HEALTHCARE

Kenton Williston: Welcome to the IoT Chat, where we explore the trends that matter for consultants, systems integrators, and end users. I’m Kenton Williston, Editor-in-Chief of insight.tech. Every episode I talk to a leading expert about the latest developments in the Internet of Things. Today I’m talking about the digitalization of health care with Peter Shen, Vice President of Innovation and Digital Business at Siemens Healthineers. How did the pandemic change health care delivery, and what lessons can we take forward? How can we better use data to benefit both patients and providers? And what can we do about the fragmentation of health care technology? I can’t wait to find out. So, Peter, welcome to the show.

Peter Shen: Yeah, thanks so much, Ken. Really happy to be here.

Kenton Williston: Peter, tell me a little bit about Siemens Healthineers. What is the company? What does it do?

Peter Shen: Yeah, absolutely. We’re a leading medical technology company with over 170 years of experience, actually, within health care, and over 18,000 patents globally. We’ve got about 65,000 different dedicated colleagues in 70 countries focused on health care, and we continue to innovate and shape the future of health care. Over 5 million different patients globally, everyday kind of benefit from our innovative technologies and services in the areas of diagnostic and therapeutic imaging, laboratory and diagnostic and molecular medicine, as well as digital health and enterprise services.

Kenton Williston: Can you tell me a little bit about your background and what you did before your current role?

Peter Shen: Yeah, sure. I’m the head of Innovation and Digital Business here at the Siemens Healthineers, specifically focused on new and innovative technologies that are coming to the forefront within health care. I’ve been at Siemens Healthineers here for now 22 years, in all sorts of different roles and capacities. Everything from sales to business management to product development, and have been living here in Silicon Valley for that entire time as well, so have seen a lot of changes in technology and everything going on right in the heart of things here in California.

Kenton Williston: Yeah, for sure. Same here. I’ve been here in the Valley for 20 years, and it’s been quite an interesting ride, to say the least. What’s your favorite thing about your current role?

Peter Shen: The thing I really like the most, Kenton, is really just being able to see a lot of these new and emerging technologies and platforms, and figuring out a way how they can influence and change health care overall.

Kenton Williston: Yeah, for sure. And obviously the last 18 months or so has been a perfect case study—in that, obviously, not the way you would want to innovate quickly, but kind of forced us all to do so. What were some of the key advancements you saw during the not-quite-finished pandemic that we’re still working through?

Peter Shen: Yeah. Certainly I think the pandemic, if anything, really just emphasized digitalization, and new technologies are playing a significant role within health care. And, more importantly, it’s helping in terms of trying to ease the burden that was on our health care workforce and a lot of the folks that were taking care of those that were sick or are sick during the pandemic here. I think some of the initial lessons that we’ve learned here are that we need to be flexible, I think, especially from a health care provider standpoint, and staff flexibility—trying to be able to manage changing demands on a temporary basis, and trying to work on demand to help scale based on the different needs of the patient population.

From a digital perspective, I think the pandemic’s really taught us to redefine the way that we deliver health care, and the way that we work with health care here—forcing us to create standards, standardize, and try to efficiently manage operations—obviously trying to maximize the safety of our patients and our interactions with them. And then trying to overall reduce that workload that’s facing us. Certainly technologies, like a lot of remote-type of solutions, I think, came to the forefront here. A lot of digital technologies around telehealth and remote capabilities to take care of the patients and to protect the patient in terms of things like real-time location services—to identify where the patient is and how do we take care of that patient. A lot of people also tend to forget that these remote and telehealth services not only benefit the patient, but they also benefit our caregivers as well.

Our caregivers could use technologies to be able to, let’s say, remotely monitor and operate a scanning device, an imaging device, let’s say, so if they needed to take an X-ray or a CT scan or an MRI scan of the patient, they could actually use some of these things like remote scanning and be able to sit physically in a different geographical location or a different area of the hospital and remotely scan or take care of a particular patient. Some of these remote solutions actually were a big benefit for, again, not just the patient, but also for our caregivers as well here.

Kenton Williston: Yeah. I’m really glad you brought that up, because I totally agree. It’s easy to lose sight of how important all these innovations are for the caregivers. Again, casting my memory back to the start of the pandemic, people were literally out in the streets cheering for our caregivers. And I think that newfound appreciation for our caregivers is still there, but it’s not as present. It’s not so much at the forefront of everyone’s minds. But you know they still have really tough jobs to do. And I think anything we can do with technology to help—of course it can help the patients, but, you know, like you said, it’s just going to help them and have their quality of life be so much better, and not get burned-out nurses and things like that.

