At first glance, many challenges in healthcare look like massive, complex, and sometimes unsolvable obstacles. But when human nature is taken into account, many of these foggy issues become clear. With a deeper understanding of behavioral economics, it becomes easier to discern why some problems in healthcare persist, and how they can be remedied. Pairing new technologies like artificial intelligence (AI) with well-established findings of human psychology is a pathway to solving some of healthcare’s biggest obstacles.
On today’s episode of In Network’s Designing for Health podcast feature, Nordic Chief Medical Officer Craig Joseph, MD, talks with Jeff Mounzer, PhD, and Peter Kriss, PhD, both of Qventus, a company that uses AI to enhance healthcare workflows and operations. They discuss their respective PhD journeys, how they ended up at the intersection of psychology and technology, and how they’ve been able to simplify clinical operations within health systems. They also talk about using technology to expand operating room availability, implementing behavioral principles within healthcare, and streamlining the hospital discharge process.
Listen here:
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Show Notes:
[00:00] Intros
[02:57] The goal of Qventus
[12:31] Perioperative scheduling optimization
[24:46] Integrating behavioral economics into workflows
[29:15] Estimating dates of discharge
[40:36] AI operational assistance
[45:53] Things that bring Drs. Mounzer and Kriss joy
[49:17] Outros
Transcript:
Dr. Craig Joseph: Doctors. Welcome to the pod. How are we doing today, Peter?
Dr. Peter Kriss: I’m doing great. Thanks for having us.
Dr. Craig Joseph: And, Jeff, how are you?
Dr. Jeff Mounzer: Also doing well, thanks so much.
Dr. Craig Joseph: All right. I thought a good way of starting would be to go over some of your minimal education that is required for appearing here with me, a distinguished physician who graduated from one of the finest medical schools in the city of Detroit. Jeff, you are Qventus’ chief product officer, and you’re the product of the PhD program at Stanford University. And further, I read, Jeff, that your focus was on mathematical modeling and optimization to solve resource allocation problems in wireless networks, IT security, and the smart grid. So, was that, like, kind of underwater basket weaving? Like, is that, what’s up with that?
Dr. Jeff Mounzer: Yeah, it was a winding road, to end up in the PhD program that I ended up in. That could be a whole different discussion. But, ended up studying queueing theory, and that is a rich mathematical discipline that has applications in all of those areas that you were talking about and also in healthcare. And that’s actually how I was introduced to the problems in healthcare operations, originally our research group at Stanford, among those various problem areas that you were just mentioning, we also studied how patients flow through health systems. And, and that ended up being an area of deep interest.
Dr. Craig Joseph: Peter, you’re Qventus’ director of product and analytics and the product of the PhD program at some place called Carnegie Mellon University. And my question to you, sir. Dr. Kriss, is what kind of football team do you guys have down there?
Dr. Peter Kriss: I think I’ve seen them once. But I can’t tell you much about Carnegie Mellon football. So I’ll just skip that question for the most part.
Dr. Craig Joseph: That’s fair. That’s fair. I think that maybe you didn’t choose to get your PhD from Carnegie Mellon based on their football team. Well, so more seriously, what kind of problems are you all trying to solve? Jeff, at Qventus? What are you working on?
Dr. Jeff Mounzer: For over a decade now, we’ve been working on problems related to what we call care operations. All of the operational challenges of running a health system that are adjacent to the delivery of care that often end up on the plates of nurses, physicians, that aren’t licensed clinical work, but are all the administrative, operational things that glue the health system together, make sure that patients are flowing, through the health system, things like care progression and inpatient or scheduling, and access to operating rooms. We have a fascination with what we call glue roles in health systems to people who are just gluing everything together. And it really is sort of a manual glued-together system in many places. And I think we all experience that as patients. But we, certainly, see that experience as nurses, doctors all over health systems. So, we’ve been passionate about solving this problem from the beginning. That’s what the company was founded to do. The mission statement is to simplify how healthcare operates. And we are really fortunate and thankful to have the opportunity to solve these problems every day with our health system partners. We work with health systems all over the country. We have dozens of partners, and we solve the hard problems. We sometimes say we’re suckers for punishment. We started out going after length of stay, perioperative scheduling, and access. All those difficult, thorny, problems that require a lot of different people to jump in, save the day, make those diving catches. How do we take all of that and smooth that out so that the health system works well?
