kind of back in
• 3:51
  Speaker 2: the paper chart days. And so you provide a lot
• 3:54
  Speaker 2: of care all the sort of notes on the care
• 3:58
  Speaker 2: goes into a binder, and to get paid on that care,
• 4:02
  Speaker 2: you couldn't FedEx the binder to a payer and say like,
• 4:04
  Speaker 2: please read through this and figure it out. And so
• 4:07
  Speaker 2: you come up with effectively shorthand to compress that binder
• 4:11
  Speaker 2: into sort of a set of codes that correspond to payment.
• 4:16
  Speaker 2: And I think that sort of initially with kind of
• 4:21
  Speaker 2: software and actually didn't transform the process. It just recreated
• 4:26
  Speaker 2: that process in a digital way. So now instead of
• 4:29
  Speaker 2: all the complexity was still there, all the complexity is
• 4:31
  Speaker 2: still there. So you know, now instead of paper, you
• 4:33
  Speaker 2: have electronic charts, but you're still going kind of tab
• 4:36
  Speaker 2: by tab. Instead of a stack of binders, you have
• 4:39
  Speaker 2: a priority list and a workque on some piece of software.
• 4:42
  Speaker 2: But fundamentally, you know, it's still a person going through
• 4:46
  Speaker 2: each chart and kind of doing all of that work.
• 4:50
  Speaker 2: And I think with and then and then the kind
• 4:54
  Speaker 2: of the second part is that they have to you know,
• 4:57
  Speaker 2: kind of each payer receives information in a slightly different way,
• 5:01
  Speaker 2: has slightly different requirements, and so it took a lot
• 5:03
  Speaker 2: of human intelligence to understand those patterns.
• 5:05
  Speaker 1: Sure, United wants it this way, and ETNA wants it
• 5:08
  Speaker 1: that way exactly.
• 5:10
  Speaker 2: And now with AI, I think there's a transformation, which
• 5:14
  Speaker 2: is it's not just humans doing the work, but switching
• 5:18
  Speaker 2: from a paper medium to an electronic medium. It's that
• 5:22
  Speaker 2: AI can take a first pass at doing the work
• 5:26
  Speaker 2: and humans can actually just supervise that.
• 5:28
  Speaker 1: Okay.
• 5:29
  Speaker 2: So as an example on the smarter DX side, the
• 5:34
  Speaker 2: average patient visit that we analyze has about thirty thousand
• 5:39
  Speaker 2: data points. There's over one hundred thousand possible ICD codes,
• 5:45
  Speaker 2: and your average patient has you know, kind of ten
• 5:48
  Speaker 2: to fifteen codes. And so if you're looking for a
• 5:52
  Speaker 2: missing code out of one hundred thousand possibilities in thirty
• 5:56
  Speaker 2: thousand data points, it's a lot like finding a.
• 5:59
  Speaker 1: Pretty big to miss.
• 6:00
  Speaker 2: It's hard. For here, it's hard. It's it's like looking
• 6:02
  Speaker 2: for a missing person in the stadium of people. Yeah,
• 6:05
  Speaker 2: that's a good name. And so this is where we
• 6:08
  Speaker 2: use AI to first identify what it thinks is missing
• 6:13
  Speaker 2: and then sort of the human intelligence that clinical experience
• 6:17
  Speaker 2: is still used to validate each and every finding, but
• 6:20
  Speaker 2: it kind of changes the nature of the workflow into
• 6:24
  Speaker 2: kind of a AI does the work and humans do
• 6:27
  Speaker 2: the supervision type approach.
• 6:30
  Speaker 1: So with that with that in mind, you know, let's
• 6:32
  Speaker 1: talk about your founder journey. You know, how did you
• 6:34
  Speaker 1: come to start Smarter d Acts.
• 6:37
  Speaker 2: Yeah, so you know, I'm of course a pretty big
• 6:41
  Speaker 2: nerd as you might imagine. So, as you had mentioned,
• 6:45
  Speaker 2: I did residency at New York Presbyterian, and before residency,
• 6:51
  Speaker 2: I had built fine Care, which was kind of a
• 6:57
  Speaker 2: version of ZocDoc but targeted toward paid without health insurance.
• 7:01
  Speaker 2: So learned a good amount of you know, kind of
• 7:03
  Speaker 2: programming in that context, and before that did a lot
• 7:06
  Speaker 2: of applied math. So I was leading a lot of
• 7:09
  Speaker 2: AI initiatives for New York Presbyterian. And it was sort
• 7:18
  Speaker 2: of actually funnily, the the doctors were arguing that they
• 7:27
  Speaker 2: the care that they were providing wasn't sufficiently captured by
• 7:30
  Speaker 2: the coders, and the coders and clinical documentation improvement specialist says, well, yeah,
• 7:36
  Speaker 2: but that's because you're not documenting it, not because we're
• 7:38
  Speaker 2: not capturing it after your documentation. So to try to
• 7:42
  Speaker 2: resolve this, I started trying to understand, you know, kind
• 7:46
  Speaker 2: of how to build algorithms that could take in the
• 7:49
  Speaker 2: raw clinical data and use that rather than just pure
• 7:53
  Speaker 2: documentation as a source of truth. So if your blood
• 7:56
  Speaker 2: pressure is one eighty over one hundred and you get
• 7:58
  Speaker 2: prescribed high blod pressure mention, whether or not somebody wrote
• 8:02
  Speaker 2: the words high blood pressure, you can infer that it
• 8:06
  Speaker 2: was likely something that was managed during that business. You
• 8:09
  Speaker 2: still have to confirm it, get it documented, you know,
• 8:11
  Speaker 2: double check it, but you can sort of have an
• 8:13
  Speaker 2: inkling that that must be, you know, true. And at
• 8:17
  Speaker 2: the same time, what was really fascinating was that. So
• 8:22
  Speaker 2: this is like twenty seventeen twenty eighteen, the one of
• 8:27
  Speaker 2: the sort of data science approaches, the transformers approach, which
• 8:32
  Speaker 2: is kind of the underpinning of LMS, started making the waves.
• 8:36
  Speaker 2: So it was still in very early version called the
• 8:40
  Speaker 2: the Burt models, but it was actually really interesting to
• 8:45
  Speaker 2: see how even very primitive implementations of this approach allowed
• 8:53
  Speaker 2: models to understand clinical data and patterns in a way
• 8:56
  Speaker 2: that you know, kind of very sophisticated implementation of traditional
• 9:00
  Speaker 2: statistics didn't, and machine learning didn't allow for.
• 9:04
  Speaker 1: What's an example of that, for a way, person like
• 9:05
  Speaker 1: myself who doesn't have a very strong math background.
• 9:09
  Speaker 2: Yeah, So I think one example of this is, let's
• 9:15
  Speaker 2: say a traditional natural language processing approach is the I'm
• 9:21
  Speaker 2: when I ask you to identify all all the all
• 9:26
  Speaker 2: times the word bank appears in a book. And so
• 9:30
  Speaker 2: what you might do is you might just search for
• 9:32
  Speaker 2: the letters B, A, N K, and then you know,
• 9:36
  Speaker 2: kind of see how many times it pops up. And
• 9:38
  Speaker 2: that's kind of in the clinical world that might be
• 9:40
  Speaker 2: akin to if the doctor writes the letters, you know,
• 9:45
  Speaker 2: kidney injury. Then you can have some sort of dictionary
• 9:48
  Speaker 2: that it corresponds to, and it sort of looks that up.
