Healthcare 2040: Operating Rooms of the Future

Speaker 1: (00:09)
Welcome to healthcare 2040 a series that matter produces, uh, in collaboration with Hillrom I'm Steven Collins, I'm the CEO of matter. We are a healthcare technology incubator and innovation hub built on a belief that collaboration between entrepreneurs and industry leaders is the best way to develop healthcare solutions today's program, which is the last, uh, matter program of 2021 is part of our healthcare 2040 series, where we look at what healthcare might look like in 20 years and how it is that we're going to get there. We have a terrific collaboration with Hillrom to produce this series. Um, it has been a big week for Hillrom. They officially became part of Baxter. Uh, earlier this week, um, today's conversation, we'll focus on operating rooms. How will operating rooms enable more efficient, more effective, and less risky surgeries over the next two decades on what do we need to invest in today in order to get there?

Speaker 1: (01:12)
Uh, we have two people today to help us explore this topic. Max Mayo leads virtual health at Parkview health. Max has worked at Parkview for more than a decade. And in the last five years, he has worked with his team to build out digital healthcare services throughout the health system matter has been working very closely with Parkview for the last several years. And in fact, just yesterday concluded our most recent, uh, collaborative innovation initiative, uh, focused on maternal and child health. We have Florian Moser, uh, brain labs, digital or director, uh, in his role, fian works closely with surgeons and nurses and senior it personnel to digitize, automate, optimize clinical workflows to enable more efficient and effective patient treatment. Last year, brain lab acquired one of matters member companies, level X, a company that started at matter and grew and became really the leader in developing me medical simulations for iPads and iPhones. Um, our moderator today, uh, for the conversation is Kirsten Emmonds, uh, Kirsten, uh, is hill Ram's director of clinical and market insights and global surgical solutions. Uh, she works to ensure that there's clinical insight into the development of surgical solutions. Uh, Kirsten has a nursing background, both in clinical practice and in leadership roles, uh, before she joined Hillrom in 2013. Thank you, uh, max, and thank you, uh, Florian, uh, and thank you Kirsten for joining us and, uh, sharing your wisdom and insights with us today and Kirsten. Uh, I'll hand it over to you.

Speaker 2: (02:52)
Thank you, Steven. Good morning. Good afternoon. And good evening to our matter, uh, folks on the phone virtually with us today. Thank you for the introduction and thank you to my esteemed colleagues for joining me today. So we are super excited to share with the matter audience, not only what we envision is that the perioperative space will look like in 2040, but we actually hope to dig in a little further and talk about the key components that are needed to make that happen. And so what do providers and clinical providers need to know or do or train to do what technology should be developed? Um, and in fact, what regulatory and or, uh, reimbursement strategies need to infer in the next few years to, to make these visions come true. So with that, I'm gonna kick it off with, um, starting us off on a macro level. Florian, can you describe how you see the, or the PA perioperative space in 2040?

Speaker 3: (03:44)
Yeah, absolutely. Thank you ki. Um, great to be here. Good morning. Good afternoon. Good evening to everyone. Yeah, so I think it's super important to understand how the operating room today looks like. And the operating room is currently the number one value driver for hospitals, but it's at the same time, the biggest efficiency leak for hospitals. So essentially an operating room for many hospitals is a big back box. So, you know, a lot of things about the patient before the patient going is going into the operating room. You can drag him, you have all the data, but then you have essentially no information about the procedure, no standardization about the procedure and when the, he or she comes out of the operating room. Again, you have a lot of data to drag, um, from reimbursement over admissions and so on and so on. So, um, again, even like really seeing your, our manager's nurses cannot predict, predict how long a specific procedure will take, because there are so many variants.

Speaker 3: (04:57)
Um, and the systems in the operating rooms are so segregated and essentially operating rooms are overcrowded with people as well, which altogether really leads to inter procedures. So I think what we at brain believe is that to overcome these current challenges, procedures need to be standardized and unstructured data needs to become more structured data. Um, and essentially there is the vision that we will have that in latest 2040, and essentially we will work in four, four domains to standardize procedures and have better patient outcomes and more efficient procedures because we are looking at like four data domains to, to standardize the procedure. So the first domain is really the anatomical data domain, where all anatomical data is available. Um, then you will have a spatial and video data domain where the anatomical data domain can be correlated to it. Um, you will have a workflow domain of data where all workflow data can be correlated to, and you will have a statistical domain where all the other data domains will feed into and you can have predictive predictive analysis.

Speaker 3: (06:32)
Um, and again, this, this data access and access to data overall is very important for the vision of the operating room 2040. So to have access to medical data, to workflow data to statistical data is what we believe the most important thing to, to get to this vision. Um, so that would be a big discussion point. I think in the talk later on as well, that we really discuss, how can we enable access to, to patient data? What, what needs to happen from lawmakers? Um, so we are able to get to all this data, but then I quickly want to talk about other areas as well in, in the field of the operating room, which would be key key drivers in an operating room of 2040. So that really will be robotics. So we see already now, um, that there are first robotic systems in the operating room by 2040, I think, um, most likely every procedure will have a part of it done robotically because we see already now for specific procedures like medical screw placement, a robot can do that more accurately and faster than the surgeon, but it would be always with oversight of the surgeon, I think as well.