Dr. Craig Joseph: That sounds like it’s a thorny, thorny set of issues that you’re dealing with. And we’ll get into some more detail, you know, in a little bit. Peter, part of your background or a significant part of your background is as a behavioral scientist. And that is not a typical role, at a digital health company. And so can you kind of tell us how your background kind of landed you a job at Qventus and, you know, the makeup of the team that you work with and what kind of work you do?
Dr. Peter Kriss: So I’ve always been kind of at the intersection of fields, went to a small liberal arts college, Swarthmore, major in math, minor in philosophy, minor in psychology. Spent a year at the London School of Economics. And, over my time there, really discovered that what really fascinated me the most was kind of the intersection, intersecting corners of those fields, like game theory, rationality, morality, human decision-making. And my key mentor there was Barry Schwartz in the psychology department. I remember building up the courage once, at the time. I was a senior, asked him, I said, Professor Schwartz, can I ask you a personal question? And he said, sure. I said, why do you wear your watch on the inside of your wrist? Like, you can look at it, you know, turn his wrist the other way, normal. And he said, Peter, there’s something that you’ll discover over the course of your studies, which is the vast majority of things that people do, they do for extremely trivial reasons. He explained he just had a watch at one point where it was kind of the weight of it always made it go to the bottom. So, he got used to looking at it there, and now, just, that’s the habit. Every new watch goes on the same way. And so, part of what really fascinated me about these fields, it’s how what seem like mistakes are not random. and they very well may be mistakes that we make in certain contexts, but they’re not just inexplicable mistakes. There’s certain patterns in the types of mistakes people make. Is there a mismatch between, you know, the way they think in one domain that works quite well and how to apply that to another domain where it doesn’t work so well anyway? So that’s sort of where my intellectual interest in this stuff started. So, to do more of that, as you mentioned, I went to this department at Carnegie Mellon, which was a very unique department called Social and Decision Sciences. There’s a bunch of economists, psychologists, others like mathematicians and historians, all studying human decision-making. So, there I did, mostly lab experiments. So, we’d bring undergrads into computer labs and have them play games for money. And I studied mostly two things which were social preferences, like fairness, punishment, lying, that kind of thing. And the other was coordination. So, how do you get, basically when interests are not perfectly aligned but not perfectly opposed, how do you get communication to work? Well, so if, for example, if interests are perfectly overlapping, you let people talk, they’ll come to the best answer. It’s pretty simple. If interests are perfectly opposed, then what people say basically doesn’t matter. Like what people say at a poker table has basically no impact. But in between, really weird stuff happens. Sometimes communication helps, sometimes it hurts. Hierarchy matters. Group size matters. some of my work was showing that the meaning of silence depends on the context. Sometimes silence is a sign of confidence. Sometimes it’s the sign of uncertainty. So that was the kind of academic programs, mostly training to be a professor. But there’d been a significant string of students before me who had gone into industry. And I felt like I could genuinely be happy either way. I ended up finding this company, called Medallia, which, broadly speaking, does customer feedback, but in a fairly unique way. So it wasn’t, not market research in the sense of do a bunch of analysis until leaders want to do, it was more oriented around how do we just get as real-time as possible feedback from the customer to the front line, in a way they can act on. And that resonated with me a lot, because then that’s arguably the largest application based on what I was studying, interest overlapping, but not perfectly, companies and customers. It’s like a massive example of that. How do you get the right information flow so that people can actually do something with it and get better outcomes? So I was on a series of analytics and research teams there. And then, after six years, I ended up following Jeff to Qventus, I was really excited about finding a place where I’d say deeply understanding and changing frontline behavior in complex organizations was going to make or break the company. I want to be somewhere where that was absolutely central. And that became clear after, you know, 15 minutes of talking to Jeff that we had a great match there. So that’s largely where I’ve been focused. Qventus is figuring out how do we tell what’s working, what’s not working, why, and what to do about it. And we built a team around that, we built both analytics of our product itself, figure out how we need to adjust it. Is it actually driving outcomes, the behaviors, and the outcomes we want? And then also we built tools for our clients themselves, to answer those same questions about their own operations. What in my hospital is working and not working. Well, what should I do about it? So that’s the couple-minute version of my path.
