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00:00:04.560 Welcome to another episode of Tech Unhinged where we dive into the raw unfiltered world of technology
00:00:10.480 innovation and the incredible people driving that change. I’m Usher and today we’re exploring AI and healthcare
00:00:15.920 disruptor of savior. I’m joined by Sabine Shik who is a global medtech leader and has built a career at the
00:00:21.279 intersection of technology and healthcare. Currently she is the managing partner at Christian Strategy Consulting and as a acts as a trusted
00:00:27.760 adviser to corporations, investors and uh international government. Sabine, welcome to the show. It’s really great
00:00:33.040 to have you on the podcast. Thank you Ashard. It’s been delightful to be welcomed to this podcast as well
00:00:38.320 and I’m looking forward to the conversation. So one of the things that I was uh you know when I was looking up at your
00:00:43.760 LinkedIn uh that kind of popped up was that you’re a honorary member at the United States Air Force. So I’d love to
00:00:50.000 you know kind of know about how did that happen? healthcare technology, Air Force. That’s a, you know, interesting
00:00:55.360 mix. Yes. Um, so in in my consulting practice, I’ve had that opportunity in
00:01:00.559 the past to work on medtec innovation that ended up having a cross industry use case uh with defense and
00:01:07.680 specifically with Air Force. When I moved back to the US um about two and a half years ago from living overseas, I
00:01:14.080 had the invitation to judge a pitch competition actually up with the um Air
00:01:19.600 Force uh commanders and I was judging the medtech side, but of course there
00:01:25.119 was defense tech that was in there and of um a tech as well. And so my introduction to the Air Force on a
00:01:31.680 personal level came through this pitch competition and I ended up becoming more engaged and invited to become an
00:01:36.799 honorary commander. you’ve worked across you know US, Asia, Australia within
00:01:41.920 corporations and kind of as an you know external executive or consultant. What’s the most surprising thing that these
00:01:48.320 three different roles have taught you around leadership and how you know people kind of work with companies and
00:01:53.520 lead companies? Um so this was a really interesting question for me to reflect on. I think that um when I left corporate, I was
00:02:01.040 used to leading teams in a very specific type of way that was taught from a corporate standpoint. And then going
00:02:07.759 into the innovation side of the ecosystem and working with startups, um a lot of these uh people had not had
00:02:14.080 that same type of training. And so for me, one of my biggest learnings were that you have to be really adaptable and
00:02:19.599 agile in your leadership abilities and capabilities and always need to bring people along on the journey. Whether
00:02:25.200 you’re working with people from a corporate setting or working with people from an early stage startup, you have to find for all of them, the commonalities
00:02:31.280 are that you have to find what the main drivers are personally and professionally and bring them along on the journey. Otherwise, they’re not it
00:02:37.280 doesn’t matter what dumb environment you’re in, no one’s going to follow you. Okay. I mean I found that there’s a very
00:02:42.800 different style of uh leadership in US versus leadership in Asia at least from my personal experience right so I mean
00:02:49.440 in US it’s more very direct hands-on to the point even I don’t know if you read it but there’s this this thought radical
00:02:55.519 cander yes so the idea was that hey so but but in Asia I felt it’s a different
00:03:01.760 style of uh you know the way they kind of communicate to people or something
00:03:06.879 did you see you know any difference there or absolutely Um so I came from coming from
00:03:12.480 the US um and initially being groomed in the US uh from a leadership uh capacity
00:03:18.319 and also being a top performer nationally and globally for my previous companies. I came with a certain I guess
00:03:24.560 leadership style that was very US- ccentric and worked for the US um teams and ecosystem. Now, when I moved to
00:03:30.400 Australia, I remember the very first uh comment that someone made to me on the Australian side when I had done a
00:03:36.159 presentation on these are the things that we’re going to change is that he said, “All you Americans think you’re
00:03:41.599 cowboys and think you can just come here and tell us what to do.” Well, that’s not how it works here.
