Many of the challenges facing the healthcare industry became common knowledge throughout the pandemic. How has industry thinking shifted with the emergence of new technologies and solutions? How can we think about tackling these huge issues at this point? Peter Shen, Head of Digital Health - North America at Siemens Healthineers, joins the podcast to discuss how emerging technologies, including AI and LLMs, can help move the needle in democratizing healthcare
Many of the challenges facing the healthcare industry became common knowledge throughout the pandemic. How has industry thinking shifted with the emergence of new technologies and solutions? How can we think about tackling these huge issues at this point? Peter Shen, Head of Digital Health - North America at Siemens Healthineers, joins the podcast to discuss how emerging technologies, including AI and LLMs, can help move the needle in democratizing healthcare. He and Yadin dive into the challenges clinicians face in utilizing vast amounts of clinical data, as well as using AI as a companion to physicians and a resource to patients.
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“So, tremendous amount of data that's being generated. The rate that's being generated doesn't even compare to the much slower growth rate of new clinicians, new doctors that are coming into health care. So as you can imagine, there's this gap that's growing…The clinician just doesn't have time to consume all this patient data that's being generated.”
“Unfortunately patients have heart attacks in all parts of the country, rural, or in the big city or whatnot. But now being able to enable the patient to go to their local hospital, get the same level of care that they could get if they went to a larger institution because you have the ability to have these shorthanded resources, now leverage technologies like remote, scanning capabilities to be able to take care of the patient from afar.”
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Timestamps:
(01:14) The unique challenges in healthcare right now
(02:37) The trend towards burn-out and “re-careering”
(04:14) The increase in healthcare data and limited capacity to utilize it
(07:58) How does Siemens approach such big problems?
(09:26) Can we leverage technologies to keep doctors informed on new clinical information?
(10:33) Ways to remove friction in the healthcare industry
(12:18) Unifying disparate clinical data
(13:13) Hurdles in unifying data
(15:41) Using AI to move the needle
(17:43) AI as as a companion for the clinician
(18:39) Peter’s hope for the impact of these technologies
(19:34) The possibilities of large language models for the patient
(22:05) The ability to scale large language models
(25:25) Distribution of AI in healthcare
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Yadin Porter de León on Twitter
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0:00:00.8 Peter Shen: There are huge challenges, and I think you've got to take small steps here, so it's certainly on the operational side. Are there tools? Are there technologies like artificial intelligence, like other emerging technologies, remote technologies that we're familiar with today that I can leverage to maybe drive more efficiency within that healthcare process, within that patient journey.
0:00:22.4 Yadin Porter De León: Welcome to the CIO exchange podcast, where we talk about what's working, what's not and what's next. I'm Yadin Porter de León. Today on the podcast, a top with Peter Shen, head of digital health, North America, at Siemens Healthineers. To dive into the digitization of the healthcare industry, we discuss the unique challenges that clinicians are facing, particularly regarding immense amounts of data and limited time to utilize it, as well as the possibilities for using AI and large learning modules to improve patient care. This episode outlines the massive challenges in the industry, as well as the hope that new technologies can help us move the needle. So Peter, some of these challenges aren't new, some of the things that the healthcare industry has to deal with, whether it comes from staffing, data silos, all these things, they're not really new problems, but I think the conversation might be shifting a little bit because there are some new solutions. So let's just pause for a second, let's talk about what actually is the challenge right now, what's unique about some of them, and then we can talk about the ways in which you're thinking about.
0:01:25.8 Peter Shen: Yeah, I think you're right, the challenges are maybe not new, but I think they've been really exacerbated over the last several years. So certainly coming out of the pandemic where I think we all recognize the importance of clinicians, nurses, everyone that's involved in healthcare, that put a big burden within the healthcare industry.
0:01:46.1 Yadin Porter De León: There were second order effects to that too, it wasn't just the individual, and I'm tired and I'm burned out and I might be thinking about doing something different, there were second order effects of, well, what's the next generation of clinicians and nurses and doctors, and how are people thinking differently about the profession.
