Hardware to Ecosystem Pivots: Problem Definition Over Features
The Next Leap in Medical Technology. Bernd Montag has led Siemens Healthineers through a decade of extraordinary transformation—spanning digitalization, AI, the Varian acquisition, and a global pandemic. Now, with its “New Ambition” strategy in motion, the company is positioning itself not just as a medical technology provider, but as a foundational force in the future of precision medicine.
In this episode, we explore the next evolutionary leap in medical technology through the lens of Siemens Healthineers’ bold moves—from AI-powered diagnostics and digital twins to the groundbreaking Therapy Command Center with Mass General Hospital. Bernd shares insights on leadership, convergence, platform thinking, and how the healthcare experience could change dramatically for a child born today.
What happens when diagnostics, data, and therapy converge into one intelligent system—and what role will Siemens play in shaping that future?
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Bernd Montag has been CEO of Siemens Health and years since 2015.
The company's 73,000 employees are deeply committed to pioneering breakthroughs in healthcare for everyone, everywhere, sustainably.
Under his leadership, it went public in 2018 on the Frankfurt Stock Exchange as Siemens Health and years AG, and just a few years later, in 2021, it joined Germany's premier stock index, The Decks.
1:47
Bernd's journey as a health in the year began 20 years ago when the company was still part of CMN's AG.
He started in corporate quality management and sales in the hearing aid business, then moved into imaging systems.
There he held several positions as a product manager for computer tomography, head of marketing for the magnetic residence business, and later head of the entire computer tomography business.
2:09
By 2008, he was named president of the Imaging and Therapy Division, overseeing diagnostic imaging and image guided therapies.
A native of Germany, Bernd holds a PhD in theoretical multi particle physics from Friedrich Alexander University in Erlangen.
2:25
Nuremberg, Behrend.
Welcome to a natural selection.
2:29
Speaker 1
Welcome.
Thank you for having me.
2:31
Speaker 2
It's really a pleasure to connect.
As we spoke before, I have a little bit of a background in the Med tech space, having been with Perkin Elmer slash Revity for for a few years.
Regarding your drive for innovation, what need or impact defines your work?
2:50
Speaker 1
So I distinguish here between two groups, one is patients and the other is customers.
Yeah, because we are our primary customer customers.
I have care providers, but everything we do is is done having patients in mind.
3:06
And you quoted our purpose statement here with this for everyone everywhere sustainably and we are and I am serious about this.
So this means really what is what is, what are the diseases 8 billion people are afraid of.
3:27
And we are focusing largely on the NCDS, the non communicable diseases, cancer, cardiovascular diseases, including stroke now also neurodegenerative diseases and and think about technologies, how to systematically offer earlier diagnosis, better, more targeted treatment.
3:55
Yeah.
So that is the patient story, the patient need.
I think it's pretty clear.
Yeah.
And I hope it doesn't sound too broad And the, and when looking at customers, I mean the biggest, the big challenge our customers have is staff shortage and also how to provide access to care.
4:20
And we are looking at how can we develop technologies which help to offer better patient care for more, for more, for more patients.
Yeah, I hope it doesn't sound too generic, but what it also shows is that the company has a big scope and it's also, in the breadth of what it can tackle, pretty unique, I believe in the industry.
4:46
Speaker 2
No, it doesn't sound too, too broad at all.
And and it's, it's great to have an aspirational goal like that.
And, and you hit two very key areas, obviously on the patient side, having some clarity and some guidance and direction when they have these horrible diseases.
5:03
And then obviously in the on the healthcare side, you're in Europe, you know, here in the US it's the same problem, the shortage of manpower and the amount of effort that's required to deliver good quality care.
5:16
Evolution of Siemens Healthineers' Core Business
If we dive into a little bit more detail for people unfamiliar with the Siemens Health in years, how would you describe the core Business Today and how that has evolved over time?
5:28
Speaker 1
See, there's different angles to this.