Dr. Craig Joseph: I love it. And it’s, I think it’s a big differentiator between successful, not only companies, but healthcare systems as well. You know, just deciding that we’ve got good tech and putting it out in the world and then wondering why you’re not moving the needle, whatever that needle is. Oftentimes, it’s related to how the humans are using your tool, and those humans get in the way all the time. Gosh, I always joked when I was in practice that my practice would run much more efficiently if it weren’t for the patients.
Dr. Jeff Mounzer: Yeah, yeah. I mean, we realized very early in our journey as a company that we really could not be successful driving the impact that we were hoping to drive without being really good at the things Peter is good at, behavioral science, we found just about every problem in care operations has some set of incentives, behavior, etc., that need to be understood and designed for very explicitly. And no matter how good our core tech was and, if I may say so myself, it’s very good, if you can’t make something that fits into the reality of the lives of care providers and navigates the complex dynamics of the relationships in the health system, the hierarchy, the way that multi-disciplinary care teams interface, the way they hand things off between each other. It just doesn’t work. And our journey as a company has been sort of a series of trying things, failing, learning, and eventually being able to put together all of these different elements: behavioral science, design technology, automation. So that these really historically intractable problems actually can be systematically solved and repeatedly solved. But behavioral science is one of the most important linchpins we found, to be able to make it successful.
Dr. Craig Joseph: So can you maybe give us an example of one of the problems that you are trying to solve or feel that you have solved and how it’s a combination of technology and, behavioral science and kind of understanding how humans tick, is there one where you can kind of say we went down this path and we thought we were going to be brilliant, but it didn’t work out exactly the way we wanted it to and, we pivoted and now it’s amazing.
Dr. Jeff Mounzer: So, let’s take how to improve perioperative scheduling and access. Interesting problem that, you discover as you start to look at this deeply is, patients and surgeons are desperately trying to get access to ORs, OR time to be able to do surgery, hospitals and health systems actually have plenty of unutilized time, and their OR is, on average, about 40% of the time goes unused. So, you look at this problem outside in and you say, oh, this is a very simple math problem that should be simple to solve, right? You have a lot of people who want that time more than they are getting in. You have a lot of available time. Just put those two things together and you should be able to really improve access and improve utilization of ORs. And by the way, it’s very expensive to run an operating room that’s not doing a surgery. That’s a huge way of losing money quickly for a health system. So, as we started to look at this, and our first instinct, of course, was, okay, let’s understand the problem. And you start to quickly realize that there are a set of ways that access to ORs is set up in health systems that create this problem, actually a queuing problem in some sense, and it’s pretty dangerous to start talking about queuing before you go down a rabbit hole pretty quickly. But, high-level, the core mechanism at play is, surgeons will have blocks, which are times that are reserved for them in operating rooms, and they can book into those times as they get certain surgeries skipped, right? So I have, let’s say, every Monday all day in operating room three is mine. And just as I’m seeing patients in clinic, I’m scheduling them in my block time. Really what you’ve done when you’ve set up blocks and most health systems will be mostly block. Right. But most of the surgeons who operate there will have a block. And so you’ve taken up that OR scheduling, chunked it up. And all these times that surgeon A can operate in this one OR, from a queuing theoretic perspective, what you’ve done is actually created a lot of small queues, which is, generally speaking, suboptimal. It’s kind of the same thing as if you’re at the grocery store, and there’s all these different checkout lines, and people don’t naturally balance themselves across those checkout line super well. You’ll see some of it, but you’ll see some that get really long, some they get really short, same