0:02:02.2 Peter Shen: Exactly. So clinicians already were overwhelmed with the amount of work that they had. And then of course, then the pandemic happened, and I think that put more pressure on them, more stress on them, so we have a lot of stories, anecdotal stories about the physician burnout, nurses being burned out. Because there was a shortage also, a lot of clinical staff, hospital staff also could pick and choose where they wanted to practice their care.
0:02:27.3 Yadin Porter De León: Excellent. Was there a great resignation too within the healthcare industry?
0:02:29.9 Peter Shen: A little bit, I don't know if it's a resignation, but maybe more of a choice. Yeah, so there was an opportunity for a...
0:02:35.1 Yadin Porter De León: A re-careering...
0:02:36.6 Peter Shen: A re-careering or finding new ways to be able to still do what they do best, but because they knew they were in such high demand, they had a lot of flexibility to go where they wanted to practice medicine or where they wanted to take care of patients. So that was a big stress, that was an added stress. And then the other big challenge is that we have this aging population, we have individuals that are getting older, and there's predictions that certainly within the next five years, the senior citizen population is going to increase substantially here. Unfortunately, that part of the population requires more focus on healthcare, so there's more healthcare... It's putting a lot of pressure. And unfortunately, we see it directly in a lot of the healthcare services that are provided, so within, let's say radiology, we see a tremendous increase year over year in terms of imaging volume, so the number of imaging exams that need to be done within the radiology service line continues to increase.
0:03:31.1 Yadin Porter De León: Yeah, 'cause there's a capability of capturing more, but just because you capture more doesn't mean you analyze.
0:03:34.0 Peter Shen: Yes. Yeah, you're touching on the other challenge, which is that there's more and more data, and certainly with every new technology advance that we have, just like within radiology, every new MRI scanner, or CAT scanner that's out there, x-ray machine, it's generating more and more pieces of information, more information that we had five years ago, 10 years ago, or whatever the case may be. So a tremendous amount of data that's being generated, the rate that that's being generated doesn't even compare to the much slower growth rate of new clinicians, new doctors that are coming into healthcare, so as you can imagine, there's this gap that's growing, the massive gap of...
0:04:11.0 Yadin Porter De León: Here's all the stuff that we're capturing and the intel, and it could be fantastic data, it could be wonderful data, be useful data, but if you don't have anybody or anything or any entity to analyze and make sense of it, and turn it into value or patient outcomes, then what's the point of capturing it.
0:04:23.0 Peter Shen: So it becomes now this huge challenge. One, operationally, because the clinician, the people taking care of the patient have less time that they can spend on an individual patient because they've got this huge volume of other exams to review, or a huge volume of patients that they need to take care of. So from an operational standpoint, huge inefficiencies there, or desire to try to be more efficient, try to do more with less type of thing, and then on the opposite end, on the clinical side, the clinician just doesn't have time to consume all this patient data that's being generated.
0:04:58.0 Yadin Porter De León: To what happens to that? 'Cause that's fascinating, 'cause I imagine that they're just overwhelmed, they're like, well, I can only work so much, I only have so much capacity, whether it's mental or physical, and there's this mountain of data that just keeps growing and growing.
0:05:07.8 Peter Shen: Yes.
0:05:08.2 Yadin Porter De León: What's happening to it? What's the conversation around that look like?
0:05:11.1 Peter Shen: Certainly there's a desire by the clinician to say, yeah, I would love to know more about the patient to make that decision, but I just, I literally don't have the time to consume all that information to be able to make that clinical decision, so in many cases, they just have to go with what they've maybe been trained with, what their historical knowledge is around that particular disease or whatnot, or go with the limited amount of information that they have about that patient to make that diagnostic decision or that treatment for that particular patient, so it becomes a real challenge for the clinician, and that's not even touching on the fact that there are new treatments that are being developed, new research that's happening.