Peter Shen: Yeah, absolutely. We’ve seen with some of our partners and our customers that they’re actually seeing kind of the pandemic as really an opportunity here to leverage digital innovations to be able to strengthen their workforce—trying to allow them to be more flexible, to be able to broaden their education and certification and training—taking online classes versus having to physically go to some sort of a class or education or whatnot. The ability, again, as we talked about, to deliver remote services so they don’t have to physically drive or go to a certain location—that’s a very helpful as well.

It’s more than just the efficiency and connectivity, but really the ability to allow flexibility, and allowing both the patient and the caregiver some ability to adapt to the changing environment.

Kenton Williston: Yeah. And I would hazard to guess that cost is an important factor here too, right? You talked about imaging, and I think that’s been pointed to as, in the US for sure, a significant factor in high health care costs—in that imaging has been a profit center for a lot of medical organizations, which, understandable, but you know, better for us as a population if we can centralize that a little bit and make it more cost effective.

Peter Shen: Yeah. I think it’s cost effective, being more efficient. So, knowing the fact that we might have to do a diagnostic test or an imaging test to try to figure out what’s going on—how can we leverage technology? How can we leverage concepts like artificial intelligence to maybe allow us to be more efficient in terms of when we’re administering that test, and more accurate or more precise in the way that we’re diagnosing those types of exams as well. I think those are real big drivers that, again, have been accelerated by the pandemic here. I think digitalization has always been a desire, I think, for health care institutions to want to move in that direction, but certainly the pandemic has really accelerated the time frame around that.

Kenton Williston: I’d like to dig a little bit deeper into that point you made about AI, because I do think it’s a pretty important part of the overall digital transformation of the industry. Where do you see AI? And, for that matter, the concept of Edge computing. That is, you putting this really powerful computational engine very close to the patient, as opposed to in the data center, or somewhere else like the cloud. Where do you see AI and Edge computing playing a role as we move forward?

Peter Shen: Yeah, it’s a great question, Kenton. I think there’s been this exponential growth of health care data that actually contains a wealth of critical clinical and operational information to help us treat the patient. The solutions that we create here at Siemens Healthineers, like our CTs and MRI imaging devices or laboratory processing units, they contribute to this challenge through this growing amount of data about the patient here. The goal now becomes—how do we process all this information, all this data, in a timely fashion so that we can deliver those important clinical results back to the physician, so that they can make that diagnosis or that treatment for the patient here.

This is where I think it’s important to figure out what is the right technology to try to address these challenges here and try to be able to find, again, those important clinical results here. So we’ve got to focus on developing technologies and solutions that process these critical clinical findings as quickly as possible. And that’s where the technologies like artificial intelligence become so important. Great example of that is a new AI platform that we’ve created here at Siemens Healthineers that we call the AI-Rad Companion, that we brought to market just a few years ago. The AI-Rad Companion actually leverages artificial intelligence here to process large amounts of imaging data, to help really identify, characterize, and quantify clinical results automatically for that physician, so they can immediately review them and create that diagnosis.

Kenton Williston: I have to say I’ve been the direct beneficiary of some of these imaging technologies myself. So, a couple of years ago I had to have a surgery and pre-surgery, and went in for an MRI because they were going to do an imagery-guided surgery, right? So they weren’t just depending on the real-time eyeballs of the surgeon to go and do what they needed to do, but were actually doing it in conjunction with a 3D image. Love that we’ve got these kinds of technologies, but that same experience was also shocking in the sense that, when I got my MRI, the way I conveyed it to my surgeon was I had to physically carry a CD to them. Really old school. Kind of made me feel like back in my college days—the sneaker net. I think that really speaks to how disjointed the industry is. One of the things you said earlier was, during the pandemic how there was a real, I think, awakening about how important standards were, and doing things in a coordinated, collaborative way.

How do you think the industry needs to continue evolving on this point—whether that’s from the perspective of digital transformation or just how technologies interoperate?

Peter Shen: Yeah. Your example is a great example of where there are still challenges in health care in terms of this digital adoption that’s going on here. Like we talked about, especially in the world of health care, we want to make these informed decisions about what to do with the patient—whether we’re diagnosing something, or we’re trying to treat the patient with something here. And I think it’s not just the sheer quantity of information and data that we talked about earlier, but it’s also ensuring that we have a high quality in this information. And that means the accuracy, the completeness, the timeliness of getting information—such as like critical results of a patient based on their exam, where if somebody is on the surgical table, trying to get that information. It’s so critical in terms of making our informed decision with this high quality.