I would one way to describe the fields in, in more, let's say technical terms is we have a very strong and we have a clear innovation and drug market leader in in the field of medical imaging.
5:44
Yeah.
So think of CT scanners, MRI, molecular imaging.
We have a strong position in lab diagnostics.
So this is more on the diagnosis side when it comes to treatments.
6:00
We are in a very, very strong position in cancer care with radiation therapy, but also interventional oncology.
And we are, we have soon after the IPO which you have mentioned combined our business or acquired Varian who is the clear market leader in in radiation therapy and and oncology in general.
6:22
And we have also a very nice business which is about interventional workplaces, think of Cath labs, Angiolabs, targeted stroke treatments.
Yeah.
So when you another way to look at it is we focus on the best possible characterisation of the patient or early detection for making the diagnosis, planning therapy.
6:52
While on the other side of the house, we have targeted treatments to deliver the precision therapy derived from these informations of the other business of the other businesses.
7:07
And the glue between this is our all our efforts and strengthen digitalisation and in a pretty cool AI programme which we are running here.
7:19
Siemens Healthineers' New Ambition and AI's Impact
Yeah, it's amazing.
And I think that probably segues really nicely into the new ambition initiative that you have.
And from my perspective, it sounds like a an evolution of the Siemens Health and years offering from pure diagnostics more into therapeutics and really a partner in decision support for the clinicians.
7:42
Is that the way?
Is that a good way of looking at it?
Maybe even morphing into precision medicine?
7:46
Speaker 1
Yeah.
I think, you know, I think the journey we are on and I'm making now maybe the time frame even even bigger a little bit before a new ambition and also maybe want to go in the future.
I think, you know, when I grew up in this business, yeah, when we were part of the bigger Siemens, yeah, I think the idea was more like, OK, we do.
8:07
The idea of Siemens back then was, hey, we do everything which requires electrical engineering and electronics.
You know, it's energy, it's automation, it's appliances, it's also certain medical technologies.
Yeah.
So, so, so that the the idea of what we need to be good at is building good machines and standing out here.
8:28
So.
And reusing the tissue of the German engineer, that's not wrong.
Yeah, but it's maybe not enough.
Yeah.
That has evolved more in what is our role in dealing with certain diseases.
8:45
Yeah.
And that is why, you know, when I answered your first question, I started with the NCDS and not with technical excellence.
Yeah, because there's a purpose why we do this.
Yeah.
And that path to look at what's really our role in fighting cancer, what is really our role in contribution, in avoiding heart attacks, was really our role in helping people overcome STEM shortage or was really our role in making our standard of care possible in Indonesia.
9:20
Yeah.
So, so to look at bigger challenges, I think that's the journey we are on.
And that includes what you say to not only think we generate an A great image, for example, but to then think, what do you do with that image?
9:36
Yes.
And here's the AI which helps to translate this image into the best possible treatment plan for that varied tumour of that patient.
Yeah.
And then to innovate not only the machine, but the procedure and with the procedure to innovate how our department runs.
9:55
And then I mean, thinking big, you know, how does a health system deal with millions of patients?
10:03
Speaker 2
Yeah, yeah, I even back when I was at Pergon Elmer, you know the I used to think also in terms of same thing as you said, right.
So, you know, you can either focus on the hardware, but then the hardware generates data.
And as we become more adapted software development and now with AI, you can actually do more value.
10:25
You can create more value with the data to solve bigger problems, right?
So the, the analogy would be, you know, do your customers want a hammer or do they want a nail in, in, in the wall, right?
So do you stop at the hammer or do you actually solve the problem that you're trying to address?
10:40
And so I think it makes total sense now that you're generating data to say, OK, how do we take it a few steps forward and start helping clinicians do their job better, maybe alleviate some of the burden and the load that they have and become more of a value added partner in the delivery of care, especially as you're looking more into the therapeutic side?
11:00
Speaker 1
Yes, absolutely.
And I mean, there's a, there's a hierarchy of, of, of needs here from basic automation tasks.
