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Building AI Solutions That Work with Nishith Khandwala, Sam Schwager, Steve Liou, & Dr. Bo Gu 8-12-26
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- [00:01]
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- [00:30]
Scott Becker
This is Scott Becker with a special edition of the Becker's Healthcare and Becker Business Podcast. We're joined today by four founders of health companies, mostly digital health, but they'll tell us more about their specific stories and what they're doing. Just thrilled to have them with us today. We've got Bo Gu, the founder, Dr. Gu, the founder of U of I. We have Nish Khandwala, the founder of Bunker Hill Health. Sam Swagger, the founder of Super Dial. And finally with us is Steve Liu, the founder of Clarium. I think our audience will enjoy this tremendously. For the brightest people that I get to visit with, I'm going to ask each of you a few different questions. We'll ask you to tell us about your company and what you do. We'll also ask about where you're seeing things work for customers beyond sort of AI hype, what's actually working. Then we'll focus on Health System CEOs, where sales system CEO be focusing the resources. Everybody's got some constraints, what solutions and where they should be focusing now. And finally, we'll ask you each to give some advice to founders. Each of you are founders. Each of you are funded and, you know, invested in by some of the biggest names in investing in venture capital investing. And so just thrilled to have the four of you with us today. I'll start by asking Dr. Gu Ulavi to take a second, introduce yourself, tell us what problem you're solving. And you've got this remarkable background from brilliant physician to founder and talk about what you're doing, what you're most focused on. And Beau, let me ask you to kick us off.
- [02:21]
Bo Gu
Yeah, absolutely, Scott. And thanks for having me again. It's always good to be on Beckers. So my background is unique. I'm a former cardiothoracic surgeon and emergency physician and a former hospital executive. You know, I think the way I look at every problem is always from a doctor's lens, which is try to figure out what's going on as in diagnose the problem with a patient and then find out what to do and how to treat them therapeutically. And that's kind of the lens that I brought into revenue cycle, having sat in the chair to kind of listen to all the vendors and services and BPOs to pitch our hospital and healthcare system. Before, in the past and very challenging times during the pandemic, while we were all being hailed as heroes, we saw some of the highest denial rates and obviously hit the reimbursement side of physician compensation. So having worked with our Chief Technology Officer, who's one of the AI pioneers for foundational architectures for AI and Transformers, he and I, as well as our coo, decided to tackle this for a true end to end AI native RCM agentic platform.
- [03:33]
Scott Becker
Thank you so much. I'll ask each of you to introduce yourselves and give us just a bit of the focus. Then I'll come back and start to ask you deeper questions about what you're doing and where you're seeing impact. Nish Khandwala, can I ask you to take a moment to introduce yourself and tell us about Bunker Hill Health?
- [03:49]
Nish Khandwala
Thanks Scott. It's great to be here. My name is Nishi, CEO, Co Founder of Bunker Hill. I was previously a researcher at Stanford on the AEI side, building different algorithms for use cases in healthcare. Saw that it was incredibly difficult to translate those ideas into clinical practice. So we spun out Bunker Hill, where our goal is to work with operational and clinical leaders to take their ideas and build AI agents for them on our platform. And it all stems from the insight that a lot of use cases are LLMs plus X. And as these LLMs get better, X becomes smaller and smaller, which really allows for there to be a platform that can do a lot of the clinical and operational use cases. Excited to be here.
- [04:34]
Scott Becker
Thank you so much. Steve Lu, let me ask you to take a second to introduce yourself and tell us just a little bit about Clarium.
- [04:42]
Steve Liu
Yeah, sure thing. First of all, it's great to be here. Scott and Bo, Nish Sam, great to share this podcast with you too. For those of you who don't know me, I'm Steve liu, founder and CEO of Clarium. I spent the first 16 years of my career as a healthcare investor before founding Clarium in 2020. Really in the early days of the pandemic, which, for better or worse, put a massive spotlight on the inefficiencies, disruptions, and really lack of investment in healthcare supply chain technology. And really, to summarize the problem that we're solving, as you know, health systems work tirelessly to ensure a supplier drug shortage never impacts patient care. But their technology and tool have always been 20 years behind what their peers had in companies like Amazon and Walmart. And Thyrium exists to give healthcare the technology and AI advantages that other industries have so that really every product skew is visible, every dollar is well spent and patient care is never put at risk.
- [05:39]
Scott Becker
Thank you so much. So we've heard so far from Dr. Gu revenue cycle end to end. Nish Khandwala in working with Bunker Hill Health on agentic AI and helping systems really accelerate their use of agentic AI. Steve Liu on supply chain and just doing brilliant work there. Sam, let me tee it up for you and tell us a little bit about what Superdial is doing.