0:05:47.3 Yadin Porter De León: That they have to stay on top of that as well, and integrate it into their practice.
0:05:51.9 Peter Shen: That's not happening at all. So a great example is in the area of prostate cancer, when patients are diagnosed with prostate cancer, unfortunately, a lot of neurologists rely on their historical knowledge on what they perceive was successful for their previous patients, and they use that treatment plan for that new patient that's been diagnosed with prostate cancer, not taking into effect all these new advances in terms of treatments, new technologies that are out there to be able to take care of the patients.
0:06:18.4 Yadin Porter De León: And from a patient experience standpoint, there was probably in the expectation that their clinician or their doctor, they're factoring in all the data they collected.
0:06:25.7 Peter Shen: Yes.
0:06:27.5 Yadin Porter De León: They're looking at all the different treatment options and they're making the best choice for that particular patient, and there's this, that trust there, but also it's a human being that's being responsible for this and there's only so much benefit.
0:06:38.9 Peter Shen: You're absolutely right. So the human, the patient wants to trust that their doctor is taking in all the latest information, knows what the best personalized treatment for that patient is. I think the other challenge that we're seeing within health care on the patients' side, is the patient is now being a consumer within healthcare, so the patient is not just saying, okay, I trust everything to the doctor, the patient says, wait a second, I have access to the internet, I can also search what's the latest...
0:07:06.0 Yadin Porter De León: I could self-diagnose.
0:07:07.8 Peter Shen: Yeah. I can ask Dr. Google what the ailment is, but the patient has a level of consumerism where they're getting involved in their healthcare process as well, and I think that's also playing in effect that it's driving this need for technology within healthcare, yeah? So you've got these operational challenges that are there, you've got this clinical need from the provider's side, and then you have now this more informed patient that is also trying to seek the most optimal outcome for their disease, so you're having this confluence of all these different pressures and here's where hopefully new technologies, emerging technologies are going to be able to save the day.
0:07:41.7 Yadin Porter De León: Yeah, which is exactly where you fit in. That's literally your job, is to take a look at all these challenges that you just outlined and make recommendations, be thoughtful about how to apply the technology to be able to create the outcomes that you're looking for. And how do you do that? How you approach this? 'Cause these are big, these are not new problems and these are not small problems, so Peter, how are you completely transforming the healthcare industry for the better?
0:08:05.4 Peter Shen: Yeah, it's really crazy, but there are huge challenges, and I think you've got to take small steps here, so it's certainly on the operational side. Are there tools, are there technologies like artificial intelligence, like other emerging technologies, remote technologies that we're familiar with today that I can leverage to maybe drive more efficiency within that healthcare process, within that patient journey? Is there a way that I can use technology so the patient doesn't have to spend so much time waiting for their exam to be done, or spend so much time trying to figure out where the closest place that they can see their doctor is?
0:08:40.9 Peter Shen: If they have to get a specialized exam, can they go to their local hospital to do it, or do they have to travel hundreds of miles to go to the big city to do it? That's what we're looking at from an operational standpoint. From a clinical standpoint, can I arm the clinician with tools, again, emerging technologies or whatnot that assist the clinician in terms of making that diagnostic decision? So we're not trying to replace what the clinician does because, hey, we don't need less doctors, we need more doctors. But we...
0:09:08.5 Yadin Porter De León: We need more of the human doctor...
0:09:11.4 Peter Shen: Yes.
0:09:12.1 Yadin Porter De León: And their unique capabilities to be able to spend more time doing that.
0:09:16.6 Peter Shen: Yes. And as we talked about, that doctor is not seeing all that clinical information about the patient, is not seeing all the latest and greatest therapies for that patient, so is there a way that we can leverage technology to help the doctor become more informed so that the clinician can make that more informed diagnostic decision, make that more personalized treatment plan for the patient? Can we use technology, artificial intelligence, other technologies that are out there to bring some of that clinical data to that clinician so he or she can make that decision? So that's a tremendous aspect as well.