idea. And so, what happens is not every surgeon fills every one of their blocks, but no one else can put into those blocks because it’s reserved for that surgeon. And by the time it might become available, and, usually a health system has a policy that says like three days before surgery, if you haven’t used your block, will release it to everyone else. Three days is not a lot of time to go find another surgery to put into that time. So we started solving this as an optimization problem. Okay. These blocks should be this size, you know, resize it to be appropriate. Actually, probably this block should become open time that anyone can look into that would be more flexible. Turns out that’s almost completely useless doing all of that, math problems, it’s nice, theoretically, there are lots of great papers about it, I have been involved in, for how to solve this optimization problem. Sharing data about this was also a natural starting point for us. Say, hey, look like, here’s every surgeon’s block utilization across the last two quarters, okay? This one doesn’t have as high utilization as this other one. Maybe take some block from this one, move it to that one. Turns out it’s all about the behavioral science for this problem. Because surgeons, and any human being sort of care about having reserved time in a place where they feel valued. There’s no really strong incentive to give up that time, especially if you’re a well-established surgeon who the health system is really wanting to keep your patients in the health system. And then the politics of navigating sort of reallocating block that’s a bear that almost no one wants to tackle, right? You’re going to go to a surgeon, say, I’m taking away this block from you, and I’m giving it to this other surgeon. That is a high emotional energy, very difficult conversation to navigate because we’re talking about people’s livelihoods here, right? How much time I have available in the OR dictates how many surgeries I can do. So, to be able to start solving that problem, we had to actually take a completely different approach. We had to start looking at this as a behavioral science problem. How do you set up the incentives so that surgeons not only be willing to release block early, for example, but actually want to do that, to be a good steward of that overall OR time, to understand that it’s actually not going to cost them anything because they weren’t going to fill that time anyways to understand that they can get time if they need it and open time. So, we set up not just the suggestion, let’s say, to release this amount of block that you’re not likely to use, but also communicate that in a way that a surgeon can say, okay, I’m not going to lose anything by doing this. And actually, this is going to help me. It’s going to increase my overall utilization. I’m going to show that stewardship of time, etc. So, the key was all in the design of the incentives and the behavioral science of it. And once we started to get that piece right, when we start to understand the interplay of relationships in the OR, the dynamics and incentives, that’s when all of a sudden we built a solution that started to make a difference and a big difference, and the math helped. It’s important we have big, important machine learning models that are running to identify who’s not going to use their time and make those suggestions at the right time. All of those kinds of things are critical ingredients. But the real magic of this thing is in the personalization that each surgeon, each person in the perioperative landscape, and making sure that we’re meeting them where they are, understanding their incentives, and designing with their goals in mind, not just solving a math problem.
Dr. Craig Joseph: So, if I put my surgeon hat on, it’s kind of like a budget in a big company. You know, if I don’t use my budget, and I’m going to get less budget next year, which is the opposite of what I want, so I better use my budget, even if it doesn’t make a lot of sense. If I give up my time, the three days prior or five days prior, then if I do get a last-minute case, I can’t do it now because I’ve given up my time. So isn’t it always in my kind of best, in my best interests to just keep that until the hospital says I have to give it back, whether that’s three days prior or whatever. So, I’m interested in what are the rewards that are out there for me? What are the incentives for me to give up this time? Because now I’m limiting that last-minute patient who I know is out there. I just haven’t found them yet. How do you figure that out?