00:03:47.519 So, I I was taken a back. It was the very first comment that my new team member and the national team I was
00:03:53.440 presenting to had said to me. And so I for me there was a learning experience about not just the Australian and New
00:03:59.360 Zealand ecosystem but then the rest of Asia as well. So as I’m working with uh people out of India or um Singapore,
00:04:06.000 Japan for example um everybody had a very different style and for me what was really helpful was learning from a
00:04:12.640 course that was presented by Aaron Myers at INSEAD and or she she was one part of a course actually I should say that and
00:04:19.120 I can’t remember the name of the book. I think it was called the cultural map and in that it talks about which countries
00:04:24.880 tend to be more direct and which ones tend to need to have that relationship built which ones are better with the eye
00:04:30.960 contact and which ones are not and tend to steer away from that or steer away from conflict. So for me, I really ended
00:04:36.320 up having to dive deep into this type of methodology, understand how I could best
00:04:41.520 work with people from all the different countries um in Asia Pacific and then transition back to the US and receive or
00:04:48.320 achieve uh global results. Moving into the topic at hand, right? So healthcare, I’ve been in technology for
00:04:54.400 almost I think now what 18 19 odd years. uh and in the last few years I’ve seen that AI has uh been kind of people have
00:05:02.880 kind of been trying to adopt AI within the healthcare ecosystem. In the last 2 3 years with Gen AI coming in that has
00:05:08.560 kind of you know that the rate of AI adoption has has really jumped right. So it’s gone from I don’t know 100 to 500%
00:05:14.560 or something on those lines. But what do you think and what’s the difference really between AI that truly improves
00:05:21.360 patient outcomes and AI that just you know window dressing or the way I’ve I’ve kind of called it at a couple
00:05:28.080 places just a bolt-on approach right? So what do you think is the true difference between those kind of two implementations?
00:05:33.600 It’s a great question. So just for context I’ve been working with AIM ML technologies within healthcare for
00:05:38.880 probably the last seven roughly years. And so I’ve been quite passionate about this space and more specifically I
00:05:45.680 generally work with regulated a IML technologies and what that means is that
00:05:50.880 it’s anything that’s predictive or diagnostic in nature has to go through the FDA regulatory process for approval
00:05:57.039 and so there is there is another additional step that needs to happen for it’s not just a pure software type of
00:06:03.600 play a IML now when we’re talking about predictive or diagnostic in nature about things that are identifying you know
00:06:10.400 like cancer diagnosis or you know things that are happening the cardoppulmonary issues that are happening ahead of time
00:06:16.800 or maybe it’s something along the lines of segmenting shoe and bony anatomy in the body right so like we’re looking at
00:06:22.960 the things that um AI and ML can do and support the physician or clinical staff
00:06:28.479 um from a diagnostic perspective predictive perspective or even workflow perspective now that being said there
00:06:34.720 are a lot of technologies that are out there a ton I think everyone is always um the end goal is to improve patient
00:06:41.440 lives, improve patient outcomes and also improve the lives of the clinical staff
00:06:46.560 because ultimately what they want is something that’s going to make their lives easier as well and not add more
00:06:51.600 work to you know the plate that they already have and already time for on. So the things that I’m seeing are if it’s
00:06:58.319 really truly a savior there are diagnostic and predictive software technologies out there that are helping
00:07:04.560 and implementing well into the workflow. The ones that don’t implement well into the workflow for the clinician or into
00:07:10.639 the patient journey are the ones that are window dressing. So while they might have an amazing use case, they might
00:07:15.759 have an amazing use case, they might have their FDA clearances, they might have clinical trials to show work, but if it’s not integrating well into the
00:07:23.280 and each system is different, right? But like if it’s not work uh integrating well, it’s never going to take off and it’ll just be window dressing and
00:07:28.960 something blingy. So that’s one thing I have to say. And then the next thing um related to this is that a IML
00:07:34.639 technologies have not been very well adopted so far in the medtech industry. I mean that from a corporate standpoint.
00:07:40.080 There’s a lot of different things that are involved uh that comes from the reimbursement play and you know like again workflow and does it fit into the
00:07:46.960 workflow? Um does it fit into the tech stack for the company? And so I think right now um there’s a big opportunity
00:07:53.199 for these technologies that are truly there to improve patient outcomes if they can get integrated correctly to do
00:08:00.160 that. But you know the healthcare or medtech industry is still quite far behind. Okay. So nurses, doctors, everybody is
00:08:06.479 so used to a certain way working. Um there’s definitely a technology factor
00:08:11.599 playing it. But how much of a factor is the human acceptance of things as well, right? So a doctor accepting the fact
00:08:17.759 okay now this X-ray has come in and I’ll have AI kind of go through it and and
00:08:22.800 tell me a diagnosis versus you know previously a radiologist is there he looks at data and looks at everything.