Speaker 3: (08:00)
Um, we will see that, um, visualization operating room will be different than we know it today. So, um, most likely this place and monitors in the operating room will be gone and you will have a kind of head up display or extended reality glasses, something like that to view your endoscopic images, your video images, your microscope images, you will have xrays to see kind of the bone structure and the anatomical structure of your patient overlaid with the real patient. Um, and I think those classes then will enable better teaching because you can have an, and essentially in surgical assistant or a rep from a company virtually with you without overcrowding the operating room. And essentially that will reduce the patient risk will reduce their, their treatment lengths will, will allow for more standardization, standardized procedures and yeah. Better outcomes.

Speaker 2: (09:09)
Thank you. So that's a bit from the technology development side of things. Max, can you fill us in on how you, um, see the, or of 2040 from the health systems point of view?

Speaker 4: (09:21)
Yeah, sure. I think, uh, good morning. Hello everyone. Uh, thanks for having me, Steve, and in the matter group group, uh, thanks Kirsten. Uh, so when we look at this, I try to break down some of these complex things into little buckets. And so I was thinking when, uh, we were, we got these questions last week as they go, well, what, how would I break this down? And I think, um, I think about it as two different people and then one physical space as I look at the, or of the future. I think what's the, what's the patient experience, uh, to this what's the clinician's experience or the surgeon's experience there. And then what is the room itself and the experience with the technology in the room? So, as I was looking through this, um, I'll, I'll kind of just pick through each one very high level, but you know, the patient experience for, for a surgery really starts far in advance of the surgical procedure.

Speaker 4: (10:11)
And so you start to look at how are we engaging the patient with their care, uh, before the surgery ever exists. Uh, when I look at, when I look at things that are available today and data that's rapidly becoming available, and the transparency behind that, I wonder, how do we get to this sort of dashboard or a digital perpetual physical that's out there. That's constantly monitoring the patient, uh, with some of these remote patient monitoring tools and those type of things that would tell us, maybe even before they need the surgery that they're coming up on, um, you know, their, their oil life like your car, does your oil life gives you an indicator that you're, you're coming up on a, an oil change need. Um, are we going to be that way in the future with some of our patient needs where we can have indicators that tell us, Hey, it looks like you're going to have, start to have problems with your knee or whatever that may be.

Speaker 4: (11:03)
Um, and then how do we bring that patient in to do various things, either presurgical or postsurgical, uh, checks where maybe they don't have to be in person at all that can all be done virtually and, um, with some of these various tools. So I think the, the patient experience is going to change a lot from that perspective. Then they show up and, and the clinician experience, what does that look like? Well, their tools are going to be more advanced obviously. Um, I would completely agree, agree with Flury and in the sense of, uh, there's gonna be so much robotic support there. So, uh, again, to use analogies, just cuz it's simple for me, but driving is not a whole lot different in the sense today than it was 50 years ago where you had a steering wheel and you turned your wheels where you wanted to go.

Speaker 4: (11:47)
Uh, but there's a whole lot more technologies that supports that with alerts and um, uh, safety measures that kinda can guide us. I think we're gonna see the same with medicine that, that support of robotics, um, is going to be there to almost autopilot us in some areas where the physician's still in control, but the technology is helping us to provide better outcomes. And then lastly, I would say, I think, again, this is obvious when it comes to our, our homes and things like that, being smart homes, uh, but the rooms themselves are going to physically evolve to support, uh, that clinician and that patient experience. So, um, whether that's able to be, uh, streamed live to family members or whether that's able to, uh, provide equipment to do some of that, uh, maybe far site robotic, uh, needs depending on the surgery. Um, all of those obviously voice commands I think will be, uh, a given, um, lots of that will change in the, in the physical perspective.

Speaker 2: (12:47)
Thank you, max. That was, um, both compelling view views and it seems like there's a lot of things that need to change in our systems to get there. Um, both of you mentioned knowing more about the patient and even max touched on the value of understanding more about the patient preoperative. So that may include six months prior up to a year prior, but I guess I would ask from a technology point of view, Florian is your organization looking at how you can capture data preoperatively and what types of technologies exist for us to enable that type of data capture?

Speaker 3: (13:27)
So, yeah, um, we kind of capture data already, already research surgically, and we have like applications we're doing kind of surgical planning for specific surgical service lines. We, we strongly believe that you need to do surgical planning for all procedures and essentially, um, it's already every, every surgeon or every, every physician is doing that already. Nowadays, even if it's just looking at like the blood, the blood levels, the, um, you know, an xray image or whatever, I think in the future that would become more complex that you have more, more data of the patient, more colored with each other and you would be able to use machine learning on, on those areas. As I briefly mentioned before, there are like the four different areas of, of data. So the anatomical, this patient video data, the workflow data and the statistical data domain. And I think especially the anatomical data domain and the statistical data domain will, will guide us in the right direction.

