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Becker’s Healthcare Podcast
How Ambient AI is Helping Health Systems Address Financial, Operational, and Clinician Pressure
00:18:35
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- [00:00]
A
Hi everyone, this is Brian Zimmerman with Beckers Healthcare. Thank you so much for tuning into the Beckers Healthcare podcast. Today we're going to talk about how ambient AI is helping health systems address financial, operational and clinician pressure. To cover this topic, we're joined by Nikhil Baduma, the co founder and CEO of Ambience Healthcare, where he leads a team dedicated to empowering clinicians and health systems with AI technology. Drawing on his deep background in machine learning and as author of O'Reilly's Fundamentals of Deep Learning, Nikhil brings a unique perspective on how technology can drive both clinical and operational outcomes across healthcare organizations. Alfred Lumsdan is the Chief Financial Officer of Ardent Health. He brings more than 30 years of experience in healthcare finance and enterprise operational leadership. He oversees financial strategy, capital planning and organizational performance with a career spanning executive roles including CFO of Quorum Health and senior leadership positions at sharecare and Tivity Health. Across these roles, he has guided organizations through major financial transformations, strengthened revenue integrity and improved long term financial resilience. Nikhil, Alfred, thank you for being here today.
- [01:12]
B
Thank you.
- [01:13]
C
Thank you.
- [01:14]
A
Alfred, let's begin with you. Can you really sort of set the stage here for our CFO listeners out there? What challenges are health systems facing right now and how is AI positioned to help? Can you give us a lay of the land here?
- [01:25]
B
Yeah. At its most basic, health systems are facing, I'd say a confluence of challenges, both operational and financial. At the operational side, we have a workforce that is stretched to its capacity. I'd say stressed out is not too strong of a word. Then financially, we've got headwinds from challenges from the payer environment. Payers have struggled on their finances over the last several years and that has shown up in a number of ways as that challenge moves downstream and gets expressed in things such as significantly more denials than we've ever faced as an industry.
- [02:05]
A
That's a significant overview for significant challenge for many health systems. When you talk about denials and sort of that, that challenging payer environment, from sitting in your seat as CFO with Ardent, how are you sort of leveraging Ambient, Right, to help address some of these pressure points and get some relief?
- [02:27]
B
Yeah, absolutely. Historically, whenever we would add technology to our clinicians, it became more of a burden. It didn't make their jobs easier, it didn't actually make delivering care any easier. Technology actually increased both the amount and complexity of their jobs. And as a consequence, of course, you've seen much higher levels of burnout, much higher levels of turnover. And then when you look at markets like the ones that Arden is in. We're in generally second tier urban markets. Markets like Albuquerque, New Mexico, Tyler, Texas, Tulsa, Oklahoma. There are at times more limited work pools from a workforce standpoint. And so managing that burnout is extremely important. And so I would say for the first time with ambient listening, with implementing the ambient product, we've seen a tool that was specifically designed to make the clinical jobs easier. And we've seen tremendous adoption that I could talk at length about, but I'd say it's the first tool in my career from a technology where the clinicians have embraced it almost in a, say, a manic way. It's been so well received.
- [03:52]
A
And that's. And to your point, Alfred, that is very unique when you think about perhaps the history of clinician adoption of technology solutions that are being brought to them. Was there any, I guess, hesitancy in the beginning when you think about sort of the history of technology integration in health care? I imagine there could have been some skepticism just based on the past experience, to your point, where technology has been brought in to make their jobs ultimately, maybe not intentionally, but more complicated.
- [04:21]
B
Absolutely. There always is skepticism and it's well earned, if you will, because again, historically we've taken, you know, these highly trained, highly credentialed workforce that is geared towards patient care and improving lives and achieving optimal outcomes and being a little bit facetious, you know, ask them to be data entry clerks and not practice at the top of license doing the things they do, which is delivering quality outcomes. So yes, I would say anytime we've had a new tool, it's from a technology, there is a normal skepticism. And again, the adoption curve that we've seen with ambience has been exceptionally high for us. It's been nearly 90% across the entire enterprise and across all specialties.
- [05:11]
A
Those are impressive numbers. Nikhil, I want to come to you now and get your perspective, really share how ambience approaches this space. And of course, we're talking more here than simple scribing, correct? I mean, not to say that scribing tools can't be incredibly beneficial for clinicians, but this is something different. Can you expand on that for our listeners?
