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Christina Cardoza: Hello, and welcome to the IoT Chat, where we explore the latest developments in the Internet of Things. I’m your host, Christina Cardoza, Editorial Director of insight.tech. And today, we’re going to be talking about the use of AI in the OR with Dennis Kogan from Caresyntax. But, as always before we jump into the conversation, let’s get to know our guest. Hey, Dennis, thanks for joining us.

Dennis Kogan: Hi, Christina. Thanks for having me.

Christina Cardoza: What can you tell us about yourself and Caresyntax before we jump into the topic?

Dennis Kogan: Well, I am a tech entrepreneur first and foremost. You know, I’ve started this company, together with my partner, Bjorn von Siemens, about 10 years ago. We had a different healthcare company right before that. I am not a physician; I’m actually more of a technology guy. So, I graduated from Carnegie Mellon University with an information systems background and then ended up working in data science consulting, doing quite a bunch of healthcare.

But my link to surgery is actually more personal. My father is a surgeon, my grandfather is a surgeon, and my great-grandfather is a surgeon. So it’s kind of a dynasty of urologists that stopped with me, but I like to think that I’m contributing to surgery in a different way, perhaps more scalable than if I were a surgeon.

Christina Cardoza: Yeah, absolutely. So you found sort of a back-end way to get into the family business, so to speak.

Dennis Kogan: Isn’t that interesting how it happens?

Christina Cardoza: Yeah, absolutely. That’s really interesting that you’ve been in the healthcare space for so long and you have so many—a deep history, family history, in the healthcare space, because I’m sure you’ve seen this space has just evolved rapidly, especially over the last couple of years. And that’s sort of where I wanted to start off the conversation.

We’ve had so many technological advancements and innovations in this space. What have you seen over the last couple of years, and how has this advancement, how has it changed expectations that surgeons and physicians and patients have coming into quality healthcare?

Dennis Kogan: Yeah. Well, just kind of piggybacking on the prior question, when I was at CMU, at Carnegie Mellon, I dealt a bit with other innovations in other industries, like sports analytics and security and military. And I was at the time already talking to my dad, who, you know, was a surgeon. And I was telling him, “Hey, you know that athletes get this and this for performance management and situational awareness and get analytics? And every decision could be supported.” And he told me, “We have nothing like this. Yes, we have very interesting and very important medical devices, and we’re continuously getting clinical innovation in our hands, but there isn’t really a lot of data usage and decision-making support.”

And I think that hasn’t changed that much up until a few years ago, to your point. I think we had a ton of innovation around medical devices. You’re probably familiar with the da Vinci robot; Intuitive Surgical brought robotics into the space. But, at the end of the day, it’s still helping the surgeon operate with his or her hands. The advancements that we are seeing, and obviously we’re part of, is actually enabling the surgical teams to not only have better tools in their hands but also have better decision-support mechanisms, right?

So, with the volume of patients I think there is more and more expectation that the surgeon cannot be just thinking herself or himself about the risks of the procedure, right? They do want support. They do want more information to stratify risks more. And doing it in their heads, as it happened before, is probably no longer acceptable, given how much background technology there is in healthcare and outside healthcare.

Christina Cardoza: Absolutely. And to your point, I could see a little bit why the surgical field may be a little bit slower to adopt some of these technologies or to advance in this space, given that you’re operating on people and it’s such a mission-critical surgery or application. And some people, consumers, may not feel comfortable having this technology being used in the operating room.

But now we’ve seen just all these advancements, how it can help, and it’s really providing more, like you said, it’s mitigating some of the risks that we did have before. So how do you see now the surgical space adding some of this technology to the OR in a safe way to ensure that it’s accurate, the physicians are comfortable with it, but then also the patients are comfortable with it?

Dennis Kogan: Yeah. I mean, I think patients, I would say patients are probably—relative to other types of therapies or chronic diseases—are less aware of what’s happening in the OR, naturally. You’re under anesthesia as a patient. But what patients really do want, they want to understand how likely are they to have a good outcome? They want to understand data about their surgical teams so that they can really weigh their choices, right? So I think they expect and they probably are surprised, would be surprised, to know that not as much integrated decision-making support is available to surgical teams that will be operating on them.

