Telehealth; Our Changing Centers of Care | Panel w/ Nadine Hachach-Haram MD & David Uffer

Joe Mullings  0:09  
Nadine, David, thanks for joining me here at beautiful LSI 2022.

Nadine Hachach-Haram  0:14  
It's great to be here.

David Uffer  0:15  
Pleasure, Joe.

Joe Mullings  0:16  
And both of you, I'm really excited about chatting about this because this is the 20,000 view 20,000 foot view of what health tech healthcare is looking at right now. And of course, a layer of background and proximity background. Both of you are insiders and outsiders from a classic medical device perspective. Right. So I was curious on both of your thoughts on these subjects we're going to talk about today. So, Elsevier came out with a study today, it's actually pretty powerful report, it's clinician to the future. And they were looking at what's been going on in healthcare today. And what are the what are the tail wagging the dog. So I want to throw out a couple of things here, and we're going to talk about it. So the global study found 71% of doctors and 68% of nurses believe their jobs have changed considerably in the past 10 years, with many of them saying their jobs have gotten worse. So you're a surgeon, and a technologist. And you are servicing the health tech community to scale that? I don't think anybody has, as of yet on the on the proximity side. So what are your thoughts on that both as a surgeon and a business person?

Nadine Hachach-Haram  1:26  
I think we can't deny the fact you know, as a surgeon, of course, working on the front line is that we you know, that supply demand gap continues to grow. And the current models of care, are starting to prove that they're limited in terms of how future proof they are. We've really tried to squeeze as much of this system as we can. But we're starting to really hit those friction points where it's becoming really clear that the standard models have limitations. And ultimately, in particularly since COVID, that supply demand gap has grown and, you know, pretty significantly, it's actually a major inflection point, which is, you know, worrying and concerning from from a kind of hospital perspective or a clinical team perspective, and that, how are we ever going to kind of bridge that gap? How are we going to close that gap? And doing more of the same isn't gonna solve the problem? And so there is there are challenges around being overwhelmed, stretched to the extreme squeezed to the extreme, and how do we try and address that we need to think more laterally. More, disruptively to think about how do we change that. And I think one of the things we saw, particularly, of course, made clear during COVID, it wasn't like the problem wasn't there, it's just it really put a magnifying glass on the promise that your typical kind of bricks and mortar approach to healthcare can only take you so far. That compounded by, you know, people leaving health care, burnout, you know, emotionally drained through all the process. It's something we need to think about and, you know, obviously got some thoughts around that. But think at a at that sort of 30,000 foot view. Those I think are the immediate challenges we need to start addressing, if we're going to be able to continue to deliver the care, we want to deliver the next even five years, let alone 10-20-30 years.

Joe Mullings  3:13  
David, you deal with a lot with technology, especially your areas of expertise are that digital side, the data side, the robotic side. What do you see as that challenge going on right now with the healthcare community?

David Uffer  3:30  
Look, I've made the transition 25 years on the manufacturer in equipment and device side to consulting and it's the same challenge of I'm not market research, we take market research and use that to make data analytics and intelligence and recommendations. And we're the same thing in clinical practice. Fortunately, our clinicians are being afforded opportunities to use so many new technologies, which is great. The downside of that is there are so many new technologies. And that's why we see some of the burnout going that you can have a first assist to or a scrub nurse, with so many different pieces of equipment to learn to train on to understand how to operate, to prepare to hand off to the next clinician in practice. And that's a lot when I'm talking when you're talking about data. There's raw data in our industry right now we're giving the clinicians in all of their step, so much raw data. And it's nice to have the data but then you have to stop, pause, interpret and understand how you're going to use that. We haven't made meaningful use of this data taking it from what I was saying I use market research to make recommendations. We haven't taken the raw data manipulated or put it into an algorithm that uses artificial intelligence uses machine learning, takes more interpretation understands that and doesn't dictate by any means to a clinician what to do, but makes recommendations or it gives them meaningful information that they can process more rapidly in more meaningfully with a better clinical outcome with a better procedure with a better utilization of whatever technology they've been adapting