0:09:47.5 Yadin Porter De León: Yeah, and those technologies being applied, because as we all know, 'cause we're all consumers, everyone listen to this too is a consumer of services, clinical services, and hospital services, pharmaceutical services, and we all know that the friction that exists there, and you just want to know, like you mentioned, what's the closest service for X, Y, and Z use case, and without getting another 100 results, or inaccurate results, or I have to log into this database for this, I have to log into another website for that, and you're already addressing some of the low-hanging fruit friction that exists within the system from an operational standpoint too. What is that hope for removing some of the friction right now, and before we can get to some of the next things, the next pieces of technology are getting applied here, what are some of the ways you're looking at right now in removing some of the friction with the way that things exist?
0:10:35.2 Peter Shen: For us, it all starts with the fundamentals, and that's with the data, the clinical data, and the operational data of an organization. So first on the operational side, the healthcare industry providers, hospitals, doctors, clinicians, they've been all tasked to do more with less. We recognize, okay, there's a shortage of this or that or whatnot...
0:10:53.5 Yadin Porter De León: That seems like it'd be a flawed request though.
0:10:55.9 Peter Shen: Exactly, right?
0:10:56.6 Yadin Porter De León: You're in charge of people's care, not only care of making sure that they have their increased quality of life, increased duration of life, and let's maybe cut your budget a little bit and work you a little harder, that sounds like a great recipe.
0:11:07.0 Peter Shen: It's crazy, right? So there's tremendous pressure on them to operationally be more efficient. So okay, you have that edict now, as an organization, as a hospital, as a provider, as a clinician, how do you tackle that? Where do you look? Where do you look for... So our view is, you first need to look at... You need to get a baseline. You need to get an understanding of, what are we doing as an organization? So we've created platforms that are looking at the operations within the healthcare enterprise, the operations within the radiology department, cardiology department, or whatever the case may be, that shiny MRI magnet that the hospital invested in the cost millions of dollars, are we actually using it to its fullest extent? And oh, by the way, are we using it with the features and functions that it's capable of doing it?
0:11:51.8 Yadin Porter De León: Yeah. I think you're touching on something really important, just having the visibility and understand what's happening right now.
0:11:56.0 Peter Shen: Exactly.
0:11:57.8 Yadin Porter De León: Without the visibility, you can't make any decisions.
0:11:58.5 Peter Shen: Exactly. So to your point, before you can even apply any sort of technologies here, we have to understand, what's the baseline? Where should we go attack this problem of being efficient or inefficient? So first, we really going to look at, where within the healthcare operation should we look at things? The other piece that we're looking at from the clinical standpoint is, how do we unify all these kind of disparate pieces of clinical data? So one of the biggest challenges within healthcare is that the patient data exists in multiple silos, even within the hospital, multiple systems, multiple databases, different systems that don't talk to each other, all that type of stuff. So is there a way that you can unify or bring together all these different disparate pieces of clinical data into one patient-centric database?
0:12:42.9 Peter Shen: And once I have that patient-centric database, then we can start talking about, oh, let's apply new and emerging technologies like AI, whatever the case may be. Large language models, all that type of stuff.
0:12:53.5 Yadin Porter De León: But if you can't access the data, then how you going to do anything with it?
0:12:56.3 Peter Shen: But if I can't find correlations within the data, find relatable data between that radiology exam, that laboratory exam, the genomic information, then those technologies can only go so far. But if I can start linking all that patient data together, that's where I've got the greatest potential for it.
0:13:11.6 Yadin Porter De León: Yeah. And what's the big hurdle there? I know there's a lot of different opinions rolling around with regards to whether or not that can even be done.
0:13:18.2 Peter Shen: Yeah, lots of hurdles, a lot of them that can be addressed with technology, some of them which we can't, because I think there's a lot of questions about who has ownership of that data, for example, so I think the hospital might say, hey, the patient came in, they did the exam in our hospital, so we have ownership of that data. Yeah, that's our data. The payer, the insurance provider might say, well, wait a second, we paid for that exam, so we should have access to that data. Even us as a manufacturer, we say, hey, that patient did their exam on our piece of equipment, we have access to that data as well. And of course, you have the patient, who says, wait a second, that's me that you're talking about, and I should be able to tell all of you, exactly...