Dr. Jeff Mounzer: I think that’s a Peter question.
Dr. Peter Kriss: Well, a couple of ways. But one of the really important things is to directly address the core concern. Like, if I have a case that I won’t be able to get back to book it soon. And so that’s where some of the other pieces of the solution come in. So, one of the pieces of the solution is like a Google flights or kayak.com for finding a case in time for a case. So, as you may know, like, right now to book a case, you know, also requires like ten plus phone calls and faxes back and forth between surgeon schedulers and schedulers to find the time. But one of the pieces of the product allows them to just basically say, here’s the kind of case I want to do here. The requirements, timing the type of equipment I need, and just shoot them back. Here are the available slots, you can identify and priority order which ones they want. Request goes to the right team, approved quickly. So, once they’ve had the experience of having a short-term case, they want to book really, really soon, and finding the time easily that can eliminate some of the anxiety about giving up the slot another piece is also on the point of kind of really core kind of machine learning models behind the scenes. It’s when we see an open slot in which there are many, whether it’s always been open time or it was a block time that was released, identifying which surgeon would be most likely to use that slot based on the past times a week they operate like the cases, etc. And then proactively outreaching to their scheduler saying, hey, here’s a slot, do you want it? So they have enough of this experience of actually getting the time for a case isn’t that hard, then the way more willing to release it, particularly if we can say sometimes in those messages, things like, you know, it looks like there’s less than a 10% chance you’re going to use this slot and give it up, and sometimes the answer is it’s like, yeah. So, these messages often go to the schedulers, and sometimes the scheduler response is essentially, oh yeah, they’re on vacation that week, and they just never would have had any other reason to release it. Just such low-hanging fruit that, that, you know, to use the overused analogy, that goes unpicked, I guess I have to continue that analogy, but some of the problems are just really, really simple when it comes down to it, they’re just hidden because of the complexity of the system.
Dr. Jeff Mounzer: And I think Peter’s hitting on a really important couple of points there. One is that overall service design is really critical to making this work. If you don’t have a system that is addressing the complex set of needs of the people that are involved, then one component element of it, I’m just reaching out and saying, hey, it doesn’t look like you’re going to use this block, can you release it so that other people use it, but there’s not that ability to then get time when I need it or know that the OR is looking out for me and trying to surface times that I am going to be able to use whenever I need it, or all of the other components of the solution, then you can’t build that flywheel of trust and belief that this is a net-net good thing for me, right? So that overall experience and building a system, not just building a point solution, it’s critically important. And then I think the other pieces, each of the interactions that we have with a surgeon, has to be thoughtful about the concerns that they are raising, right? When we reach out and suggest, hey, it looks like you’re not going to use this portion of your block, we’re not going to say you can use the whole block, this portion of your block, and there’s less than a 5% chance that you’re going to use it. And by the way, if you are able to release this, you know, 20, 30 days in advance, someone else is going to be able to use it. But also, your overall block utilization metric, which most institutions are tracking, is going to go up, not down. So, you’re going to look like a good steward of your block better. Each of those, if the design, not just the system, but each interaction to address the needs and concerns that aren’t going to be naturally raised, right? What you said is exactly what most people think. That’s what I would think. Like what is my incentive? Least setup? I just hold it hold on to it, to the last second, just in case. But you have to build that environment of trust in each interaction and then in the system that’s around it. And then you can start to see pretty transformational change, which is what we know.
Dr. Craig Joseph: It sounds like with the data that you have access to, you’re kind of doing what Peter’s professor, came up with, you know, with that, wearing his watch on the inside. Although, Peter, it sounds like your professor knew why he wore his watch on the inside. He said it was kind of silly, but, you know, it’s based on a heavy watch many decades ago, but he couldn’t change after that. I think that it sounds like you’re able to come to surgeons and say, with that kind of 5% certainty or, you know, it’s unlikely with only with high certainty, there’s a small chance that you’re going to actually use this. I think over time, it sounds like they’re trusting you. And when they see that kind of 5%, they’re like, yeah, you’re right. You know, Friday, or even Thursday afternoon. I’d rather not really fill this with big cases because I don’t want to come in on the weekends to have to go around on these patients that are post-op. And so, kind of, maybe they start to see, explain to themselves how they behave, even though they really never would have normally sat down and kind of thought that through.