- [06:07]
Sam Schwager
Certainly. Thank you, Scott. It's great to join all of you here today. Sam Schwager, co founder and CEO of Superdial we build voice AI agents for RCM teams. We actually got our start as an RCM company. We were called Superbill before Super Dial. And Super Dial was actually born as an internal automation. At Superbill, we set out to solve our biggest administrative problem, which was the phone calls that we had to make to payers to verify benefits, follow up on claims and more. This was the biggest source of time and cost expenditure in our business and we, we realized pretty quickly it was a generally applicable solution to other RCM companies and RCM teams. So we ended up pivoting from Super Bill to Super dial back in 2023 and the rest is history. We are now working with some of the top RCM teams in the industry. We've handled over 7 million phone calls and we've been even expanding the capabilities of our platform to solve other problems similar to the payer phone calls faced by our RCM customers. So it's great to be here today and to talk about what we're seeing in the industry in general.
- [07:19]
Scott Becker
Thank you. When you talk about sort of the phone calls, are the outbound calls, inbound calls, how does that work today and how does that interaction work? And is most of your work with other RCM companies or directly with providers and payers, what does that look like? Sam?
- [07:35]
Sam Schwager
Yes, the core of our platform is the outbound call capability to the payer. So our customers, our combination of the RCM teams within provider organizations and then, yes, RCM companies themselves, you know, companies like Think Omega, which is one of the largest RCM companies in the industry and one of our, one of our clients. Yeah, the calls are, you know, you can think of Them as, you know, calls to gather information from payers that could not be gathered via digital modalities. So these are calls related to benefits verification, prior authorization, claim status denial, follow up, credentialing and enrollment. They're calls where you navigate a phone tree, wait on hold, authenticate yourself to a payer representative, potentially get transferred around to a couple of different departments and then conduct a very domain specific conversation with the payer representative about one of the use cases I listed. These calls can be up to an hour long, so they're very nuanced and frankly, before the advent of LLMs and a lot of the increases that we've seen in voice capabilities, this just would not have been something that was possible to handle. So it's very exciting time to see this automation working at such scale.
- [09:00]
Scott Becker
That's amazing in terms of working with the payers and so forth. This must take a lot of the. Typically somebody in doctor's office or revenue cycle office is calling a payer. In addition to needing information or trying to get information through, they've got tremendous frustration, there's emotion. This must help cut to the chase some too because you're taking the human element out of that.
- [09:21]
Sam Schwager
That's exactly right, Scott. When you think about the core experience of the conversation on the payer representative side. Our voice agents are trained in the revenue cycle. Given we are a voice AI company built for RCM teams. Our voice agents are domain experts in rcm. They're very polite, friendly and they don't spend time, you know, looking up information. They have it all in context so that the conversation is faster, it's more pleasant and it's, you know, really expert to expert so that the payer rep has a frankly a good time or a better time talking to our voice agent, I think than in a lot of, you know, cases talking to a person.
- [09:58]
Scott Becker
Simply remarkable. And Steve will tell us what you're seeing on the supply chain side. Where is this working? I know you're both great investors, I think General Catalyst and some others great customers. And then where's this working so far? Where are people using your solution and what does that look like?
- [10:21]
Steve Liu
Yeah, great question, Scott. Let me start by giving a quick overview and recap of Clarium. So Clarium is an AI native supply chain intelligence layer. We unify supply chain finance and clinical data for health systems, allowing them to automate and transform business processes, reduce unnecessary spend and prioritize patient care. Supply chain, for those who don't know, is the second largest expense for hospitals with over 450 billion annually spent by US hospitals. And we feel that approximately 60 billion of that is what Clarium is looking to eliminate. And we categorize that as excess spend, procedural waste and unnecessary administrative burden. Now what I'm most excited about is thym's rapid deployment of agentic AI. And we believe that it's at its earliest stages in healthcare, especially within supply chain. Just last week McKinsey published a report that found 85% of organizations are pursuing AI adoption in healthcare supply chain today, 79% have adopted it and 66% are already seeing ROI from it. And for us the top use cases in that survey, for example, supplier performance, predictive demand planning, inventory management are all areas that Clarium's AgentIQ platform is working across. And just to give a few real world results of what we've seen at our Health Systems 1 at St. Luke's Health in Boise, Clarium's AI agents have helped automate 79% of tasks during critical supply disruptions and have reduced their total disruption resolution time by 71%. At Yale New Haven Health we identified more than 3 million in annual supply waste on preference cards with a little over 1.3 million in savings already achieved from our platform. And at Cleveland Clinic Clarium has helped identify over 26 million in net new annual savings and we've achieved 7 million of that in less than 12 months. And this really leverages our AI powered procedure card optimization for system wide standardization of supplies used in all surgeries.
