 Morning everyone, welcome to theCUBE's day one coverage of UiPath Forward 6 from MGM in lovely Las Vegas. Lisa Martin with Dave Vellante. We're going to be talking about some great industry use cases for AI and automation healthcare at first. We're going to be talking about relieving doctor and nurses workloads, reducing processing time and saving lives. Two guests join us, Jared Stahl, Senior Director Advanced Analytics and AI at Mayo Clinic and Jason Whirlman, VP Global Health Care at UiPath. Gentlemen, it's great to have you on theCUBE. Happy to be here. Let's dig in, Jared. Give, everyone knows the Mayo Clinic, so many amazing advances in healthcare have come from Mayo Clinic over the decades. Give us a sneak peek into its automation program. Yes, it's been a remarkable transformative journey over the years, evolving from experimental to really building out a centralized model for care in the lens of using automation with efforts to reduce administrative burden, to give specialized tasks back to humans, really helpfully optimize the areas that we can focus in on. Again, to give that time back to our patients, which is, of course, what's most important for us. Talk a little bit about how at Mayo Clinic you've successfully built an operating rhythm that's really producing results for the clinicians, the patients, everybody involved. Yeah, so the model itself is essentially a robust series of vetting to be able to ensure that we maximize our abilities with our investments, with our strategic partners, creating the space for innovation to be able to make the right choice in terms of where we're going for directional purpose as it relates to, are we using the systems that we invest into the best of our abilities? If not, when we see those gaps, where can we fill those gaps with augmented workflows, with AI layered in to help that decision-making process or serve up some of those tasks that can then be given over to an individual for decision-making? Jason, we were talking in our open about one of the early RPA days. You had big long tail where customers maybe had just a couple of bots kicking the tires and so forth, but then there were some real successful examples of scale. What are you seeing in healthcare? It's a two-part question. What are you seeing in healthcare in terms of greater adoption in that scale? And are you seeing a shift broadly from back office, any indication from back office to front office? Yeah, absolutely. And we have seen that shift and it's taken a while. I think, to Jared's point, a lot of the programs were very tactical, plugging in gaps, optimizing costs. I mean, there's a lot of interoperability in healthcare today. There's still a lot of interoperability in healthcare today. But what we're seeing is automation becoming a part of the mission of care. And when you put a mission behind anything, you become visionary and when you drive vision, then you see things at scale. And now we're seeing automation becoming a part of enterprise programs, not just the technical program, but also the way they deliver business, the way they transform. I don't think it's a secret of the impact the world has had on our clinical staff, the shortages we've had. And now they're rethinking the way that clinical workers come to work. You know, the work that they do, the administrative work that they do, how can we alleviate that type of outcome? So where it used to be, I have a process, I want to optimize that process. Now it's, I want to optimize the whole staff so I can give more time back to my clinician. And to that, automation has become key to that strategy. And Rob actually alluded to that in his keynote this morning. Jared, I want to kind of double click on the use case here. Revenue Cycle Management, or RCM. Why did Mayo Clinic start there? Why was the focus from automation there optimizing the financial optimization of the healthcare of the health system? Give us that kind of strategic vision. Great question. You know, when you look at the healthcare landscape and the opportunity to bring this technology in, in arguably the most regulated industry on the planet, you know, where's a good place to start, you know, to prove the concept to the clinicians? That's Revenue Cycle. When you make a change in that domain, you almost instantly always see an ROI, right? Where maybe people are checking claims manually to a federal site to see if there's updates, statuses for overturn. We have APIs, we have bots that are putting those things together, automatically serving up details and data in the system so that people don't have to go out and check. The value add work comes to them instead of the other way around. And again, that creates capacity. You know, to Jason's point, we have a shortage in various domains across clinical and non-clinical enterprise. The answer is not filling it with more warm bodies. The answer is creating capacity in different ways and more innovative ways. And this is a very key strategic way to do that. So regulation compliance, was it a catalyst? Was it meaningful use, for example, that drove that? Or was it really more sort of just get better productivity instead of have to hunting and pecking for regulation changes? Yeah, I think it's really about, again, going back to that element of creating capacity, there are individuals who have varying levels of specialization. They weren't working at the top of their license. And so they had to do all of this other work to get to that value add work. And our innovations through the course of automation brings them closer to that value add work. And so, again, some of this work, we didn't even have the capacity to get to before implementing solutions, especially in that claims status domain or denials processing. That was only possible by implementing tools like this. To create new work, to augment existing work, and to reimagine what the future of work looks like after that, really taking them out of the element to see, it's kind of hard to look at your kitchen while it's on fire and say, gee, we really need to