00:08:28.400 Have you seen this some how welcoming have those professionals been you know in terms of accepting yes this is
00:08:34.159 something that we can work with and not having this thought that hey it’s going to replace my job or anything the
00:08:39.279 constant fear that people have today. Yeah and so and that’s a really good question that’s a really big uh concern
00:08:44.320 that comes up and I I would say that radiology was probably one of the very first areas for you know a IML to be
00:08:50.480 adopted into the healthcare ecosystem and that’s like where it’s making biggest waves or has been making the biggest waves but there are a number of
00:08:56.720 other areas as well. Um so if we’re looking at um in the cardiac space or orthopedic space for as examples I would
00:09:03.920 say that there is a lack of trust factor when it comes to the clinicians uh from an adoption standpoint. So, you know, of
00:09:10.240 course, we’ve got the workflow and now there’s what is a IML? How is it actually going to work? You know, like what are the guardrails? You know, is it
00:09:16.640 safe? You know, is it accurate? They like to still be involved with the decision-m so is it going to take away
00:09:21.839 their decision-m or is it going to aid their decision-m um which of course is going to aid their decision-m, but I can
00:09:27.760 understand why a clinician that’s trained through medical school and years as a resident would think that it’s taking away that decision-m um that
00:09:33.680 they’ve uh grown to love. So I think there’s there is this lack of trust and acceptance that’s happening on the
00:09:39.360 clinician side when it comes to the advention of these technologies but ultimately there and there’s also a lack
00:09:45.200 of trust and acceptance that’s on the patient side as well to to use this and what’s happening with the data right so
00:09:50.800 and it goes two ways like I think you know the ones that are going direct to consumer and patients are actually
00:09:55.839 buying those things directly that that’s one completely different category because there is already some type of
00:10:00.880 trust and acceptance from a patient or consumer on that side but the ones that need to go through this entire clinical
00:10:06.240 workflow which are usually the ones that I’m working with there’s a lack of trust from both the clinician and the patient
00:10:11.680 you know their data what’s happening with their data you know is accurate and I think that part is going to have to
00:10:17.200 change like we have to really build that trust first in the ecosystem before you know these uh stakeholders continue to
00:10:23.360 try to be comfortable with adopting this technology so my niece is a radiologist so that’s why you know I’m very familiar that she
00:10:30.959 works uh she she’s also based off in London UK So yeah, so that’s where this uh radiology example came on from. So
00:10:38.079 now you mentioned a few different places, right? So where do you think based on what you’ve seen so far, the people you’ve worked with is where a IML
00:10:45.120 has really proven itself as a genuine savior in the healthcare industry? I know there’s predictive, there’s uh
00:10:51.040 diagnostic abilities but you know one or two real world examples where yes this has an absolutely you know deal breaker
00:10:58.240 or this it’s been really really you know a very good implementation of a IML. I think um one of the examples that
00:11:05.200 comes to mind is uh an a IML technology that I’ve um seen in the IVF space.
00:11:11.839 Okay. And this particular technology is able to identify uh which embryos are
00:11:17.360 going to have the highest likelihood of success. Wow. Okay. And so that that is something that’s
00:11:22.399 quite interesting because you know of course the IVF journey is quite stressful for a lot of couples and
00:11:28.160 costly as well and timeconuming right like you can couples can end up going through years of IVF therapy before they
00:11:35.600 can effectively conceive. So this one particular technology is able to shorten that duration by really identifying
00:11:41.760 embryos that would have the highest likelihood of success uh based on its predictive technology. That’s actually very interesting and
00:11:47.839 that’s something that directly impact a lot of things in terms of uh increasing success ratios and everything. Okay,
00:11:54.079 that’s very interesting. You mentioned uh that you know you’ve kind of worked predictive AI in orthopedics, neuro
00:12:00.800 cardopulmonary use cases among others. How do you judge where I mean this case
00:12:06.560 that’s that’s perfectly I think 100% suitable where you’re able to identify embryos that can be that have the most
00:12:13.680 successful ratio but other than that particular case what other predictive tools are really helping patients and is
00:12:20.000 just not kind of you know adding another screen for the doctors to look at or to soo through another opportunity that I’ve had the
00:12:25.519 chance to work with is um an a IML technology that uh is able to predict
00:12:31.519 early onset of Alzheimer’s years before that it actually happens and this is something that hasn’t existed and it is
00:12:38.240 absolutely gamechanging right so now um because it’s able to identify who’s going to have Alzheimer symptoms about 5
00:12:45.360 years before those symptoms actually manifest you know entire care team is able to get involved earlier and start