Dr. Jeff Mounzer: Yeah. I think it’s really important to meet people where they are starting from, just as a general behavioral principle. You know, as an extreme example, we could come in and say, all right, mathematically speaking, we should change the block allocations completely every quarter. That actually is probably mathematically right. We’re going to take five hours off of your block, move it to this other person based on their utilization pattern, etc. That’s a huge change to try to put on a health system all at once. Eventually, it would be great if they can work their way up to that, but it is meaningfully different. If you’re asked once in a while and you release a couple hours out of your block, not a systematic change to the block allocations, but it’s a small change that actually ends up yielding the same net result. In the end, you’re creating more liquidity in that marketplace, but you’re not doing it in a way that requires a complete change in behavior, in the way the system is constructed, that kind of, thinking about how do you incrementally change behavior, but in a meaningful way that actually benefits the system and comprehensively, but also doesn’t make each person have to completely change the way that they think about the world. That’s where a lot of the magic is when it comes to care operations. You have to remember that care providers have very difficult, complex jobs and care most about caring for patients. And the more that we are able to allow them to focus on that and not have to change all of their habits so that they’re solving operational problems, the more successful any of these kinds of changes are.
Dr. Craig Joseph: 100% agree. And you know, especially when we’re talking about surgeons and I can say this because they’re not here to defend themselves. There was a joke when I was a resident, that a surgery resident would pose a question asking, you know, what is the worst part about being on call every other night? Because many of them back in the day when I was a resident, they were they were on call every other night. And do you know what the answer was, Jeff or Peter?
Dr. Jeff Mounzer: I do not.
Dr. Peter Kriss: Yeah. I’m not familiar.
Dr. Jeff Mounzer: I’m not that funny. So, it’s hard for me to come up with the punchline to a joke.
Dr. Craig Joseph: Yeah. The only bad part about being on call every other night is that you missed half the good cases, and it really, and you can smile. So that’s a joke. That’s a joke, right? And they’re like, I don’t know, maybe I don’t know.
Dr. Jeff Mounzer: There’s a real, there’s real truth to that.
Dr. Craig Joseph: They love being in the operating room and operating. And that’s why, like, this is core to what they do. It’s also how they make a lot of their money. But that’s really a very low secondary or tertiary reason. The main thing is they love operating and anything you can do to get them that to maximize their time is appreciated. And I guess they would probably walk through fire for you if you help them do that.
Dr. Jeff Mounzer: That’s entirely the point. And that’s what we’re being able to do, consistently. That’s something we’re very proud of. And that’s why we’re very fortunate to have supportive surgeons that we work with. Because there are real benefits to them being able to do what they want to do.
Dr. Craig Joseph: Let’s pivot a little bit to maybe surgeons care about this, but physicians that admit patients to the hospital, let’s talk about the estimated date of discharge. All right. So, I was taught, back in 1926 when I was a resident, that date might not be actually, fully accurate, but it was a long time ago, I was taught that discharge planning began at the time of admission and so, yeah, you need to start thinking about getting them out of there the second that they just got in there. At least thinking about it to make sure that you got rid of every obstacle that you could in a timely fashion. And so, one part of that planning is understanding what is when do we think this patient is going to go home? You know, patient came in with community-acquired pneumonia or had a car accident. But you generally have a sense that, yeah, I have three or four days. And so, you can, okay, I’m estimating no one’s holding you to it, but I’m estimating that they’ll go. They came in on Monday. They’ll probably go home on Thursday. And that’s really important, right? Because t