Speaker 3: (14:40)
So we will know, um, beforehand. So we will take, let's take a brain tumor patient. We will get all this CT, CT, MRI images, all of those things will do an automatic planning and we will see essentially, um, automated what is the best way to the tumor to remove, remove the tumor based on like the anatomic model, but the, and we'll be able then use this anatomic model in the operating room, of course. But, um, additionally to that, I think we will see out of the statistical domain as well, that right, this patient is a male, um, 65 years old, wide and so on. So we will have data from there and can correlate that with, with the same or a similar patient group to understand what is the best approach and what is, what is the highest risk essentially for that procedure. And can then based on that information already adjust, um, our procedure to, to those specific needs. That's, that's an area where brain lab is actively working into to kind of get those areas, um, to get, but we, we believe at the same time, it's like, as I said earlier, um, data and data access is really most important thing, which is currently challenging for, for hospitals, for research institutes, for startups and for, you know, other industry players.

Speaker 2: (16:25)
Yeah. Thank you. I think, um, when I consider with a nursing background or health systems background, max, that efficiency obviously is the key component of the or, and the, the better the throughput, the more valuable the surgical organization is to your overall health system. Do you see though patient outcomes being impacted by knowing more about the patient prera and postop like Lian mentioned?

Speaker 4: (16:56)
Uh, I certainly think that if we can hyper personalized things that it seems like we should be able to attack outcomes the same way. So, um, the more we can use that data, um, the information given to us beforehand, we're not just measuring one point in time, perhaps we're doing things with devices that's capturing, um, a whole several months or several thousand data points before the surgery occurs. Um, all of that I think will help flow into, uh, delivering a surgery that's catered specifically to that patient, which I think will in and of itself drive, um, drive outcomes specific for people, which seems to me like has to be better than just a, um, a blanketed format, uh, where there's, there's more variation just, uh, by nature.

Speaker 2: (17:47)
So you can standardize the procedures. More Florian mentioned that in his technology development and max, he mentions the value of, um, decreasing variation. But the idea of customization of those standards to that patient may help you provide better care for them postop and improve outcomes. If you're UN if you have an understanding of what have an interop or preop.

Speaker 4: (18:08)
Yeah, I think, I think so. So the, the topic of standardization is, is hard for me because we're, it, it's kind of this balance right, where we we're moving into this world of hyper personalized healthcare. Right. So everything needs to be, uh, very specific to someone's, um, genetics essentially. And we, and what's great is we have the foundation to do that, right? So, um, the genetics are there, the, uh, the tools are there to do various, uh, monitoring and, and get thousands of data points like fian was mentioning, like, you need that data to do that. Um, so on one hand, you, you almost have this drive not to create standardization and then, um, uh, which as a consumer, right? Like you want something very catered to your, your care. Um, a and then there's this idea that standardization has to occur in order for us to deliver lower costs, um, and, and better outcomes.

Speaker 4: (19:04)
And how do you have robotics or, or something help in that delivery. So it's, it's this delicate balance, I think, between, um, using the personalization of the patient to deliver a standard surgery. And I almost wonder if what that looks like in a, in a or room is something along the lines of, you know, the, the equipment that's delivered is, is personalized for that patient. Um, uh, the, the tools in the, or is specific for that patient. Um, and we almost measure waste or cost in terms of the equipment that we're not using. The things that get, uh, repurposed that we didn't have that almost seems like waste to me. Um, when you start to get into that, um, that realm of personalization. So, um, then, you know, the tools are going to help us become very standardized kinda, uh, again, using analogy like, like GPS does with us to get from point a to point B, there are lots of ways to do that. Um, but GPS essentially tells us what's the most efficient, or what avoids tolls or whatever we want is the outcome. And I think we'll start to see that with the tool set. So that'll help us deliver a personalized care plan that does have some, uh, some basis in standardization around the tools. So whether that's Google glass and the display technology there, or some, some version of, uh, whatever that technology looks like,

Speaker 2: (20:28)
Max is a good point. You kind of, um, emphasize the fact that it's an interesting dichotomy of personalization versus standardization. Gloria, in that intraop space, you mentioned your four data domains that, um, brain lab is particularly interested in and the notion of anatomical S spatial workflow and statistical domains. Um, can you speak a little bit about specific technologies? You mentioned a few, but if you could dig in a little deeper on how you intend to track those different domains or how you collect that information and what types of technology should enable that in the future.