Nadine Hachach-Haram  5:09  
I'd love to add 1.2 It's your first comment then once your second. I mean, on the first comment, I think you're absolutely right, like feeling supported, coached mentored through these, there's just plethora of new devices. I mean, there's some graphs out there to just show you the, you know, the the new technologies, the new techniques that clinical teams have to acquire is exponentially growing, you know, every other month, we hear about a new device that comes to market or a new system or a new technique. And we need to make sure that the teams behind that they need to deliver that are supported, trained, coached and mentored to deliver that. And, you know, from my experience, having worked in supportive training for medical device companies, for many years, it was really part of the impetus that made me start Proximie to think about, how can we provide more of a crowdsourcing opportunity to share knowledge is that knowledge together, and support the training and upskilling rapidly of these teams, you know, if you're a scrub tech or a first assist, with a big, you know, new orthopedic tray have a whole new set of kit, I mean, it can be quite daunting, let alone the doctor that has to use it, let alone the juniors that are training alongside them. So really thinking through how you bring in and almost better understand the ethnography of the operating room and pull that all in, I think, is really important. You know, I think we've tried to bridge that with Proximie. And we've shown that we can, you know, accelerate that time to training by you know, 60%. And that is one way that we can try to sort of bridge that gap on the other side, in terms of the being overwhelmed by data, I think, you know, clinical teams don't want another dashboard. Like that's not what we're looking for, you know, more and more dashboards, more and more charts that tell us just a lot of data and information. It's how do we then interpret that into what is what is it really telling us? You know, I can have a lot of graphs and charts that tell me things, but what is it actually telling me about the care, we're delivering the optimization, the effectiveness of how we're treating our patients, and the clinical diagnostics that we need to sort of deliver behind that. So, you know, when we look at sort of raw data, there's a lot of raw data out there. But how do we actually synthesize that to something really meaningful, that is going to shift the trajectory of care, and the pace of care, I think, is really how we should be starting to look at data and, you know, the, the, the kind of the avenue of data and analytics and AI and in healthcare is still nascent. You know, we're still learning and there's a lot to grow in there. But I would really urge people to really think about, what what are we trying? What question are we trying to answer? And how does that shift the the trajectory of care on the back of it,

Joe Mullings  7:37  
You know, Pablo Garcia, from Verb Surgical, Pablo is one of the early Yodas of digital. And Pablo said, last year at SDTS, in Houston, at Eric's meeting said, Everybody's talking about data. But nobody can agree upon the language of data right now. Like if I say, cat, everybody can envision a cat in their head. And if I say, yellow bus, you got it. But we're irresponsibly throwing around the word data in the med tech health tech industry. And we're also applying data right now. And I wonder what both your opinions are on this? It seems like most of our data fixation is on the acute procedure itself. And are we spending enough time on both ends of that pre and post operatively? On what outcomes mean? So what are your thoughts on that? And especially in the robotic world where you carry some really good experience? Yeah,

David Uffer  8:30  
we talked about this a few months ago, Joe, and it's, it's the entire pairing up and a process that you have to take in to account, you can do a perfect surgery, you can have a surgeon and all their staff do something absolutely perfect textbook in the O R. But maybe the patient wasn't prepped properly, and they came in suboptimal. They were undernourished, they were not prepped, right, they are going to end up with a post operative infection because of prep, or after they're completed in the OR, the post op or maybe even at discharge, or even the care following discharge is sub optimal. It all goes back to well, was it the surgical procedure or the surgeon doesn't care, it's their patients, they're gonna not gonna have a great outcome. So it has to be the entire process perioperative, pre, during and post and through discharge, when a clinician said I do such great care at this institution with all the staff, and we wheel them so caringly in a wheelchair to the door, and then wave goodbye and hopefully do well.

Joe Mullings  9:32  
Where's the data follow up there? Yeah, you know. So So here's an interesting stat while we're on data. Clinicians predict that over the next 10 years, technology literacy will become their most valuable capability, ranking it higher than clinical knowledge, which is really interesting. In fact, 56% of clinicians predict they will base most of their clinical decisions using tools that utilize AI. However, 69% report being overwhelmed with the current volume of data in the digital health technologies, and it's even becoming more of a burden than helping. As a result, 83% believe training needs to be overhauled, so they can keep pace with technological advancements. So are we trying to improve a system that should be thrown out anyway, in the first place? Right, we're putting data on a model that hasn't changed in over 120 years surgery. And we're overlaying data analytics on that, should we look at it healthcare being more than just a nurse and a doctor? And can we change and start to hire higher fidelity talent pool within the healthcare system? Now, you deal with that every day?