• 9:51
  Speaker 2: But if I say, hey, Jonathan, can you search for
• 9:56
  Speaker 2: all the times banks appear? But what I mean is
• 9:59
  Speaker 2: specif typically not just a word, but the bank that
• 10:04
  Speaker 2: you go to deposit money, as opposed to a river
• 10:07
  Speaker 2: bank or a bank shot or a promise you can
• 10:10
  Speaker 2: bank on, the context is actually very hard to understand.
• 10:15
  Speaker 2: And similarly on the clinical side, to use that blood
• 10:18
  Speaker 2: pressure example of sort of one eighty over one hundred,
• 10:21
  Speaker 2: that must be high blood pressure, right, well, not necessarily.
• 10:24
  Speaker 2: What if you just broke your arm and you're in
• 10:26
  Speaker 2: the er and you're in lots of paint. I guarantee
• 10:28
  Speaker 2: your blood pressure will be high white, right, but it's
• 10:31
  Speaker 2: not high blood pressure. And so kind of very similarly,
• 10:36
  Speaker 2: understanding the context before and after a specific measurement is
• 10:41
  Speaker 2: actually really key to interpreting the clinical data. And so
• 10:48
  Speaker 2: the way that transformers work is that instead of just
• 10:53
  Speaker 2: searching for the letters B, A, and K, it's converting
• 10:59
  Speaker 2: all the words using the words before and after it,
• 11:02
  Speaker 2: or the sequences before and after It's have to be
• 11:04
  Speaker 2: words you can be numbers into, you know, kind of
• 11:08
  Speaker 2: a digital representation that that you can actually map to
• 11:13
  Speaker 2: things like a physical bank versus a promise you can
• 11:16
  Speaker 2: bank on versus a river bank. And that context also
• 11:21
  Speaker 2: helped us understand pick out specific abnormalities and clinical data
• 11:28
  Speaker 2: that were sort of not otherwise explained by the context
• 11:32
  Speaker 2: around that value.
• 11:34
  Speaker 1: No, it makes a ton of sense. Thanks for that analogy.
• 11:36
  Speaker 1: Very queer. So I guess what did you have to build?
• 11:38
  Speaker 1: What was the first pilot program that you did or
• 11:40
  Speaker 1: first product that you came out with.
• 11:42
  Speaker 2: Yeah, so, you know, first I left the hospital, went home,
• 11:49
  Speaker 2: and you know, told my then fiance that she thought
• 11:52
  Speaker 2: she was marrying a doctor. But guess what now I'm
• 11:57
  Speaker 2: now an entrepreneur slash unemployed. And but luckily, you know,
• 12:03
  Speaker 2: it was able to be fairly convincing, not just for
• 12:06
  Speaker 2: my now wife, but for you know, kind of other
• 12:09
  Speaker 2: people who join along the journey. And so I think
• 12:11
  Speaker 2: actually the first thing to build was really, you know,
• 12:16
  Speaker 2: kind of a data science team because you know, I'm
• 12:19
  Speaker 2: good at data science for a doctor, but we have
• 12:23
  Speaker 2: people on our team who are you know, Stanford PhD s,
• 12:26
  Speaker 2: so I guarantee understand this at a much deeper level
• 12:29
  Speaker 2: than I do. And the first way that we did
• 12:33
  Speaker 2: this was actually, it's very hard to sell into health
• 12:37
  Speaker 2: systems before they know who you are. So we actually
• 12:40
  Speaker 2: did consulting based approaches where we would go to a
• 12:43
  Speaker 2: health system and you would say, let us analyze your
• 12:46
  Speaker 2: data and sort of identify areas of opportunity for you
• 12:52
  Speaker 2: to improve. And then that kind of gave us the
• 12:56
  Speaker 2: data to build the algorithms to them build a product
• 12:59
  Speaker 2: where which now scans all of that data in real
• 13:04
  Speaker 2: time and kind of continuously surfaces kind of gaps over time.
• 13:08
  Speaker 2: So that was the kind of first smarter pre build product.
• 13:12
  Speaker 1: So the unlock there was not trying to go in
• 13:14
  Speaker 1: and sell them a service you know that changes their
• 13:16
  Speaker 1: workflow tomorrow, right, but really to build that, build that
• 13:20
  Speaker 1: set of solutions on top and show that it has
• 13:22
  Speaker 1: an ROI. And so what we're what sort of ROI
• 13:25
  Speaker 1: were you were you seeing in those early test cases.
• 13:27
  Speaker 2: It was actually really really fascinating, even right off the bat,
• 13:33
  Speaker 2: I think, take so, one of our first customers is
• 13:37
  Speaker 2: a hospital uh here in New York about you know,
• 13:41
  Speaker 2: three hundred or so bad So for context, it's kind
• 13:44
  Speaker 2: of a small to medium sized hospital and on an
• 13:49
  Speaker 2: annualized basis, I think we went in and identified five
• 13:52
  Speaker 2: million dollars, big number for them, and it's you know,
• 13:57
  Speaker 2: one hospital of a eight or nine hospital health system.
• 14:02
  Speaker 2: And so translating that up to the health system you're
• 14:05
  Speaker 2: talking you know, kind of thirty or forty million dollars. Yeah,
• 14:09
  Speaker 2: and you know, I think what I would say is
• 14:13
  Speaker 2: that the care for for many health systems, I think
• 14:18
  Speaker 2: average margin across the country is like a two percent
• 14:22
  Speaker 2: operating margin, which is a lot like at the end
• 14:26
  Speaker 2: of your biweekly paycheck, only saving two percent after paying
• 14:29
  Speaker 2: for you know, rent and utilities and food, and so
• 14:35
  Speaker 2: many hospitals feel like they're kind of living paycheck to paycheck.
• 14:40
  Speaker 2: But at the you know, business level, and so this
• 14:45
  Speaker 2: actually ends up being you know, thirty to fifty basis
• 14:49
  Speaker 2: points worth of revenue lift. But because they already provided
• 14:54
  Speaker 2: the service and you know, had the expense of providing
• 14:57
  Speaker 2: that care. It actually mostly flows down onto margins, so
• 15:00
  Speaker 2: it's actually a pretty big impact to.
• 15:03
  Speaker 1: The bottom line, right, that's right. So when when a
• 15:06
  Speaker 1: customer comes to you now now and says, I want
• 15:08
  Speaker 1: to implement a Smarter solution, you know, do you have
• 15:12
  Speaker 1: to customize it for them? I mean, how much? What
• 15:14
  Speaker 1: does a sales cycle look like? You know, how does
• 15:16
  Speaker 1: implementation work? Can you just walk me through maybe the
• 15:19
  Speaker 1: broader process?