That means things like accessibility is real critical. And other examples are active participation of patients in their health care—whether it’s the wearables that we’re all familiar with, or your active engagement in terms of monitoring your vital signs and whatnot. Getting that information to the clinician becomes very important as well. And trying to figure out a very easy, technology-enabled way of getting that information directly to your clinicians is just as important as well. We have to have almost like a digital health platform, if you will, that could gather all these different, disparate, growing, large amounts of health care data that we’re talking about, and trying to consume all this information in a timely manner—easy effort, so that the provider, the clinician here, your doctor, can effectively see all this information, gather all this information, digest all this information in a real simple way.

Kenton Williston: Yeah. What’s Siemens specifically doing towards that end? One of the things that comes to mind for me is how there’ve been all these different EHR systems, right? You kind of have a platform, but the problem is there’s multiple flavors of these things. Just like the personal example I experienced, I was going between different health care organizations, and that linkage totally broke down between them. What’s Siemens doing to create more of a standardized ecosystem that folks can plan together?

Peter Shen: Like you talked about, there are several characteristics that are necessary in order for this digital health platform to be successful. In the digital transformation of health care, I think this platform, like we talked about, needs to be accessible to broaden the digital portfolio of clinical and operational tools that might be available to the clinician or the end user here. It needs to be flexible in order to leverage technologies, to allow for the ease of deployment without the dependency on technical limitations or infrastructure. And then it’s got to be scalable to be able to facilitate organizational growth. And, finally, it’s got to also provide this interoperability that you mentioned, as well. So, being able to drive the connectivity amongst different systems and to simplify the whole concept of information sharing.

Here at Siemens, we actually created a digital health platform that we call the teamplay digital health platform. It’s a bit of our ecosystem, if you will, that brings together data and evolving applications, providing this unifying platform for accessibility, flexibility, scalability, and interoperability that we talked about here. And that platform leverages the latest different computing technologies that are out there, provides the flexibility to not only be both a cloud-based solution that we’re used to, but also maybe an on-premise solution, or within the walls of the institution for security purposes, or whatnot.

And the other important thing is that there’s some independence there that allows a consistent performance to deliver those critical results, regardless of the type of platform, or the solutions that are involved in that particular platform, to be able to deliver the right results to the physician without worrying about technology challenges.

Kenton Williston: I want to come back and talk more about that performance angle. You’re definitely triggering some questions in my mind. But before it gets to that, how do people actually partner up with you? Is this something that Siemens itself goes out and asks people to join, or are partners coming to you? How does that work?

Peter Shen: Yeah. It’s a combination of both. Certainly, I think, we at Siemens Healthineers, we pride ourselves in innovation, and certainly we’re developing a lot of clinical and operational solutions organically within our organization. But there’s also an opportunity for inorganic growth as well, where we recognize that some of our customers, some of those different health care providers, they want specific solutions that either we haven’t fully developed yet, or may not be in an area of expertise for us. And that’s where it’s important to kind of have, again, third-party clinical and operational partners. Certainly we love reaching out to certain partners to be able to explore what we might be able to do together.

Conversely, many partners also reach out to us, knowing the fact that we’ve got a digital health platform here that is quite ubiquitous in a lot of different health care settings already throughout the globe.

Kenton Williston: As promised, I do have questions about this idea of performance. What does it mean that you’re resolving performance concerns and making that a non-issue?

Peter Shen: From a practical standpoint here, we talked about this example earlier of trying to identify a nodule in a static chest CT image, and then maybe looking at the heart ventricle while it’s beating, or whatnot, in a functional MRI exam. The technology requirements to analyze and characterize, let’s say that nodule within an image—a picture, if you will—those might be vastly simpler, versus trying to take now a beating heart and trying to analyze, let’s say, the volume of that heart as it’s in motion and as it’s beating to try to figure out whether it’s getting enough blood to survive, let’s say.

And I think those different processing requirements, those needs to process all that information, whether it’s a static image or it’s this beating heart now, those are vastly different. And in the big picture, though, for our customers, for the clinician, for that doctor, he or she actually doesn’t care, especially in a critical situation, doesn’t care about how complex it is to interpret that image or that moving image or whatnot. He or she really just needs to get that critical clinical result. Is it malignant or is it benign? Do they need to go to surgery or not? And so this is where what we want to do with our digital health platform is we want to really move away from having our end users, those clinicians, having to worry about the technical limitations and the technology challenges that are there in order to get that critical clinical result.