And so I think the challenge has been, to your point, is that surgery is a real-time intervention, right? And so in order to integrate new technology and AI, any automation to be able to jog decisions. You really have to get a lot of new infrastructure in that would be able to pool data and create the necessary backbone to actually make the software and AI run in that real-time setting. And there is a pretty high threshold of quality and operational effectiveness, right? So, for example, anything that is used in the operating room should have almost no lag, right? So these should be real-time, near real-time decision-support mechanisms. And that, by itself is a higher hurdle than a lot of other information technology that has been used in healthcare.

So I think over the last decade, I would say, probably, since we started, I think the biggest change that’s happened in the operating room is actually bringing live all these different disparate devices into one system that is able to, A: drive the safer and more automated workflow in the operating room, but also be able to capture data and receive data so that you can start building even more advanced innovation around AI to really bring even more decision-making support to the surgical teams.

Christina Cardoza: Yeah, and that’s always seemed to be a big hurdle in the healthcare space, is having these silos of data—if patients have different data, depending on the devices they’re using or the doctors they’re seeing or the space they’re in. So I can see why it’s been a little bit difficult on the physician side to be able to get access to all this data and really make those real-time decisions. And of course with automation and AI, like you mentioned, it’s all bringing it in one place so that they can react quicker, they can make better decisions, they can have that top-of-mind rather than information getting lost.

But when we’re talking about AI in the operating room and technology advancements, I think a lot of people automatically go to what we were talking about earlier, about the robotics and robot arms and robots being used to do the surgery. In this context we’re not necessarily talking about AI-guided surgery, right? It’s more AI-assisted surgery, where AI is providing the up-to-date and accurate information for the physicians to actually help surgery and improve patient outcome. Is that correct?

Dennis Kogan: Yeah, no, that’s 100% correct. I mean, I think there isn’t a very high probability of surgeons and surgical teams being replaced by technologies for a long, long, long time. The environment is extremely dynamic. And it’s not only quantifiable activities and techniques; it’s also communication and teamwork, right? I mean, it’s actually a team sport. So, part of the outcome depends on risk stratification and how well a surgeon does a certain maneuver. But part of it is how well does a surgeon communicate with the nursing staff and anesthesiologist, and how do they adapt to changing clinical picture during the procedure? It’s so complex that it’s almost impossible to foresee how this could be replaced by artificial intelligence in the foreseeable future.

But because of that same dynamism, AI has a lot to give in terms of bringing the right information and options to the fingertips of physicians in this dynamic setting, right? I mean, a procedure that’s lasting hours or, say, an hour, and a physician team that may be operating from early morning into late evening with very different types of patients. You could be having a healthy 25-year old female or a very sick 85-year-old male, right? And you have to be able to adjust a lot of inputs and a lot of decisions throughout the procedure dynamically.

And that cognitive overload often does cause mistakes or suboptimal decisions. So at the end of the day there is—we call this variability. The change, the risk, in surgery is, unfortunately, there. There’s probably one out of seven cases has some sort of significant complication, so over 15%. And so proactive risk management through situational awareness, through certain automation, is what we’re talking about. It’s about reducing and removing variability that was unwarranted, that’s driven by the cognitive overload and changing clinical picture.

And so I think the best use cases that we see right now for AI are really proactively managing risk by showcasing specific information about that given patient, about that procedure—before the procedure, and in real time, and after the procedure—to be able to guide the entire pathway, and the outcome to be better than it would be without that support.

Christina Cardoza: I love all the patient examples that you provided, because it really just showcases not only do physicians have a number of different things going in their heads with different patients and just different operations that they have to do throughout the day, but not every patient is a clear-cut case. Not every surgery is a one-surgery-fits-all. And there’s unforeseen complications and decisions you have to make while you are performing the surgery.

So I can see how AI being brought in really provides that real-time information that allows them to react fast and to give that best possible outcome, surgical outcome, for those patients. We were talking a little bit about the infrastructure that is necessary for implementing AI into the OR. So I’m wondering if you can tell us a little bit more about how we can actually get this information—we can get AI into the OR and have that combined with the patient data and the human expertise to really transform the surgery and the outcome.

Dennis Kogan: Yeah. And the operating room is obviously the place where the actual therapy happens, and it’s very important, but because a surgery is actually a treatment to a disease, everything that happens before and after is also extremely important. So actually the best-integrated kind of platforms allow for the connectivity between the operating room and the pre- and post-operative space and time and activities, right?

Because decisions you make right before the patient enters the operating room are extremely important—about preparing the right tools, the right medications, the right people at the table. And then, of course, knowing with what level of risk that patient is exiting the OR may change the protocol of how that patient is going to be taken care of. Maybe that patient can go home; maybe that patient needs to be in ICU; maybe they need an extra dose of antibiotics because of extra bleeding, right?

