AI and the Future of Healthcare - A Conversation with Arka Roy

How can artificial intelligence and analytics transform the future of healthcare?

In this episode of Inside Alvarez Business, Dean Jonathon Halbesleben sits down with Arka Roy, associate professor of operations and analytics at The University of Texas at San Antonio, to explore how cutting-edge research is improving clinical decision-making, hospital operations and patient outcomes.

Roy shares the personal experiences that inspired his work in healthcare analytics, including optimizing anesthesiology schedules, enhancing cancer radiation therapy with AI and designing resilient surgery schedules for children's hospitals. Learn how predictive and prescriptive analytics, optimization models and interdisciplinary collaboration are reshaping the business of healthcare.

Whether you're interested in healthcare innovation, academic research or the real-world applications of machine learning in medicine, this episode offers an inspiring and insightful deep dive.

Stay connected with the UT San Antonio Carlos Alvarez College of Business to learn more about how we are empowering the next generation of business thinkers. Follow us on social media or visit us online at business.utsa.edu

Transcript

Arka Roy, PhD 0:00

That's how I like to think of AI assisting doctors in making decisions rather than giving them the decisions.

Jonathon Halbesleben, PhD 0:07

Healthcare is a unique industry. From the start of our lives to the end, we are all destined to interact with the healthcare industry at multiple points in our lifetime. With growing concerns about affordability and access to high-quality care, it has become increasingly important to think about where healthcare is headed in the future. For example, what does the future of healthcare look like with the advent of tools like AI? And how can business researchers contribute to the challenges ahead? Welcome to Inside Alvarez Business, a podcast dedicated to bringing you the stories behind the research. I'm your host, Jonathan Halbesleben, Dean of the Carlos Alvarez College of Business at the University of Texas at San Antonio. This season we'll focus on the business of healthcare, sharing the stories of our faculty, the real-world impact of their research, and what led them to study these important topics. I'm delighted to bring you this conversation with Arka Roy, Associate Professor of Operations and Analytics here in the Carlos Alvarez College of Business. Arka's research in optimization, analytics, and AI is focused on finding ways to improve the work of healthcare clinicians. Hope you enjoy this discussion as much as I did. Well, welcome to Inside Alvarez Business.

Arka Roy, PhD 1:16

Yeah, it's really a privilege to be here and talk to you and share some of my insights. Hoping for a wonderful fireside chat.

Jonathon Halbesleben, PhD 1:23

Okay, well, I'm excited to hear the sort of the stories behind your research. So that that's really what we're kind of focused on with the podcast is understanding the research, but really kind of the story behind it and and where it came from. So before we get into your research, though, I want to ask you a question. I don't think anybody has ever, when they were a kid, said, When I grow up, I'm gonna be a professor. Or you know, like they don't invite me to a career day at school, right? How how did you end up going down that path?

Arka Roy, PhD 1:49

So growing up in India, they value a lot of education. And education was always in my family. My parents always told me to get more as much as possible. And coming out of undergrad, I did an internship at Motorola, and things I was doing at work didn't really have a tangible output that could really make a difference. And so I kind of thought, okay, what how can I make something that I felt full ownership over? And so I went back into graduate school to do a master's, and probably about halfway through, I was like, you know what? If I'm gonna get all this time and spend effort into it, I might as well go do a PhD. You get to solve a bigger problem, something that really makes an impact. I loved teaching, I loved mentoring junior people, I loved seeing that light bulb go off in students where they learned A nd it was just the perfect fit of all those things that come together in academia where you have the flexibility to have your own schedule while also impact young minds. Financial freedom or reward wasn't necessarily my top thing that I prioritized. And so the academic job felt like a perfect fit.

Jonathon Halbesleben, PhD 2:55

Did you have were there people kind of along the way that encouraged you down that path, or did it you just kind of figure that out on your own?

Arka Roy, PhD 3:01

I remember my possibly 11th grade math teacher in high school was a big impact in like kind of making me feel confident in my abilities more than just, you know, just not about the material, but rather building my self-confidence.