Kenton Williston: Is this something you’re doing by making sure, for example, they’ve got on-prem sufficient compute power? Is it extending things into the cloud so you can flexibly handle whatever they need to do? What are you actually doing to make sure they’ve gotten the performance they need?

Peter Shen: Yeah, absolutely. It’s a combination of both of those things. I think it’s creating on-premise solutions to make sure that we’re delivering those results in a timely fashion. It’s leveraging cloud computing to be able to always make sure that our customers and those clinicians have the latest and greatest AI algorithm to be able to process those studies that are there. It’s Edge computing, where we’re able to take a hybrid of those scenarios to be able to, again, deliver the result in a timely fashion, leveraging whatever technologies that are out there. It’s certainly doing those aspects. I think the other piece—also leveraging a lot of the technology partners that we have as well to make sure that those capabilities are there too.

We have a wonderful partnership with Intel, for example, that is so critical to how our digital health platform works here. The processing performance that we get from Intel to help us design our AI algorithms and deliver those critical findings that are assimilated by solutions like the AI-Rad Companion are so important. What’s great about our partnership with Intel is that their OpenVINO toolkit allows us to configure and optimize those different AI algorithms for our platform here. And, quite frankly, it allows us on the Siemens Healthineers side to actually focus on developing our algorithms and clinical solutions to process those clinical findings, without having to worry about technical or infrastructure limitations here.

Kenton Williston: Peter, I feel like you just totally read my mind, because as you were talking I was thinking about—this is kind of a conversation about scalability, right? You can put computing at the Edge, in the cloud, in the data center. And I was already thinking about OpenVINO, and was going to ask you about that very thing. Because one of the things that’s so cool about that platform is that it’s designed to scale all different kinds of compute hardware, so that you can put things on the Edge, if that’s where they need to be, put them in the on-prem data center, put them in the cloud. It can do all of the above, right?

Peter Shen: Yeah, absolutely. I’ll give you another practical example of where Intel’s OpenVINO toolkit really helps us out. If we go back to that AI-Rad Companion solution that we mentioned earlier, one of the hallmarks about the AI-Rad Companion is that, with that solution, we actually run multiple AI algorithms at the same time on a particular image that’s being processed by that particular solution.

This is important from a practical sense, because in the real world the clinician actually might not know what’s the disease or ailment that’s affecting the patient that he or she sees. This is where, now as I create these AI algorithms, I have to have multiple computing powers, multiple capabilities here to—if I want to run these algorithms all at the same time. And, again, this is where OpenVINO makes things so much easier for our team. Because it really is now—with OpenVINO I can actually create these AI algorithms that really address these different clinical areas, and then have them all run or operate at the same time—all to meet this physician’s need at the end, which is that they need to get those critical results back so they can figure out how to best diagnose this patient.

That’s kind of a real-world example of how something like OpenVINO is really helping us in terms of the development of a technology like AI, and then the practical application of that technology in the real world.

Kenton Williston: That’s really cool. I should mention, too, for our listeners, that this podcast is an Intel production. One thing that really, I think, is interesting about this example you’re giving of somebody coming in and there’s a lot of different directions you could explore. A lot of the conversations about AI to date have been around sort of the point solutions—like, I need to see if there’s a nodule in the lung or not, right? That’s a single question. But really, caregivers can benefit from having a broader perspective on what the patient’s issues could actually indicate.

And I’ll never forget when my wife was pregnant, we went in—we’re talking to her OB-GYN, and we’re talking about this and that and the other. And she got out her little pocket book and flipped through to do a little quick analysis of what these symptoms could add up to. And this is somebody who is very experienced, really knew what she was doing, but it’s like you just can’t keep it all in your brain. And I think there’s some really interesting opportunities for AI to go beyond individual diagnoses to do things like better understand the patient as a whole, and even know what questions to ask to begin with.

Peter Shen: Yeah. You’re absolutely right, Kenton. I think that’s where we at Siemens Healthineers actually see the greatest potential for technology like artificial intelligence. And I think here at Siemens Healthineers we really strive to be that leader in clinical decision support, not only in the point of diagnosis, but through the entire care continuum for the patient. And that also means driving concepts like personalized medicine. It’s not just trying to figure out what’s the right diagnosis, but also maybe trying to figure out what’s the optimal therapy plan or treatment plan for that individual patient.