- [12:23]
Scott Becker
Thank you. Let me ask you a follow up question there. Now I don't want to put you on the spot but Nish, Sam and Dr. Ku all are Stanford grads of one sort or another. You're a Columbia grad and how does Columbia compare to Stanford today academically? And any thoughts there? Should there be an anti Stanford bias given how much they're dominating the call?
- [12:52]
Steve Liu
Not at all. Stanford is one of our critical and important clients and co development partners and so I have the deepest respect for Stanford Clarium. You know I was a native New Yorker, so born and raised in New York. It was always my goal and dream to go to Columbia, stay close to the family but also great academic program and the other benefit it afforded me was it is the finance hub of the world. And as you know Scott, I spent the first 16 years of my career as a healthcare investor and worked at large hedge funds like Millennium Citadel 72, worked at JP Morgan in investment banking and healthcare before that and also in healthcare venture capital. And so having that home base in New York and going to Columbia afforded me a lot of opportunities from a job perspective that I may not have gotten if I attended another school.
- [13:45]
Scott Becker
No, no. Simply remarkable. And I have a daughter at Columbia grad school there now, so I have great, great respect for just. And with the relationship to 0.72 or back in the day, are you able to get Mets tickets or not?
- [14:01]
Steve Liu
Unfortunately, I don't have Steve Cohen on speed dial anymore, so probably not. But I'll try my best for you, Scott.
- [14:07]
Scott Becker
No, no, I don't need them. But maybe we could use Super Dial to get us tickets through. He could get Steve on auto dial.
- [14:16]
Steve Liu
That's a good call.
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Scott Becker
Nish, let me ask you. Bunker Hill Health talk a little bit about the thesis and how it's going so far and how it's rolling forward. And again, I mean, it's an amazingly bright group of people we've got on the call today. Nish, tell us a little bit about where you're at with Bunker Hill Health and how it's coming along.
- [14:39]
Nish Khandwala
Yeah. So Bunker Hill is the enterprise AI agent platform for hospitals. We partner very closely and become their operating model and the governance around AI. What I mean by this is when different operating leaders and clinical leaders at hospitals we partner with have ideas, we work with them to implement those into AI agents. And so we integrate a lot with different EHRs, ERPs, the Internet, and actually not only read but also write back into them as well. And so to till this date, we have partnered with about two dozen health systems, some of the largest ones like the Cleveland Clinic, Intermountains and Tara Health. And at each of our health systems that we partner with, we have more than 10 agents that are live across the enterprise. At some health systems like UTMB, we are live with over 20 different agents, some very clinical, some very operational. A couple of examples to share. We at Cleveland Clinic, we have had agents identify patients who are at risk of cardiovascular disease and get them in front of the right care at the right time. One of their other AI agents that are live there is actually helping the radiology department make sure that all patients with actionable findings have received appropriate follow up. We are processing over 3 million radiology reports there annually and helping over 60,000 patients get to the right care. At UTMB, one of the agents that are live is helping different service lines ensure that patients who are at high risk actually end up getting appointments sooner. So we are looking at referral cues and seeing which patients are high risk, which patients are low risk. How do you match the right patient to the right physician at the right time. And to give you a concrete example, in the case of nephrology, an average sick patient would wait for about 79 days for an appointment after this agent that reduced down to less than 35 days to 2x improvement. And so we've just seen wonderful success across clinical and operational use cases across different health systems that we partner with.
- [16:53]
Scott Becker
No simple remark. I'm going to come back to you in a second and talk about this from a health system perspective, a CEO perspective in how many different AI companies are they going to work with? Because I look at over the years years a lot of systems had, you know, five to 12 enterprise programs running and then, you know, it could be a thousand to three thousand apps or modules running of different types. Then you see people trying to sort of claw back on how many different things are running. I'd love to in a moment get with each of you on some of your thoughts on a CEO perspective, a CIO perspective, an operating perspective and how many different AI companies is somebody to work with, how much you're going to use them not just for a point, but like for example, niches company works across a wide range of areas with helping them develop agents. And I want to come back to that discussion in a second. Before I do that, Bo, tell us a little bit about, you know, another Stanford medical school graduate. Tell us a little bit about, you know, where you're working currently, how it's going and where things are going good.