Jonathon Halbesleben, PhD 3:18

I think back to my own education and career, and I guess but probably education and the professors who I remember the most, I don't know that I really remember much of anything from their classes. I just remember kind of the interactions with them and the way they approached learning and...

Arka Roy, PhD 3:35

So the way I teach, like when I teach topics here at Alvarez College of Business, I set up sometimes like competitions for analytics, and students will try to figure out how to build best models that they can kind of have a friendly competition towards. Those kind of things help you learn.

Jonathon Halbesleben, PhD 3:50

Then in your research, you've done a lot of work in healthcare. And I'm curious what what led to that particular industry as far as kind of focusing your work.

Arka Roy, PhD 4:00

So I think there are probably three pivotal moments in my life that led to my healthcare research. So, you know, coming from a traditional background home in India, medicine is always one of the top career choices that you can take. So being a doctor was always at the forefront of something that I thought about growing up. But then about 10th or 11th grade, they did, I forget what the procedure was, but they put a camera up my nose and then they showed it to me on the television and I almost fainted when I saw that, right? It was just not my field. And so I went down the math part and I did an industrial engineering undergraduate. And then going into graduate school, I came across a professor who did research in applying industrial engineering concepts or you know, really optimization concepts in the field of cancer radiation therapy. So that was my kind of way back into healthcare, if you will, really in healthcare, not just operations, but healthcare treatment decision making. And that was important for me because I wanted to be in somewhere where I could actually help a patient in their treatments, right? To help them overcome that. So that was a big moment for me to come back into healthcare. The first probably pivotal moment. And then the second pivotal moment was as I was getting into this field, one of my close friends, who was about 26 years old, she developed breast cancer. After a mission for a while, the tumor metastasized to her lungs and then ultimately to her brain, and she passed away after about a few years.

Jonathon Halbesleben, PhD 5:31

I'm sorry.

Arka Roy, PhD 5:32

So that was another second very important moment that kind of gave me purpose towards doing research in this area. A lot of people ask me, how come you do clinical, you know, research that not necessarily is traditional business? And I'm very fortunate that at Alvarez I get to do that. But that purpose gets kind of sent down, and I've enjoyed being able to give back to that community to do research there. And the third pivotal moment was when my son was about I think maybe eight months old. Every time I would put him down, he would really start crying and he would keep his right leg kind of furled up and have some kind of trouble putting pressure on it. So we called uh the pediatrician's line, the nurse told us to take him in, so we rushed him to Dell Children's and we do an MRI after two hours of waiting, and it turns out to be a septic hip.

Jonathon Halbesleben, PhD 6:24

Oh no.

Arka Roy, PhD 6:25

He needs surgery the next morning. Every time they try to put a needle in him when he's trying to get him fluids and stuff, he starts crying uncontrollably. We turn - we find out that he has something called breath holding spells where kids can cry and eventually not take breath because they're in this weird zone and they turn blue in your arms and the entire hospital rushes in to resuscitate him. And I learn that there's all these chaos that are happening in a hospital, in the operations of a hospital. And in the middle of those, there are teams of nurses and surgeons that essentially have to clock out and clock in. And you have to explain your situation to the different people multiple times.

Jonathon Halbesleben, PhD 7:08

Yeah.

Arka Roy, PhD 7:08

There's just a lot of, I wouldn't say inefficiencies, but just uncertainties that are involved in the system that you have to take care of and you have to account for when you're building schedules and, you know, any kind of operations decisions. So that that was the third pivotal moment where I thought, you know, I could probably make an impact in the research that I do in these areas and I've like and I'd get the reward that I originally had for working in the field of medicine.

Jonathon Halbesleben, PhD 7:35

If you recall, within the first hour of a handoff is like the most dangerous time if you're in a hospital setting because of that transition and the lack of communication. So I am glad that your son's doing all right.

Arka Roy, PhD 7:49

Very healthy and there's a seven-year-old that's running around and yeah, jumping off the building almost.

Jonathon Halbesleben, PhD 7:55

And, you know, those stories are so powerful. I can definitely see why that would have led you to want to make a difference in healthcare in particular. And while I wish those things hadn't necessarily happened to you, I in a way I'm glad they are because it's led you to do some really, really cool work. I know one of your recent studies was about anesthesiologists and how they get scheduled. And so I'm kind of curious, like what was it like before the study? And then, you know, how did what did you guys kind of figure out there to make it better?