0:13:53.9 Yadin Porter De León: Who should have access to my data. Where's the licensed agreement on for when I get into that MRI machine? And you just had accept before reading of course, and you just skim through it though, 'cause I think you touched on a really important point, that it's not just a technology problem really, there's a much more fundamental problem with regards to data access, visibility, privacy, choice that really governs whether or not you're going to be able to solve some of these problems.
0:14:19.4 Peter Shen: And you have to remember that that's the case for every single piece of patient information that's out there, so you're going to have that same battle within the radiology department, versus the cardiology department, versus the laboratory results you had, versus where you got your vaccination or whatever, these are all now disparate discussions that are happening around disparate pieces of patient data that's there. So huge challenges in terms of how do you manage all that aspect.
0:14:47.0 Yadin Porter De León: So even before you do something like, say, leverage technologies like artificial intelligence, which has all sorts of promises and means different things to different people, but it excites people, it gets people engaged and gives people hope, and I think that's the big word is hope, it gives people hope that some of these problems can be at least mitigated or approached where they couldn't be before, but do you have to get the data clean before it?
0:15:14.3 Yadin Porter De León: Do you have to get all the different entities from the equipment maker to the healthcare pair to the healthcare provider on the same page before you can even apply that technology? Or can AI start to move the data in the right direction, move the process in the right dire... Move operations in the right directions so that you can meet in the middle, so that when the patient and the healthcare pair and the provider meet and agree, then you've got something that allows you to actually do something.
0:15:40.3 Peter Shen: Yeah. Technology like artificial intelligence can already start to move the needle for us, and we can see it's impact already within, in individual service line, and how AI plays a role to help with a diagnosis. You see that, right now in healthcare, there's a lot of focus in radiology and the radiology service utilizing artificial intelligence because they've got the ability to use simple AI techniques that we're familiar with like pattern recognition, so the same technology that you and I are used to in our personal lives where we take a picture of somebody and hold the camera up to their face, that says, oh, that's Peter there, and that's him smiling, that type of stuff.
0:16:15.6 Yadin Porter De León: I think you can search Peter in your photos, and it brings all the photos up, and then sometimes it brings up a picture of a dog, and you're kind of wondering what it's thinking that some sometimes.
0:16:24.7 Peter Shen: So radiology is already using that same technology to say, wait a second, we can use that AI to actually start to identify different different anatomical structures within the patient, so we can say, hey, that's the patient's knee, or that's the patient's shoulder, or that wait a second, that's a lung nodule in that patient's lungs. So you've got pattern recognition right there that's able to do some of that work, and it's already advancing, it's helping that clinician, that clinician who is pressed for time, can't spend as much time as they want to with the patient, if AI can come in there and help the clinicians say, hey, I look through all these images for you already, Mr./Mrs. Radiologist, and we've actually found this particular nodule, you should take a look at this. And then these other images, we didn't find anything, so you don't have to worry about that, that's going to help the clinician save a little bit of time, be more efficient, all those things that they're under big pressure to try to achieve.
0:17:11.9 Yadin Porter De León: The radiologists have been in the headlines, of course, as everybody knows too.
0:17:14.4 Peter Shen: Yeah.
0:17:15.4 Yadin Porter De León: It's like radiologists are, watch out. But I think it's good to pause and think, like the point you're making, is it's not AI or humans, AI plus human, and I know that's definitely within the conversation, but I think it's worth pointing out that a lot of what we're talking about is augmentation not replacement, and it is a bit hyperbolic to start saying, well, we don't need radiologists anymore, that's absurd. It's absolutely.
0:17:39.2 Peter Shen: Within The Healthineers, we term it as AI being a companion for the clinician.