• 15:20
  Speaker 2: Yeah? Absolutely, I mean certainly have a range of solutions
• 15:23
  Speaker 2: within the Smarter Technologies portfolio now, so I think on
• 15:29
  Speaker 2: the on the product side, so that would be you know,
• 15:33
  Speaker 2: kind of pre bill notes through the pieces, acquisition, utilization
• 15:37
  Speaker 2: management and things like that. We've baked it down to
• 15:42
  Speaker 2: pretty standard configurations versus customizations. Okay, And that's really key
• 15:48
  Speaker 2: to scaling a software products because you're not doing you know,
• 15:52
  Speaker 2: you're not writing a bunch of new code to support
• 15:54
  Speaker 2: every new customer. And then at the end of it,
• 15:56
  Speaker 2: it's very hard to maintain and it's you have to
• 16:00
  Speaker 2: obviously be enough of the market and understand things well
• 16:03
  Speaker 2: enough to understand, all right, what are the common flavors
• 16:06
  Speaker 2: that you know, kind of of of this workflow that
• 16:08
  Speaker 2: people want as an example, and then we have kind
• 16:13
  Speaker 2: of more of a sort of a solution side. So
• 16:17
  Speaker 2: you know, AI is very spiky today, as you know,
• 16:23
  Speaker 2: somehow we have models that can solve you know, Imo
• 16:27
  Speaker 2: Gold Medal math problems, but still can't reliably order you
• 16:31
  Speaker 2: a plane ticket. And so I think on the automation
• 16:36
  Speaker 2: side that uh sort of uh sort of ridge between
• 16:41
  Speaker 2: what is possible and what is not possible, or that
• 16:43
  Speaker 2: that boundary is you know, kind of always moving, okay,
• 16:47
  Speaker 2: and so on that side, it's actually harder to not
• 16:52
  Speaker 2: customize because the technology is always changing. And so what
• 16:57
  Speaker 2: we and I think this is where you know, kind
• 16:59
  Speaker 2: of our scale delivery function is so key because what
• 17:02
  Speaker 2: we can say is we will you know, effectively achieve
• 17:07
  Speaker 2: the outcome, whether a volume basis or you know, kind
• 17:11
  Speaker 2: of a dollar's collected basis. And then on our side,
• 17:15
  Speaker 2: we'll deal with the spikiness of changes in workflow relative
• 17:20
  Speaker 2: to changes in technology.
• 17:22
  Speaker 1: No, well said, maybe just segueing here because I got
• 17:25
  Speaker 1: in the weeds a little bit on the products and service.
• 17:27
  Speaker 1: You know, if we think about your journey, you start
• 17:29
  Speaker 1: smarter DX. A couple of years later, you know, you're
• 17:32
  Speaker 1: now part of Smarter Technologies. What was the pitch to
• 17:35
  Speaker 1: you as the founder or founders of Smarter DX, of
• 17:39
  Speaker 1: Winding Mountain Capital? You know, why make this combination? What
• 17:42
  Speaker 1: really resonated to you about the opportunity? And I don't
• 17:45
  Speaker 1: know not to put words in your mouth. Is it
• 17:47
  Speaker 1: the scale? Is that the capital? Is that the capabilities?
• 17:50
  Speaker 1: I'd love to hear more about why this all fits together?
• 17:53
  Speaker 1: So well?
• 17:54
  Speaker 2: Yeah, absolutely, I mean I think you know, when we
• 17:58
  Speaker 2: were Smarter d X, we were moving as quickly as
• 18:03
  Speaker 2: we possibly could. Of course, Uh, but healthcare is big
• 18:07
  Speaker 2: and thorny, and so you know, really I think the
• 18:11
  Speaker 2: question was, how can we you know, kind of for
• 18:15
  Speaker 2: lack of a better words, shortcut the addition of capabilities
• 18:19
  Speaker 2: and you know, kind of the breadth of how we
• 18:21
  Speaker 2: can help our customers. And so we started to sort
• 18:27
  Speaker 2: of look for other companies that we would want to
• 18:31
  Speaker 2: join forces with and and sort of think about, you know,
• 18:33
  Speaker 2: potential capital partners. And uh. The other thing that I'll
• 18:39
  Speaker 2: say is that you can do this in the VC
• 18:43
  Speaker 2: world by raising at a super high valuation. And you know,
• 18:51
  Speaker 2: I think there are sort of incentives sometimes to grow
• 18:55
  Speaker 2: faster than what the kind of like underlying business actually
• 18:59
  Speaker 2: allows for, and you get companies that move into the
• 19:02
  Speaker 2: kind of unsustainable growth sort of phase of things. And
• 19:07
  Speaker 2: so I was worried that just continuing down the pure,
• 19:13
  Speaker 2: you know, kind of venture capital journey wouldn't be the
• 19:17
  Speaker 2: right answer. Either you kind of you know, promise the
• 19:20
  Speaker 2: sky and and you better achieve it or else. And
• 19:26
  Speaker 2: so I actually it was. It was actually very interesting
• 19:29
  Speaker 2: because when we first when I first met you know,
• 19:33
  Speaker 2: Matt Holt and some of the New Mountain team. You know,
• 19:37
  Speaker 2: speaking as a founder that's not been in finance, traditionally,
• 19:43
  Speaker 2: you think of pe as, okay, great, they're gonna come in,
• 19:47
  Speaker 2: You're gonna ruthlessly maximize Ebita and then you know, flip
• 19:51
  Speaker 2: the company and call it a day. But actually it
• 19:55
  Speaker 2: was kind of very very interesting because of all the
• 19:59
  Speaker 2: capital partners I spoke with, Matt actually had in some
• 20:04
  Speaker 2: ways like the deepest product understanding of healthcare and kind
• 20:09
  Speaker 2: of was trying to reimagine kind of how healthcare works
• 20:15
  Speaker 2: and and sort of think about the components it would
• 20:17
  Speaker 2: take to decrease the trillion dollars that we spend on
• 20:20
  Speaker 2: healthcare administration. Yeah, and so it was actually kind of
• 20:26
  Speaker 2: great because you get the sustainability of kind of private
• 20:31
  Speaker 2: equity capital, but the ability to imagine of venture capital money.
• 20:40
  Speaker 2: And I thought it was actually a really sort of
• 20:44
  Speaker 2: great capital partner, and you know, kind of a conceptual
• 20:50
  Speaker 2: and imagination partner as well.
• 20:52
  Speaker 1: So if I think about the three business being combined,
• 20:54
  Speaker 1: and again I don't sit in the four walls of
• 20:56
  Speaker 1: your company, but I think of Access as more of
• 20:58
  Speaker 1: a legacy business in the space with scale. Is that
• 21:00
  Speaker 1: is that a fair way to.
• 21:02
  Speaker 2: Think about it?
• 21:02
  Speaker 1: And then smarter you know, newer technology on top, and
• 21:06
  Speaker 1: then the third thoughtful team thoughtful team, sorry, thoughtful AI
• 21:11
  Speaker 1: as you know, the agentic piece that's coming forward. Is
• 21:14
  Speaker 1: that is that a right way to think about the
• 21:16
  Speaker 1: three legs to the stool?
• 21:18
  Speaker 2: So I think sort of dividing it into the clinical
• 21:21
  Speaker 2: intelligence and then the automation of the fifty hoops side.