Yeah, using AI and digitalization for this, which is it sounds mundane, but it's super important, Yeah, in a world of step shortage and physician burnout and so on and so on.
11:20
But also when it comes to access to care, when it's, you know, in in Lmic's where maybe the the the trained physician doesn't even exist.
Yeah.
So, so putting AI for the routine into the system.
But what we step by step do is solving, I mean, solving big challenges.
11:40
I mean, one of the topics I'm really pretty proud of, and normally I'm on a diet when it comes to the word because it's always the beginning of complacency.
Yeah.
What the risk is, It is, yeah, we have four months ago or so signed a 10 year agreement with the province of Alberta in Canada, which is 800 million Canadian dollars worth in terms.
12:14
But but the topic is not so much about the money, but what is the content.
It's about how can we team up to elevate cancer care for 6 million people?
Yeah.
And then that shows, yeah, that basically when it comes to including setting up an AI program in Alberta and so on and so on.
12:32
Yeah.
But that shows that it's possible.
And it is a need, yeah, to, on the one hand here to stand out with great technology on a product or what you call hardware level.
Yeah.
But then to step by step with on the one hand consulting, but then also with digital offerings and so on and so on, helped really solve system wide challenges.
12:57
Cultivating Innovation and AI at Siemens Healthineers
Yeah, and we've obviously been touching on AI.
You know, the course of my career I've seen and I'm a computer scientist as well, I've seen the application of software in the Med tech and medical field go from early data capture and automation, IoT and now obviously with AI the emergence.
13:21
So you know, AI's been around for a while, but this version of LLM based AI has been around for just a few years now.
As we enter this next phase of AI and we're talking already how Siemens Health in the years is using this.
But how do you see the field of medical technology evolving generally?
13:39
Is it something that you've invested more into and brought in more AI experts?
Are they distributed across product lines or do you have a central command unit, if you will, on AI that works across these units?
13:52
Speaker 1
We are blessed with having a very strong central unit.
I mean, central always has a bit of a connotation here, but a real strong competence center in Princeton, NJ I'm extremely grateful, yeah.
14:10
Because the person running it basically has put us on the map when nobody, when basically nobody was here talking about AI.
Yeah.
So when, when, when, when it was called machine learning, pattern recognition and so on and so on.
14:25
And, and this is as a very, very strong centre.
And in the combination, the starting point was basically the combination of the capabilities of AI with our strength in imaging.
Because let's say imaging is one of the 1st and most obvious benefitor of AI.
14:47
Yeah, because Bing data are very standardised.
It's in specialist language.
There's the DICOM data centre that is the, the, the standardised way of how to how to store and and send wax based images around.
15:03
So it is very structured data.
And there's the problem of ACT data set being super big and so on and so on.
And then you can start looking at you have the AI algorithms to detect line modules to find the coronaries in, in, in the thorough arcs and look at and, and present it in a way so that the physician can make decisions very, very quickly.
15:27
Yeah.
So, so that has evolved a little bit naturally, but it's now a little bit the Mecca for everybody who on who is closer to the business to discuss topics.
Yeah.
And this can be from speeding up MRI reconstruction to early attempts to to predict response control to certain tumor treatments and so on and so on.
15:53
Yeah, So it's a competent center, which is close to the medical application.
But when it comes to the deep, deep use case, this this competence sits in the respective businesses.
16:06
Speaker 2
Interesting when when you think about the innovation strategy for Siemens Health and ears having been in a in a large corporate environment, they know that there are often very important demands on the people to upgrade a curtain, a current product, fix bugs, ship out new features and new versions and so on.
16:30
And those are very important things, you know, because they keep the businesses thriving and moving forward.
But then that also some competes with the attention you need to do true de Novo innovation.
How do you think about the ability or maybe you already have a structure in place, but how does Siemens Health and Ears satisfy the ongoing needs of the organization and its current clients while also investing in the future and future technologies and exploration?
