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Becker’s Healthcare Podcast
Advancing Cardiac Care Pathways with Innovation and Collaboration
00:21:14
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- [00:00]
A
Everyone, this is Molly Gamble with Becker's Healthcare. Thank you so much for tuning into the Becker's Healthcare podcast series. Today we're going to talk about how to elevate cardiac care pathways in a shifting healthcare landscape. Joining me for today's discussion is Nick Wilson, Vice president of product and marketing at Philips Ambulatory Monitoring and Diagnostics, and Darren Botera, manager of nursing innovation and informatics at a leading academic medical center. Nick. Darren, thank you so much for joining me today.
- [00:28]
B
Thanks for having us, Molly.
- [00:29]
C
Yes, appreciate it. Looking forward.
- [00:31]
A
Well, to kick us off, I just shared your names and your titles, but I know there's much more to the story here. Can you start by sharing a bit more about yourself and your work in healthcare? Nick, I'll turn to you first.
- [00:43]
B
Absolutely. So I've got the privilege of leading a team who's really focused on supporting patients who are outside the clinical setting. So we work with large health systems practices providers to identify and help diagnose particularly cardiac patients who are suffering from arrhythmias or other cardiac conditions and get them back into the health system so they can get the treatment and support they need.
- [01:10]
A
Such a great overview, Nick. Thank you. And Darren, let's turn to you.
- [01:13]
C
Yes, I'm Darren Batara. I work as an informaticist at a large academic medical center. I've got a long background in critical care, worked in a medical, surgical, trauma, neuro, ICU and then had the privilege of doing a lot of leadership roles as a manager and then had really my first taste of technology deployments while I was in that role, which made it really applicable when we were opening up a new hospital wing in 2019. So right after that we went into a large pandemic and were thrusted into a lot of telehealth technologies and really standing up a lot of technologies that would better serve the needs of our patients. And that's kind of the work that we're continuing on today.
- [01:54]
A
Darren, I'm probably going to pick up with that through line you just drew for us for the past six years, your time at your AMC and your leadership roles. Health systems today continue to still face a lot of uncertainty. You mentioned the new hospital opening a pandemic. Now you've got rising costs, workforce shortages are still with us. Obviously some different funding changes that are coming down the pike. From your perspective, Darren, what do you still see as the most critical factor to really ensuring that your teams are delivering consistent, high quality care in this environment where there's just so many unknowns.
- [02:30]
B
So many variables yeah, that's a great question.
- [02:33]
C
I think this has been proven over time. I've been in my institution for well over 20 years and I can say with the confidence that one thing that hasn't changed in my experience here is that we've always maintained a patient centered approach. And I think at the genesis of all that, whatever we pursue in terms of system upgrades or culture, those two factors really sit at the heart of what we're doing. So putting patients at the center of all that, all these things that are in the air right now on technology deployments and these IT modernization products, all of that is great. And I think it's necessary in the day and age, but it can't be without the people who are delivering the career and incorporating them into the process of how we're making these technologies actually work. Because at the end of the day, it's still care for a patient and it's still human beings and nurses and doctors who are delivering this care. And it needs to work along with the workflows that they're doing. So anything that we do really needs to maintain and be in the spirit of that.
- [03:29]
A
I like the clarity of that, Nick, that non negotiable that Darin just outlined for us. Do you see things any differently from your work that you said is outside of the health system system?
- [03:40]
B
Oh, not at all. I, I was in New York visiting a large, a large number of Darren's peers a couple of weeks ago and almost to a man, they said exactly what Darren just said. And, and one of them even went so far as to say, look, unless what you're bringing to me is clinically 10 times better, I can't disrupt my workflow. Right? I mean, there's too much, there's too much kind of downstream effects to changing things. So if you're not outsized, clinical differentiated workflow is going to eat everything else for lunch. And I think that's very much along the lines of what Darren was just expressing when they're making decisions at his system.
- [04:22]
A
I'll stay with you here, Nick. That 10 times figure, 10 times plus it sounds like, is really what health systems need to even consider changing the workflows like you said, or adopting different tools. Right now, given what we're seeing with the care teams, the workforce, in addition to the budget constraints, efficiency is just becoming even more essential. Efficiency is table stakes. And now it's become even more important. When you say technology needs to be 10 times better, can you go into a bit more detail about how the tech really needs to Support faster, better decision making, really help clinicians stay focused on patients, not disrupt their workflows too much, especially in the world of cardiac care.