- [05:35]
C
Yeah. Even taking a step back, what's probably kind of wild for a lot of folks to witness for the last couple of years is just how rapidly we've become accustomed to AI in our daily lives. My mom is not a particularly tech savvy user and yet she uses ChatGPT and Claude almost every single day now. And it's One of those things where even though we've seen AI sort of take off across our personal sort of use of technology, more often than not you step inside of a health system today and it almost feels like the organizations are largely operating in a pre AI world and yet the opportunity is so big. Alfred talks about this sort of workforce crisis. Most organizations are also facing a profitability crisis. And I think for us, what we've realized is that even though we are on this exponential trend, that models are getting smarter and smarter and smarter every quarter. There's still so much work to do to be able to translate that increase in intelligence to AI technology that's actually usable inside of the context of the health system, and then to tie that usage of technology to real value that that makes a dent in that operating margin crisis that most of these organizations are navigating right now. The first part of that, Brian, I think we think about how do we earn the right to be the window in front of the clinician? And you're right, this is much more complicated than just described. Partially because doctors are doing all sorts of work before they even walk into a room preparing for a visit. They're navigating all this complexity in the billing system of the ehr, thinking about what codes to pick. They're chased down by CDI folks to make sure that they change their documentation to support the codes. And all of this work today really eats at the psyche of the clinician who went to school to take care of people, not to wrangle with technology and not to become an expert in coding and billing. And so in many ways what we think about is how do we take AI technology that deeply understands the medicine being practiced, understands the workflow of the clinician in that specialty area, has the full context of the patient, and also understands what the payers are looking for and what compliance is looking for, what auditors are looking for in coding and documentation. And how do we make it easy to do the right thing in workflow so that doctors can focus on what they do best and not the stuff that really drains them of their energy.
- [08:10]
A
Yeah, and Alfred, I want to come back to you here in a moment and get to some results. But Nikhil, follow up for you there. Just thinking about how, to your point, how fast this technology is moving, how advanced it is. It is just anecdotally from my personal experience too, in the work that I do, completely different than what I was doing just before, before it takes off. That said, a lot in the healthcare system has, you know, not necessarily been Designed to move fast. I guess I'll say. The way I've heard it put it before in terms of the disparity in terms of the technology landscape is you've got robots in the building and you've got fax machines in the building. And so, Nikhil, want to get your take in terms of how I guess you think about ambience meeting folks where your partners where they are, you know, where they're able to move, how fast they're able to move, no matter how awesome the technology is, you kind of got to meet folks where they are. Right.
- [09:03]
C
Look, I think one of the things what we found is that part of the reason why it's been hard to diffuse technology across the health system in the past is that it came with so much friction and you weren't solving a burning enough pain point and you were not solving it well enough for an end user for them to want to pull the technology out of the institution's hands. And I think one of the things that we found that's probably one of the most exciting revelations is that if you actually create something that's iPhone, easy to use in the workflow and it solves a burning pain point, the rate at which this stuff gets adopted is shocking. I think Alfred talked about sort of the adoption and utilization numbers at 90% across the enterprise. We've seen organization go from zero to that level of adoption within months of just saying that, hey, we're ready to release the technology to our clinicians and make it available. I know those are not the numbers that most institutions are seeing with AI technology that they're buying. And I think part of it is there's a massive gap between what the ideal solution looks like for a specialist versus what sort of a general purpose model or a very sort of limited ambient listening capability actually looks like in the workflow. But what we found is if you solve that technology gap and that user experience gap, it just really takes off like wildfire. That's one component of it. I think the other component of it is this like, like implementing an ehr. Implementing AI does require change management. And so we've built this incredible sort of care transformation team inside of ambience that works hand in hand with our partners to walk through what are the best practices of how you actually roll this technology out, achieve adoption, and actually achieve value creation from that adoption all the way down to when a clinician has an issue sometime in the workflow. Our median Response time is 90 seconds to being at the elbow and resolving that issue for that clinician. And so we get all those pieces right, which is a magical user experience. Strong change management and then extremely strong at the elbow support in workflow for that clinician in real time 24, 7. And I think that's the foundation to get these kinds of results.
- [11:09]
A
Excellent. I appreciate you going a layer deeper there. Nikhil Alfred, coming back to you. Now we've touched on sort of the 90% number with adoption, which is excellent. Can you share any other sort of results you've seen at Arden Health since deploying ambience?