17:01
Speaker 1
Very important question.
And, and I think I mean, I, I, I give an answer which maybe sounds fluffy, yeah, but I think it's, it's largely a culture and value topic.
And it's, it is not necessarily something you can solve by creating a future department or by setting aside a certain envelope of money which you spend into somewhere.
17:27
So I think there by by what it culturally means, I mean we are a company which is which values depth, yeah.
And domain knowledge.
Yeah.
So we are and I think this is in our industry super important, yeah, because I mean our customers are and, and best combination partners are people like yeah, the radiologist of MGB or the, the, the oncologist of, of MGB or, or of any academic medical centre.
18:02
Yeah.
So, so they spot empty suits very quickly.
So, so on the one hand, it's always clear, yeah, that it's not only a commercial success, but also the idea of how do we feel move the field forward as an example.
18:18
Yeah.
And this may be the second time I should use the word out, but I'm careful of right.
I mean, one of our breaks, one of our signature innovations currently maybe absolutely alone this, this so-called photon counting CT.
18:33
And that is a, a, a breakthrough technology which basically has redefined CT and keeps on redefining.
We started the program in 2003.
Yeah, in with material science.
18:49
Yeah.
Looking at how can you have a cadmium Telluride material which directly converts X-ray into an electric signal.
Yeah.
And it took 18 years to bring this to a commercial product.
19:05
Yeah.
So.
So it was a burden in the P&L.
Yeah.
Through don't to say yeah, you at the startup, you call it cash burn.
Yeah, it it was a burden, yeah, for many generations of people running that unit.
I was also one of by the way.
19:22
Yeah.
But it was part of the culture, the only thing here.
What's the next big thing?
Yeah.
What, what, what is this?
And, and we held the course, yeah, despite financial crisis or a deficit reduction act or our IPO or COVID pandemic and so on and so on.
19:42
Yeah.
So and there is, I think it is important that in a company like us, you have something which you treat like a campus, Yeah or or you, you look at yourself that on the one hand and it's about a business, but on the other hand there is.
20:00
The real, the real thing we're changing is comes from, from, from innovation, which by the way very, very often happens in collaboration with our leading clinical partners.
Yeah.
20:14
Scaling Theranostics and Boosting Physician Productivity
Speaking of which, I believe you have a therapy command center that you're working with Massachusetts General Hospital, which it from the outside, it looks like a a compelling example of a deep collaboration with one of your with one of your clients.
20:30
Can you tell us a little bit more about that?
20:32
Speaker 1
I mean, we are what we are working on.
I mean, as an example is we want to set up diagnostics.
Yeah, as a business or as a business.
I mean, business may be the wrong word.
Yeah.
But when it comes to new fields of medicine, yeah, in this case, theranostics, I don't know whether everybody knows what that is, but it's basically the idea of having having a, a, a special radioactively labeled molecule which detects typically a certain species of cancer in a pet image.
21:09
But then you can use that very molecule, label it differently, and then it becomes a treatment.
Yeah.
Because that molecule goes exactly to the type of cancer.
So that is in a, in a, in very super, super simple terms, what diagnostics is.
21:27
Yeah.
So it means this requires there's a new way of doing things.
And now it's the question, what, what is our role needs to be to help, not only to say, hey, this is not available, but to think through how do you do this?
21:43
What is the information system for it?
How do, how does response control look like?
How do you know two months later what you have done with which patient, when, when they come back and so on and so on.
Yeah, so and how do you make this?
22:00
I think there's one topic which is extremely important also in this aspect of stack shortage.
How do you turn things from an art into something which is a repetitive process?
Yeah, as Germany's this may sound, yeah.
22:18
But it's important.
Yeah.
That that it scales.
Yeah.
That it's that.
And that it doesn't depend on the individual learning curve of great physicians.
Yeah.
22:30
Speaker 2
I love that, Yeah.