So, first and foremost, truly integrated surgical-decision support touches on all points of the perioperative cycle, right? You have to connect clinical and operational information that’s coming directly from the workflow to really get a sense for how you can reduce this variability. And so that means that you actually have to connect a lot of different systems, right? We’re talking about—and we did talk about—the inside-the-OR situation, where you can connect medical devices and video cameras. The real, unstructured data, just like pilots or athletes use it to really understand, in more granular detail, how exactly that intervention is done.

But it also includes the classic, usual suspects. The electronic medical record, because it has a trove of data about the patient and his or her predispositions. The ERP, the operational data to understand the length of certain things, because that also can lead to important insights. So, the truth of the matter is that in order to get the best, smartest insights, you have to have a full perioperative clinical and operational record, with the crown jewel being the intraoperative space, because that is the most mission-critical piece where things can really go wrong.

And so because it’s real time and because it’s mission critical, it has a specific, added level of, let’s say, sophistication that’s needed. And, of course, it’s not, in technical terms, a cloud-friendly territory, right? Like, we’re used to a lot of innovation being rolled out very fast using cloud technologies, and you can open your phone and you can use it. And even in healthcare that’s been the case, right? With desktop usage—maybe not cellphones, up until recently.

In the OR, it’s all on the edge, right? You cannot rely on two-second upload and download from a cloud. So this edge computing, the Internet of Things–technology toolkit, is extremely important. And, again, it’s very similar to mission-critical segments outside healthcare: you have to have very high level of service.

And at the same time it has to be very robust and attractive from the perspective of deployment and cost solution, right? Because at the end of the day everything that is overly expensive or unwieldy—another huge machine being rolled in into already a very packed operating room—is just not, doesn’t work, right? You’re expected to have ergonomics in the OR, you’re expected to have plug-and-play capabilities, but you’re expected to have a high threshold of real-time, high-integrity flow of information.

So it took us at Caresyntax, for example, with the help of a few technology partners, years to develop this platform in a way that achieves these parameters that I just mentioned. And early adopters, of course, learned with us, seven, eight years ago. But at this stage we’ve achieved that level of quality and efficiency and are able to integrate operating rooms and add the context around it to create this proactive management of risk.

And so I know it’s possible. I think it’s still sort of in the beginning in a way, right? I think the next decade will probably have every OR being equipped with these kinds of systems. And in 10 years physicians will be wondering how they were doing work without it.

Christina Cardoza: So, given that most operating spaces or most healthcare organizations, they have these devices that don’t really talk to each other or play nicely with each other, what type of investment does a hospital or physician need to make to make everything plug and play, to be integrated, more interoperable? And how can they make those investments with Caresyntax, ensuring that they are future-proofing any investments that they do make? That they can add more capabilities, take away capabilities if they need to. They can take advantage of the latest and greatest technology and innovations coming out without making these technological investments that are going to put them in a vendor lock-in or to stagnate their innovation.

Dennis Kogan: Yeah, that’s a great question. I mean, every industry has gone through a cycle of having first a few vendors create kind of a walled garden, and then gradually the users expecting more and more flexibility and open nature to be able to add value and add new applications. And I think surgery and healthcare need to undergo the same change.

I mean, I think the medical device world, for good reasons as well, has a lot of proprietary intellectual property. And so a lot of device owners and vendors are naturally quite protective of the ecosystem they create around their therapy and their device. And so historically that’s been a dominant mindset also for physicians, is to a certain degree align with specific vendors and think of the operating room through a prism of a device.

So the first investment that needs to be made is to reinvent and recalibrate the mindset towards the operating room being not an extension of a leading device platform, but actually a care setting that belongs to that horizontal process of achieving outcome. And that having infrastructure that is vendor neutral, having infrastructure that is open to capturing any kind of data and feeding any type of algorithms into it, regardless of what vendor you use, is of course a significant change in historical patterns of acquiring such platforms.

But from a perspective of capabilities, obviously flexibility, but also total cost of ownership, having a neutral platform that is really there to add value, regardless of what inputs it’s connected to, vendor neutrality doesn’t mean any compromise in quality and safety of these platforms.

Christina Cardoza: So I know this is still a little bit early days, and more hospitals and physicians are looking to add this in the OR, so I’m curious if you have any customer examples already or any use cases of how Caresyntax really came in and helped an OR setting that you’re able to share with us or talk a little bit about.