• 21:26
  Speaker 2: I think of, you know, smarter DX and you know,
• 21:29
  Speaker 2: more recently pieces as the clinical intelligence side got it,
• 21:33
  Speaker 2: which is in just clinical data, you know, have cutting
• 21:38
  Speaker 2: edge AI algorithms that can understand that data and parse it.
• 21:42
  Speaker 2: And then you know there's actually a very interesting thing
• 21:45
  Speaker 2: on the automation side, which is it turns out that
• 21:50
  Speaker 2: what you automate is every bit as important, of course,
• 21:54
  Speaker 2: as how you achieve that automation. Okay, And so if
• 21:59
  Speaker 2: you imagine if you go to a thousand different customers
• 22:03
  Speaker 2: who do things a thousand different ways, and you try to,
• 22:08
  Speaker 2: you know, kind of write a bunch of custom code
• 22:10
  Speaker 2: for each customer, it actually becomes incredibly hard to maintain
• 22:16
  Speaker 2: over time. And that's that's actually part of the reason
• 22:19
  Speaker 2: why historical attempts at automation and healthcare have actually failed.
• 22:25
  Speaker 2: And when I first heard about Access Healthcare, I had
• 22:29
  Speaker 2: the same impression that you had, actually, which is, oh,
• 22:31
  Speaker 2: it must be you know, kind of like a legacy
• 22:34
  Speaker 2: workforce company. And Matt kept explaining, you don't understand to
• 22:39
  Speaker 2: have the lowest cost of delivery, and I was like, okay,
• 22:42
  Speaker 2: that's cool, but it's still a legacy workforce company. It
• 22:45
  Speaker 2: turns out, though, that if you imagine anyone can sort
• 22:52
  Speaker 2: of have a, as an example, a productive ar follow
• 22:57
  Speaker 2: up person. This is somebody who calls insurance companies, understands
• 23:00
  Speaker 2: why there's a denial, and sort of goes to that workflow.
• 23:04
  Speaker 2: If I give you somebody who's you know, who has
• 23:07
  Speaker 2: ten years of experience and a clinical background. Of course
• 23:10
  Speaker 2: that person will be relatively expensive. And so if I
• 23:15
  Speaker 2: start saying, hey, Jonathan, you know, you're only allowed to
• 23:20
  Speaker 2: hire the fresh college graduates who have no healthcare experience,
• 23:26
  Speaker 2: but they have to do this job well and fast
• 23:29
  Speaker 2: and at a high quality. The only way that you
• 23:32
  Speaker 2: would actually be able to achieve that is by building
• 23:36
  Speaker 2: in tons of sort of workflow tools and guard rails
• 23:41
  Speaker 2: to kind of abstract in some sense a lot of
• 23:44
  Speaker 2: the decision making away from the person doing the work.
• 23:50
  Speaker 2: And so the link between lowest costs to serve while
• 23:55
  Speaker 2: still maintaining high quality was that what they've actually built
• 23:59
  Speaker 2: over time was actually an incredible workflow engine in which
• 24:04
  Speaker 2: a lot of the standard operating procedures and how to
• 24:08
  Speaker 2: and you know, payer patterns and things like that were
• 24:12
  Speaker 2: abstracted away from kind of inside the cranium of the
• 24:17
  Speaker 2: worker and then onto you know, kind of software and
• 24:21
  Speaker 2: databases and manuals. And actually that substrate is incredibly powerful
• 24:32
  Speaker 2: for building automation because you're now building automation based on
• 24:36
  Speaker 2: best practice, kind of armed with insights on how to
• 24:40
  Speaker 2: do something well, not just recreating you know, kind of
• 24:44
  Speaker 2: existing workflows.
• 24:45
  Speaker 1: Now, I see that makes a lot of sense. So
• 24:47
  Speaker 1: I guess you have the chassis now to apply the
• 24:49
  Speaker 1: further level of technology on top of it.
• 24:52
  Speaker 2: That's right, that's great.
• 24:53
  Speaker 1: So now you know, we're in two thousand, the end
• 24:56
  Speaker 1: of twenty twenty five today, you've been at this for
• 24:59
  Speaker 1: five years. Now, you're the head of a very big company.
• 25:02
  Speaker 1: I don't know the exact number of employees you have overall,
• 25:05
  Speaker 1: but it's probably a lot more than Smarter d act
• 25:08
  Speaker 1: That's right. You know, as you think about what your
• 25:10
  Speaker 1: mandate is, you know, for the next couple of years
• 25:13
  Speaker 1: as a CEO, you know, what are you guys looking
• 25:16
  Speaker 1: at from a strategic comperative perspective for the next I mean,
• 25:19
  Speaker 1: you're only in combined company for what eight or nine
• 25:22
  Speaker 1: months now?
• 25:23
  Speaker 2: Yeah, it's you know, it's been it's been relatively short
• 25:27
  Speaker 2: from a kind of combined company perspective, and so I
• 25:32
  Speaker 2: think really in the near term for the next two years,
• 25:36
  Speaker 2: and actually I hope it's always our north star. My
• 25:39
  Speaker 2: strategic comparative is really, how do we solve this is
• 25:44
  Speaker 2: gonna sound silly, how do we solve customer problems? Right?
• 25:48
  Speaker 2: And and this is just where going back to that
• 25:52
  Speaker 2: trillion dollars of healthcare administration, Like it's not like the
• 25:56
  Speaker 2: hospitals want to spend all that money on administration, you know.
• 26:02
  Speaker 2: Uh and and the payers though either for what it's
• 26:04
  Speaker 2: worth uh and And so I think, you know, kind
• 26:08
  Speaker 2: of what we want and what our customers want, which
• 26:11
  Speaker 2: is to arm them with the tools to get paid
• 26:17
  Speaker 2: correctly not too much and also not too little on
• 26:21
  Speaker 2: the care that they've delivered. And to do so in
• 26:25
  Speaker 2: I think of costs and and frick and kind of
• 26:28
  Speaker 2: like friction a similar concepts. And to do so in
• 26:30
  Speaker 2: a way that with like decreasing friction over time really
• 26:34
  Speaker 2: fundamentally is our north star. And I do think of
• 26:41
  Speaker 2: us as you know, less just a r c M,
• 26:49
  Speaker 2: you know, kind of services with a little bit of
• 26:52
  Speaker 2: technology inside it company, and more as a company that
• 26:55
  Speaker 2: is trying to sort of use and reimagine how providers
• 27:02
  Speaker 2: go through this process. And in that sense, i'm you know,
• 27:07
  Speaker 2: in a sense maybe this is just my startup hat
• 27:09
  Speaker 2: still being on. I'm not sure there's a perfect comparitor
• 27:14
  Speaker 2: for us out there, gotcha.
• 27:16
  Speaker 1: So maybe just thinking about that in a little more detail,
• 27:18
  Speaker 1: I mean, can you sketch out the broad strokes of
• 27:21
  Speaker 1: how big the market is just for RCM today, and
• 27:24
  Speaker 1: you know, as we think about that landscape, how would
• 27:27
  Speaker 1: you how would you kind of bifurcate that between you know,
• 27:30
  Speaker 1: the legacy mature, established players and then there's a couple
• 27:34
  Speaker 1: newer players like yourselves on the in the marketplace these
• 27:37
  Speaker 1: days too.