I mean, trying to get a repeatable process that scales is so critical across so many areas of healthcare and the discoveries that you make in any given hospital are going to be so valuable.
So it begs the question, and I don't know if you have an answer, a solution for this yet, but a lot of times when hospitals will, when they purchase or license a platform such as this, they'll want to keep all of the learnings, all the data internally for obvious reasons, right?
22:59
It's like they're, they claim patient privacy, but if there's also IP involved, but if you're building this intelligence system that's a combination of diagnostics and therapeutics decision support, is there potentially a way of extracting some generalizable learnings from hospitals so that the whole industry could benefit?
23:21
Or is that kind of taboo when you can't touch that?
23:25
Speaker 1
One aspect which I find fascinating is by the way a quote of the of of Dorian Komanicho who was running our AI centre here.
He says in every in every medical interaction, whenever a patient talks to a physician, knowledge is created.
23:41
Speaker 2
Exactly.
23:43
Speaker 1
Basically in every, every so.
And the question is what happens with this knowledge?
Yeah.
Does it, does it evaporate or whatever I'm making, you know, traditionally this knowledge with a little bit of luck, Yeah.
Or part of it stays in the brain of the physician at least.
24:00
Yeah.
So that there's a learning curve.
Yeah.
If if he or she knows what's really happening after the interaction that she had with with the patient.
Now how to use I think, but the challenge is that it's just not enough to collect all kind of data.
24:25
Yeah, you need to know is this good data?
Yeah.
And and, and and that is why just saying, hey, we are the Zeeman self in years and we have the biggest installed base on the planet in terms of imaging and linear accelerators.
24:43
And and intervention is not doing the trick here because you basically need to need to very clearly look at what is the special topic you want to solve.
Yeah, because, as you know, over the last 100 years, the number of medical subspecialties has mushroomed.
25:04
Yeah, and rightfully so.
And now the question is detecting this version of lung cancer, how do you do this in the best possible way?
And now let's look at the best possible data of the best possible institutions and make sure that there's no bias because of the ethnical background or whatever of the patient.
25:30
So you need to define the problem very clearly.
And you cannot just throw a huge computer on all the data and try to and hope that then you get smarter in some kind of an unsupervised learning.
25:46
Yeah, I want to make one, one additional comment.
Yeah.
And and this maybe sounds pedestrian, yeah, but that is still important.
I learned in my early years a lesson from a colleague of mine, you know, by the way, he's from Boston and basically said, you know, when you when when you have the choice between something where you're the claim of your product is I make somebody a better physician or I make a physician more productive, more productive always wins here.
26:32
And and and I think there's a there's there's some good reasons for it.
Yeah.
So when in in a lot of the use and I and I think it's, it sounds pedestrian, but I think it's super important when we focus a big effort of our AI activities on taking away the burden of routine work from physicians.
27:05
Yeah, instead of trying to solve problems the physicians cannot solve.
Yeah.
So I have respect for all the topics of clinical decision support and so on and so on and that physicians are so grateful.
27:21
Yeah.
I mean, you know, the discussions and they don't have to document.
Yeah.
I mean, there's now that whole topic.
We are not in this field here of you talk to a patient and automatically AI writes the documentation.
27:37
The topic of coding, Yeah, I mean the topic of prepare as a radiologist, the topic of preparing the images, getting the right user that I can make a diagnosis, measuring something and so on, that steals a lot of time.
27:56
Yeah.
So in a way, when it I think is super important first, but not exclusive, not necessarily sequentially, but in a super important step is making physicians more productive or taking away the routine.
28:14
And that also makes them better physicians.
Yeah.
Because they can they can solve the topics.
First of all, why they become became physicians here.
But the harder problems.
Yeah.
And sometimes we are too carried away by saying, oh, you know, I, I, I do this makeup correlations and so on and so on.
28:35
So we need to find the right balance between what I call pedestrian.
Like what?
What are more visionary solutions?
28:43
Speaker 2
I actually don't think it's pedestrian at all.
