Vanguards of Health Care by Bloomberg Intelligence
Smarter Technologies’ Blueprint for AI-Driven Revenue Cycle Management
15 Jan 2026
Chapter 1: What is the main topic discussed in this episode?
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 thrilled to welcome today's guest, Dr. Michael Gao, the CEO of Smarter Technologies.
Prior to assuming the CEO role, he was the co-founder and CEO of SmarterDx, one of the three predecessor companies that were combined by the financial sponsor New Mountain Capital to form this new automation and insights platform. Dr. Gao completed his medical degree at the University of Michigan,
and his residency and fellowship at New York Presbyterian, where he was also a medical director focused on transformation before his foray as an entrepreneur. Welcome to the podcast, Michael. Thanks, Jonathan. It's great to be here. So why don't we start with a 30,000-foot view of what Smarter does and where you fit into the ecosystem?
Chapter 2: How does AI transform hospital revenue cycle management?
Yeah, absolutely. You know, I think to understand what Smarter does, one has to understand a little bit about what healthcare revenue cycle even is. And I promise it's not going to be an arcane two-hour sort of pre-ramble on this. You know, fundamentally, what revenue cycle is, is that for hospitals to get paid for the care that they deliver—
They have to first take that care and generate a receipt for the care. So that's the process of forming the claim and understanding what the clinical care all corresponded to. And then they have to jump through the 50 or so hoops for the payers to actually deliver payment for that care.
And so Smarter Technologies really is about solving both of those challenges with, you know, kind of AI and scale delivery.
Chapter 3: What challenges do hospitals face in revenue cycle management?
And so the first component of that, which is really generating an accurate receipt for the care, is principally about clinical intelligence and being able to ingest care data and understand what that means. And then, you know, use that to either help facilitate documentation or understand if there's misses in the receipt. And then the second is really the jumping through the 50 hoops process.
And that might be submitting things through a specific website, following specific rules and guidelines and things like that. And so on that side, it's really about sort of the scale needed to understand those patterns And then the automation needed to do that in a low-cost manner.
So at the end of the day, it's just about helping hospitals make sure the receipt is correct and then decreasing the cost of collecting on the sort of payment for that care.
No, well said. So maybe thinking about this as kind of a journey, if we think about the evolution of RCM, you know, we have the pre-EMR days, which I would think of as paper. post the High Tech Act. And now we're kind of in this AI evolution.
Chapter 4: How does Smarter Technologies utilize clinical intelligence?
So how has it changed along that longitudinal journey? And I guess, when did you get the idea or the light bulb to tackle this problem? Maybe that's the ending point.
Yeah, yeah, yeah. So That's a really fascinating framework, actually. So, you know, the reason why we have claims or, you know, if you've heard of ICD codes or CPT codes in the first place is kind of back in the paper chart days. And so you provide a lot of care. All the sort of notes on the care goes into a binder.
And to get paid on that care, you couldn't FedEx the binder to a payer and say, like, please read through this and figure it out.
Chapter 5: What is the significance of automation in revenue cycle management?
And so you come up with effectively shorthand to compress that binder into sort of a set of codes that correspond to payment. And I think that sort of initially with kind of software, it actually... didn't transform the process. It just recreated that process in a digital way. So now instead of... So all the complexity was still there. All the complexity is still there.
So, you know, now instead of paper, you have electronic charts, but you're still going kind of tab by tab. Instead of a stack of binders, you have a priority list and a work queue on some piece of software. But fundamentally, you know, it's still a person going through each chart and kind of doing all of that work. And I think with...
Chapter 6: How does the evolution of RCM reflect changes in healthcare?
And then kind of the second part is that they have to – kind of each payer receives information in a slightly different way, has slightly different requirements. And so it took a lot of human intelligence to understand those patterns. Sure. United wants it this way and Aetna wants it that way. Exactly. And now with AI, I think there's a transformation, which is it's not just –
humans doing the work, but switching from a paper medium to an electronic medium, it's that AI can take a first pass at doing the work and humans can actually just supervise that. So as an example, on the smarter DX side, The average patient visit that we analyze has about 30,000 data points. There's over 100,000 possible ICD codes.
Chapter 7: What role does human supervision play in AI-driven workflows?
Okay. And your average patient has, you know, kind of 10 to 15 codes. And so if you're looking for a missing code out of 100,000 possibilities and 30,000 data points, It's a lot like finding a... Sounds like a pretty easy to miss.
It's hard. It's hard.
It's like looking for a missing person in a stadium of people. Yeah, that's a good analogy. And so this is where we use AI to first identify what it thinks is missing, and then sort of the human intelligence, that clinical experience, is still used to validate each and every finding. But it kind of changes the nature of the workflow into kind of a...
Chapter 8: What is the future outlook for AI in healthcare administration?
AI does the work and humans do the supervision type approach.
So with that in mind, you know, let's talk about your founder journey. You know, how did you come to start SmarterDX?
Yeah. So I'm, of course, a pretty big nerd, as you might imagine. So as you had mentioned, I did residency at New York Presbyterian. And before residency, I had built FindCare, which was kind of a a version of ZocDoc, but targeted toward patients without health insurance. So learned a good amount of, you know, kind of programming in that context. And before that, did a lot of applied math.
So I was leading a lot of AI initiatives for New York Presbyterian. And it was sort of actually, funnily, the doctors were arguing that the care that they were providing wasn't sufficiently captured by the coders.
And the coders and clinical documentation improvement specialist says, well, yeah, but that's because you're not documenting it, not because we're not capturing it after your documentation. Right. So to try to resolve this, I started trying to understand kind of how to build algorithms that could take in the raw clinical data and use that rather than just pure documentation as a source of truth.
So if your blood pressure is 180 over 100 and you get prescribed a high blood pressure medication, whether or not somebody wrote the words high blood pressure, you can infer that it was likely something that was managed during that visit. You still have to confirm it, get it documented, you know, double-check it, but you can sort of have an inkling that that must be, you know, true.
And at the same time, what was really fascinating was that... So this is like 2017, 2018. One of the sort of data science approaches, the Transformers approach, which is kind of the underpinning of LLMs, started making the waves. So it was still a very early version called the BERT models. Okay. But...
It was actually really interesting to see how even very primitive implementations of this approach allowed models to understand clinical data and patterns in a way that, you know, kind of very sophisticated implementations of traditional statistics and machine learning didn't allow for.
What's an example of that for a person like myself who doesn't have a very strong math background?
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