- [17:56]
Bo Gu
Yeah, you know, I think every co design question really starts with that financial pain point and owning the outcome, I think less and less. Are you really seeing point solutions per se that's kind of heterogeneously distributed within the ecosystem, whether it's revenue cycle or supply chain finance. So I actually just had a whole long two hour discussion with one of the largest healthcare system in the northeast and for them what they really care about is not just reducing your days in AR, which we've done by 51 days and increase the appeal letter success by 80, 90%. But really if I spend a dollar on Yubo, would you be able to bring how many dollars back? So what is that true ROI from overturn, rate of denials, whether it's really increasing that cash flow? For us that number is five and that's what we have for the utilify yield coefficient for all our healthcare systems. And when we do that cost analysis, that's what we're looking at. Secondly, from a technology and from a broader vision they're looking for how do you really anticipate things, how do you predict things before they happen? And one very astute CRO was just saying that, hey, can be something like the Minority Report from that movie where you anticipate a crime before it happens. So really building the prediction of analytics, the revenue integrity and working more upstream, where everything is interlinked within revenue cycle, is what we're building and have built in terms of metric success.
- [19:38]
Scott Becker
Thank you. And talk about, I know you got a huge vote of confidence from one of the most selective early stage investors in the world. Talk about how that impacts you, how that impacts confidence in what you're doing and sort of what that means for your growth as you continue to pursue growth and where you're going.
- [19:59]
Bo Gu
Yeah, I think I'll be honest. You know, when you have These conversations with CEOs, CFOs and SVPs and VPs of the healthcare system, we rarely talk about who our investors are. I think there's a little bit of personal validation. I think fundraising is a very important piece of a founder's success. But that doesn't really translate into why does it matter for the cfo, why does it matter for the CEO and why does it matter for the VP of rcm? I think we're very lean in terms of how we spend our money. We're very efficient. So in terms of the capital, it's more about scaling our growth, supporting our agentic deployments. But I think from a personal standpoint, you know, it's helping the confidence. But from a enterprise deployment, it still goes back to the pain point and financial outcome.
- [20:55]
Scott Becker
Thank you. And who are you working most closely with at Systems? Is it the cfo, somebody in charge of revenue cycle? Who's the core contact point that you end up working with?
- [21:06]
Bo Gu
Yeah. And you know this better than anyone else, Scott. You know, there's sometimes always that segregation between finance as well as revenue cycle. And in many ways, as we're kind of relaying the success of revenue cycle, a lot of those reports becomes more understandable to the CFO and the finance team. And they're not working against each other, but the decision makers are the revenue cycle VPs, senior directors. And a lot of times we're working with the billers, the denial specialists on the ground from the forward deployment perspective. But ultimately it's the CFO who sees that in terms of budgetary benefits.
- [21:48]
Scott Becker
And let me ask each of this question, just 30 to 60 seconds each. You had mentioned the word for deployment. I think that could be so critical to make sure these things actually work as a Customer expects them to work. Give me 30 to 60 seconds on the importance of forward deployment and what that looks like. And then I'm going to ask Sam, Steve and Nish the same question about forward deployment and how that relationship looks like with your customers, with your hospital systems.
- [22:13]
Bo Gu
I think the success of forward deployment really comes down to are you able to speak the language? Do you understand what it means in terms of a denial subcategory of medical necessity? Do you understand utilization management? Being able to speak the language, being able to understand the clinical context, being able to sit down next to them whether they're making a manual phone call to the payers or whether they're trying to come up with an appeal letter or trying to use a pivot table or slice your dicer for EPIC and understand literally with your eyes and ears of what that really means. And forward deployment is actually more listening understanding rather than doing so. For me, I think that's our key to success and I would say 90% of our times we're just listening. And I think they understand that we have deployed operators who have been in revenue cycle, including myself, and that that conversation goes a lot smoother.
- [23:16]
Scott Becker
And Sam, take a moment on a similar question of how important is that engagement with the, with the health system? So don't feel like you're throwing it a solution at them, but you're working with them to make sure it gets deployed correctly. Sam, can you take a moment on that?
- [23:29]
Sam Schwager
Certainly, yeah. So this is something that we're very proud of in our company that's, that's evolved a lot over the past year. And I think Ford deployment is kind of a term that gets used monolithically but means different things for different settings. So I'd say in the superdial context and the RCM context, what we found works best is a combination of technical and RCM subject matter experts coming together to both identify integration needs and configuration requirements and then to build, you know, project plans to, you know, execute that kind of last mile work that ends up unlocking a ton of value for the customer. So, you know, we built a base. So back to the, you know, core outbound payer calling solution, right? We, we have a gen, you know, a voice AI agent right now that, you know, has access to hundreds of millions of data points from the, you know, millions of previous calls that we've, that we've run. But you know, customers have, you know, specific preferences and requirements. So yes, we have a base that is expert in making these outbound calls to payers, but also there is a need for our RCM experts to come meet virtually or in person, ideally with customers, to identify exactly the behavior that they're targeting. And then our technical subject matter experts, the four deployed engineers, to then execute on those requirements. So I'd call that rather than building something custom, you're configuring what you already have. But that configuration again is absolutely critical because without it, you're trying, as you mentioned, to kind of push a general purpose product onto a customer that has very specific needs. And I do think that the overarching theme here is that we're doing work for these customers. We're not just delivering software, we're delivering outcomes and labor via these AI agents. So in the same way you'd want new employees to be trained and ramp up on your systems and configurations, you require the same thing from an AI world workforce. So we've taken it very seriously and it's definitely paid off across our, across our deployments.