Dennis Kogan: Yeah, no, I mean, I think there are multiple examples. I think the ones that I’m really excited about are the ones that we’re able to achieve things at scale. So we have an example of a medical-insurance company, actually, that insures surgical risk in terms of medical malpractice and safety aspects, partner with us to create proactive risk-management solutions that are using some of the understanding of safety that they have, and processes and governance, with our technology, creating a proactive risk-management offering that, both in the OR and in terms of governance, is fostering this culture of improvement and safety using the data feeds from our platform.

And this partnership has been very successful in Europe—actually in multiple countries, in hundreds of operating rooms. Basically convincing the hospitals that the combination of technology and good governance of how to achieve a high-reliability organization—if you combine this into one solution set—it’s in their best interest. And we’ve had a lot of positive traction and a lot of indications of actual improvement in technical skills of physicians, improvement in outcomes, improvement in managing surgical-site infections.

So I think that’s kind of a more complex partnership example, but of course there are numerous end users that have seen the same, right? We’ve published several studies with surgeons—I think from 20 different countries—about how usage of intraoperative data, including video, can improve technical skills of physicians, which is one of the biggest determinants of success, right? At the end of the day, you may have a very sophisticated robotic assistant or a Gamma Knife, but at the end of the day it’s still the skill set many times that decides whether it’s an excellent or an average outcome.

And so we’ve been able to show that using these advanced platforms in the OR can lift that performance level. And that’s not only surgeons; it’s also other physicians and clinical collaborators. So, for example, nursing, right? We’re increasingly starting to deploy almost like interactive, step-by-step navigation guides in the OR.

After the pandemic a lot of folks entered the workforce without maybe as much training as they would have beforehand. So there are a lot of new, for example, nurses who very quickly need to catch up in the environment, where there’s a lot of new volume because so many surgeries came back after the pandemic. So being able to quickly ramp up and actually get the step-by-step and move-by-move support in the right moment of the procedure is extremely helpful for somebody who is still lacking confidence and the experience to know what to do next, right?

Christina Cardoza: You mentioned a lot of different technologies that go into this, so I imagine there’s a technological partnership that goes into this. You know, I should mention that the IoT Chat and insight.tech as a whole, we are sponsored by Intel. But I can see you’re using the edge, you’re using the AI. So I can see there’s probably some Intel hardware and Intel software that goes into this, and there’s a lot of different moving pieces to make this happen. So I’m curious, what’s the value of working with partners like Intel to make this a reality, to bring this to ORs in a safe, secure, and accurate way?

Dennis Kogan: Yeah, no, and even though it’s sponsored, I can kind of rave about some of the support we’ve been getting here, because at the end of the day, being a surgery specialist, we have a very good view for what the end application and use case should be, but we don’t have as much experience building that infrastructure, and we don’t have the benchmarks and comparables from other use cases that may be similar in terms of the rigor and in terms of the actual architecture.

And so Intel is indeed an important partner that helped us and is helping us to meet those criteria that I described, right? I mean, having an integrated smart-surgery platform that is sort of plug and play, that is very smart and not very heavy in terms of hardware content, something that is able to generate information but also have the capability and the bandwidth to receive algorithm and actually produce AI and showcase it in real time—it’s a pretty sophisticated set of requirements.

And Intel has been one of the partners who have really plugged in with us, almost inside our team, to make this happen, right? So being able to design the architecture, find the right components, utilize some of their components that they developed, like OpenVINO, which allows for this AI penetration and usage—all of these were very important. I think without a partner like Intel we would’ve been, at the least, much slower, looking for every piece ourselves, probably making more mistakes.

In the end, I think, if you really think about the speed at which we’ve created some new, very key, new-generation components together, I think it’s probably half or even less than if we would have tried to embark on this ourselves. But we’re excited to be working with them. And I think there’s going to be continued innovation.

And alongside Intel, of course, we also work with cloud-solution providers—AWS and Google Cloud. Because, at the end of the day, you kind of have to have an edge-to-cloud transition. As I mentioned, it’s a preoperative, intraoperative, and postoperative space. So if you really wanted to have an integrated performance or decision-support system, you continuously have to go to the edge and back to the cloud and make the information interchangeable.