00:12:52.320 managing that patient and trying to delay the onset of symptoms or prevent it altogether um and that’s still pretty
00:12:57.839 early um they do have FDA clearance but um I would say say it’s still pretty early but very promising and that would
00:13:04.480 be a game changer that is absolutely going to be a game changer how do these uh systems or studies kind of get access to the right data right so
00:13:12.320 let’s say if somebody has had Alzheimer’s and we’re looking at five years 5 years old data to predict that
00:13:18.320 hey if you have this XYZ symptoms then there’s a you know X percentage chance for getting Alzheimer’s I mean you you
00:13:23.839 need to have some data right so you need to have some valid data how do how do they get access to that data is it small
00:13:29.519 studies large scale studies yeah and that’s a really Good question. I think this is where um the key
00:13:34.800 difference between medtech, so anything that’s software as a medical device, regulated a IML versus um something
00:13:41.760 that’s just general a IML and wellness. This is the key difference in this and I think this is like where there’s a lot
00:13:47.839 of um misunderstanding from the investor side as well. Medical technology, even if it’s a software technology, still
00:13:53.680 takes quite a bit of time to get to market. It needs a lot of clinical studies uh to prove out what it’s
00:13:59.040 working on. So this is not something that was just invented five years ago. This is something that’s been in the works for about 15 plus years and ga
00:14:06.959 gathering that data to be able to prove out these end points, you know, and follow the patients that they’ve been identifying. And now this particular
00:14:13.440 technology is able to capture over 400 different digital biomarkers with their platform and they are able to understand
00:14:20.480 that certain combinations of these digital biomarkers result in you know
00:14:25.839 someone’s going to have these symptoms in the next five years. Okay. So there’s a difference between health tech and
00:14:32.399 medtech, right? So health tech can be just a technology SAS product, anything around health and wellness like you
00:14:38.160 pointed out and medtech is more like uh how maybe pharmaceutical companies create drugs and might have to go
00:14:43.839 through that similar kind of a process for a you know a medtech product to come into place. Okay. Interesting. So I was
00:14:49.680 I was uh in one of your Forbes article you you warned that you know personalization could actually fragment
00:14:56.560 health care further. So A can you elaborate a little bit more on that and
00:15:01.600 what’s that about and then B how do we avoid that crowd? I want to preface this answer with I’m a big fan of personalization. This is I’m
00:15:08.480 a fan of the direction that healthcare is going with the personalized opportunities and the predictive opportunities to make sure that care for
00:15:15.839 myself or everyone you know in the world is more tailored to each individual
00:15:21.360 person’s needs and systems that they are in. Now when I think about all these technologies that are coming to market
00:15:27.279 and there are very impressive technologies right they’re all in isolation. You’ve got big device companies. So, we’ll talk about like the
00:15:33.440 big uh corporates that own a lot of the ecosystem tech stacks, right? So, they’re the ones that have the
00:15:39.519 infrastructure set in place. They’re the ones that are having like the robots or, you know, ultrasound machines. You know,
00:15:45.440 all the technology stacks are with the medical device manufacturers. And then you’ve got the EHR systems like Epic and
00:15:52.160 Cerner that have it from the digital health record side. Now you’ve got all this innovation coming in that is being
00:15:58.320 developed by innovative startups but none of those actually integrate into these technology stacks. So while I can
00:16:04.560 get you know predictive analysis of you know what my brain waves are doing and identify fatigue a little bit earlier
00:16:11.120 which you know that’s out there or I can you know identify things that are happening to me through some wearable
00:16:16.880 sensor technology that might be accessible to me you know through other means that is still software as a
00:16:22.320 medical device right like it’s connected to some type of software algorithm platform none of these are actually integrating with the tech stack so I
00:16:29.199 what I’m saying is that um it fragmentation I think will grow. We already have a fragmented ecosystem
00:16:35.040 where tech stacks don’t talk to each other. And now with all this personalized innovation that’s coming
00:16:40.320 into play, you might have a different wearable than I do. And neither one of these two wearables might connect into
00:16:46.720 the tech stack that you know of the um hospital system or the groups that we’re going into. So this is where I’m
00:16:52.639 thinking it’s going to be a little bit more fragmented and it will be fragmented. And the way we can fix this,
00:16:57.920 it’s not necessarily through us on the consumer side. It’s actually going to be at the medical device corporate side,
00:17:05.199 you know, and on the EHR system side. They’re the ones that are going to have to be open to allowing these technology
00:17:12.079 stacks to integrate into their systems. And the hospitals will have to be open to letting these technology stacks
00:17:17.599 integrate into their systems that their clinics and hospitals are using for all the information and the data flow to