Arka Roy, PhD 8:26

So actually, this study started through discussions with the chair of anesthesiology, Dr. Frank Rosinia, who was at UT Health at that time, and he was very aware of the impact of analytics and informatics that helps in hospital operations. We started to identify some of the challenges that they face every day in creating these schedules of anesthesiologists. They have contracts that they have to meet in order to supply anesthesiologists to go essentially meet the demands that shows up at UHS. And so a lot of times there are certain inefficiencies and also preferences that come from anesthesiologists that are unmet. The discussion we had was how can we build a fair, equitable sort of a workload that is balanced across people, across time, while making sure that the hospital system is as efficient as possible.

Jonathon Halbesleben, PhD 9:23

Mmm-hmm.

Arka Roy, PhD 9:23

So we built a mathematical model that uses multi-objective programming a field that has existed for a long time, where that looks at trade-offs and different objectives. So one of the objectives was can we make the shifts as equal and balanced as possible across all anesthesiologists? Another objective was can we make it as equal and even over time? So if you had a really rigorous, complex cases shift that you worked on, on Monday, perhaps you don't get it again till Thursday or Friday, right? To do that manually would take you hours just because there's so many different decisions that you're doing. You have a set of 40 to 50 anesthesiologists, you have multiple days of the week, you have multiple shifts, you have multiple other constraints that you have to take into account. So we built this multi-objective program and solved it using, you know, some of the state-of-the-art computation that's available at Alvarez and was able to actually implement a solution and show that it actually led to improvement in satisfaction.

Jonathon Halbesleben, PhD 10:29

That's cool. So satisfaction among the team of anesthesiologists.

Arka Roy, PhD 10:35

Correct.

Jonathon Halbesleben, PhD 10:35

Well, that that's really cool.

Arka Roy, PhD 10:36

Our our takeaway from that was actually dollar savings because that was the time that they could have been spent in the clinic treating patients.

Jonathon Halbesleben, PhD 10:44

Yeah, I imagine insurance doesn't reimburse for someone sitting down building a schedule.

Arka Roy, PhD 10:49

Exactly, yeah.

Jonathon Halbesleben, PhD 10:50

Did you - so you measured satisfaction. Did you have you extended that to other kind of markers of well-being, stress, burnout, any of those types of outcomes, or maybe that's a future study?

Arka Roy, PhD 11:03

That's a really good point. We haven't really extended that per se. And we've talked to them about it. We have shifted direction a little bit more recently. So at UT Health, there are three stages of the problem that we study. One is at a you know much more long-term, six month, seven-month scale, where the chair of the department wants to know how many anesthesiologists do they need to have in order to satisfy demand six months later. The midterm problem is how can I assign different people, the anesthesiologists, different schedules on different days so that someone's leave that they want to take two weeks later or three weeks later is also satisfied. And the immediate problem that they study is someone who's called a board runner who essentially on the day off after all the emergency service, you know, demand has come in.

Jonathon Halbesleben, PhD 11:55

Yeah.

Arka Roy, PhD 11:55

How do they reallocate everyone so that all the necessary supply is met by the anesthesiologists? Right. So that we sort of shifted from the long-term, midterm view after solving that problem to a much more short-term view where we're looking at can I meet preferences on an individual level of anesthesiologists on that day-off problem and create teams of people where they get along much better with each other. And we use some of the more advanced and modern machine learning models that from recommendation engines. Think your Netflix, when you watch a movie and, you know, movies that have certain genres, you get sort of your thumbs up and you give it right and they recommend it back to you.

Jonathon Halbesleben, PhD 12:37

Oh, that's interesting.

Arka Roy, PhD 12:38

So that's the kind of work we're doing. We haven't expanded it to a burnout or stress environment, but those would be really cool things to look into.

Jonathon Halbesleben, PhD 12:46

Yeah. That last piece is so interesting to me. Then to schedule them as teams is not really feasible. I mean, if somebody needs off one day, then the whole team can't go. But to use that kind of recommendation system, that's really that's really fascinating.