remodel, right? You have to pull them out of that and give them the ability to create and think about that future outlook after you augment and change and innovate the area that they live in currently. How is that, I'm curious, Jared, continuing on that, how is that from a cultural perspective to change the behaviors of the clinicians? Obviously, it sounds like what you're aiming to do is give them the capacity that they haven't had, but that's a cultural shift, I imagine. It's a huge cultural shift, and there's a lot of thoughts and ideas, both with the clinicians, our workforce, with our strategic partners, thinking about, is this all right? Is this something that we should be doing? And we have had a lot of experience with this anonymously with other solutions that we've worked with in the past, so sometimes it's creating the understanding of the similarities between the two, but also creating a clear, transparent understanding of what this is and what it can do, and that's really what we've done on our journey in the revenue cycle domain, as sort of an example for the rest of the organization to say, this is what this is, this is the art of the possible. When you think about automation, when you think about AI, this is truly what it can do, and we've seen people's minds open up to that over time as they mature in their understanding of that, come to the table more and more with those ideas, with those thought processes about how to transform and how to innovate and bring those things forward, but it's a journey. Well, Jason Jarrett kind of alluded to it earlier in his remarks, I mean, we saw it in COVID, I mean, just a nurse burnout. I have a personal experience, my daughter was studying nursing, and then through the whole COVID experience, changed her mind, and now she's doing global health, and I know many nurses that are just, were so spent, are you seeing automation having, it sounds like it is, but you could double click on that, having an impact on that sort of clinician burnout factor. Is there light at the end of the tunnel? Absolutely, as Jarrett said, capacity is really the key word here. You know, when we talk about revenue cycle, a lot of organizations start there because it's all about risk, being the largest regulated industry in the world, and also the largest employer in the world, you want to introduce risk into areas that are going to impact patient care. So you normally start in revenue cycle on your work way up, but through COVID, we did see that dynamic shift because the capacity wasn't there, nor did we have the clinicians, it wasn't even about capacity, it was about not having enough clinicians, and it still continues on that way. I mean, there was just a statistic out saying that medical colleges are rejecting nursing applications, not because they're not qualified, they don't have enough educators to educate the clinicians that bring them into the marketplace. So it's not just the clinicians, just not the shared service centers, it's also the educators who are even educating our next round of clinicians. So every little bit definitely makes a change, but from a clinical perspective, we have this principle of pajama time, which is the time when clinicians are doing work at home. A lot of times it's data entry-based work, they're scheduling appointments, they're looking at pre-appointments before you actually go in the next day and talk to your physician, because if you've ever had this experience in going and talking to the physician, and they're typing on the computer at the same time, clicking boxes, there's a number of reasons why they might do that during the appointment, but a lot of times it's because they're closing these gaps that are essential to your care, especially if you have a disease state that they're tracking. So what happens is these nurses in the evening, they'll close that so the clinician doesn't have to, so they can have a meaningful, value-based conversation with you. Those are two to three hours worth of work for administrative nurses for most hospital systems, because they can't do it during their daytime. So these types of processes could be highly automated. They're not clinically impacting, they're not making clinical decisions. What they're doing is they're accelerating the work and turning a researcher into a validator, and a validator into a clinician at the end of the day. So those two to three hours really save a lot of time and effort for these clinicians. A good night's sleep, spending time with family, doing extracurricular activities, these are the burnout principles, I think for anyone, let alone clinicians, but the stress that they feel during the day, of course they need that relief time and when they're still working to the moment they go to bed, that has a significant impact on just the clinical culture at hand. So data entry, I mean, that's a, I mean, who wants to do data entry? And then the other, I mean, something else that you said that AI has the potential to effect is education. I mean, MOOCs and massive online education systems can definitely pick up some of that slack, right? Yeah, absolutely, there's a concept out there that says that with a lot of the emerging technologies, there's going to be a difference between a knowledge worker and an intuitive worker. I think clinicians are absolutely an intuitive worker, right? But I think we all got started as a knowledge worker, right? So as we really bring more of these technologies into play and we start to move these knowledge workers to an intuitive worker, I think there's a lot of change management to Jared's point that's going to be required because there is a comfort in clicking boxes sometimes. There is a comfort in doing data entry. We say we don't like it, but at the end of the day, we all got started that way. We all filled our career that way and now we're being asked to move it off our plate and we don't kind of want to give that up. But we also do want to be creative. We do want to be intuitive. We want to be skilled. We want to come into a job and within a few months