• 27:38
  Speaker 2: Yeah. Absolutely, So, you know, I think the RCM market
• 27:43
  Speaker 2: broadly so hospitals spend somewhere on the order of one
• 27:47
  Speaker 2: hundred and twenty five maybe one hundred and fifty billion
• 27:50
  Speaker 2: dollars on revenue cycle, and the majority of that cost
• 27:57
  Speaker 2: is you know, as you might imagine, therefore, I'm kind
• 28:00
  Speaker 2: of internal cost, so you know, kind of a lot
• 28:04
  Speaker 2: of the people going through each of those processes, and
• 28:09
  Speaker 2: then there's some software costs, and uh, some fraction about
• 28:13
  Speaker 2: of that, maybe about ten percent or so, is kind
• 28:18
  Speaker 2: of like quote unquote outsourced. So it's you know, somebody
• 28:22
  Speaker 2: who's either either it's a managed services play, which is
• 28:27
  Speaker 2: to say, I will take over the function and do
• 28:29
  Speaker 2: it for you, or it's a staff augmentation play, which
• 28:33
  Speaker 2: is you can't hire enough people. So I'm just going
• 28:35
  Speaker 2: to add kind of two more people, right, just fts
• 28:39
  Speaker 2: for something like that so to to to your uh
• 28:44
  Speaker 2: to your book. And so I think that for us
• 28:49
  Speaker 2: there are certainly, uh we we kind of have a
• 28:53
  Speaker 2: mix across those different categories. So with you know, kind
• 28:58
  Speaker 2: of like smarter pre bill and smarter notes, these are
• 29:03
  Speaker 2: inherently products that is more akin to other software spend
• 29:10
  Speaker 2: that they have. And then of course with you know
• 29:14
  Speaker 2: kind of access, there's comparisons to other services that they
• 29:20
  Speaker 2: might sort of offshore. And with the combination of the two,
• 29:27
  Speaker 2: there's also the you know, kind of the sort of
• 29:31
  Speaker 2: more of a solutions based approach to it.
• 29:33
  Speaker 1: Just wrap it all together.
• 29:35
  Speaker 2: Yeah, that's right. So I think are in a sense,
• 29:37
  Speaker 2: I think our sort of you know, TAM TAM is
• 29:45
  Speaker 2: truly in that one hundred and twenty billion dollar range.
• 29:49
  Speaker 2: And and I say that because again, hospitals would prefer
• 29:57
  Speaker 2: to spend more of their attention on how to deliver
• 30:00
  Speaker 2: better clinical care and you know, contingent on not needing
• 30:05
  Speaker 2: to worry about it, would rather not worry about revenue
• 30:08
  Speaker 2: cycle management.
• 30:09
  Speaker 1: No, well said, what do you guys typically find is
• 30:12
  Speaker 1: the tip of the spirit to get you in the door.
• 30:15
  Speaker 2: Yeah, there's so I think there's you know, there's very much.
• 30:21
  Speaker 2: I think. I think what I would say is when
• 30:23
  Speaker 2: you make decisions, especially when you're getting to know somebody,
• 30:27
  Speaker 2: you want sort of to take on less risk, of course,
• 30:31
  Speaker 2: and then there are processes that are relatively low lift
• 30:36
  Speaker 2: operationally and then financially lower risk as well. So as
• 30:42
  Speaker 2: an example, on the smarter pre bill side, for that
• 30:47
  Speaker 2: five million dollars, you're talking about assigning sort of four
• 30:53
  Speaker 2: total three or four total FTEs to review the AI applications,
• 30:59
  Speaker 2: and it's a it's kind of like a little QA
• 31:01
  Speaker 2: process that you insert, so you're not redoing any of
• 31:05
  Speaker 2: your traditional operational workflows. Another good example would be low
• 31:10
  Speaker 2: balance or aged ar. These are accounts receivables that either
• 31:16
  Speaker 2: because of the number of dollars involved or the number
• 31:20
  Speaker 2: of days past, you've kind of given up on. So
• 31:23
  Speaker 2: you might as well give that stack to somebody else
• 31:25
  Speaker 2: to try to see if there's something in there that
• 31:28
  Speaker 2: you know they can get right because you've kind of
• 31:30
  Speaker 2: completed all of your normal processes. So that's kind of
• 31:33
  Speaker 2: another good place to start.
• 31:36
  Speaker 1: Is there ever a scenario where you take over more
• 31:39
  Speaker 1: of the workflow, I mean down the road, do we
• 31:42
  Speaker 1: automate more and more, and this just becomes, you know,
• 31:45
  Speaker 1: the standard standard operating procedure.
• 31:48
  Speaker 2: I think so, and I think, you know, to be clear,
• 31:52
  Speaker 2: I don't think we ever want to be in the
• 31:55
  Speaker 2: kind of quote unquote end to end business where we're
• 31:58
  Speaker 2: taking over the entirety of any cycle. And I and
• 32:01
  Speaker 2: the reason for that is, I think it's actually, you know,
• 32:05
  Speaker 2: I think a large fraction of the market on that
• 32:09
  Speaker 2: that the fraction of that we're targeting, kind of wants to,
• 32:12
  Speaker 2: for lack of better words, retain that expertise internally and
• 32:16
  Speaker 2: so they can choose how to do things their way more.
• 32:21
  Speaker 2: And so I think there might be kind of whether
• 32:25
  Speaker 2: it's AR or coding or you know, denials management or
• 32:31
  Speaker 2: something like that, I think there will be potentially components
• 32:34
  Speaker 2: that we take over. And and so I think, you know,
• 32:37
  Speaker 2: you start with the easy stuff, which requires very little
• 32:41
  Speaker 2: operational lift, which in which the outcomes are all additive.
• 32:46
  Speaker 2: And then I think as we what we hear a
• 32:48
  Speaker 2: lot from our customers is that as they get a
• 32:51
  Speaker 2: sense of the power of our technology and the quality
• 32:54
  Speaker 2: of our work, they tend to want to give us
• 32:57
  Speaker 2: more and more. So actually, on the on the access
• 33:01
  Speaker 2: side the It's interestingly the vast majority of their growth
• 33:08
  Speaker 2: and revenue are from existing customers rather than new customers.
• 33:13
  Speaker 1: And what exactly are they growing? Is it just the
• 33:16
  Speaker 1: underlying number of cases that they're serving or or you know,
• 33:20
  Speaker 1: where's that growth coming from?
• 33:21
  Speaker 2: Both both breadth and depth, so both different areas that
• 33:25
  Speaker 2: they start working on as well as kind of like
• 33:30
  Speaker 2: the fraction of work within a given area that they
• 33:34
  Speaker 2: that they get.
• 33:35
  Speaker 1: So how how has consolidation in the provider space played
• 33:40
  Speaker 1: out for you guys? Is that helpful or headwind to
• 33:43
  Speaker 1: the business? I don't really know sitting here today.