Speaker 3: (21:08)
Sure. So, um, again, maybe talking quickly about what max said as well is like, yeah, I'm totally agreeing. There's like, you know, a little bit of a contradiction between, um, standardization and, um, you know, having specific, specific, um, procedures for the patient for the individual. Um, so I think in that, in that, in atomical domain, if we talk about that a little bit, it's, it's really an automatic alas segmentation to segment different parts of the brain to really create like, you know, a Google maps of the human body. So essentially, you know, if you just look at, at, um, you know, a map itself, it doesn't bring you too much value. So Google maps really just brings you value because essentially you see where the coffee shop is. You want to go and then you can plug in my location. And it tells me where, how far it is to the coffee shop.

Speaker 3: (22:17)
So I think like that's similar for like CT and demo data, and then therefore the Ana anatomic domain without having the additional information, it's just the CT and the MRI image. So that's why the anatomic domain is so important to get like, you know, this kind of unstructured, MRI, CT, xray data to tructure data and, um, overlay all the relevant information there. Um, in the, in the video domain and the spatial domain, I think it's very important that we are able to kind of overlay this anatomical data to this video data and the spatial data and direct, direct information in operating room to really understand, um, what procedures, what steps in the procedure are done to simplify documentation essentially, and, and document it. I always look at like, you know, American football there, and especially the NFL is like, if Tom Brady throws a big or , so the big or a touchdown, the first thing he's doing, if he's going out of the field is like, he's taking a tablet and looking at his last plate.

Speaker 3: (23:29)
So that's currently not happening in, in the hospital or in the operating room. You know, you document a lot of procedures, but nobody is looking at it again. So I feel that's very important in the future to drain surgeons, to drain residents as well, um, to have better outcomes, to look at video images afterwards and learn from your mistakes, but see as well, what you have done done well, and being able to teach, to teach residents, students and so on. So that is a very, very important area there. And at the same time, all this video data can really reduce the effort and documentation. There are currently studies that like nurses and surgeons spend 40% of the time in documenting in the hospital. So it's like, you know, the less time they're documenting the easier you can make it for nurses and surgeons to document procedures and have like, you know, as we have it in the, in the airplane, the black box recording where like the whole procedures just recorded and you do that smartly and use machine learning in the eye to extract important parts, it will be easier and create an automatic documentation report after the procedure easier.

Speaker 3: (24:47)
It will be then later on for a surgeon to document it, to share it with referring frustrations, with families and so on. And at the end, they will have more time to spend with the patient and less time to spend for documentation. So that's, that's about like, you know, the, the video domain and essentially, yeah. The spatial domain, um, to track all the information there, um, from a workflow domain similar, then max was said, it's like, I think that operating room itself would be smart. And we will get information on like when the patient automatically, when the patient entered the room, when he left the room, what's the humility, what's the temperature correlated with like specific risks, you know, is like, what's the surgical site infection because the temperature wasn't right. And the humidity was too low, things like that, but essentially the room itself will, will adjust.

Speaker 3: (25:47)
And at the same time, you will have information about what, what I implants do you need, what instruments do you need? What equipment do you need in the operating room beforehand? And, um, will use, set up time and be more efficient essentially in the operating room, because everything is connected and essentially is an open standard with using fire other technologies, which currently, you know, a lot of systems are proprietary. And, um, yeah, this open standard will feed into like this workflow domain to understand the specific workflow for a specific procedure. And again, all this data will then go into like a statistical domain where you really can, can use machine learning on understanding what is happening for specific procedures. What is as well, maybe the best hospital to go for. Because I think in the future, there will be more specialization on a hospital level, but as well on like a surgical stuff level.

Speaker 3: (26:54)
So I doubt that we will see an orthopedic surgeon who is performing an knee implant, and he's performing a hip implant because, and hip impact because, um, I believe in the future, we will know exactly who is the best search of an knee implants. And the patient will know that as well. And you will go there and maybe for like, as an example, again, brain tumor, or you will in the Chicago area, low Northwestern, and you will not go anywhere else. Cause that's the best, the best hospital for, for brain tumors, but others are better for brain aneurysms.

Speaker 2: (27:34)
Do you actually see this helping an aiding more specialization versus a broader ability to perform surgeries?

Speaker 3: (27:41)
Yeah, I think so. And the think is as well, like every surgeon, as everyone in the world has, you know, it's good in specific things and might not be as good in other things, you know, I think that's, that's very clear and we have seen this shift already from like, you know, a little bit of from specialization in the last 20 years. So, um, essentially I hope it will further increase and it will be more transparent to the patient because I think that's, that's important as well that the patient understands, you know, where can I get the best possible treatment, not based on yeah. Previews.

Speaker 2: (28:22)
So, um, we had a question from one of the, um, listeners, so you spoke a lot about data capture there, and that was from a lot of different disparate pieces of equipment of people, of places of the environment. So do you see, um, systems opening up for interoperability? And do you think that hospitals might add third party made components to a surgical system, for example, instead of single companies controlling an entire system? So I think the point here is kind of the notion of open access and sharing data across systems and equipment and vendors. And how do you see that changing? Yeah, overcoming that?