- [25:45]
Scott Becker
I mean, so, so important that you're actually delivering results, working so closely with the customers. It's not just delivering a product, it's really engagement. Nish, I know you and I have talked about this before, the forward deployment model, Give us a moment there. And Nish, because you work across a bunch of different agentic AI situations, give us a moment. Also on how many AI companies, when you guys talk to health systems, how many different AI solutions are health systems talking to as well? Maybe not in your same space, but across their ecosystem? I don't know if you have a sense of that niche, Steve, you probably do as well. But just talk about for deployment and what that looks like, engagement with the customer. And then also when you're dealing with customers, how many different AI companies are they each dealing with?
- [26:30]
Nish Khandwala
Absolutely. So we pride ourselves in the fact that we offer the breadth of a platform, but the depth of a point solution. And the forward deployed team is the one responsible and needs to be applauded for definitely the latter half of that sentence, which is the depth of a point solution offered in a platform. What I mean by this is when a new use case, new problem is identified, the forward deploy team builds the agents. That's the most fun part and arguably probably the easiest as well, maintains the agent, which is like, you know, you're not building a toy, you're building something that's mission critical, that is going to be deployed in a production setting, at an enterprise, in an enterprise manner. So you really need to be responsible if things go south at 4 in the morning, if AWS goes south or something like that. So they are responsible for building the agent, maintaining the agent, but perhaps most importantly, the change management that comes with all of these agents being deployed. And so we help the health system with that as well. And that's what the forward deploy team is instrumental and responsible for doing.
- [27:38]
Scott Becker
And are those people mostly engineers? Who are the people in the forward deploy team? Are they customer people, engineering people, A mix of people? It's got to be really bright people to be able to take context, feedback and make sure you're delivering what's needed.
- [27:54]
Nish Khandwala
Yeah, this is where you have to tread the line between being a platform play versus a consultancy play. Because we have architected our platform in a way that is very composable, that has the mission critical components that you can repurpose and reuse. None of our forward deployed team are engineers by training. They are sort of X startup ex consultants, really folks that are incredibly nimble, that have the grit in them and they work on our platform and configure it for different use cases that a health system is interested in. And so that's how we manage to bring scale to our work. And that's why we offer the breadth of a platform, depth of a point solution.
- [28:42]
Scott Becker
And that's so important because anybody's been in the startup world knows that most people that do point solutions, niche solutions, can do them better than broader solutions. But at the end of the day, systems don't want to work with a thousand different point solutions. So everybody ends up morphing into something beyond a point solution at some point. So if you could offer the breadth that people need, but the strength and the agility of a point solution, that's a fantastic combination. Nish, one more question for you. When you talk to health systems, how many other AI companies are they working with and dealing with generally? And I know that's got to be all over the board, but what do you see out there?
- [29:22]
Nish Khandwala
Yeah, we work with health systems that are, you know, that are very forward with their AI strategy. They have been early and as a result have worked with a lot of companies. We work with health systems that are just trying to get started on the AI front, but actually we see that a lot of use cases come from point solutions trying to sell to health systems. I'll give a very concrete example. We had a point solution company approach, the chair of Vascular surgery saying, hey, we have a solution that can help you track aneurysms over time. It's good for the patient, it's good for the top line for the health system. You should really consider using it. The Vascular Surgery group was very excited. They brought it to the AI Governance group which said, hey, we already have carebricks as our enterprise AI platform. Carebricks is Bunker Hill's platform. Why don't you, the Chair of Vascular Surgery, give Bunker Hill a shot. Why don't you take the next six to eight weeks to see if carebricks, the platform, can do what this point solution can and if not, happy to entertain onboarding that point solution. Our forward deploy team worked with the Vascular Surgery Group, implemented the aneurysm tracking solution and as a result that health system was able to solve that problem, not need to onboard a new point solution. And the Vascular Surgery group got what they wanted perhaps eight to 12 months ahead of schedule.
- [30:51]
Scott Becker
One last question. You mentioned AWS. Is everybody touching AWS and should we keep on investing in Amazon? You don't have to answer the second part of the question, but is everybody touching AWS one way or another?