Arka Roy, PhD 12:59

Yes, you need you need a sort of a secondary layer of an optimization precisely of what you said, right? Suppose you create these teams, but then someone's out on that day because they're sick or something. So you need an optimization to re-impose those constraints so that you get re-optimized solutions where with people that are available.

Jonathon Halbesleben, PhD 13:17

Yeah. What was your experience as you worked with those groups?

Arka Roy, PhD 13:20

Honestly, I think the success of the project has heavily relied on the buy-in and engagement of the providers that was there. One thing we learned was like, let's keep the math and the optimization systems as simple as possible. We had to break this up into instead of a more complicated two-stage model, into a two-step system where the two steps were actually not connected from one another.

Jonathon Halbesleben, PhD 13:45

Okay.

Arka Roy, PhD 13:46

And let them see the output of the first step, which was to design shifts that were sort of balanced and met the necessary demand that we were forecasting. And then in the second step, assign providers into those shifts that we designed in a more equitable fashion. That allowed them to kind of replicate the manual process that was happening in an automated way.

Jonathon Halbesleben, PhD 14:07

Yeah.

Arka Roy, PhD 14:08

Right. So to have that buy-in and engagement that we had from them through every week, weekly discussions and meetings, and to iteratively improve the model from little steps from a single-step solution to a two-step solution, and explain to them how this is sort of the simplest way to deal with it was probably the most important thing towards the success of that project.

Jonathon Halbesleben, PhD 14:30

Yeah. Well, it's so thoughtful. And they might not have seen that it really all of the same thinking that went into the manual process went into it just much better.

Arka Roy, PhD 14:39

Right, right.

Jonathon Halbesleben, PhD 14:40

But they wouldn't have seen that. And so I can that actually that's really thoughtful to kind of build it that way. I can see how that would have made it much more successful.

Arka Roy, PhD 14:49

100%. And I think, you know, sometimes we solve these problems using really, really complicated methods, and then we implement it, and then perhaps it gets used for a week or two, and then if that student or person leaves the system, it just doesn't get used, right? So you need to have the right culture in place with people who are going to be analytics driven or think about it quantitatively. You also need to have simple steps in process because in the healthcare setting, particularly clinicians are thinking about saving lives and treatments. They're not thinking about how can I optimize these things.

Jonathon Halbesleben, PhD 15:25

Oh, that's really cool. So switching gears just a little bit to another topic of your research. I know you've done some really interesting work with radiology. Can you talk a little bit about some of the work you've done there, some of the problems you've been working, you know, working to solve in in that arena?

Arka Roy, PhD 15:39

Let me give you a little bit of background about cancer and radiation therapy and how operations research comes into it.

Jonathon Halbesleben, PhD 15:44

Okay.

Arka Roy, PhD 15:44

So typically in radiotherapy, there are, I would say, multiple modalities, but two of the more modern and popular modalities is proton therapy, where think about really, really large particles being shot towards you from an external source. And you can control the depth of the energy that's deposited in your body. And so it gives you some additional degrees of freedom to control the radiation. Okay. You're able to optimize and figure out exactly how to deliver that dose. So you have a really high dose on the tumor and you try to minimize any surrounding dose on your healthy tissue. Now, instead of using proton beams, you can also use photon beams. That's also the probably the most popular choice. And there, it's a little bit harder to control the depth of the energy deposition. Think about it like a cookie cutter just goes in and destroys the tumor cells.

Jonathon Halbesleben, PhD 16:34

Okay.

Arka Roy, PhD 16:35

But you try to give lots of radiation from multiple different angles so that when they converge on the tumor, there is a high uniform dose. And so to do that, there is a very, very large-scale optimization problem that gets solved. And you try to solve this optimization problem over a large scale so that you maximize dose to tumor, minimize dose to surrounding healthy tissue. So that's really where my optimization background comes in. Now, this is this field has existed for decades. The more modern problems that people study are uncertainties that exist in this space. So, for example, when you go in for treatment, if you have lung cancer, you can't really hold your breath for the entire treatment.

Jonathon Halbesleben, PhD 17:14

Right, right.