Speaker 3: (29:03)
I think, I think we, we clearly see that. I don't think like, um, that that is a model for the future. I think we need to have open access. We need to have open standards. Fire is a really good first step into that direction, but at the same time we as brain let don't believe that like, you know, you need you as a single company can provide everything for every surgical subspecialty, you know, so there might be a foundation layer and I'm taking our robotics as like a good example. You know, there should be a robotic based system let's say who do some robotic procedures, but essentially there might be one company who is really good for like a robotic system for a brain procedure. And let's say the robotic system is essentially similar than like the human, the human arm. It's like maybe just, just the hand is robotic. The arm itself is non robotic and is, um, a standardized system, but then different companies can provide the hand. And essentially it reduces that the cost for the hospital and still gives a standard for the procedures. Because again, I don't think that like companies like intuitive or others are able to provide specific, specific surgical outcomes for each procedure. So I think startups are from that point of view, just quicker in developing and the, there must be the opportunity to provide them a base level of, of doing that.

Speaker 2: (30:53)
And I would imagine that we would need to see some policy changes or involvement from health systems and or government government payers that start kind of forcing the hand of that access to, to data across the,

Speaker 3: (31:08)
Yeah, I think, I think we've seen it already with like the, with like, you know, um, the fire standard and, um, and other other first steps in that direction where we know that like big EHR companies try to, to, to count our initiatives there. Um, I think we, we see that already. Um, I think at the same time, it's important that we go to a, to kind of like a data donor level, similar than organ donor, where, you know, essentially lawmakers are enabling patients to donor the data. And I think as a patient itself, you decide what data you want a donor to home because, you know, for myself as an example, I would donor my data. If I have a knee implant to add knee implant manufacturer who wants to do research data and stuff like that. But I most likely wouldn't donor my data to like the same company I'm ordering food list or I'm, you know, they are, they're directing my, my internet search or stuff like that. So again, I think it's important that the patient has control over the data and is deciding where it gives the data to. Um, but on a really easy, easy level.

Speaker 2: (32:39)
I love that notion of a data donor. Actually, sometimes I don't love that. I feel like when it comes to my own, uh, practices across social media and how it can be monitored. Right. But, um, I do see the value of opening those systems up for that. Here's what data we have that would impact an outcome, right. From each technology developer. So max, I'm sure you have thoughts on this wide world of data that's being collected, that's being, um, aggregated and then needs to be presented out to actually inform and provide clinical insight clinical decision support. So how do we ensure that the right folks are involved in that type of analysis of technology and ensuring that it's developed with the users and the folks who are receiving that information in mind?

Speaker 4: (33:25)
Yeah. I tell you this, uh, this is a gray space that really concerns me, right? Like, uh, partially cuz I don't know if patients fully understand the value of their data and the, um, uh, what they should do to protect that. And uh, and maybe what they're giving away if, uh, if they are donating that for free. I love the concept of, uh, of that data donation. Um, but I think this is a, a world that's really unregulated right now. And not that I'm, I'm generally not for more regulation, but I, I think that it's, um, really tricky. And I think by the time we get to a point where we're regulating it or have some of our, um, arms around this, that it may be too late, that all that personal data may have already been given up at that point or the most valuable we'll have.

Speaker 4: (34:14)
Um, so I, uh, I really struggle with this arena, um, uh, of, in, in balancing between development, cuz we need clear data, we need transparency, we need, um, uh, thousands of people to fuel those AI engines and to, to better correct that, uh, data computation, but at the same time, um, I, I worry that, uh, that, that could also go sideways pretty quickly and that patients don't entirely know, um, what, what they're giving access to. Um, and there's a course biasness there if you don't get all the data. So as you create those AI engines that you're, uh, you're then creating a, a naturally biased component, uh, that, that may have left out some data. Um, I don't have the answers here, but it is definitely a, a gray area that gives me a, a lot of heartburn.

Speaker 2: (35:10)
I agree, agree. So, um, we've talked a lot about the technology. We've talked about the outcomes we think we can, um, improve with increasing technology. I think there's going to be the notion you guys have pointed to some of the speed bumps, I'll call them. I won't call them roadblocks or else we wouldn't be innovators, right. If we saw them as roadblocks, but the speed bumps that, that we have to overcome in the future. So you talked a bit about policy and regulatory. Can you talk a bit about the people in the, or that you're designing for Florian as an example, surgeons, you mentioned standardizing the way they provide care or even potentially automating types of procedures or workflows within that procedure for them. Can you speak to how, um, surgeons react to that? How clinical staff in the, or react to that type of clinical decision support or even the notion of automation?

Speaker 3: (36:11)
Yeah. Um, I think it's, it, um, really, really depends a little bit. So I think, um, especially from an administrative and from like in a while leadership perspective, they wanna have more standardization surgeons typically, you know, do I, do, I, I wouldn't say avoid standardization, but they, they want to have the freedom of choice to do what they, they do best in treating the patient. I think they need to have this, this freedom of choice. So I don't think standardization in the operating room needs to go down to like, you know, every step is standardized. Everything is standardized. I think it's more like, you know, the standardization of documentation, the standardization of specific checkpoints in the procedure and learning, learning based off that, that standard of care a little bit, I would say, and understanding like, you know, we had a knee implant with Dr.