Nadine Hachach-Haram  10:45  
I mean, I think there are two things stood out to me from the point I mean, one on the technical literacy, which we'll talk about, but I think we need to be careful not to over index, like you said, in the current model, the role that AI is going to play in the current model, I mean, we, you know, there's definitely a promise of what AI is going to deliver in surgery. But we have yet to see what that isn't going to actually do. And, yes, it can be useful in very distinct cases about, you know, identifying specific paths or anatomical structures or others. But, you know, just layering in AI into surgical workflow, and assuming it's all going to kind of work is challenging. And, you know, we haven't seen that really take off, we've been hearing about AI in surgery for quite a while now. And it hasn't really taken off because the underlying infrastructure doesn't exist. And this was something you know, I thought about with Proximie me years ago, I remember, just 2014, I was sitting in my OR thinking this is a hugely analog  undigitized environment. And I really echo your point on the pre in the in the postdoc, because you the connected surgical care has to happen. It can't just be in the OR, its everything. And so focusing on, you know, building AI algorithms on some videos of an operating room independently, are interesting and academic, you know, at this point at best, but they're not going to shift the way we deliver care, unless you really think holistically about, you know, how are we going to generate enough, you know, data sets the fidelity, the heterogeneity of the data set? So you eliminate bias, how do we make sure that these algorithms are sustainable? There's still a lot of thinking that has to happen, how do we make sure the labeling is standardized? What Pablo talked about when we're at that same meeting together? And so what I decided to really focus on back then is, let's not try to what I would say, put the cart before there was this, let's try to get to the AI. But let's first build the foundation like have we actually built the foundation, the data pipelines that connect the operating rooms, the pre op, the post, stop the data layer that brings it all together? And we haven't. And so it's still sitting in quite a silo, the AI element. And that's why I think in that approach, can very hard for it to just disrupt surgery, and we haven't seen it happen yet. And most clinicians are still quite skeptical if you talk to surgeons about the role that AI in and of itself will play. The bit that I think is also more important is the technical literacy. You know, when we look at medical school programs, you know, look way upstream, before you get into the the surgeon completing his training, but even at medical school, even in that early intern year, you know, is our the program is being redesigned to include technical literacy to include med tech, digital softwares and others, they haven't yet at large, there are areas and pockets that are doing a good job of pulling it in. But I think that's the bit we need to overhaul it's not just medical school, it's nursing programs, it's physician's assistant programs. Now we're talking about AI and the nurse on the ward is still filling out a clipboard or at best an iPad. So you know, there's a big disconnect still.

Joe Mullings  13:40  
Do you think if we started all over again, would the healthcare system be designed as it is today, if we had a blank sheet of paper? With what we know today?

David Uffer  13:51  
No, I think we're so inefficient. It could not start from where it is today. I think that's where we're struggling to evolve it. Because we're at such a nearly broken system. And look, I'm always hopeful that there's ways to improve things. So when I say nearly broken, we've got a lot of issues with the system, the disparity between different standards of care and access to care. But not only just that the amount of information that we're able to use when somebody enters the system, they could have been at four different touch points in the healthcare system. And you're starting to ask the same questions that has already been populated into another system. So no, I don't think we would have started from where we are today, whatsoever. I remember 15 years ago to somebody show me a little barcode on the back of a driver's license. And here's what I'm trying to build for a user health information system. There's a lot of HIPAA requirements. It's a whole day seminar to have that discussion, but we don't have access to patient information. So how do they make meaningful decisions even when they're getting more data in inputs that Nadine was just talking about. I mean, I'm very hopeful that this is going to transform the industry. You know, people use information every day and evolves every decade, Joe, when you look at SPO 2, you didn't have that parameter 40 years ago, you're looking at different pieces of information that everybody has to learn how to use, it's not going to replace a clinician, you're saying clinical decisions will be less important than learning how to use data, you're still going to, that's why they're clinicians here, they're still going to have to make medical decisions based on their clinical knowledge, their clinical knowledge, will direct that care based on the inputs they get, and when they use inputs all day to day everyday with their decision making, based with their clinical knowledge, and that I don't think will ever change.

Joe Mullings  15:55  
Let me challenge that for a second. So the clinical knowledge by the 10,000 hour surgeon takes that surgeon 25 years, 30 years to accumulate, and he or she gets an algorithm. Well, why can't I use narrow AI? Let's be smart here, AI in general, is not really usable. But if I can take narrow AI, and give it to a three year out, surgeon, she's she's just out. She now she's in surgery for three years. And I can take that 50 years of surgery, and then give it to her clinically for her to get advisement on, she still pick strategy, right? Because strategy in the surgery and surgery, right? You're a surgeon. So you can have all the clinical data you want, and a highly predictable environment. And you could make the chess move from here to here. But what happens when things go sideways,

David Uffer  16:50  
that's the point. That's the exact point I was going to talk to you Joe, you can say that I don't need clinical decision, clinicians making decisions in the middle because we could automate, we could standardize everything. But these are human bodies that are unpredictable, and everybody is different, we are not homogeneous, by any means. Now you can say, for 60%, for 80%, this approach will work great, what happens when it doesn't. And that's where the clinicians become very much involved. So you can have a specialist or you can have a generalist, a specialist is there for your more difficult or more nuanced cases. But even with a generalist, when something goes south, you're gonna want a clinician making decisions, not an algorithm, well, they could get more information and inputs that are going to be helpful to that decision making. But they have to make a clinical decision.