I think it's critical, right?
I mean, sometimes we can overlook true value to our customer by thinking too big and too complex.
And I think that your perspective is spot on, right?
28:59
It's, I, I work with a lot of clinicians here at Harvard and Mass General Hospital in the Mass General Brigham system.
And when I talked to my, my friends and colleagues that are clinicians, the part that they don't like about their work is all that mundane stuff that could be automated.
29:16
They don't like the note taking, they don't like the, you know, the, the record keeping and all of the entry level work that they do.
The stuff that motivated them to become clinicians in the 1st place was the human experience is giving good quality care and developing a relationship with their patients.
29:33
And so the more the technology now can start alleviating some of that administrative burden on clinicians, it, it eventually opens them up to be more productive and deliver better quality care.
Because now the 15 minutes or so that they have with a patient can be spent more having a conversation and really getting to know them, not only to diagnose and treat, but potentially to also prevent and, and look how the foresight to anticipate.
30:02
OK, what are your biggest risks and how do we keep you healthy in the 1st place?
Yeah.
30:06
Speaker 1
Absolutely.
What do you agree?
30:10
Speaker 2
But I'm really excited about this command center.
You know, it really represents the beginning of almost like an intelligent care platform is, is that the way that you're thinking about this is kind of like an all-encompassing integration partner with the clinicians, alleviating some of this burdensome work and opening up clinicians to deliver really better care.
30:31
Because now not only are they more focused on the human, but you can start providing decision support.
You're capturing data and providing analytics and decision support on top of that.
30:42
Scaling Healthcare Through Systemness and Collaboration
A word which, which by the way, is also very often used in MGB is the the word systemness.
Yeah, so, and what I like about the word is not only an MGB word.
31:03
Yeah.
It's like then how does a health system get better by being bigger.
Yeah.
And not only bigger now, in other words, how do you scale medicine?
31:21
And I think this is one of the biggest future challenges, but also also exciting opportunities for healthcare.
And that, I mean, currently we see a lot of, I mean, we have seen and continue to see a lot of consolidation in healthcare globally and also especially in the US.
31:40
Yeah.
And there are good economic reasons, you know, from, you know, the aspect of this 'cause there is better negotiation power against the pair or the topic of I keep the patient in my system.
Yeah.
But we all know that basically the second wave is yet to come and the second wave is I ioffer better patient care.
32:03
Yeah.
So if if I I use an example.
So I'm inventing an example.
Yeah.
How are 100 radiology's?
Yeah, not just G's better in one, Yeah.
32:19
Then 100 separate ones.
Yeah.
So you can think of, there's some some on the one hand who can think of, you know, scheduling.
So there's more, you know, there's some financial efficiency, but then there's the topic of who should, how do you know what is you?
32:42
You, you have a benchmark.
Yeah.
How long?
What are the best protocols?
How many patients?
What is it the best throughput here?
And how do you, how do you have learning in this system?
Yes, it's also an efficiency tool.
And then comes the next thing.
When you have 100 radiologies, you have maybe two people who are really good at pediatric lung imaging.
33:07
Yeah.
So how do you make sure that in this system every image finds the right specialist?
Yeah.
So, so, so this idea of how do we, how do we make the delivery of healthcare a system?
33:27
Yeah.
And how do we benefit from this is I think something we are just at the beginning.
Yeah.
And, and we all know, yeah, when somebody in a smaller city or in rural Germany, US or I don't know, it's a cancer diagnosis, you get some.
33:46
And then the question is, oh, I need a second opinion or I want to go to MD Anderson or, I don't know, to the big names because I have the feeling the care in Des Moines, IA is not the same as at Memorial Sloan or at MGB or at MD Anderson.
34:09
But basically that assumes that is in the head of the physician I need to visit.
Yeah.
But the technology to diagnose and treat is basically available everywhere.
Yeah.
So how can we scale the knowledge of what is the best treatment of that very patient without the patient having to travel?