- [31:06]
Nish Khandwala
Perhaps. Yeah, what I meant was I think last year we had a couple of instances where some of the major cloud providers went down for some time and those are typically events where it's not just us as a company, it's many industries that go down simultaneously and those are the types of instances where the health system needs a partner to know what's going on. It could that, that's where when you know, when you think about build versus buy, this is the downside of the build aspect, which is who do you go to? You can't go to a researcher, you can't go to a physician builder and be like hey, it's gone down at 4 in the morning, can you go fix it? You really need a partner that is responsible for the day to day operations and maintenance of the solution.
- [31:55]
Scott Becker
No, that, that needs a deeper team. And my, my question was not so much about the, the outage because you have that everyone's want all these tech companies, it just happens about is everybody touching AWS and should I invest? But we'll, we'll touch on that at a different time. The that. Steve, your thoughts on CEO's biggest priorities now and how many different AI solutions are you seeing when your team starts to work with systems? You know, what does that look like? And also you had mentioned that supply chain is the second largest expense for health systems. So obviously a priority or should be a priority. What are you seeing out there from a CEO's perspective and what are you also thinking about when you think about systems and how many different AI solutions are working?
- [32:38]
Steve Liu
Yeah, Great question. When we talk to CEOs and importantly CIOs at Leading Health systems, they really stress that they see a wave of inevitable point solution consolidation happening over the next three to five years. I just spoke to the CIO of a leading system the other day who said they have 1800 different applications and are looking to dramatically consolidate that. So it's definitely something that's on everyone's minds. I think that there will be the core systems of record that everyone's invested in that they'll continue to use and they will really look to a small subset of trusted vendor partners that really can Deploy high value AI solutions over the next 12 to 18 months that can deliver immediate ROI within the first 12 to 18 months of deployment.
- [33:32]
Scott Becker
How important is it when working with health systems that they start to receive ease of use and results fairly quickly? How much patience do they have? And when you mentioned 1800s and 1800 different AI solutions, or 18 different apps
- [33:47]
Steve Liu
and modules of all sorts of applications overall, I meant.
- [33:51]
Sam Schwager
Right?
- [33:51]
Scott Becker
No, no, no. Absolutely, absolutely. And everybody's looking to consolidate some of that. But how important is it with your customers, your health systems, that they start to see results fairly quickly? And ease of use? How important is ease of use?
- [34:05]
Steve Liu
Both are incredibly important. I would say that for us we are dealing with our end users, being nurses, clinicians, supply chain teams, inventory teams, incredibly busy with their day to day operations and jobs. And so you really need to show immediate improvement in terms of their workflows, in terms of time savings, in terms of cost savings. Otherwise they will quickly move on from your solution and look at the next best thing. So that is really important. And I would say, as NISH highlighted, change management is just as important as having a technology. And for us that's where we really focus on two major things. Back to your initial question, Scott, which is, you know, how important is forward deployment for Clarium? Forward deployment has been incredibly important for us over the last five years. For every onboarding, Clarium assigns a dedicated team of forward deployed engineers and subject matter experts. And as you know, every health system IT team is incredibly bandwidth constrained with a hundred different ongoing projects, a massive EHR ERP upgrade, and they just don't have the time to implement a new platform. So we make that easy. We take on all of that work for them in a highly secure environment. And that allows Clarium to deploy incredibly quickly on average in 8 to 10 weeks, which then allows us to deliver immediate hard dollar savings within the first 12 months, which is exactly what the CIO is looking for these days.
- [35:33]
Scott Becker
Thank you. I'm going to ask each of you one last question and I'll focus more on health systems than on founders from a health system perspective. And Paul, you're working with health systems as well as practices, correct? Not just health systems, but practices too.
- [35:51]
Bo Gu
Yeah, correct. So PE platform, MSOs, large physician groups and healthcare systems.
- [35:57]
Scott Becker
Thank you very, very much. When you're working with large practice groups, large pm, PE funded practices and health systems, what's the one thing that health systems should be thinking about as they start to work with an AI company, a vendor in the AI world? Where should they, what questions should they ask? What's one or two things they should be focused on and starting to work with you? Then I'll ask each of you the same question. You know, Sam, Steve, Nish, what should people thinking about as they start to work with a vendor or particularly your company?