00:17:22.880 happen. Right. Yes. So that was my one of my other questions. You mentioned this before technology being problem. Why? I
00:17:29.600 I’ve always struggled to understand this. I mean you have Athena, you have Epic, you have Curemd, you have I don’t
00:17:35.440 know how many EHRs, EMRs there are out there and then you have EHRs for cardio patients, you have EHRs for surgical
00:17:41.360 patients and is so much segregated. I have never been able to really understand that why healthcare of all
00:17:47.760 industries is so bad at connecting the dots. If you look at US generally their process systems are amazing, right? So I
00:17:54.160 think it’s by far one of the one of the best countries out there who’ve been able to create single source of truths
00:18:00.320 for data for processes for everything but healthcare is just spread out. What
00:18:06.640 do you think is the reason behind I mean can’t they have one federal EMR system for every citizen of theirs or
00:18:12.559 that would be amazing when I was living in Australia I remember that they had implemented a national system you know
00:18:18.960 like I I moved back to the US shortly thereafter so I actually don’t know like how that implementation has gone and to
00:18:24.400 me I thought that was a great idea that everything goes into this one system and that you as a patient can access that
00:18:30.960 system and pull out whatever information you need whenever you need it. I think the issue is that um first of all all
00:18:36.880 all the different companies and EHR stacks offer a different value proposition or a slightly different
00:18:41.919 value proposition right so which is why they’re vying for customers and trying to sell to this hospital ecosystem or to
00:18:47.520 that clinician you know while they all do something similar the value propositions are like nuanced right of
00:18:52.960 what they’re trying to achieve with their technology and in the US um so I’ll talk about the US specifically um
00:18:58.480 each of the stakeholders in the ecosystem like the payers so health funds or the insurance companies I
00:19:03.919 should say the corporate, the hospital systems, the clinicians all have
00:19:09.200 different priorities and levers that they’re trying to achieve. So I think this um lack of alignment and and it is
00:19:15.520 what it is, right? The payers are looking to achieve something from like how much they’re paying out and how much they’re saving money and like the
00:19:21.360 healthare cost like clinician is you know of course like doing their procedures and you know trying to earn
00:19:26.400 what they’re meant to be earning. the hospitals are trying to save cost like you know everyone’s got different competing interests and so I think this
00:19:32.880 is where that fragmentation also happens like unless we have one unified system that all of these players are you know
00:19:39.520 aligned to we’re going to always have fragmentation right or or maybe even if it’s very hard to put them all together maybe you know
00:19:46.400 they create this law where any update that happens to my profile in Athena or
00:19:51.440 any update that happens to my profile in Epic has to go to some central source of truth right so these all these techn
00:19:57.280 technology companies have to make sure that my data whenever it gets updated in their portal or in their system because
00:20:03.679 you go to one hospital you know you have they’re connected to Epic you go to an emergency for some is this connected to
00:20:09.440 Athena it can maybe push to some federal space where at least all that data is being consolidated or something but yeah
00:20:15.840 that’s uh I think it’s it’s one of the problems that they’ve been trying to solve and they haven’t been able to solve or they don’t want to solve that’s
00:20:22.559 you never know u but okay that’s interesting so you’ve also written about funding gaps for software as a medical
00:20:30.000 device, right? So, what do you think investors still kind of misunderstand about these regulated AI products and
00:20:35.600 why does that gap kind of exist? Yeah. So, I think um when software as a medical device like had been coming to
00:20:41.919 fruition, it’s been so new and it’s still fairly new to the investor world. The challenge that we were seeing is
00:20:48.159 that most investment funds are either deep tech, so they really know the medtec space on the hardware side and
00:20:54.559 really enjoy the deep tech um opportunities. to understand that regulatory process and the clinical trial needs or the funds are on the
00:21:01.280 direct to consumer side and SAS. So they really understand the SAS vertical and something that’s direct to consumer
00:21:06.799 that’s not regulated. Now software as medical device doesn’t neatly fit into either of those categories. You go to a
00:21:12.159 deep tech fund and they say sorry you’re software we don’t understand software. So then you go to the software fund and
00:21:17.280 they say no no no you’re regulated medical technology we don’t understand the regulations around that. And for
00:21:23.120 them it’s scary right? They don’t understand like how long something will take to get to market and where they’re used to seeing something get to market
00:21:28.320 in two years. That’s not going to be the same thing with medtech. And then on the deep tech side, they know exactly what happens with, you know, production, um,