• 33:47
  Speaker 2: I think consolidation has been sort of helpful overall. And
• 33:54
  Speaker 2: I think it's because a lot of times with consolidation,
• 33:58
  Speaker 2: health systems suddenly find themselves. You know, when you consolidate,
• 34:02
  Speaker 2: you add all the vendors that every individual hospital you
• 34:05
  Speaker 2: consolidate hit work with, and so they suddenly find themselves
• 34:08
  Speaker 2: managing you know, hundreds or literally even thousands of vendors.
• 34:16
  Speaker 2: And and I think this is where, you know, I
• 34:19
  Speaker 2: think part of the value that we provide to health
• 34:21
  Speaker 2: systems is that we have a range of sort of
• 34:25
  Speaker 2: you know, technologies, products and solutions and services and you know,
• 34:32
  Speaker 2: with sort of one I sort of data feed from
• 34:36
  Speaker 2: your EMR, you can unlock multiple themes.
• 34:39
  Speaker 1: Got it? Maybe taking a step back, you know, one
• 34:42
  Speaker 1: of the things that we always hear when we go
• 34:44
  Speaker 1: to these conferences is that EMRs are built for billing.
• 34:48
  Speaker 1: But it seems like, at the end of the day,
• 34:50
  Speaker 1: the EMR is actually not that great of a tool
• 34:53
  Speaker 1: for revenue collection. Is that a fair statement. It seems
• 34:57
  Speaker 1: like you need this additional overlay over top of it
• 35:00
  Speaker 1: besides just the raw EMR from EPIC or Cerner or Athena.
• 35:04
  Speaker 1: I don't know.
• 35:06
  Speaker 2: I think it's certainly much better than the pre EMR days.
• 35:12
  Speaker 2: And you know, I think sometimes, uh, just to maybe
• 35:17
  Speaker 2: give you a funny anecdote, when I was a resident,
• 35:20
  Speaker 2: I remember one of the attendings was like, Mike, when
• 35:23
  Speaker 2: we were residents, we saw you know, fifty people a day,
• 35:27
  Speaker 2: and you guys have it easy only see twenty people.
• 35:29
  Speaker 2: And you know, I was like, well, when you were
• 35:32
  Speaker 2: a resident, the sort of you know, like the tradition
• 35:36
  Speaker 2: the care for a heart attack was bed rest, and
• 35:39
  Speaker 2: you just like saw them in bed and wrote two
• 35:41
  Speaker 2: piece two words on a piece of paper, you know,
• 35:43
  Speaker 2: and called it a day, and so I think I
• 35:48
  Speaker 2: think some of the reason that we don't feel like
• 35:52
  Speaker 2: there's been progress in terms of simplifying revenue cycle is
• 35:59
  Speaker 2: that we took a process that was built in a
• 36:02
  Speaker 2: sense for a much simpler world, and patients are and
• 36:06
  Speaker 2: the treatments and everything we do is so much more complex.
• 36:10
  Speaker 2: And I think if you you know, we we've had
• 36:14
  Speaker 2: I remember when I was a resident, we would occasionally
• 36:16
  Speaker 2: have you know, a few hours of downtime and I
• 36:21
  Speaker 2: literally wouldn't even know how to you know, like compose
• 36:26
  Speaker 2: the solution to deliver our medications. I just don't memorize
• 36:29
  Speaker 2: the percentage of sailing versus you know, kind of like
• 36:32
  Speaker 2: other fluids. And so I do think that the EMR,
• 36:36
  Speaker 2: you know, has been helpful in that transformation from manual
• 36:42
  Speaker 2: to digitization. But at the end of the day, it's still,
• 36:48
  Speaker 2: to go back to what we were talking about in the beginning,
• 36:50
  Speaker 2: it's still a person who has to do all the
• 36:53
  Speaker 2: cognitive work and the care of these days is just
• 36:57
  Speaker 2: really complex, especially at these you know, tertiary academic medical
• 37:01
  Speaker 2: centers or you know, like these level for trauma centers
• 37:06
  Speaker 2: as an example, And so I do think that's where
• 37:10
  Speaker 2: the fundamental problem of how do we find the capacity
• 37:15
  Speaker 2: to do that Cognitive labor doesn't get solved by converting
• 37:19
  Speaker 2: something to digital. You can have more standardized workflows, you
• 37:22
  Speaker 2: can have you know, priority lists. But I think that's
• 37:26
  Speaker 2: why AI is sort of so exciting for its potential
• 37:30
  Speaker 2: to transform this.
• 37:31
  Speaker 1: So maybe thinking about that a little bit further and
• 37:33
  Speaker 1: teasing out is the ROI that your customers typically see
• 37:37
  Speaker 1: higher the more complex the cases or the higher the acuity,
• 37:42
  Speaker 1: because there is that all that more care and nuance
• 37:44
  Speaker 1: being given to that episode.
• 37:47
  Speaker 2: Yeah, I think that's I think that's fair. There's a
• 37:49
  Speaker 2: little bit of a confounding factor, which is that the
• 37:53
  Speaker 2: sort of larger health systems are the ones that tend
• 37:56
  Speaker 2: to have higher acuity and they're better resource so their
• 38:01
  Speaker 2: teams are bigger, and yep, they're just more sophisticated. Yeah,
• 38:03
  Speaker 2: they're more sophisticated in general. But I think even with
• 38:09
  Speaker 2: that that is still true.
• 38:11
  Speaker 1: So shifting gears to the technology which you just mentioned
• 38:14
  Speaker 1: that you know, we've had this paradigm change with with
• 38:16
  Speaker 1: AI and automation. You know, as you look out for
• 38:18
  Speaker 1: the next three to five years, what are you excited
• 38:20
  Speaker 1: about from a technology perspective as it applies to your business.
• 38:26
  Speaker 2: I mean, I think that what I'm excited about is really,
• 38:33
  Speaker 2: you know, kind of the evolution of that jagged frontier
• 38:37
  Speaker 2: that I was talking about, where it's good at solving
• 38:39
  Speaker 2: IMO math problems, but can't order you a plane ticket.
• 38:42
  Speaker 2: And you know, right now, there's still a lot of requirement,
• 38:46
  Speaker 2: for lack of better words, glue in between different steps.
• 38:52
  Speaker 2: And I think as models get better, that requirement will
• 38:57
  Speaker 2: go down, and in a sense, I think that I'll
• 38:59
  Speaker 2: actually be sort of deflationary from a cost perspective, which
• 39:06
  Speaker 2: is what we all want as you know, patients and people.
• 39:12
  Speaker 2: So I think that's you know, kind of the most
• 39:15
  Speaker 2: exciting part. This is maybe not a direct answer to
• 39:20
  Speaker 2: your question, but I think the other thing that I'm
• 39:24
  Speaker 2: really excited about, and this part adds complexity, is that
• 39:29
  Speaker 2: I think we will see sort of faster advances in
• 39:35
  Speaker 2: a sort of drug discovery, and then you'll be bringing
• 39:40
  Speaker 2: more new things to market. And whenever there's a new thing,
• 39:44
  Speaker 2: there's kind of the ripples of wait, how do we
• 39:48
  Speaker 2: account for this new thing in the context of revenue cycle?