- [36:31]
Bo Gu
Yeah, I think the first thing that should pop up in their mind and same thing in my mind sitting at the opposite side of the table and previously as a healthcare executive is do I foresee a future, a long term future with this vendor or more quote unquote, partner, meaning that do they have the ability to quote, unquote, forward deploy? Can they understand my pain points and adapt to that pain point from financial to operational or back to financial? Do they have the technology flexibility to build more agents or to build a more holistic, comprehensive platform? And can they absorb honestly a lot of what the previous BPOs were doing? And can you agentify that and show a very measurable cost savings from FTEs being reduced or repurposed? That's the first thing that's brought up to us implicitly. And that's the first thing that I always asked when I was listening to the vendors. That's the first question that should pop up in mind. And secondly, you know, can I, can I trust them? Right. Healthcare is always built on trust and trust is not just a handshake. Trust is really, can you be transparent when your metrics don't look good? Can you be transparent and work through the hiccups of some of the integration or HL7 data feeds or you know, a cloud server being down? So I think honesty is the basis of the trust as well as adaptability and having a sustainable future with the CEO and healthcare system?
- [38:11]
Scott Becker
Well, no, I absolutely love that because the concept of anybody who's hiring somebody or starting to contract with somebody, do business with them, doesn't want to be going through 13 different vendors over the next year. They want somebody who's growing, who's thriving, who's continuing to improve what they do so they can hopefully work with them for a long time, not be in and out and, and obviously you want that the same as a company that's providing services, you want that as well. So I love that. Is this a long term relationship? Can we sustain it? Can we grow? Can we thrive together? Sam, what's the first thing a health system should be thinking about as they start to work with yourselves with Super Dial or another company providing services?
- [38:52]
Sam Schwager
Yeah, so I mean, I think especially in the revenue cycle context, RCM is obviously a very exciting area. There's a lot of innovation and a lot of promise around the impact that AI agents can have in the revenue cycle. I do think when engaging with any AI vendor, a revenue cycle leader should say, are there a handful of other revenue cycle leaders you have delivered for that I could call that you've worked with for, let's say at least a year. Right. I think that's, that's a good way to weed out, you know, companies that, that have not really spent a lot of time, you know, building the, you know, RCM expertise that, that is required to come into this, this space. It's extremely complex. There are people with, you know, decades of experience who are still, you know, learning a ton in, in the space. I think the, you know, the promise of agent in RCM is, is true. Agentic AI could have a massive impact, but it's still really complicated and nuanced and you need RCM expertise in the company's DNA in order to be able to deliver. So I'd say that's one thing, I'd say the other thing that RCM leaders should be asking is what is your company really the best in the world at? I think that of course you want to try to limit vendor bloat. It's good to have vendors that can handle more and more. But I do see a pattern of companies maybe that started out doing something very specific and then have built around that. And I think if a vendor can't really answer very specifically what it is that kind of is at the core of their platform. Maybe it's a couple of things, but something that truly they have more experience than other companies building more data with which they can train their agents. I think if you don't have that, then it's hard to believe that, that you're going to achieve that really fast. Time to value with the initial use case that I think is so critical because I do believe that starting with A well defined proof of concept or initial use case or small set of use cases really delivering value quickly. So within the first few months seeing the true hard ROI and then expanding from there with a vendor is better than starting with 20 initiatives that can lead to that kind of failed deployment. We all know that MIT article 95% of these enterprise AI deployments are failing. I think a lot of that comes down to eyes bigger than stomach scenarios at the beginning where folks want to try everything all at once rather than going deep on a couple of things and then expanding once they're really seeing the results. So again, I'd say two things. RCM expertise and then what is your company really set itself apart doing at the core of the offering. Really important for RCM leaders to have in mind as they evaluate vendors.
- [41:43]
Scott Becker
Thank you very very much. And just a couple quick notes on the concept of making sure that somebody's got references, somebody else you could talk to, they've worked with for a year or some period of time. I love that whenever I'm a buyer technology I never want to be a first user. I always wanted first question I always ask is who else is using it? How is it going? Can I talk to them? The other thing I'll note, I mean both you and BO are in different parts of the RCM business for people that, I mean people are mostly aware of this but healthcare is about a 5.4 trillion a year part of our economy. The latest data I saw was 200 to 220 billion spent somehow or another on revenue cycle billing and collections in healthcare. So a huge, huge different areas within revenue cycle. And no wonder it keeps on getting so much attention because there's so much money to be saved, money to be moved and so important to making things work for providers. So I love both your inclusion and comments. Thank you. Dish the first thing that a health system should be talking to you about or anybody they're talking to as they start to decide who to work with on these kinds of solutions.