00:21:35.600 you know, uh, product design, all these different things. They understand these steps, but when it comes to software, they have no idea and nor should they at
00:21:42.320 this moment understand, you know, like what all the different iterations mean and, you know, like how to do how to safeguard, you know, the data that’s
00:21:48.320 coming through and protect patient privacy. You know, there’s so many different nuances in there. So I think that that that is why it’s not I think I
00:21:54.400 know that is why the funding gap exists for software as a medical device. Now that being said you know I think we’ve seen a number of SMD companies fail you
00:22:01.360 know over the last few years because they weren’t able to get funding. I think it’s ripe for private equity to come in and you know pick up some of
00:22:08.480 these different opportunities and consolidate them and roll them up into you know one type of company or value
00:22:14.640 proposition that’s more interesting to them. And we are seeing now that there are some funds that are starting to look
00:22:20.240 into software as medical device as part of a thesis and bringing on the right expertise or right external expertise to
00:22:26.320 be able to work with that type of portfolio investment. Right. Okay. Yeah. So it kind of falls somewhere in the middle and you need
00:22:32.480 somebody who can understand the software side and he can understand the T tech side and you know he has perspective on
00:22:38.960 both views. So maybe somebody like that can make it better. All right. You’ve worked across different continents. Can
00:22:44.640 you in your opinion tell who’s leading the way in adopting AI in healthcare? US, Europe, Asia, Australia, New
00:22:50.799 Zealand? Yeah, this is I I uh I actually feel like this is probably like a bit of a loaded question because um I think we
00:22:58.000 could segment this out in a few different ways. I feel that China actually I haven’t worked in China, but
00:23:03.280 everything that’s coming out of China and the way they’re implementing AI opportunities into that ecosystem, I
00:23:10.320 think is further ahead than any of the other countries right now. So, okay, you know, like they’ve got um lately like
00:23:15.840 robot a IML robot uh ran hospital now and I don’t know if you’ve seen that
00:23:20.960 patient actually robot. So, they’ve made Superman crew, right? So, that first scene in Superman, the new one, I don’t know if you’ve seen it,
00:23:27.200 but uh you haven’t seen it. All right. So, when he he gets uh injured, so he
00:23:32.559 gets to his uh that fortress of solitude, right? So, there’s robots that pick him up and they they kind of start
00:23:38.080 treating him and saying that you need sun and everything. So, they’ve kind of made that real then. That’s a direction that um AI is going as well, right? So
00:23:45.520 we’ve got AI of course and like the generalized AI opportunity and now you’re looking at um and of course the
00:23:51.919 locked A IML opportunity for software as a medical device or for the healthcare um because it has to be locked right for
00:23:56.960 the FDA clearances and then you know it does its predictive diagnostic actions. You’ve got agentic AI systems that are
00:24:03.360 coming into play, you know, like within vertical AI which falls into that. And then the next the next phase is physical
00:24:09.200 AI and embodied AI which incorporates the robot and so China is you know leading
00:24:14.880 but there’s an element of empathy and uh you know when you’re treating patients right so there’s this there’s this
00:24:21.120 sensitive element to that as well but robots leading becoming a doctor don’t you think that’s a little too harsh
00:24:26.320 maybe on patients or I mean yeah look I think a hybrid model could actually work you know that I think that
00:24:32.640 um there is a sense of empathy and you know there are certain situations of forest that you don’t want a robot
00:24:37.919 there. You want a human, you know, human interaction. You want the entire human care team. I mean, I think about that
00:24:43.279 like from an oncology perspective, you know, of course, like, you know, or, you know, something as uh as close as like
00:24:49.600 um pregnancy, right? I I think that those are in my opinion like things that um or areas that people would want that
00:24:56.159 empathy. There are other areas where some people just want a decision, you know, tell me what diagnose me, you
00:25:01.440 know, do I have, you know, chest infection, you know, you know, something very quick and they don’t want to wait
00:25:06.640 hours or days to get in to see someone, right? So, basically segregating between
00:25:12.000 where you need more care versus where you just need a treatment. So, right, maybe where you just need some sort of
00:25:18.400 treatment, some sort of initial diagnosis and, you know, go in, go out and just get done with it versus where
00:25:23.760 there’s care. So maybe that part can be entirely automated and that actually can improve your overall healthcare uh speed
00:25:30.480 and process too. All right. Okay. So but that’s that’s news. Yeah. So in addition to that, I think
00:25:35.919 that another area that um it could be triage. You could have robots that are initially doing the triage um in a
00:25:41.919 facility and identifying then know you know that patient needs to see. So now you’re taking away you know um the main
00:25:48.799 bottleneck that happens for ERS and hospital systems is that triaging area. Yeah. And you’ve actually seen um so I