Joe Mullings  17:43  
But if the if the clinician that you want bedside with you, you know, in the middle of a procedure, and she has 20,000 hours and thousands of procedural experience, she basically is an algorithm, an inefficient one, but she's an algorithm. And so if I could take all that learning from all these cases, and you and I wax on robotics all the time, all those learning on all those cases, and I can put them as an advisement on the screen with that new three year surgeon there who might not have the 20,000 hour expert next to them. I'm actually given them more than that. And I'm giving them a machine who makes less clinical mistakes, and somebody who is working purely on their own algorithm, doesn't that add value to the equation? And isn't that actually better than the surgeon bedside? Who's got all those procedures? I'm just, I don't know,

David Uffer  18:37  
it adds value without question. Example, you're in a procedure and the blood pressure is going down and you turn to the anesthesiologist say, I think I've lost some blood, call it how much blood do we need to give a unit, you're guessing it's pixelated today, with some technologies that you can say 700 ML, or you're up to a liter point three. So you can actually do that today. But technology didn't tell you that before. And you could say we need to add a unit of blood now. But that's still a decision. If I think I bring too much volume to the patient. I'm going to have respiratory issues, and these are clinical decisions. We can give them the information, but they have to make the call.

Nadine Hachach-Haram  19:18  
Yeah, I think what we see is, you know, and and perhaps there's two points that you're touching. I mean, one is, if we go back to the original point about that, you know, supply demand of healthcare workers to deliver the care, we need to find ways to accelerate that learning to make sure that we have competent, safe, effective, you know, healthcare workers in the field delivering care to patients. You know, in the USA, it takes I don't know, 5-10 years to train a surgeon in the UK, it takes about 10 years. That's a long time. It's a huge investment. And it takes a long time to get that ROI for the patient. And so we need to be looking at where safely and with quality, we can accelerate that learning curve. And there's already enough evidence that's showing that inputs, you know, bringing human and technology together can accelerate that. There's already evidence that shows, you know, video performance metrics can accelerate learning curves and drive down, you know, variation or harm to patients. And there's evidence that shows that by bringing together you know, I always say this approximate want to bring the best of human expertise, with the most advanced technologies to save lives. And I think that's that medium that we should be aiming for that almost very seamless inner interplay between human and technology, that's going to really help us shift to that new pattern of healthcare. You said, if we had a blank canvas, what would we design it to be? Because ultimately, there are things within a clinicians or a healthcare workers day that can be automated, that can be driven through algorithms, supportive decision making, like you said, How much blood getting more real time inputs, and equally, really starting to shift, this opacity of knowledge and data, you know, I can speak for the OCR, there's a huge amount of opacity of information, you know, how do we make that more transparent, you talked a bit about the information, we still don't have that you know, all the patient information, it's, you go through different systems, it's, it's all linked, we need to start to connect those dots. But we should be shifting and aspiring to that world where we do bring that seemless interplay between human and device or human and technology or human and robots, where it can augment and play its its role. But makes, you know, I would say not replace the doctor, but definitely augment accelerate what we can do.

Joe Mullings  21:32  
As I take us out here, I've asked each of my guests today, and I will tomorrow, the following question. And David, I'll start with you on it. What are you most worried about over the next 24 months in the health care system?

David Uffer  21:48  
In the US our rising costs and the ability to keep up with all the costs. It's continuing to skyrocket. And the more we bring new technologies there, the more we bring patients that can be solved for their health issues, the more costly we get.

Joe Mullings  22:10  
Yeah, aren't we? Aren't we just graduating patients to more complex diseases, because in the past, they just died earlier. Really, that's what we're doing is very much so we're, we're it's expensive, balloon angioplasty, help stop the Widowmaker and then the stent, stop that, and then the aortic valve and mitral valve repair replacement. Stop that and we're kicking everybody downrange to heart failure now. So our our efficiencies are actually causing a bigger burden and a bigger cost on the back end of this. So it's a really complex, interesting quandary we're in we're graduating more people to living longer to put a bigger burden on the healthcare system. So what is your biggest concern?