- [42:53]
Nish Khandwala
Yeah, I think there needs to be a sort of, we need to as an industry need to be self aware that this is an incredibly new field and it's not just oh, before we didn't have LLMs, now we have LLMs. Even as these LLMs have progressed and gotten better, we just see new capabilities be unlocked. So the technology underlying all of this is moving at a pace that is very fast now. What does that mean? It means that you cannot be of the opinion that oh, you can't afford to make mistakes, you can't afford to take risks. You have to be how do you set yourself up in a way where the cost of iteration is very low, where the speed of iteration is very fast, where the cost of making mistakes is fairly low? And that's what I would encourage most health system CEOs to think about in terms of their AI strategy. Like set it up in a way where you don't feel like you're getting into a lock in period with a three to five year contract, you know, six to seven figures annual basis with no way to get out of that that makes it such that you can't afford to be nimble. Every decision seems like a one way door, not something that you can never reverse. And so the way we think about this at Bunker Hill is we do not want to lock in a hospital with something like this. We offer health systems to buy AI credits upfront and decide where do they want to spend those AI credits on. So if you have an experimental use case in mind, you know, you can just try you doing that use case and if it doesn't work out, you can stop spending AI credits on it and just move on to a different use case. And so that way rather than always feeling that it's a one way door, you feel like the cost of iteration is fairly low and as a result you can move fast.
- [44:46]
Scott Becker
And I love that because people don't have to think of this. I was on the phone yesterday with the CEO who's doing another latest epic transformation in their system and they've had great luck with epic, but every time they do it, it's very gut wrenching and there's a lot that goes into it here. You shouldn't think about it as though you're capturing a whale. You're starting with pieces and moving from there. Nish, is that a fair. So it's easy to get started. It doesn't become, you're not, you're not planning for a multi billion dollar conversion.
- [45:18]
Nish Khandwala
Yeah, it's easy to get started, but it's also easy to expand and move fast very quickly. Like if you, there are many use cases that are just white spaces and you can easily see the application of AI into those use cases and those problems. And you know, to the earlier point there are, there would be zero companies in that space who can say, oh, we've been doing it for three, four years. You can ring up 30 customers and they will tell you how much they love you. It's just, it's not possible because it's so new. The capabilities are so new that have been unlocked. And so for those kinds of use cases or those kinds of problems, rather than say, oh, we're going to wait three to five years to solve that, you could potentially get started now. As long as the cost of failing, the cost of making mistakes is low.
- [46:03]
Scott Becker
Thank you, I love that. And Steve, let me wrap up with you Steve. How should health systems be thinking about this? I mean again, as you mentioned, just like revenue cycle is a huge expense, supply chain is the second largest expense in health systems. How should all be thinking about working with an AI supply chain company like yours? What are the first things they should be asking and thinking about?
- [46:25]
Steve Liu
Yeah, there are a few broader recommendations I'd make and then a couple supply chain specific ones. In terms of the broader wrecks, I would say one fix your data before you deploy AI. Every health system is under massive pressure to have an AI strategy, as you know. But putting AI on top of dirty, fragmented data only produces expensive noise faster. And the first real move is cleansing, enriching, unifying all that disparate data from all your systems of record into one unified intelligence layer. And once you have that strong foundation, then AI can be incredibly impactful. Two, by outcomes, not software. CEOs, CFOs and CIOs should demand that vendors show measurable ROI within a predefined window and walk away from anyone that won't commit to those results in writing. And three, separate the signal from the noise, as Sam said. And you echoed Scott. CIOs are getting pinged with thousands of AI vendors claiming to do things they can't or simply showing vaporware. And really my advice would be to focus on the startups that have built deep trusted relationships with your peers and also have the real world case studies to back up their claims. Now, as it relates to supply chain specific recommendations, I'd say one elevate supply chain to a strategic function. Amazon and Walmart treat their supply chain as a competitive advantage, while many health systems still treat it as a back office function. And CEOs should give their supply chain leaders real seat at the executive table. And the second supply chain specific recommendation would be invest in technology now, not before the next disruption. Disruptions are not episodic anymore. They're the new status quo. And if teams don't have the right technology in place proactively, this is going to lead to significant financial strain and inevitable workforce burnout.
- [48:09]
Scott Becker
Thank you very, very much. I want to thank all of you. I know I took more time than we'd anticipated, but just a pleasure visiting with the four of you. For me, nothing better than getting to speak with with four brilliant people who are making a huge difference in healthcare. Dr. Gu Stephen Liu Nish Khanwala Sam Swager Super Dial Clarium Bunker Hill Health Ulfi I want to thank all four of you for joining us on this special episode of the Becker's Healthcare and Becker Business Podcast. Thank you so much for taking the time with us today.
- [48:41]
Steve Liu
Thanks Scott. Pleasure to be here.
- [48:43]
Bo Gu
Thank you Scott.
- [48:44]
Sam Schwager
Thanks Scott. Great to be here.
- [48:49]
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