Speaker 3: (37:25)
Do on Monday, it took one and a half hours on, on Tuesday, Dr. Ado, a similar, although it a similar em, implant with a similar patient two and a half hours, why was the variance there? What was the reasoning for that? And there might be very good reasons, you know, but essentially I think, as I said earlier, currently it's a big black box and nobody really understands why, why those things happened and to do, to direct it more accurately and to drain them, you know, specific areas and specific people in the operating room and see what are the needs, you know, could be that like, yeah, there was a new nurse in the operating room and she wasn't aware of the tools he needed and not all instruments, very well, stuff like that. And um, that discussion clearly is happening already now where the dog, after the case about those things. But again, I think the better we document that the better we can standardize and the better it is than for a patient outcome later on. Cause those things can be avoided. Um, at the beginning, if that nurse would be drained beforehand, or if that nurse wouldn't be in the operating room for that procedure, because she wasn't as an, as an example. And again, that would reduce the risk for patients because in our long, under anesthesia is clearly a higher risk for the patient.

Speaker 2: (39:06)
So it makes sense then that we could utilize the data aggregation, um, provide the types of clinical insights to both the surgeon clinical decision support, the support automation potentially, and to the clinicians in the room that we're increasing proficiency across the, across that care space. Um, or at least providing kind of that baseline level of knowledge and care, thereby improving outcomes in all patients. I think, um, there's a question and we would be remiss if we didn't mention COVID, but max, can you speak a bit to the variables that are leading to that lack of proficiency or that change in proficiency in the, or,

Speaker 4: (39:50)
Uh, with specifically you mean,

Speaker 2: (39:52)
Or just post COVID maybe world.

Speaker 4: (39:55)
Yeah. So it's created such a bottleneck for moving patients. I think at least if any other hospitals are going through what we're going through. Um, I think you see that, um, you're having to make decisions on, on who gets or time right now, or be mostly because you're making decisions on who gets bed time in certain rooms. Um, because at least in our facilities, the, the beds are at a pretty high premium right now, especially as you get into some of the ICU, uh, areas. So I think that, um, C is really bottlenecking some of the surgical opportunities that we have just because medical, uh, patients are, um, not no fault of their own, but there there's a log jam there with, uh, with medical patients right now, um, with that, with that piece of it. And then of course you, you add on all the complexities of cancellations and things like that due to, uh, someone testing positive. Um, if there's a, an elective surgery of some sort, uh, we would opt not to bring the patient in who's COVID positive, uh, at times. And, um, uh, reschedule that later on. So you do get into this, um, just operationally, these inefficiencies that, that pop up in there.

Speaker 2: (41:09)
Yeah. We're seeing a higher acuity of patients, um, come through the, or it seems due to the backlog. So folks who waited longer to do surgery or to have their surgery, some somewhat elective, somewhat semi elective. Right. And so, um, it's a notion of how do we respond and react to those higher risk patients, which I think when you all mentioned knowing more about the patient pre-op, that would be an interesting notion of DEC, you know, decreasing that risk by having that knowledge before they come to the, or the other is, um, we are seeing significant turnover and burnout of, um, nurses, particularly in the or space. So fian mentioned elevating the proficiency. And when you have new folks in the, or coming through or new, um, providers supporting surgeons in the, or you start to see those variations in care a little more profoundly. So as we move, um, through time, I suspect that those would be really key elements to focus technology on as well.

Speaker 3: (42:09)
Yeah. Yeah. And I think, think what we've seen with COVID as well is like restricting access from hospitals for like the industry as well. And essentially, um, I think that's a big, a big area in the technology world as well. We need to develop on is a virtual assistance. You know, currently a lot of, you know, surgical reps are in the operating room, assisting surgeons and nurses, like, you know, what implant to use, how to use the instrumentation, how to use equipment, stuff like that. And therefore you have a, an over crowded operating. And I think we have seen it already and for COVID now with restricted access stuff for, for those folks to get into the operating room. And I think especially with like, you know, remote, remote access, remote, um, learning opportunities, remote, um, assistance, um, that is about where COVID pushed, pushed that development forward really a lot.

Speaker 3: (43:17)
And I think it would be more than like assume a WebEx or Microsoft teams call in the future. Um, as, as said earlier, like we will have, you know, some extended reality in the operating room and you will have most likely a virtual system instead of like a real, a real folk there and will have them like, and, you know, a virtual technique guide or something like that, essentially available to guide you through the procedure. And yeah, that's, that's one part where like, for us explain lab level X, the company would just bought out of, of matter essentially, or they were part of the matter group, um, yeah. Comes into play because the technology is key for that area as well.

Speaker 2: (44:08)
So gamification of those opportunities for it, I think is part of the level X discussion, right?