Nadine Hachach-Haram  22:50  
I'd say? Well, two things. One is, you know, I'm definitely and you know, seeing this firsthand, you know, the the burnout and the ability for us to scale care. So, you know, we've seen a mass exodus of, you know, healthcare workers, nurses, doctors that have been really burnt out, they're sort of mental well being during this time has been difficult. So I want to make sure that we can actually continue that that inflection point that I described, that we bridge that and we need to embrace solutions, you know, like proximate others to try and do that. The second I would say, is inertia, I mean, one of the things that we did see was, I guess, one could call a silver lining out of COVID, is that we were able to accelerate and push through the adoption of some innovation and technology that historically there was a lot of friction. And I, you know, we were getting optimistic about the opportunity to really start to disrupt a pretty complex system health care. And, you know, I am concerned that that sort of starts to wax and wane and that we see inertia sort of picking and again, the system's turning back to their complex ways, and they're sort of traditional ways. So I hope that that appetite to grow and disrupt continues, and that healthcare leaders and their hospitals and health systems and ideas continue to push that narrative or that strategy as a key strategy for how they redesign or reimagine healthcare for the future.

Joe Mullings  24:06  
You bring up COVID. And I just want to again, one more question before we go out. How important is that shifting centers of healthcare to the home going to be and is the consumer patient ready for that?

Nadine Hachach-Haram  24:21  
Huge, we have to decentralize healthcare. You know, we saw it firsthand the traditional big central bricks and mortar approach everyone comes into that it's just not the model for the future. It's about pushing care closer to patients homes, closer to their support infrastructures, remote monitoring, digital technologies, empowering patients with tools in their hands, whether it's on their iPhones or the digital solutions. So this is something you know, we're seeing quite a lot of and, you know, the systems are completely burdened when, when they when when COVID hit and I think it's really made us think, you know, how do we accelerate it support like dialysis closer to home or other other needs diagnostics, point of care testing all of those models, I think are important.

Joe Mullings  25:09  
You do a lot of strategy for companies, what are you seeing right now on the shift to home health? And is it here to stay? And is the patient consumer really ready for that complex relationship?

David Uffer  25:20  
One of the one of the silver linings to this horrible COVID Cloud has been the understanding of how we can access and treat patients outside of the acute care facilities. By necessity. We don't, we didn't want them accessing the facilities, they were gonna get exposed, they were burdening the system, we wouldn't be able to take them through procedures, because we looked at them as optional, and they weren't that they needed this care. So we've learned how to deal with patients outside of the acute care facilities. And I think that was a really great lesson for all of us. All of this digital technology that was getting a little bit of a look see for a while is now getting implemented. And I'm not going to say every buddy is going to succeed. But at least the clinicians are starting to adopt their trialing. And they're saying brought supreme value, not for me. So that's what the COVID has brought to us. And yes, we're right down the road here, Scripps, they have a chief medical officer just for hospital at home, bringing patients out of the acute care facilities and being able to treat them ASCs booming around the country. Let's do these not highly complex in patient surgical procedures outside of the acute care facility, let's set up just surgery in home. It's going to expand exponentially. And then next five years without question, in my estimation, and everybody that we're seeing in, I would say all the large commercial entities are asking us project this and how do we set up to take advantage of it? You asked our patients ready to take care into their own hands? No, they need a lot of involvement. We can give them the tools, but they're still going to need involvement. It's just a change in the way that they access their clinician and their relationship with the clinician, and the people who are directing their care. We have a lot of other companies that are setting up care models that are alternative to just the physician patient interaction that can facilitate that understanding of the patient how to maintain their best care, so very hopeful of that shift.

Nadine Hachach-Haram  27:33  
I think even when that decentralization you know, we're going to see how that might even impact all the surgical backlog as well. I mean, it's one thing about monitoring and supporting patients with care at home, it's also thinking about that, just immense surgical backlog that happened. I mean, it's we're years behind where we need to be with that. And so we're going to have to think more laterally about scale delivering that care and in bridging that gap on the backlog, you've got patients who've been waiting years for a hip replacement, knee replacement, you know, things are gonna affect their activities of daily living, and we need to try and address that.

Joe Mullings  28:05  
Well, I'd like to thank you both for this. I love the 20,000 view foot and a strategy of the health tech med tech industry. And both of you bring a lot of value to our viewers. I appreciate it. Thank you. Thanks Joe. We got it. This is Joe Mullings. LSI 2022 Dana Point be well