We’re in the process right now of developing another solution that we call the AI-Pathway Companion, which is really looking to leverage, as you mentioned, patient data from all these multiple sources. Not just imaging data, which we’ve talked a lot about here, but also: let’s look at the patient’s laboratory results, let’s look at the patient’s pathology report, or even their genomic data and history to maybe take in all these different disparate pieces of data. Leverage AI to ingest all this information. And then, more importantly, find correlations between all those different, disparate pieces of data. And then analyze that data to actually then try to either create kind of a guide, or predicted or optimized personalized treatment plan for that individual patient.

Kenton Williston: Yeah, totally. And this reminds me of something you said earlier in our conversation about even integrating data from wearables—which now Apple watches and the like have a lot of really sophisticated sensors in there, and, you know, can really help understand on an ongoing basis how an individual’s health is trending and how a larger population is doing. One of my aunts who’s in a retirement home now—they had a cohort wearing Apple watches for a while there to do various kinds of studies and understand how they could help them stay healthier longer. And I think that’s really cool.

Peter Shen: Yeah, absolutely. And, again, if we can leverage things like AI to help us figure out, “Hey, if this worked for your aunt, can we take the same regimen, same treatment, or whatever it is, and maybe help all of her colleagues also who are maybe either in the same environment or suffering from the same challenge or whatever it is.”

Kenton Williston: Yeah, totally. And this does lead me to a question. I mean, it’s really great to think about bringing all these different data points together. And I love what you guys are doing with this teamplay idea. But I do have a question about how you ensure all these different solutions coming from different partners are able to be integrated, and to make sure they’re secure, to make sure they’re compliant. Because, of course, as you well know, in the health care space there’s just a lot of regulations, and you have to take really, really good care of your patient data. How is Siemens handling these issues?

Peter Shen: Yeah. It’s a great question, Kenton. And obviously one that’s sensitive, to not only the patient and their information, but then also to the provider themselves—the health care giver as well, in terms of making sure that their patient’s information is in good standing and safe.

The great part within Siemens Healthineers is that that data security and patient information security is actually built in and inherent to even the design of our solutions. As we develop things like the teamplay digital health platform, as we design applications like the AI-Rad Companion or AI-Pathway Companion, security is kind of inherent in it. The ability to compartmentalize the data—if it’s not necessary, then let’s remove kind of all the patient information that’s there—so, all their PHI information or protected health information associated with the patient. If there isn’t a need for that information, then there’s no need within our applications to actually keep that information. That design thinking is inherent in all the products that we’ve created. And then, of course, as we implement these solutions and roll these solutions out, we’ve created different—either physical barriers or even technology barriers—where we’re isolating individual patient data or institutional data so that they’re separated from other organizations or whatnot as well.

It’s really principles that we adhere by, even from the initial development of our solutions, to make sure that we address data security and data privacy from the get-go.

Kenton Williston: Got it. Well, it’s certainly comforting to hear. Looking forward, what else can we expect in this area of health care digitization? And what should health care organizations be doing to prepare themselves for what’s coming?

Peter Shen: The future of health care continues to be dynamic, and digitalization plays a significant part of it. We talked about a couple of different concepts around leveraging technologies like AI in terms of simulating all the data that’s there. I think what we see in the future here is also the ability to—if we can assimilate all those different, disparate pieces of data—we can not only use that to do some predictive analytics, to figure out what works and what doesn’t work. But if you think about it, if we have information about your imaging exams, if we have information about your lab results and your pathology reports, and let’s say genomic makeup or whatnot—what we’re doing here at Siemens Healthineers is we can actually start to create, if you will, a digital twin of the patient. Kind of a digital replica of the patient.

And by creating that replica here, that digital twin could be used to simulate different diagnostic or therapeutic decisions to test them virtually, and see what the response is of the virtual patient before we do that exam or do that procedure on the patient for real. The excitement here, again, is all based on the data, but if we can leverage these technologies to gather all this information, not only can we, again, make diagnostic and therapeutic decisions, but we can actually then create this digital twin of the patient and use that digital twin to help us actually drive concepts of wellness going forward.

Kenton Williston: All right, well, listen, Peter, it’s been really great getting your perspective. Really appreciate your time today.

Peter Shen: This was fantastic, Kenton. Really appreciated the time as well. Enjoyed the conversation, and all is very exciting here in the world of health care.

Kenton Williston: And with that, I’d just like to thank our listeners for joining us. And to keep up with the latest from Siemens Healthineers, you can follow them on Twitter at Siemens Health and on LinkedIn at linkedin.com/company/siemens-healthineers. And, of course, if you enjoyed listing, please support us by subscribing and rating us on your favorite podcast app. This has been the IOT Chat. We’ll be back next time with more ideas from industry leaders at the forefront of IOT design.

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