00:25:56.000 think te telly health systems were a first step to towards that stage where you’re able to access remote areas via
00:26:02.080 teley health solutions. You’re able to deal more patients and everything. So that that kind of uh process already started with that all those teley health
00:26:08.480 solutions coming up. All right. So let’s talk about a little about women in tech. You’ve led metronics women network um in
00:26:15.279 APAC region. unique perspectives do you think women are able to bring to the overall AI healthcare conversation that
00:26:21.679 are you know kind of often overlooked or what’s the unique perspective from our side? Yes, I think first of all it’s
00:26:27.360 unfortunate that uh women are oftenimes excluded from these conversations and you know and I don’t think it’s been by
00:26:34.799 you know by design. I mean I I don’t know what the right word is to say on this one like I don’t I don’t think it’s been mal intent to do that. I think it’s
00:26:41.679 just, you know, conversations are happening, someone’s connected to someone, they bring in this person, and it happens to be most fields are
00:26:47.919 male-dominated. So I I, so I think like there’s that element, but I think with women in the healthcare ecosystem in
00:26:53.279 general or in the family system, they’re the ones that tend to be the caretakers. They’re the ones that are taking the
00:26:58.799 kids or looking after the husband, you know, taking them to the doctor’s appointments, you know, interacting with
00:27:04.159 the physicians, the nurses, uh, usually the ones that are organized about the medical records, making sure that, you
00:27:10.159 know, they have everything that they need, you know, for this doctor’s appointment or for this visit or transition. So, I think the myth is that
00:27:16.640 not having that female perspective of how does it make their life easier? what are the needs you know for a system or
00:27:23.679 technology to be integrated correctly from the user case side and on that specific side so it’s not just the
00:27:29.279 clinician or patient workflow but on the female side on you know what are we missing and like what else do we need to
00:27:34.559 have that and I think that’s probably one of the biggest missed opportunities interesting so since they have to take
00:27:39.840 care of all of these things what are the things that we really need to do to make them you know for them to be able to do
00:27:45.840 that better or easier that kind of perspective yeah I think it’s like they’re understanding what some of the gaps are
00:27:52.080 more so than someone that’s not doing that, right? So, they’re the ones that are understanding, wow, it is so hard for me to get my medical information
00:27:58.240 from from this health system to transfer to this system. You whatever it is, they’re the ones that are actually
00:28:04.399 living the challenges and obstacles in their healthcare journey compared to someone that not having to deal with
00:28:10.080 them. They can just show up for an appointment and show up for the next appointment because everything is organized for them. All right. So what is the one change
00:28:15.440 that you’d like to see in innovation labs to you know in places where this happens which where this voice can then
00:28:21.919 shape the overall future of healthcare. You know I think the labs or the companies need to be more uh intentional
00:28:29.360 about finding a non-clinical non-b businessiness advisor that’s actually on
00:28:34.799 this adjacent use case. Right? So it needs to be in an advisory role that they brings a female in or a few females
00:28:41.520 in into that from a brainstorming perspective and guidance perspective on
00:28:46.720 you know how how this could actually work into their lives and like going in the gap product design. So on a personal note what’s one lesson
00:28:53.679 that you know you’ve kind of learned or faced as a as a woman in Vette that has you know that you feel that has really
00:29:00.320 shaped you or that really influenced you on what you are today? Yeah, I think one thing that comes to mind is that I’ve
00:29:06.640 always I’ve always prided myself on the success that I’ve had in my roles, right? And so for me, I never wanted to
00:29:12.320 be identified as a female in medtech. I wanted to be identified as one of the best leaders in medtech. And so for me,
00:29:19.840 I’ve always worked hard to ensure that my numbers or my skills are at the top,
00:29:25.360 you know, of the industry, you know, from a quant quantitative perspective, qualitative perspective, strategic
00:29:30.960 perspective. I’ve always been at the top. And for me that’s that’s shaped that. So like I don’t like I happen to
00:29:36.559 be a female in medtech but I don’t that’s not my identity. I’m a very strong leader in medtech. And I think
00:29:41.840 that’s something that often times gets missed. I think that instead of focusing on being a female in medtech or female
00:29:48.320 in business and you know like needing more opportunities which of course like as women I think like there are missed
00:29:54.000 opportunities um for you know training and education in certain ways and to level up the playing field. I I think
00:29:59.919 everybody um in whatever field they’re in needs to strive to be at the top, not
00:30:05.039 use the gender as something that uh they want to point a finger to. On a closing note, if you had to place