34:31
Yeah, because I mean, the Internet is accessible everywhere, Yeah.
And check GPT.
So I think it's a super, again, a mundane topic, Yeah, but creating systemness is, is one of the big topics.
Yeah.
34:46
And it's something we take very, very serious.
But again, I mean, it it, it's not something you switch on from one day to the other.
But I think it's a super relevant topic, Yeah.
34:59
Bernd Montag's Vision for Global Healthcare
Oh, absolutely.
You know, as you were saying that I was thinking about rural clinics in Indonesia, you know, it doesn't even have to be in the US And you know, as I think about the command center that you're working on with MGH, does it require a big infrastructure investment or do you have a lighter versions that could be used at smaller hospitals and clinics that may not have the facilities or or ability to implement something that heavy?
35:32
Speaker 1
And eventually this all needs to be, and I'm saying that maybe overused or this needs to be cloud based.
Yeah, I mean, this is and, and I mean, of course in healthcare there's always special attention and rightfully so to be to be given to topics like patient privacy, cybersecurity topics and so on and so on.
35:57
Yeah.
But but in the end, the best way of doing this is in a, in a, in a cloud based deployment.
Yeah.
Because then the learning of the system, yeah, we're feeding in the global standard, Yeah.
36:12
Or, or the evolving knowledge becomes a problem of the back office like we are used to When, when, whenever we we, we use our smartphones.
Yeah.
So this is not a heavy topic.
36:27
Yeah, I think that from my from my team geology point of view, I believe what is a bigger challenge to overcome is and I don't mean this negatively is convincing physicians.
36:44
Yeah.
And, and because this also is it's it's about new habits.
Yeah.
And, and, and I don't, again, this is not meant to be critical.
37:02
It's a very natural thing.
Physicians are trained to be physicians.
Yeah.
I mean, this is their profession.
This is their identity.
Yeah.
So you don't take a change in how you do things lightly.
Yeah.
So there, there is, This is why, you know, there is the FDA for everything, which is changing and, and and so on and so on.
37:24
But it's deep in the system.
Yeah, of how people think.
Yeah.
So and, and and how to make sure that that this is really, and the system really needs to be good.
So many people feel like, OK, I trust that system instead of making my own call.
37:45
So it's also a lot of homework for us.
37:48
Speaker 2
Yeah, yeah.
And you know, to that point, a big part of what I've been doing over the last eight years has been at the implementation science side, so way downstream and really thinking about how health systems adopt sustainable innovations at scale.
38:04
And, and I completely agree.
I think changing behavior for clinicians is hard.
As you know, it's a human thing.
But generally speaking, it's also because CMNS is not the only company trying to change their behavior, right?
There's also competitors, other companies.
38:21
Everybody's coming out there with new technologies.
And so doctors have to be very judicious about which products do I use and how do I change my protocols, because otherwise I would be doing it every single day, 50 times a day.
38:33
Speaker 1
Yes, I absolutely agree.
But I think the the additional, you know, the same challenge is that it's not only one physician because typically it only makes sense if everybody changes when you include, you know, when you do a change.
Yeah.
So and that also means, I mean, I don't know, you know, we are now on a Zoom call.
38:53
Yeah, we are working, we are in team with health and is working with Teams.
OK.
Yeah.
So, and there are, I'm very sure that there are fanatics who find Zoom better and others who find Teams better.
And then, yeah, then you have that debate within your physician group.
39:10
Yeah.
When, when, when So, so it's not only convincing the single physician, but it's also then getting to to an agreement.
And and I believe there's something also which I, I learnt to, I hope learning.
39:26
Yeah, I think so.
What is the difference of somebody who grows up in, in, in a company, right.
In an industry.
Yeah.
So I don't like the term industry so much for what we're doing because so much about patient care and science and so on.
39:43
But and let's call IT industry.
And when you when you grow up as A and get educated as a physician, yeah, in, in industry, you learn very fast.
39:59
That's always about improvement of the system.