- [05:04]
B
Yeah, I mean, I think there's a couple of examples, and you can take this a couple of different routes, but one is where do clinicians live every day, all day? They live in the emr. Right. So, first and foremost, basic principle, anything that is not in the EMR has to be fully interoperable with the emr. That's why, you know, at Philips, one of the things we've done this year is we've signed a large partnership with EPIC to fully integrate into their EPIC aura system. Because we really believe that if you're not kind of EMR native, you've already introduced friction to the clinicians. The second big one that we're really focused on, and I risk going into the buzzword territory, but is AI, but operational AI. Right. So a simple example. When you're doing a workup on a cardiac patient, you want to look at a bunch of different modality, and ideally, you want to look at their history. If you're a clinician, the best clinicians, I mean, that can take an hour if you're dealing with a patient with a long history. If you look at some of these AI tools, what are they really good at? They're really good at summarizing data. We've developed a tool that we call the HEART program. It's like ChatGPT for ECGs. You press a button and it runs a model and it says, hey, over the last five ECGs, you know, AZ's QT elongation has gotten worse, for example. Right. They don't have to go through all the ECGs. And I think the third one is really, frankly partnering with people like Darren.
- [06:34]
C
Right.
- [06:34]
B
I mean, one of the things I love every time I meet Darren is we were able to go deep and kind of really hit the pain points and the challenges that he and his peers have. And Darren set up some amazing forums to really go deep on some of these solutions with a multidisciplinary team to allow folks like me to really actually understand what are the problems we're trying to solve and where are we accidentally creating new ones.
- [06:57]
C
Frankly, I'll add to that. I think one key piece too, that I think Nick touched on is that, you know, these partnerships that we're creating, I think it's clear to say there's a bit of a market saturation for a lot of technologies that are in these spaces, particularly around AI. And I think one of the key differentiators that we're learning is that some of our vendors who are showing that extra commitment and that willingness to meet, you know, our needs here. And I think, like I said, I outlined at our institution, we focus a lot on these workloads and making sure that these clinicians have some part in the process. And it can be varied. Sometimes if they really want to be involved in the design, maybe they want to be involved more in the implementation. A lot of them really are interested in the education maintenance piece of it. And so wherever that is, it's really about creating sort of that culture with them. And then I think vendors that allow for that space and that time make for a much better experience and ultimately that pays off in adoption.
- [07:54]
A
That's so helpful, Darren and Nick. I'm hearing interoperability to your first point, nick, the operational AIP. So ChatGPT for ECGs, for instance, consolidating information, lifting time away from clinicians and unpacking those trend lines and making them more detectable, partnering the partnerships, so solving real problems. I think so much attention paid to it sounds like to those that may be unintentional, but still still very real for care teams that you also need to keep in mind. And then I think to your point, Darren, with the over saturation, sometimes it might be easy to underestimate the role of education, but it is so, so important. Darren, can you share a specific example from your work where any one of these traits of a strong tech partnership really emerged to the forefront?
- [08:41]
C
Yeah, I mean, our partnership with Philips is very much ongoing. I've worked with Nick and his team for years now, and I'll just say from a reliability perspective, we lean heavily on our Philips partners to make sure that from a cardiac monitoring perspective, but beyond some of the basic principles around patient monitoring in the inpatient, easier interventional spaces, that the next evolution of what that may look like is really around the AI principles. Right. What are we doing around prediction, predictive modeling. And I think as Nick outlined, even just in a simple example for EKG synopsis, there's a lot of opportunities to look at the way that we're monitoring patients and the methodology of doing so, and just to reimagine and rethink what that could look like. So, for example, one of the big key things that we're really excited about in the future is just implementing a lot more of our analytics dashboards and really being more data driven around how we're going to manage alerts and alarm fatigue. This is a continuing ongoing problem, not only at our institution, but many institutions across that remains a joint commission target to reduce some of these noises. And so partnering with them to make sure that we can reduce a lot of the noise that we've put into these environments can be distracting to our clinicians. It can definitely cause some ailments to some of our patients are trying to rest. And so how do we balance sort of that need of clinical monitoring so that everyone is getting them what they need to monitor these patients accurately? But then how can we also find that sort of space around, like, where's enough? Where's too much too much? I can't really do that without an analytics engine to do that. And then once I have that information, then it's really easy for me to then inform that, put that into our own forums, into our own venues. And this is where the culture piece comes back into. Now I can go and speak with department manager. Hey, did you know that you're running a pretty high rate of alarms over here between 0600 and 0800. Did you know that? And I think traditionally we haven't been able to do that. And I think now what we're starting to see is a lot more advancements in these monitoring systems that will really change the way that we deliver our practice and care for our patients.