00:30:11.840 one big bet, right, what would be that bet be on, you know, in terms of one single AI breakthrough that you truly
00:30:18.080 believe that will redefine patient care in the next decade? What would that bet be? Something I’m very excited about this. I
00:30:24.559 think it’s going to be the agentic AI systems and vertical AI that’s going to transform the patient engagement patient
00:30:30.080 care process in what capacity in the sense of you know one’s first visit to the end
00:30:35.600 treatment or I think it’s going to change the way that the patient engagement is happening
00:30:41.600 and the answers patients are receiving it it’s going to improve their experience and outcomes because they’re
00:30:48.480 receiving these answers in real time in real time through agentic AI systems right like so
00:30:53.679 instead of having to wait for the doctor or the nurse to respond back to you. Everything’s in a closed loop ecosystem
00:30:59.200 where you’ve got a system set up of a number of agents that are right acting on behalf of the doctor or clinician or
00:31:05.360 you know like within like a medical device company like when it comes to the patient engagement side of things and responding in real time you know within
00:31:12.000 the privatized system uh for that patient collecting the digital biomarkers you know digital markers you
00:31:18.000 know like of you know what’s relevant for that patient their symptoms as well you know you know aspting everything
00:31:24.880 into there. So now you’ve got this entire entirely new um vertical that’s being created underneath AI and the way
00:31:31.840 this can transform the patient experience and keep patients involved in their therapy longer term transform
00:31:38.640 outcomes for sure and have better involvement from the company and the clinician right awesome yes absolutely so I if I
00:31:46.000 were to place a bet I don’t know if it’s happening right now or not but you know what I would want to wish is if
00:31:51.279 disruption can really happen in preventive healthcare so rather than having reactive healthcare there are a
00:31:56.399 lot of things that you if you can know your markers or if you can identify like the Alzheimer example right so for
00:32:02.480 instance diabetes for that matter is a completely lifestyle prevention disease right so if you if you change your
00:32:07.519 lifestyle you can 100% prevent it stop it or even reverse it or things like that my bet really would be if if it’s a
00:32:14.399 if preventive health care can be a place where if you’re taking medicines when
00:32:19.519 you’re 40 45 rather you you know you’re able to push that to when you’re 70 or 60 or 80 or whatever. So that’s that’s
00:32:26.159 the way u I would love AI and these systems with the data all of the data
00:32:31.679 that they have if uh you know something of that can be built and put together. I completely agree with you. I think
00:32:37.200 that that’s really the most idyllic way for us to reduce our chronic diseases
00:32:43.360 right or you know issues that are coming up but especially the chronic diseases. And I think like the prevented side
00:32:48.399 preventative side is it’s absolutely exciting. I think um I hope that you know this is something that um us as
00:32:55.120 consumers are able to and willing to put money into as well as employers like
00:33:00.480 because right now un unfortunately like the way the reimbursement systems are set up in the US um so I’ll talk about
00:33:06.240 specifically the US is that it’s not based on preventative care it’s based on point of care once someone has a disease
00:33:12.159 diagnosed right so the whole reimbursement system is based off that unfortunately and until that changes I I
00:33:18.320 think the preventative care is going to is going to be addition additional cost and this is like where it’s going to cause some of that fragmentation that we
00:33:24.320 were talking about as well. You know, some people some groups will have that and implement that for their own lifestyles and some other groups won’t
00:33:30.159 be able to afford to do that. I’m with you like that. That is absolutely the vision and the ideal state is that you
00:33:36.640 know we’ve got this robust implementation of preventative care and we’re able to identify Alzheimer’s ahead of time. We’re able to identify diabetes
00:33:43.919 you know like earlier on you know um reduce you know make lifestyle changes to have healthier populations. For sure.
00:33:50.080 Correct. Correct. Correct. Absolutely. No, the entire system has to change. It has to go from react I’m a big farmer
00:33:55.919 for that matter, right? So they that’s what they sell on, right? So it has to kind of completely move into the other
00:34:01.440 direction. All right. U I think that’s it from our side. Sabine u thank you for
00:34:07.200 joining us today. It was uh it was been a real privilege you know kind of hearing your perspective. You’ve managed to cut through the hype and uh we’ve
00:34:14.079 been able to talk about what AI in healthcare really means. I always thought that robots treating patient was a hype, but apparently it’s not. I’ll
00:34:21.599 definitely read up on that. Uh, and I appreciate the honesty and depth you
00:34:26.879 kind of brought to the conversation. Thank you so much, Asher, for having me on here. Like, this was this is also a
00:34:32.239 pleasure on my end. I love speaking about this stuff and, you know, I I I can speak for hours about this. So, um,
00:34:37.760 awesome. Pleasure is all mine. Thank you. Thank you so much.