• 39:50
  Speaker 2: And this kind of like back and forth, you know,
• 39:53
  Speaker 2: kind of on sort of what the proper payment should be.
• 39:57
  Speaker 1: I could only imagine for gene therapy or CARTII or
• 39:59
  Speaker 1: the sorts of things that's forth, let's just be so
• 40:02
  Speaker 1: much friction.
• 40:03
  Speaker 2: Yeah, that's right. And so I'm actually, you know, I
• 40:08
  Speaker 2: actually think that in a sense, the lack, for lack
• 40:13
  Speaker 2: of better words, deflationary work that I think AI will
• 40:17
  Speaker 2: bring to revenue cycle is necessary for the health system
• 40:21
  Speaker 2: to actually absorb what I hope is sort of an
• 40:26
  Speaker 2: increase in the velocity of, you know, our kind of
• 40:29
  Speaker 2: understanding of how to care for patients.
• 40:31
  Speaker 1: Another question that just came to mind is just as
• 40:33
  Speaker 1: you as you think about scaling up your your infrastructure
• 40:37
  Speaker 1: around AI and automation, I mean, you have peers doing it,
• 40:40
  Speaker 1: so I'm sure it's competitive for talent, but also the
• 40:43
  Speaker 1: payers are doing it as well. And how does that
• 40:44
  Speaker 1: friction work with And this is a naive question, but
• 40:48
  Speaker 1: AI on the provider side and AI on the payer side.
• 40:52
  Speaker 2: Yeah, yeah, I have a joke here, which is that
• 40:56
  Speaker 2: forcing two different AIS to argue about you know, the
• 41:01
  Speaker 2: kind of the right payment over a phone line is
• 41:04
  Speaker 2: actually the genesis for the Terminator movie, where they solve
• 41:09
  Speaker 2: the problem by saying you know what, this would be
• 41:12
  Speaker 2: much easier without the people. But I actually, I'm actually
• 41:18
  Speaker 2: sort of you know, our loyalty is to our customers,
• 41:25
  Speaker 2: who are the providers, but in some sense our greater
• 41:30
  Speaker 2: loyalty is just to make the payments accurate, right Like,
• 41:36
  Speaker 2: providers shouldn't be reimbursed more than the care that they
• 41:39
  Speaker 2: actually provided, either, And so actually on the smarter DX
• 41:44
  Speaker 2: PREVIEIL product, we identify down grades as well as upgrades
• 41:50
  Speaker 2: when they underlying care isn't justified, and hospitals actually like
• 41:54
  Speaker 2: that because what's what's not great is we don't get
• 42:01
  Speaker 2: paid for you know, ten dollars that you thought you
• 42:05
  Speaker 2: would get paid. What's much worse is if you first
• 42:07
  Speaker 2: spend sixty days arguing about it and then you realize
• 42:10
  Speaker 2: you're in the wrong, and then you don't get paid
• 42:12
  Speaker 2: the ten dollars anyway. And so I think there's you know,
• 42:17
  Speaker 2: kind of I think what I what I kind of
• 42:20
  Speaker 2: hope is that the arc is that as we get
• 42:22
  Speaker 2: more and more accurate about the connection between the underlying
• 42:27
  Speaker 2: care and what's on the claim, that we can start
• 42:31
  Speaker 2: convincing payers to say, hey, look, this is a claim.
• 42:34
  Speaker 2: We have extremely high confidence in all here's all the
• 42:38
  Speaker 2: evidence connected to every single claim for lack of better words,
• 42:42
  Speaker 2: on that claim, and I actually hope that over time
• 42:47
  Speaker 2: that reduces the friction as well. Makes sense.
• 42:52
  Speaker 1: I'd be remiss as an analyst if I didn't ask
• 42:55
  Speaker 1: something about the business and the financials here. You know,
• 42:58
  Speaker 1: how do you charge your client? You know, what does
• 43:00
  Speaker 1: leverage look like in the operating model? All those good things?
• 43:03
  Speaker 1: What keep KPIs are you looking at from a financial
• 43:06
  Speaker 1: perspective to manage the business day to day?
• 43:09
  Speaker 2: Yeah? Absolutely, so. You know, I think we work across
• 43:12
  Speaker 2: a broad range of uh sort of contracting, so we
• 43:19
  Speaker 2: have contingency, We have more traditional SaaS models, and then
• 43:25
  Speaker 2: we have you know, some of our tech enabled services
• 43:29
  Speaker 2: work is more a sort of on a per transaction basis.
• 43:34
  Speaker 2: The key here is obviously you want to you wouldn't
• 43:38
  Speaker 2: want to have a purely an FT based business model,
• 43:44
  Speaker 2: and then you automate away the FTDs and and you know,
• 43:47
  Speaker 2: kind of your revenue decreases, and transaction based allows us
• 43:51
  Speaker 2: to share some of the upside of automation with our customers,
• 43:55
  Speaker 2: but then you know, kind of keep some of the
• 43:57
  Speaker 2: you know, upside for us as well. And you know,
• 44:01
  Speaker 2: we are. This year we've implemented it's actually been you know,
• 44:10
  Speaker 2: sort of a banner year. I think we've booked in
• 44:17
  Speaker 2: this year alone several hundred million worth of I guess bookings,
• 44:24
  Speaker 2: and so are in the process of implementing a lot
• 44:27
  Speaker 2: of that and and and so a lot of what
• 44:30
  Speaker 2: we track is, you know, kind of the the impact
• 44:35
  Speaker 2: per customer, so the number of dollars we find corrupted
• 44:39
  Speaker 2: for the size of the customer, so dollars per discharge
• 44:43
  Speaker 2: is one. The quality of what we do, so the uh,
• 44:49
  Speaker 2: you know, kind of the the error rate, the fraction
• 44:53
  Speaker 2: of charts that we're able to q A. And then
• 44:56
  Speaker 2: I think third is just our own internal cost kind
• 44:59
  Speaker 2: of or unit of service that we deliver, got it.
• 45:04
  Speaker 2: And so it's it ends up being basically, you want
• 45:07
  Speaker 2: sort of more impact.
• 45:09
  Speaker 1: Out of higher quality for lower costs. Well, that just
• 45:11
  Speaker 1: leads me to one, I guess maybe natural question that
• 45:13
  Speaker 1: I didn't think of before, but, uh, you know, how
• 45:16
  Speaker 1: does your solution set help hospitals in your provider customers
• 45:21
  Speaker 1: improve quality?
• 45:22
  Speaker 2: Yeah? Absolutely, Yeah, So it's actually you know, very interesting. Actually,
• 45:32
  Speaker 2: the longer story for how I got into revenue cycle
• 45:37
  Speaker 2: and clinical documentation improvement was that as a hospital, based
• 45:44
  Speaker 2: on our internal metrics, we were being inefficient at the
• 45:47
  Speaker 2: care that was delivered. So we were keeping patients in
• 45:50
  Speaker 2: the hospital longer than they needed to stay in the hospital.
• 45:54
  Speaker 2: And so actually, the slightly longer story is that doctors
• 45:59
  Speaker 2: were arguing that they were being efficient and keeping patients
• 46:02
  Speaker 2: the right amount of time, but the patients are more
• 46:04
  Speaker 2: complex than they ought to be.