- [11:23]
B
Yeah, no, happy to rattle off some of the underlying statistics in terms of what we've seen from, I'd say our core KPIs. And then more importantly, what is that translating into, from an impact to the organization given the pressures that we talked about from both the, and a workforce perspective. So you know, just from a KPI perspective, we've seen the clinicians who are utilizing ambiance, we've seen an over 44% decrease in documentation time, 56% decrease in after hours documentation, almost a 20% increase in patient face time. So more quality. You know, again, clinicians doing what they were trained to do. 95% of charts being closed on the same day and over 18% faster chart closure. So what does that mean? You know, this has translated directly into increased coding accuracy, fewer queries, more defensible claims from an audit perspective, which ultimately leads to a stronger and more robust revenue cycle. So when we really think about this, then, you know, because, you know, we don't, at the end of the day, you know, we are a mission driven company and in order to execute on our mission, we have to have a margin and so we have to have tools that, you know, accrete to that overall mission. And we, we don't view ambience as an incremental cost, but actually as, you know, part of our whole revenue cycle process, you know, it's early in our implementation, but as you've already heard, We've got almost 90% utilization today. Our early analyses would indicate over $5,400 in incremental top line revenue from E M coding alone. Approximately 0.4 to 0.5 additional patient visits per per day. And we value that at Approximately an additional $8,400 per clinician per year. We're still, of course, you know, attached to all of that. There's incremental cost of, you know, providing those services, of seeing an additional patient or half a patient a day. So we're still working through the net benefit to the Organization, but it's actually, it's very, you know, I'm a very bottom line driven person. It's very easy to see how this is creating value to the organization. And it's early, right? We're talking about outpatient only, you know, when we look at the bigger opportunities still, you know, yet to be harvested, you know, we're really only at the tip of the spear.
- [14:10]
A
Yeah. Fascinating to see those results so early. And it really does, you know, what's to come should be very interesting. And we look forward to checking in on that as you continue to have this experience with ambience. Alfred, before we close here, I want you each to maybe look to the future as Alfred, I think your comment suggests there's a wealth of opportunity out there for this technology to come in and make a difference for patients and clinicians. So as you look ahead and maybe putting on your CFO hat, Alfred, you can answer this question first. What does the future of ambient intelligence look like? What's your take?
- [14:48]
B
Yeah, of course, my crystal ball is probably no better than anybody else's, but it's easy to say again, the persistence of the headwinds that we've already talked about and how we as CFOs need to prioritize compliance on a proactive basis, creating defensible documentation and implementing technologies that stabilize both the workforce as well as the revenue cycle. I do feel confident that underlying audit intensity will rise and that platforms like Ambience that support coding and compliance are critical if we're going to again deliver on our mission from a next generation. I do see ambient listening extending and really ambience intelligence, not just listening, of course, extending into the inpatient realm. And of course the complexity is incrementally or exponentially higher on the inpatient side and that actually then will yield the greatest potential opportunity to harvest.
- [15:56]
A
Excellent. Nikhil, what's your take? What would you add to what Alfred said? What do you think? Where are you thinking in terms of what's next for ambient technology? But for ambient and for partners like
- [16:08]
C
Alfred, I think Alfred hit the nail on the head. When you've built a machine that can deeply understand every single one of the patients in your population, because you can analyze all of that past context that's loaded up in the EHR and unstructured and structured data, when you can listen to a conversation and reason about the medicine that's happening in that room, when you can distill the expertise of your back office experts, your CDI analysts, your prior AUTH folks, your, um, advisors and you can then take that intelligence and you can distribute it as far upstream as possible. So you can make doing the right thing easy and make doing the right thing obvious proactively at the point of care, as opposed to retroactively in many ways. That sort of creates a lot of the foundational stability and opportunity that Alfred's talking about. I think the other opportunity that we've started to discuss is as you think about your organization and you've got a really deep understanding of your population and what they need, and you have a really deep understanding of your physical infrastructure, an understanding of your clinicians and what they practice and what they're good at, the schedules and the work cues. And there's also an opportunity to extend sort of the second order and third order opportunities from this intelligence layer, including how do we orchestrate care effectively across the system and solve for throughput and access at a system level, because you've got a self learning AI machine that's able to solve this logistical optimization across the entirety of the population, the entirety of the physical infrastructure of the institution. And so in some ways, we think once you've built this infrastructure, you've got an endless set of opportunities to both deliver better care with a better experience, but also at a lower cost and greater efficiency than we've ever done before. And so I think, you know, the Ardent team and the Ambience team feel like we're barely 5% of the way through everything that is possible to create from the foundation that we've built together.
- [18:10]
A
That's exciting to hear and again, all of it's excited to hear more as you continue to build this foundation and build upon it. Nikhil Alfred, thank you so much for coming on the podcast today.
- [18:21]
B
Happy to. Thank you.
- [18:22]
C
Thank you.
- [18:23]
A
We also want to thank our podcast sponsor, Ambience Healthcare. You can tune to more podcasts from Becker's Healthcare by visiting our podcast page at beckershospitalreview. Com.