Arka Roy, PhD 17:15

Right. So your lungs are moving in a cyclic pattern. So with motion, perhaps sometimes you sneeze, that goes out of also that cyclic pattern. Can I capture those patterns or even the worst case sneezes in my modeling of the treatment so that in the worst case, your dose is still going to be acceptable? While in the best case, you're as close as possible to the optimal. We have taken some of those tools and extended it past motion to look at tumor shrinkage. Can I build AI models to predict those shrinkages? Can I adapt my treatment based on those predictions of how the tumors will shrink? We've built optimization models to model how will the biology of the tumor cells change over time. Can I also account for uncertainties in those predictions and then build optimization models to account for that? So I've done a lot of work in uncertainty management in cancer radiotherapy from the optimization side.

Jonathon Halbesleben, PhD 18:13

Wow. And are you seeing some of this get applied? How does how do you get that into practice?

Arka Roy, PhD 18:18

That's a really good question and a hard one because so , you know, in healthcare, in the operations field that I talked about early, that's a quality improvement project with anesthesiologist. That is really easy to implement. But when it actually impacts a treatment, then it you have to go through multiple, multiple layers of protocol and clinical studies.

Jonathon Halbesleben, PhD 18:40

Right.

Arka Roy, PhD 18:40

So what people study today, I would say 10 to 20 years later, after clinical studies have gone on, I have seen vendors adopt that. So the AI models that the vendors are using now have been studied for many, many years before in cancer radiotherapy.

Jonathon Halbesleben, PhD 18:56

Yeah.

Arka Roy, PhD 18:56

And so the research is definitely way ahead of actual practice, but the onslaught of the research that we do today definitely does impact what happens in the U.S.

Jonathon Halbesleben, PhD 19:08

So you you mentioned AI models, and I know some of the work you've done in the past is and doing now. It relates to implementation of AI models or machine learning models in healthcare context. Kind of broadly speaking, maybe you know, maybe extending beyond your your research. How do you feel about the future of healthcare you know relative to AI?

Arka Roy, PhD 19:34

I'm very optimistic because I think doctors are trained really well in their fields that they know what are the limitations of AI when they use it. When I have discussions with physicians, they want to know what are the limitations that AI will have when it makes recommendations. Eventually the argument is AI is going to at least automate a lot of the tedious and more time-consuming tasks that the doctors do themselves.

Jonathon Halbesleben, PhD 20:06

Yeah.

Arka Roy, PhD 20:07

So if I can, if the AI can do that while producing some sort of confidence in its predictions, the doctors can always make an evaluation on the actual prediction by saying, okay, you know, the AI is 90% confidence, I'm fairly okay with it. Let's go ahead and implement it. When the AI is 10% confident or 20% confident in the predictions it makes, I'm going to review it. So at least it reduces that burden on the physician themselves when they're making the treatments. I'm looking at how some of these models can perhaps be translated and used in lower economic settings where treatments to cancer therapy is quite expensive. If we can take AI models that's been built at, let's say, for example, MD Anderson or in May's Cancer Center, and be taken to East Texas and be implemented there, it can make access to those treatments much more affordable. It can perhaps reduce the geographic distance that a patient has to travel to get the treatment. And so it helps kind of overcome some of those barriers.

Jonathon Halbesleben, PhD 21:09

Healthcare seems like it's a little unique that way in the sense that there's already dramatic shortages in healthcare. So I see it taking many of the parts of the job that aren't the desirable parts of the job and taking care of some of those things. I think about my primary care physician uses the system where it listens in and does the notes. And he just loves it. He said it saves like three hours of his day, not having to do all the charting. I'm optimistic in that sense that it it will help protect in some ways the well-being of the people who are in the industry while also helping just to kind of increase the capacity in a lot of different areas.

Arka Roy, PhD 21:50

I mean 100%. I actually have a close friend who does exactly the same, he uses the same system on scribing and automates that whole process. And that's how I like to think of AI. It's assisting doctors in making decisions rather than giving them the decisions.

Jonathon Halbesleben, PhD 22:04

Yeah. Just more to the the process of your research. A lot of the work you've done has involved postdocs. And some really some of the really cool studies you've done have involved postdocs. Talk a little bit about the the value of that mentoring relationship.