• 46:06
  Speaker 1: And the provider view of that that scenario, I'm sorry,
• 46:10
  Speaker 1: the pay review of miss.
• 46:11
  Speaker 2: Yeah, and and the the sort of the pair of
• 46:16
  Speaker 2: view was like, you're just not I don't see that
• 46:20
  Speaker 2: complexity in what you in the domation and so thanks
• 46:24
  Speaker 2: for your opinion, okay, but but you know you have
• 46:27
  Speaker 2: to justify it. And actually, you know that applies more broadly.
• 46:34
  Speaker 2: So if if I show you two hospitals with a
• 46:39
  Speaker 2: you know, one percent mortality rate each, you know, like,
• 46:45
  Speaker 2: which hospital is the better hospital? Well what if I
• 46:47
  Speaker 2: told you one was you know, Memorial Sloan Kettering and
• 46:52
  Speaker 2: the other was an urgent care center, Well one percent
• 46:55
  Speaker 2: would be freaking amazing for but it would be pretty
• 47:00
  Speaker 2: terrifying if it was an urgent care center. And so
• 47:03
  Speaker 2: the way that hospitals monitor their internal performance is actually
• 47:09
  Speaker 2: inherently tied to sort of the documentation and codes that
• 47:14
  Speaker 2: they submit, Like there's not another source of truth that's
• 47:17
  Speaker 2: separate from process. Or another way to say it would
• 47:21
  Speaker 2: be if you were responsible for maintaining you know, low
• 47:27
  Speaker 2: food wastage from shipping beef at McDonald's, but the receipts
• 47:34
  Speaker 2: for how many quarter pounders were ordered at each McDonald's
• 47:37
  Speaker 2: were all jumbled and inaccurate, it'd be impossible to do
• 47:40
  Speaker 2: that job. So I think the data that we create
• 47:45
  Speaker 2: actually gives hospitals a more accurate measurement of lots of
• 47:51
  Speaker 2: different components of the care that they deliver, from class
• 47:54
  Speaker 2: to fitting place to you know, kind of secondary effects
• 47:58
  Speaker 2: on mortality.
• 47:58
  Speaker 1: Yeah, the revenue is just the out put of a
• 48:01
  Speaker 1: job well done. The technical side, that's right, excellent. You know,
• 48:05
  Speaker 1: we're getting towards the end of our time here. And
• 48:06
  Speaker 1: one of the ways I like to wrap these questions
• 48:08
  Speaker 1: are these discussions up, is to ask a question about,
• 48:11
  Speaker 1: you know, what you see as your mission. Is there
• 48:13
  Speaker 1: something from your personal life or your professional experience that
• 48:17
  Speaker 1: you really see as you're you use the word nerve star,
• 48:20
  Speaker 1: but you know, how do you think about what your
• 48:22
  Speaker 1: personal north star is.
• 48:24
  Speaker 2: Yeah, I appreciate the question. You know, when I was
• 48:27
  Speaker 2: in medical school, I helped found a student run free clinic,
• 48:34
  Speaker 2: and you know, it was incredible to see the amount
• 48:42
  Speaker 2: of physicians that volunteered, you know, the number of students
• 48:47
  Speaker 2: who put their heart into soul and we set up
• 48:50
  Speaker 2: the care labs, radiology and you know, all of that.
• 48:54
  Speaker 2: And this was uh, you know, kind of sort of
• 48:59
  Speaker 2: twenty ten and twenty eleven. And I remember at the
• 49:06
  Speaker 2: end of the first year, you know, we were like, okay,
• 49:10
  Speaker 2: so how many patients did we treat this year? And
• 49:14
  Speaker 2: we treated I don't know, a few hundred patients, and
• 49:18
  Speaker 2: a lot of people were like, oh my god, this
• 49:20
  Speaker 2: is amazing, you know, incredible work. And I remember at
• 49:24
  Speaker 2: the time the ACA had passed, but I think it
• 49:28
  Speaker 2: wasn't implemented. And I remember doing the math of like,
• 49:31
  Speaker 2: all right, what's three hundred divided by the forty million
• 49:35
  Speaker 2: people without health insurance, and it was like, turns out
• 49:38
  Speaker 2: that's a relatively small fraction. And I think one of
• 49:42
  Speaker 2: the you know, amazing things about being a physician is
• 49:47
  Speaker 2: that ability to make a direct impact. And I think
• 49:51
  Speaker 2: one of the hard things about healthcare, and maybe related
• 49:55
  Speaker 2: to why it's so expensive is the difficulty of scaling
• 50:00
  Speaker 2: sort of past that I work five hours, I can
• 50:03
  Speaker 2: see you know, fifteen patients, you know, kind of relationship,
• 50:08
  Speaker 2: and so you know, I think, really, I think since
• 50:13
  Speaker 2: that experience, my north star has always been how do
• 50:17
  Speaker 2: we use technology to help healthcare scale in a way
• 50:23
  Speaker 2: that just people with really good intentions don't quite scale.
• 50:30
  Speaker 2: And I think, you know, that's where what gets sort
• 50:35
  Speaker 2: of me out of bed is looking at that sort
• 50:38
  Speaker 2: of one trillion dollars of spending healthcare, which you know,
• 50:43
  Speaker 2: my personal opinion is four and a half trillion if
• 50:46
  Speaker 2: it all goes to research so better future care and
• 50:50
  Speaker 2: the provision of care for patients. Who's to argue that
• 50:53
  Speaker 2: twenty or twenty five percent of GDP is or is
• 50:56
  Speaker 2: not the right number, right, But it's the it's the
• 50:59
  Speaker 2: sort of trillion that gets burned up in the administration
• 51:02
  Speaker 2: that you're like, well, that really improve somebody's life. And
• 51:08
  Speaker 2: so I think, you know, kind of reducing that friction
• 51:12
  Speaker 2: of healthcare very much, you know, with technology very much
• 51:15
  Speaker 2: aligns with that sort of fundamental north star.
• 51:17
  Speaker 1: That's great. Thank you so much for sharing, and thank
• 51:20
  Speaker 1: you for giving us a masterclass on RCM and automation
• 51:23
  Speaker 1: and where the market's going.
• 51:24
  Speaker 2: Thanks Jonathan. I hope your listeners all stayed awake during that.
• 51:27
  Speaker 1: I'm sure they did. I know my mom is still
• 51:29
  Speaker 1: listening here. That's my mom, so thank you very much.
• 51:32
  Speaker 1: That's doctor Michael Gao, CEO of Smarter Technologies. Thank you
• 51:36
  Speaker 1: so much for joining us for our latest episode, and
• 51:38
  Speaker 1: please make sure to click the follow button on your
• 51:40
  Speaker 1: favorite podcast app or website so you never miss a
• 51:43
  Speaker 1: discussion with the leaders in healthcare innovation. I'm Jonathan Palmer,
• 51:47
  Speaker 1: and you've been listening to the Vanguards of Healthcare podcast
• 51:50
  Speaker 1: by Bloomberg Intelligence. Until next time, take care
• 52:11
  Speaker 2: Cases are conserases US mosses