Yeah.
Productivity.
How do you make your your Rd. faster?
How do you make the factory leaner?
40:14
How do you.
Yeah.
So it's that the spirit of constant improvement of processes is it's not even written somewhere.
It's just what a company does.
Yeah.
In healthcare, this is not how people get trained.
40:33
I think the individual paradigm is mastery.
I want to be a really great neurosurgeon.
I want to be a really good urologist, but one is not trained to me.
40:52
How can we do this?
Neurosurgery department, how can we change as a group in order to be better Yeah, it's not that they don't it's not a type of thinking yeah and it's not even, you know it's not even an established role yeah.
41:09
Like in, you know, in a company, you have the process engineer and so and so people, yeah.
And so on and so on.
Yeah.
So, so we also need to make sure that we don't expect things, yeah, from and now with all the technologies which we also meet people who, who, who, who had the chance to get trained in that type of thinking to some extent.
41:37
Speaker 2
I totally agree.
But yeah, you know, that last mile of implementation is really hard.
And and the more you think of it upfront as you're designing and creating your products, the better chance you have downstream and say, OK, let's not think about implementation at scale as an afterthought.
41:54
Let's think of it upfront.
And so I love the way that you're thinking already about how clinicians think it would make them good at what they do and what it is that incentivizes them.
So with that burned, you've obviously had such a profound effect on Siemens Health in years from the very beginning, from the its inception and for the last 10 years.
42:14
Thinking about your long term vision, what imprint do you want to leave on Siemens Health in years?
And then, if you achieve it, what does it mean for the future of human health globally?
42:26
Speaker 1
What I hope you know is I mean, I think there are certain topics which which I hope which will always stay in the company.
So, and I think this and, and, and you know what I talk about, you know, a culture of domain knowledge of valuing science, long term technology development on in addition, of course, of beings financially successful and the and the spirit of collaboration with academic partners.
43:00
But really having patient benefit in my day.
I hope I don't sound fluffy, but I think that this, you know, this, this, this, this is super important here to maintain and, and and further grow.
And I in that sense, I'm just continuing what I have been taught in this company and more from a content point of view.
43:25
Yeah.
I, I think that that we are in a really unique position and I say you need not only to say look how good, how great we are, but also how big the opportunity and also the responsibility is we have.
43:46
Yeah.
And the topics I discussed in the beginning, Yeah.
Managing the NCDS, Yeah.
Meaning.
Taking a wave, maybe not completely by by step, but step by step.
44:03
The fear of cancer, avoiding that people suddenly have a heart attack because we find it earlier.
Yeah, making sure that stroke can get treated within 60 minutes without, without a, you know, without longer term damage to the patient.
44:27
These are super important case topics.
And to not only do this in New England here, but also in Indonesia.
Yeah.
And I truly believe, yeah, that there's no company better equipped in, in, in, in being the, let's say, the the mastermind here or having the technology behind all this.
44:53
Yeah.
Bigger, potentially bigger companies here.
Who in here.
This is the one device you need for this.
This is the one drug which is now the blockbuster and so on and so on.
Yeah.
I always say we work on making.
We work on the healthcare system or we make money by making the system better by others make money in the system.
45:15
Yeah.
By pushing certain procedures and so on.
It's all.
There's nothing bad with it.
Yeah.
And and my hope is here that we can live up to that promise here so that we really have the the impact I described on patients, but also on customers here.
45:34
Speaker 2
Well, that's marvelous.
I mean, it's a, it's definitely a grand vision and, and I admire you for having such an enlightened view.
Obviously, you need to be profitable as a company, but it looks like the way that you're looking at this, you really are looking at it at the ground level with clinicians and focusing on delivering better quality care in the long run.
45:57
So with that, Baron, it's been a privilege to get to know you and having you here on a natural selection and I look forward to meeting you in person at the upcoming World Medical Innovation Reform.
46:09
Speaker 1
Looking forward to see you and thank you for the conversation.