And so it’s been very rewarding as well to kind of build with our partners, because actually they all collaborate in between themselves—Intel and Google, Intel and AWS. So there’s a lot of big-technology-company support here to enable this, and then some of the secret sauce and the knowledge of what really makes it different for the users that we bring to the table.

Christina Cardoza: Yeah, it’s great to see all the different partnerships happening, because it’s not that you have to solve this alone, especially in a sensitive environment, like the surgical environment that you’re working in. It’s good to see that you’re using the expertise and the knowledge that you have as a company, but then also leveraging support and expertise and technology from other companies as well, to really make this as high quality as possible, as it can be.

Dennis Kogan: Of course the pandemic has been an impediment to any innovation, given the distraction on real, existential/day-to-day issues, but that’s subsided. And I think everybody’s really looking at surgery and saying, “Okay, well, it’s very important. You cannot prevent surgery very often. And yet it’s still not as safe as it could be.” It’s not as safe as flying; it’s not as safe, even as some other medical procedures. It’s time to improve it. It’s time to let everybody change certain ways of doing things.

And to your point about partnership, it takes an ecosystem of players to achieve that. And I think we’re shaping that, and Intel is one of the partners we are extremely grateful to have alongside us.

Christina Cardoza: Absolutely. I can’t wait to see how else this space continues to grow, and where else that partnership with Intel and other partners you’re working with will bring these technologies, make this more mainstream, and see that adoption over the next couple of years. And like you said, physicians in the future are going to say, “I can’t believe that we were working without this.” And I think patients are even going to say, “I can’t believe I had gotten surgery back in the day without all of this information at the physician’s fingertips.” So it’s great to see all these transformations and innovations happening.

Unfortunately we are running out of time, but before we go, Dennis, this has been a wealth of knowledge in this conversation. Is there anything you want to leave our listeners with? Any final thoughts or key takeaways?

Dennis Kogan: Well, I mean, I think I just want the ecosystem to kind of acknowledge a couple of things, right? I mean, I very often see that folks—and I think there are reasons for that—think of surgery as something that’s been figured out, something that’s reached maturity and doesn’t require innovation. It doesn’t give me any pleasure to say that this is not the case. I think 15%-complication rates, mortality rates, going into a procedure not knowing how it’s going to turn out, is still a daily occurrence.

And yet, it’s a stark size that surgery has in terms of a share of volume in treatments, right? Next to pharmaceutical therapies, surgical therapies are the second-most-used way of correcting a disease, right? If you think about spend, I think it’s over 20%, 30% of all of healthcare spend in the US is connected to surgery. And so if you think about the variability and the risk that’s still in the system, and you also even convert this to spend, this is a huge problem that has clinical implication; it has cost implications for our society, for the government who’s insuring a lot of people. So it hasn’t been fully solved, right? It hasn’t been fully solved, and it has opportunity and room to get to the same place as we have gone with aviation, you know? I don’t think you and I would accept getting on the plane with a 15% chance of something going wrong in that flight.

Christina Cardoza: No. Absolutely not.

Dennis Kogan: I think we should have that feeling going into surgery: that everything is going to be okay, backed by real statistics. And in order to change the statistics, you have to let all boats rise. And in order to do that, you have to deploy these kinds of solutions into your workflow. And—together with the ecosystem of providers, insurers, technology vendors—you really can make surgery safer and smarter, and it will have broad impact on patient health, millions of cases. And it’ll have broad impact on cost as well.

So I think my message is, please think about surgery as part of innovation, where faster, better cures to certain diseases can be achieved. And, ample room for improvement in every provider organization, as long as the mindset is there. And we’re always happy to be there together with our partners to help move forward towards that objective.

Christina Cardoza: When you think about all the risks and the complications that can happen in surgery, and then all the benefits that patients and physicians get by having this data, having AI assistance in the operating room and at their fingertips, it seems like a no-brainer to do some of this stuff. So I’m excited to see how else this space moves forward.

I invite all our listeners to visit the Caresyntax website to see how else you guys are going to continue to innovate, or how they can partner with you to add some of these technologies into their operating room. And I just want to thank you again for the insightful conversation, Dennis. Thank you for joining us on the IoT Chat. And thanks to our listeners for listening and tuning in today. Until next time, this has been the IoT Chat.

The preceding transcript is provided to ensure accessibility and is intended to accurately capture an informal conversation. The transcript may contain improper uses of trademarked terms and as such should not be used for any other purposes. For more information, please see the Intel^® trademark information.

This transcript was edited by Erin Noble, copy editor.