Speaker 3: (44:15)
Yeah. Correct. Gamification, but as well, like, you know yeah. For draining, but at the same time, just providing really a virtual technique guide for specific procedures.

Speaker 2: (44:27)
Cool. Yeah, go ahead, max.

Speaker 4: (44:29)
Well, uh, it was gonna go off, um, script a little bit as I sit here and listen to Flo and I've just been writing down points and as I read the chat and stuff, it makes me just think of little one off. So, um, I was kinda just gonna do a rapid fire of, of thoughts here. Um, I, you know, I think Florian had mentioned just some of the data coming in when I think of or scheduling and how it exists today. Um, some of the efficiencies that I think we'll gain is in the scheduling side of things where we will be able to determine, uh, based on, uh, just better transparency before the surgery, uh, of what outcomes we can expect or the duration of the surgery that we may expect, uh, based on better inputs. So, um, it's, there's some of our challenges I think, occur because we get backed up in surgeries that, uh, we find something that was unexpected or, uh, the patient was more complicated than we expected.

Speaker 4: (45:22)
And I think that'll help us just from a, a scheduling standpoint alone, which I know is not a real sexy thing, but it, it matters in the terms of delivering that care and time efficiency and stuff for our providers. Um, I, going back to some of the, the policy and the patient's understanding their data, I read the other day that if, uh, there was an infographic I saw, and the only number that I remember is Microsoft's, uh, terms and conditions. But if you could read 220 words per minute, I think if you sat down, it would take you a solid hour and 35 minutes to read your terms and conditions with Microsoft, um, on your, uh, on your computer. And it had, it basically had every tech company in their zoom and, and all the others. And, um, none of them were short. So to, to, again, to emphasize, um, patients or people don't know what they're giving up to some of these tech companies, um, I think that's really illustrative of how legally they have really defined everything.

Speaker 4: (46:19)
Um, and so how do you make that, uh, transparent to in a, especially with medical language that patients already, uh, may struggle with, how do you make it, uh, transparent and easy for them to understand what they're, uh, what they're agreeing to in those TNCs? Um, and then, uh, again, Flo had mentioned some of this earlier, but, uh, all of this I think is driving towards patient choice. Um, he mentioned that, you know, this, you may not, uh, get care locally. You may, uh, see someone who's a specialist somewhere. Um, and I really think that that is where healthcare is moving. We already see that today. Um, but this idea that transparency is going to put some of that decision making power back in or into the patient hands. Um, they're going to see who has the, uh, whatever scores, however, we're grading these surgeries in the future, and they're going to be able to make choices on that.

Speaker 4: (47:15)
And, um, this is where I think policy is going to struggle to support some of that. Um, insurance and payers is all very much based. I saw there was an out of network question. Um, it's all very much based on a network, right? A physical constraint that we put people in. And, um, we saw this with the explosion of video visits across the country and state li lines and licensure. Um, I just, as the, as the earth flattens here from a delivery standpoint, um, I, some of that's going to feel real antiquated very quickly and, uh, the policies and the ability for patients to flow into other areas, uh, we may need to evaluate what in network means and, uh, where patients are able to go for care and how virtual can support some of them.

Speaker 2: (48:06)
Yeah. Great point, same similar notes on my side as well. Max, I think, um, payment and reimbursement for specialized procedures for enhancing technology may not be that you do benefit from increased payment. It may actually be that you set yourself apart from a different organization by providing the technology and the outcomes that, um, are best for those patients and the notion of healthcare shopping, um, certainly is a trend that continues over time. I thought it was interesting. Um, we kinda had a question about it too, but we didn't, we kept the traditional or intact. It felt like, and primarily because we were focused on some higher risk surgeries with the, the brain lab, um, folks on the phone. But if we were to look at technologies and enablement of, um, surgery outside of that traditional acute care space, what do you see as the trends there? And can we potentially move higher risk patients to an outpatient or ambulatory type center for surgeries?

Speaker 3: (49:18)
I think, I think ambulatory care centers will, will most likely focus on like lower risk patients, essentially, because again, it's like, you know, they, they are not set up to, to, to do high risk patients. And I don't see them being set up for high risk patients because you need to have like, you know, intensive care units and stuff like that. Um, the typical ambulatory care center is really like, you know, you're, you're leaving after your procedure. So I see, I see that, um, involving, because, you know, the more procedures become minimal invasive, the easier it is to do that in an ambulatory care center. But again, from a patient perspective, I feel like, and I think ABRO care centers are, uh, a really good choice for specific procedures. But I think knowing data then as well, and being able to predict if the patient is a high risk or a low risk, even if it's just a simple procedure like knee or hip implant might, might give us the opportunity, decide if he needs, or she needs to go to ambulatory care center, or she needs to go to the hospital because she's a high risk patient and would reduce cost because the lower risk patients can be, can be, um, treated in an ambulatory care center setting, which reduce the cost for healthcare or more, um, and higher risk patients will be treated in the hospital.

Speaker 4: (50:53)
Yeah.