Arka Roy, PhD 22:17

And I think mentorship is a big part of the appreciation that I have for research. So I've always enjoyed working in teams when I'm kind of looking at how to solve a problem. I've loved talking about it. I've loved thinking through things with other people. And so locking myself in a room and just doing individual research has never been appealing to me. I kind of share a lot of that with the mentorship that I give, not just talk about research, because otherwise I think it's very easy to fall into that trap of just like publish, publish, publish. Get into these top journals all the time. And sometimes that can get unhealthy. The people that I've worked with, I think have appreciated that fact that I can talk to them openly about it, I think. I can talk to them about my failures that I've had plenty of over my academic career that I think are the things that I struggle with, the things that I have uh issues of imposter syndrome, or, you know, how do I handle that? How do I handle the stress of working and the stress of tenure track life? How did I have two kids while going through tenure track life? A wife who's an academic who also has a very stressful job is tenured now. So talking about all those things with PhD students and postdocs who are kind of either going to go through it or are going through it has allowed me to create sort of, I would say, mentorship, relationship, friendships that works well for my personality.

Jonathon Halbesleben, PhD 23:50

Yeah. I have to think that given the the nature of the work that you're doing, the sort of practical application and even if it takes quite a while for things to actually happen in practice, you can you can see it happening in practice. I have to think that helps with the kind of plateauing piece of it.

Arka Roy, PhD 24:11

Because it's so practical, there's always a next question that comes out of the research that I do.

Jonathon Halbesleben, PhD 24:17

Yeah.

Arka Roy, PhD 24:18

I think that's what probably motivates me to keep going and be curious and continue the research on that side. You look at my research output, really. I've have a diverse output set, and some people might find that good, some people might find that bad. But I think to me, I want to be able to reach the clinicians that are gonna read my work because I want them to use it.

Jonathon Halbesleben, PhD 24:42

Yeah.

Arka Roy, PhD 24:42

There is no better outlet for me to than to get some of this work out in the radiation oncology journals or in some of the anesthesiology journals, while making sure that I'm also staying in my field of business and publishing in operations research journals.

Jonathon Halbesleben, PhD 24:58

I appreciate that you've taken that approach. I mean, I think the the best thing we can do is get the work to the people that need to use it. So I imagine it also helps with motivation. I know for myself, part of the reason I reason I'm - I kind of went into psychology, besides that I couldn't pass organic chemistry and become a different kind of doctor. But you know people are always gonna do weird things that are gonna be interesting to study, you know, and so there's like never like we're never if we ever reach the point where people stop doing weird things and we're just like fully predictable. Well, I guess then I can just be done. But I have to imagine sort of in healthcare too. There's just there's so many, so many things that go on, and then it changes. And as new technologies come into play and all this, you know, it always gives you something.

Arka Roy, PhD 25:45

Especially with like AI, I think the field has changed so much that there is now probably way more open questions than closed questions.

Jonathon Halbesleben, PhD 25:53

Yeah.

Arka Roy, PhD 25:54

I really think there is a gold mine of more research to do now in healthcare.

Jonathon Halbesleben, PhD 25:58

Yeah. On that note, what's up next, research wise? Or, what are you working on now that you're you're excited about?

Arka Roy, PhD 26:04

There's two pieces of research in healthcare that I'm really excited about. One is very practical, collaborating with Children's Health, which is a very large hospital out of Dallas with some collaborators out of Cox Business School at SMU and University of Washington Engineering School, as well as postdoc Catherine Adams, that's here. We're all a team that are looking at building reliable and resilient surgery schedules for their surgeons, implementing machine learning models and some of the more set-of-the-art models to make predictions and then make decisions based on those predictions. And then on a probably less implementable but more of a conceptual question is something in the area of cancer, but also on a larger scale, how do we bring these AI models and predictive models along with into a decision-making framework? How do I make my subsequent decision-making model or optimization model that can handle those errors in the machine learning model? So conceptually, that's what I'm really interested in is that this intersection of predictive and prescriptive analytics where you make decisions based on the predictions, but from an uncertainty management perspective.