- [10:45]
A
I love that example. And Nick, I'll turn to you. We'll stay on AI here for a bit because I'm sure there's a lot more stories to be told under this big topic. But I'm hearing from leaders at Health Systems all the time about the questions they're navigating with AI. There's still a number that are unanswered right now. Also, practical application, making sure that results are being recorded and achieved. And first and foremost, just adoption is getting somewhere. Nick, any thoughts to add on what meaningful scalable AI looks like in the context of cardiac care specifically?
- [11:19]
B
Yeah, well, so I think there's. First, it's important to define the AI we're talking about, Right. Because it's become such a. It's kind of a generic tool now. Right. I was in a meeting and I use the analogy of using AI is like kind of using the word Excel, Right. You can Excel to do a whole bunch of things, but it's become a general purpose tool. I think, particularly with the new versions like ChatGPT and others. You know, I'm seeing three major applications right now for AI as what I would call the first wave. And I would also say cardiology is behind other Specialties. So if you look at radiology, kind of five years ago, AI is now used in radiology, as per, for the course to speed image interpretation.
- [12:05]
C
Right.
- [12:06]
B
It's, it's every, every radiology scan is using some form of AI cardiology. Now we're starting to catch up, right? So if you just look at patents, there's a lot more patents for clinical interpretive AI in cardiology now than there was five years ago. So it's kind of the wave that we saw in radiology is starting on the clinical side. I think where I'm actually, though, super excited is in the operational application. Clinically, we're going to just move slower. It's regulated as it should be. We need to make sure positive predictive values are there. And also I think importantly on the clinical side, bias is something really important. How do we control for that? Tune the models so there is not inherent bias. Because the challenge with bias in AI versus bias in an individual, bias in an individual scales to an end of 1, bias in AI scales to an end of end. Right. It's an infinite bias operationally, though, that has a lot lower risk and can move a lot faster. Right. So where I see things being really, really exciting, for instance, is how do you manage a patient? So when someone like Darren goes into a patient's room, how do you quickly give them the signals so they're not trying to find out if the patient's decompensating? So, for instance, at Philips, we have an avatar that instead of having to go look at the SpO2, the temperature, et cetera, it's literally a kind of a picture of a virtual human. And if the patient's temperature is dropping, it turns them blue. Right. If the patient has a low SpO2, it turns them purple. Those things, I think are really, really exciting because what do they do? Darren cited it earlier. Cognitive load of all the data we give clinicians every day now, they help with that. I think that's a really immediate impact that we can move really fast with, while we're also developing some of the more regulated, slower actions that are on the clinical side.
- [14:12]
A
Thank you, Nick. Darren, I'm going to turn to you. I want to pick up on that example that Nick just illustrated for us. Do you have any thoughts on the difference of experience for both the clinicians and the patients? If that information about whether a patient is deteriorating, if it's immediately available before you enter the room and you don't need to go searching to piece that together, what kind of difference would that make?
- [14:34]
C
To clinicians, it makes a substantial difference. I mean people, if you're new to a hospital setting, there's a lot. I think yesterday we were doing some rounding on the unit and it was astonishing. I was rounding with a few people and the nurses were at the desk and they're sitting there, they're doing the documentation. From the outside it doesn't look like a whole lot is going on, but you actually can't see cognition. And one of the things that I picked up on when we were doing the rounding is that sometimes in the spirit of doing rounding, it's disruptive. They're in their mindset for it. And you can imagine one thing that Nick talked earlier about is that documentation does play a large part. I think in a 12 hour shift there's a fair amount of hours that go into just the documentation piece, let alone giving the actual delivery of care to the patient. If you stack on interruptions onto that, it forces these clinicians to then have to pull all of that cognitive load and then reprioritize and reshuffle for every interruption. We know that interruptions can be in the double digits in a 12 hour shift. So if you scale that when we're thinking about technologies that are going to be make that workflow easy, I'm also excited. Just as Nick said, the operations piece for me is really where the value proposition is. If we can make a technology do something easier for a clinician that doesn't require as much cognitive loading. And we have a lot of evidence of that, this incorporation of large language models to extract and to have a think partner to be able to do that quickly. There's a perfect example of a tool that an everyday clinician can use for their care. Now we're saying that they're being used for clinical decision care, but maybe it's just reference that you're checking and you don't have the time to peruse through eight Google searches. So there's a real, I think speed that is being gained from the incorporation of things like large language models. But I think on the prediction side of things and alarms, you know, trending of alarms right now, there's a lot, I think operationally that can save time as well as reshifting a priority in a way that is meaningful for clinicians because right now what they deal with in priorities can range from getting a warm blanket and ice cubes to there's a code blue happening in either code too. So that whole range in that middle of it, there's a lot of opportunity then to surface Some of these technologies to reduce some of these things that we might consider everyday, normal tasks. Maybe those are no longer tasks of today, those are tasks of yesterday. And we operationalize automations to be able to do so. There's lots of opportunities with AI to create that. So very excited about what the opportunities are.