be just as good as somebody that today took five to 10 years to get there. And those are sort of the gaps we need to close in care. And I think the technology is starting to close that gap quickly. You mentioned the word comfort. And it's from a change management perspective, that's a hard force you're fighting against. I always say to people, whether it's something personal or professional, get comfortably uncomfortable because change is obviously the one constant. Jared, I'd love to, on that front for you to kind of share some of the best practices if there are folks out in the audience who might be where you were a year or two ago, what is your, some of the best practices that you've learned to help drive a culture of automation within Mayo Clinic and really get those folks comfortably uncomfortable? Absolutely. I think it's about being transparent. It's about education. Again, this is something that we would view exists for everyone at Mayo Clinic, right? And so we offer that ability for individuals to explore, to surface ideas, to be part of the process. We have liaisons on the business side that we start to grow up and establish our citizen program. Again, exposing people to what is possible, familiarize them with these tools so they have a certain comfort level and understanding of what would be applicable in this domain. And that really, I think, allows us to continue to accelerate as we mature that population and get them comfortable with the change. To have them understand that this is not something that replaces anything. This is something that enhances your work, right? And that's a very key distinction is to not think about that mindset. Get out of the universe of AI will replace this and it will replace you versus enhancing the work that you do, augmenting it in meaningful ways so that you can be the best version of yourself in your respective domain. Well, it's certainly been our early experience with AI, right? We've all played around with it. So this is good. This has given me ideas and it's definitely not replacing. At this point, it's hard to predict the future, but I wanted to ask you, Jared, you said you went from experimental to I thought I heard a centralized model and that's how you began to scale. As you start to enable citizen creators, does the pendulum swing and does that become more decentralized? How do you manage adherence to those standards and compliance, but yet let 1,000 flowers bloom? Great question. That is a particular unique challenge in the healthcare domain because we can't let everyone go forth and conquer without some level of enablement. And we look at the areas where people can work freely, create pre-approved levels of activities that you could explore in the automation and AI domains where maybe you don't have to go to a committee for approval because we've already cleared that pathway and then have other triggers and safeguards for those areas that might have risk associated with them. So there's the ability to allow those flowers to bloom, as you're saying, within certain pathways, but also have that guidance and oversight when it comes to some of those more sensitive areas in healthcare that we all know about. So. Can you give us a peek, Jared, in our final minutes here into the future of AI and automation at Mayo Clinic? What are some of the things that are on the horizon for you? Oh, so much. You know, Mayo has just a magnificent innovative culture. We have the ability to work in a platform mentality that gives us this ecosystem to allow multiple different groups to thrive and go in different ways by utilizing that same platform mentality, right? We have our strategic partners, but we don't look at it from a pipeline standpoint. We look at it as an ecosystem to be able to create and leverage and the communities that partner with us also allow that to happen. So I think there's a lot coming down the pike, you know, as it relates to generative AI, large language models, ambient listening, process automation. We're really looking at what the next step will be in Mayo and all of those domains, and there's a lot coming up down the road. Jason, last question for you. Give the audience kind of an understanding, RPA plus AI together, how does it reduce the barrier to automation? Why should folks be thinking about this combination? It's really the barrier to complex automation. And Jared, I've been in a lot of these conversations recently. I mean, a lot of the technologies he just mentioned were really difficult to implement for the health, let alone healthcare, any organization. I think anytime you make multiple investments and you're trying to bring it together and orchestrate it, you're upside down typically on the business case to actually do it, or it extends so many months or years that you actually lose the momentum or the interest. And what we're seeing is RPA plus AI, it really becomes a practical way of delivering these complex use cases. Things that used to be nine to 12 months with a large army of experts trying to deliver. Now it's a few months with some creative thinking because that barrier to energy is lower, there's pure play integration. And like I said, my favorite word is practicalness. There's a lot of practicalness still needed in healthcare. Art of the possible is absolutely needed. But I'd say let's move to the science of practical and RPA plus AI I think actually enables that now for the first time in a long time. Whereas that's not a great thought on paper, it's something we can actually put in our hands and deploy. Yeah. Guys, thank you so much for joining Dave and me on the program talking about really what Mayo Clinic and UiPath are doing together to really enable clinicians to treat patients. This is truly impacting lives and lives saved. We appreciate your insights and your time and best of luck on the project in the future. Pleasure. Thank you so much. Our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live tech coverage covering day one of UiPath Forward Six. Stick around, our next guest joins us in just a minute.