Jonathon Halbesleben, PhD 27:19

Yeah. I'm so glad on the I guess the first part you talked about that you're working with children's hospital. And because I think you know, reliable scheduling is important and helpful in any context. But I mean, it you know, you're a parent, I'm a parent. You know, we're talking about children's surgeries. It's you know, it feels like the the reliability of the schedule and knowing that the surgery is gonna happen when it's supposed to happen or at least close. The sort of stakes they go up a bit more when you're dealing with the parents and just you know the emotions with it. So I'm glad that that's the setting that y'all are are working in.

Arka Roy, PhD 27:57

There is like multiple stakeholders in this, right? So there is the patient's perspective, which includes their parents, which includes making sure that the schedule that you set out is reliable, like you said. So the patients that are coming in from low-income backgrounds that have to travel multiple hours to get there, and someone has to take off days of the work week, right, to bring their kids in. That is reliable. But then there's also the reliability from the physician side, which is they want their schedules to be as accurate as possible, so that when they wake up at :5 30 a.m. and they have to rush in for the surgery, they know that they are not going to be waiting around for someone else to finish so that you know they meet their necessary obligations. So and then there is the hospital perspective, which is they want to get as many patients as treated as possible, right? Without having to push surgeries on to the next day. So...

Jonathon Halbesleben, PhD 28:47

When you're not working on research or thinking about research and and teaching and all of that, what what other things do you enjoy in your life? Well, one of the things I kind of want to emphasize is that you know we are humans doing this work.

Arka Roy, PhD 28:58

I've got two big loves. One is Manchester United watching soccer. So I do watch a lot of soccer on the weekends with my kids in the mornings. And my probably the biggest love is traveling.

Jonathon Halbesleben, PhD 29:10

Okay.

Arka Roy, PhD 29:11

So we've traveled around the world a lot of times. My seven-year-old has been to 17 or 18 countries.

Jonathon Halbesleben, PhD 29:17

Oh, wow.

Arka Roy, PhD 29:18

Every chance we get, we try to be somewhere else in the world while doing our work at the same time. And I think that's that's the biggest sell for academia for me. I want to be able to be somewhere else where I can show my kids different cultures that exist, different food that exists, different types of humans that live there. But they need to understand, you know, there are a lot of other people that live in this world from different con... different backgrounds, different socioeconomic statuses, and have very rich lives from different cultures that they grow up in. So we try to show that to them every opportunity that we get.

Jonathon Halbesleben, PhD 29:53

Well, that's wonderful. I appreciate that you're doing that. I mean, just...

Arka Roy, PhD 29:58

Thank you.

Jonathon Halbesleben, PhD 29:58

Yeah, thinking of the future of humanity, the more of that we can, the more of that we can instill in people in our children. I think it's just gonna make for a better world overall. So I really am appreciative that you're...

Arka Roy, PhD 30:08

And academia allows me to do that. So I appreciate academia for letting us have these flexible schedules and being able to show up to my kids' dance recital or some of the other things that I do and still be able to be successful in academia.

Jonathon Halbesleben, PhD 30:22

Yeah, and while still also making a huge impact with the work that you're doing.

Arka Roy, PhD 30:26

I appreciate it.

Jonathon Halbesleben, PhD 30:27

Well, Arka, it has been such a joy to talk with you today. Thank you for the work that you're doing and and your drive to try to make it a better system for all of us. So I really appreciate that and wish you the best of luck as you continue your work.

Arka Roy, PhD 30:40

It is a pleasure, and I'm so thankful to have the chance to sit down with you and talk about these things.

Jonathon Halbesleben, PhD 30:45

Thank you for listening to the Inside Alvarez Business Podcast. Special thanks to our producer, Brittney Johnson, and for the support of Wendy Frost and Melissa Lackey to help make this podcast possible. Stay connected with the Carlos Alvarez College of Business at the University of Texas in San Antonio to learn more about how we are empowering the next generation of business thinkers and conducting groundbreaking research to ensure their success. Follow us on social media or visit us online at business.utsa.edu. Until next time, I'm Jonathon Halbesleben. Thank you for listening.