Speaker 3: (50:54)
Not if you read in higher risk patient in like, you know, an ambulatory care center and then he needs to go to like an ICU, then you have to transfer it to the hospital. So that's like the, the big challenge. And then immediately we increase the costs. Um, tremendously.

Speaker 4: (51:15)
Yeah, I was gonna add, I, I think, uh, the orf today is gonna diverge and I think you're gonna have, um, kind of surgical light and surgical heavy sort of mentality. Um, and to one commenter's point here was this all costs money, right? So, um, even if you, even if you replace the surgeon, the entire surgeon, um, the technology to do that is very expensive. Um, so it's not like you're, uh, you're just getting a different resource. It's not like you're getting a cheaper resource necessarily. And so, uh, I think though to the, the points being made is that you're gonna have procedures that may be light on resource needs. Um, kind of those, those quick, easy, relatively, uh, not meaning that in any offense to anyone, but, uh, some of those easier surgical procedures. And then you're gonna have ones that are far more resource intensive with far more anesthesiology needs or, um, uh, or different technology needs that are going to be in those, uh, tertiary care centers. So, um, I think money and technology is definitely going to drive those types of, uh, of procedures to where the normal or room of today will kind of, uh, diverge into one of, of two models in the future.

Speaker 2: (52:34)
It was interesting. One of the questions centered around, do you see then some organizations kind of getting out of the surgery business and max, I don't know. And Flo, and you started with, it's one of the number one value drivers. So would we see that as a feasible

Speaker 3: (52:52)
Trend? No, I don't. I don't think they will go out of surgery. I think they will just specialize on things, you know, I mean, we see that already now that not, not every hospital has like a cardiology or a neurosurgical department, you know, from that point of view. And I think we will see that even more in the future, um, where it would be more specialized. But again, if you have, let's say it again, simpler procedures, you know, um, you will have more throughput and it would be more efficient essentially, and you still, and you still can make, make the necessary money. So I think essentially it will become a win, win, win situation. So like, you know, it would be a win for like the hospital or the healthcare system. It would be a win for like patients because the outcome will be better and more standardized and it would be a win for, for surgeons and nurses as well, because essentially they are more specialized. They have, um, less, um, documentation burden and, you know, can focus more on the patient. And that's, that's what they decided on when they chose to become a nurse or a surgeon.

Speaker 4: (54:10)
Yeah. I, you know, as a healthcare administrator, what, what scares me a little bit is what we've already seen with some of the low acuity stuff with like the Teledoc and others, which are really tech companies that can then deliver care. I would not be surprised if over the next 20 years we see the emergence of tech companies that also deliver surgery and, um, that will be real competition for healthcare, uh, hospital systems. And where do they play a role in that? Um, so, uh, again, we, as health systems are probably far more financially constrained in some of those tech, uh, not, not probably, we are more constrained than some of those tech companies that have more at their disposal or, or can move and pivot faster on the innovation scale. And, um, it would not surprise me to see, um, someone who starts in a niche area then explode very quickly with surgical capabilities that is perhaps more led by technology than led by, um, than led by healthcare. And, um, we already see that today in the medical space, it wouldn't surprise me to see it and the future space with surgical.

Speaker 2: (55:25)
Wow. Thank you guys. Like that was a lot to cover in a short amount of time. I feel like we could probably go on much longer. Is there anything I missed that you all would like to have a parting word on before I wrap this up max or,

Speaker 3: (55:42)
Yeah, I think we, we really could have gone on for like another hours.

Speaker 4: (55:49)
Yeah.

Speaker 2: (55:51)
Think

Speaker 4: (55:51)
We, thanks, Kirsten. Uh, thanks everyone for participating. That's a great questions. Um, appreciated the opportunity to share and discuss.

Speaker 2: (55:59)
Thank you, Flo. And you froze for, for your last statement there. I think you're back. Sorry.

Speaker 3: (56:07)
Yeah. Um, yeah, no great discussion. Um, really appreciate the time. Um, yeah,

Speaker 2: (56:13)
Looking forward. Thank you so much. Well, it felt realistic like that we could really change technology over the next few years. I apologize. You froze again, Laurie right at the end, but I think that we have the opportunity to develop the technology and it seems like those pathways are well underway. Um, we talked a lot about the need to understand more about the patient to customize, uh, patient care throughout the care continuum, but especially in the or space and the acknowledgement though, that that creates a really interesting dichotomy with how then do you standardize cases as well? You know, being able to be in tune with that patient's, uh, unique characteristics and needs and improve their outcomes. Um, we also talked about though the notion of, if we're gonna collect data and share it, we need to do it safely. Um, with respect to the policies that exist and, or look for policy changes in the future that support that, uh, data acquisition and sharing and the need to become more interoperable, uh, between devices, which I think again is a whole nother topic that we could spend quite a bit of time on. So, um, several great points. I appreciate so much your expertise and your knowledge and contribution today, guys. And I hope you all have a good afternoon or evening.