0:17:44.4 Yadin Porter De León: Yeah.
0:17:44.5 Peter Shen: So the clinician is still going to make that ultimate diagnostic or therapeutic decision here, but can we have a technology like AI be that companion for him or her, so that they have actually a more informed or more confident diagnosis or a more confident treatment plan for that particular patient, so that's where we see, again, the technology working hand-in-hand with the clinician rather than replacing that clinician there.
0:18:07.1 Yadin Porter De León: I'll come back to the word hope, 'cause I like to inject hope in the conversation too, and there are other capabilities that artificial intelligence has, and the new... And the reason why there's been an inflection point is because some of the large language models have produced results that have excited people, that have inspired people, that have astonished people. And so looking at, I think this is more future gazing though, because there are the hurdles that you're talking about, those have to be addressed before you can really apply technology, otherwise you're just turning technology at flawed data and it's garbage in garbage out. What are some of the hopes that you have for how you would love to apply this type of technology in order to create better outcomes for patients?
0:18:46.2 Peter Shen: Yeah, in the area of large language models, we see a lot of exciting potential there from a healthcare standpoint. And one of the first areas of interest is the fact that if you think about a large language models, you think about the input for a large language models and the output of large language models, they're actually kind of simple language, colloquial language, so we can actually feed these LLMs our natural language.
0:19:14.2 Yadin Porter De León: Exactly. Where we're training them to talk like people and output stuff that sounds like people, and that's one of the big inflection points, because it's far more approachable from an interface standpoint, from a consumption standpoint.
0:19:24.6 Peter Shen: Yeah. So think about also the output, the output, aside from the input, the output can also be kind of this natural language or layman language that's there. So now think about the possibility it means for the patient. So now the patient who, again, overwhelmed with the whole healthcare process, all this clinical stuff that's happening to the patient, let alone the worries that that patient might have because there might be something good or bad that might happen from the diagnosis, now instead of being fed a bunch of clinical reports where they're trying to make sense of it and going back to Dr. Google or whoever, just help them, guide them, what does this mean for my doctor, we can actually use LLMs to be able to convey results, clinical results in a meaningful way to the patient, so that the patient actually understands what's happening to him or her. That's a great potential for it.
0:20:17.6 Yadin Porter De León: And at scale. And the great thing is it's at scale. And it'll be available as much as that person wants to be able to ask questions and ask follow-up questions, and it's interesting, there's lots of different use cases, like for example, someone writes a book and instead of posting the book or posting a review or a summary of the book, they feed an LLM the book, and you can ask the book questions, how would be great if you could have a diagnosis? Feed LLM with the diagnosis for a personal plan, and they can ask a question of the diagnosis and have a conversation. Now whether or not the person is actually going to do what they're supposed to, that's a different question. I only had a conversation with an AI once, I said, "Give me a plan to help me lose weight." And it said, "Okay, diet and exercise." And I said, "Well, what if I don't want to exercise?" And it said, "Well, I'm sorry, you have to exercise." But I don't want to.
0:21:00.2 Peter Shen: Right. What you touched on is, I think the other beautiful point about LLMs is the scale aspect. So we talked about earlier, there's all these innovative treatments, these innovations, research that's all happening within the healthcare side, so much that the clinician, the patient even can't keep up with anything that's going on. Now we've got this opportunity with large language models to say, let's have the LLM consume all this, let's have the LLM stay up-to-date with the latest cancer research that's happening, the latest that's going on in terms of treating this disease or that disease, and be able to summarize it in may be more simplistic terms for that clinician to say, "Hey, what's the latest treatment for the prostate cancer?"
0:21:40.5 Yadin Porter De León: Here the LLM's done all the, consumed millions of research studies and all this type of stuff, and says, here are reviewed papers, articles, videos, audio, and then send you a weekly report saying look at, there's five different things you might want to look at this week.
0:21:55.5 Peter Shen: Exactly.
0:21:56.1 Yadin Porter De León: Not like a voluminous thing, let's... We know 'cause we're humans, and we're limited and we have bandwidth. And at a certain point, like you said, the machines will do so the heavy lifting so that the humans can then spend more time with the human interaction component of it, that...
0:22:08.9 Peter Shen: So think about the impact that has to, again, that urologist or that clinician that sits in community hospital in any town USA, now has the ability to have a weekly, a daily summary of, hey, here's the latest and greatest ways to treat patients who have breast cancer. And now that clinician has that knowledge, has that information so that when he or she is now talking to that patient, he can inform her, hey, there's great solutions that are out there that we can take care of this problem. There's clinical trials that are happening, all this type of stuff. So great opportunity for the clinician who may not be in touch today with all the latest emerging technologies, all the latest treatments, all that stuff like that, to now be up-to date with what's going on in healthcare.
0:22:52.4 Yadin Porter De León: And I think we're running... That's we're running into the uneven distribution of AI, where you're going to have certain companies, certain clinics, certain entities that will have certain types of systems in place, but I guess at scale is, how do you create that ubiquity because you will have certain disparities of, if you go to some clinic, they may not be utilizing these pieces, there's going to be period of time with that uneven distribution. And so, I guess that hope is, from your standpoint, that you're going to be working towards solutions in which they can be far more ubiquitous so that you don't have that disparity.
0:23:26.9 Peter Shen: Yeah, we're looking to really try to democratize healthcare, and the healthcare technology in particular here. And certainly I think there's emerging technologies that are assisting in that space, so even beyond artificial intelligence and large language models, you look at the whole remote technology concept that we've all embraced...
0:23:45.3 Yadin Porter De León: It's the telehealth.
0:23:46.8 Peter Shen: Through the telehealth, through the pandemic, all that stuff. We have the ability now to have not just clinicians be able to review exams from remote locations or anything, but we can actually leverage those same technologies that we are used to do conference calls and all that stuff to actually operate remote scanners. So I could have a clinician who might be an expert at looking at cardiac events or whatnot be able to operate a CAT scanner or MRI scanner that could be thousands of miles away. So what's the benefit there is that now that patient... Let's say that patient who's suffering from some sort of cardiac event doesn't necessarily have to drive hours to the big city to get that cardiac study done, they can go to that neighborhood community hospital for them. So this is really, again, democratizing the access to care, so that again, unfortunately, patients have heart attacks in all parts of the country, rural or in the big city or whatnot, but now being able to enable the patient to go to their local hospital, get the same level of care that they could get if they went to a larger institution, because you have the ability to have these short-handed resources now leverage technologies like remote scanning capabilities to be able to take care of the patient from afar.
0:25:01.3 Yadin Porter De León: That's fabulous. Alright, well, this has been a fantastic conversation. Peter, I could talk to you for probably another five hours, I would imagine, 'cause this is not a small problem.
0:25:10.9 Peter Shen: No.
0:25:11.6 Yadin Porter De León: And it's not going away, and it's exciting that you get to be in the epicenter of this, the great work and the solutions that are hopefully going to be available to a large population of clinicians who can then provide the patient outcomes that are needed. Peter, where can people go out in the world to find more about you, your work, what you're doing?
0:25:29.2 Peter Shen: Yeah, absolutely. We're very proud of all the work that we're doing at Siemens Healthineers. So certainly, folks can learn more about everything that we do to take care of the patient, and all the innovation that we're doing within healthcare by going through a Siemens-Healthineers.com, they can see all the great innovations that we're creating in healthcare.
0:25:46.0 Yadin Porter De León: It's fabulous. Peter, thanks for joining the caching podcast.
0:25:48.9 Peter Shen: Alright, super. Thanks.
0:25:50.7 Yadin Porter De León: Thank you for listening to this latest episode, please consider subscribing to the show on Apple Podcasts, Spotify or wherever you get your podcasts. And for more insights from technology leaders as well as global research on key topics, visit vmware.com/cio.