- [17:13]
A
I mean, that is exciting. I think, to Nick's point, the clinical AI development will continue to move slowly, as it should in some ways, with the regulatory involvement that's needed there. But the operational side, I guess. Nick, Darren, any thoughts on where we stand with that? Are we just in the early innings of this? Do you expect expect the next 18 months to a year to be rife with a lot of developments on this front?
- [17:35]
B
Yeah. You want to go ahead, go first. Aaron, I think you're on the front line of the experiment.
- [17:41]
C
I'll just say it's probably more fast than we can manage. There are every day there just seems to be some new advancement and some new requests that comes in from our clinicians about how we manage it. We're actually having to put intake processes in place for how we prioritize these asks as they come in and then evaluate them so that they're, you know, fairly deployed and prioritize accordingly. I think one thing, I just read this today. One of my colleagues shared this with me today. On the wearable front, like, outside of the walls of the hospital, there are regulatory components that I think aren't necessarily barriers to care. But like, one thing that I think could help is that the AMA put this announcement that that 16 consecutive days of data that's required to be billable is now going to be on docket for removal come next year. And I personally experience walking in during my rounds that patients are frustrated the devices that they work aren't integrating with the systems that we have. And I just think when we think about all the work that is being done outside of the walls in the hospital, we need to sort of acknowledge that that work that's being done out there there will come a day when all of those technologies will provide a seamless experience for the patient. I'm using my. I'm using a generic example CGM device, a heart monitoring device, whatever it might be. And I walk into the hospital and that synonymous flow of data is able to go to clinicians. And just in that example alone, we may see some reduction in time. The chart inquiry, just to review a patient's chart thoroughly without interruptions in a real practice care environment, very challenging to do at times. And I think whatever we do in that example, can make for about submission to where the patient needs to go, maybe the right part of the patient to come into the hospital versus the patient that needs to go home. So a lot of opportunity there.
- [19:31]
A
I think both of you have done such a great job in this conversation helping me understand where things stand today and what's to come. So I want to thank you for your thoughts. Is there anything we didn't touch on or any final thoughts that you'd like to leave our listeners with?
- [19:43]
B
I think from my perspective, I think it's just honestly to my peers in industry, find partners like Darren. I mean, all the technology in the world doesn't really matter if you don't have people like Darren who can share their, their expertise, their experiences and the problems that need to be solved. Because otherwise you're, you're, you're solving for a theory versus, you know, ultimately it's, it's humans like Darren and other other clinicians who are on the front line treating the patients.
- [20:10]
C
Thank you, Nick. I appreciate that. And I'll also quote Molly by just also saying, like, I think important change is really hard and things are so dynamic in healthcare right now. And I think it's important that we be really mindful that at the end of the day, there are patients in the bed and we need to make sure that whatever we're bringing into the environment, we're doing that due diligence of work outside before we bring these technologies into the house. And that is not always easy to do. It takes considerable dedication, commitment, and a lot of collaboration with people internally and also. But generally, like people like Nick, it's.
- [20:44]
A
Kind of like another version of the cognitive load you described. Daring, where you can't see it, but it is a very strong commitment that takes a lot of energy, focus, and staying with it. I want to thank both of you so much for your time and your insights in this conversation today. We also want to thank our podcast sponsor, Phillips to our listeners. You can tune into more podcasts from Becker's Healthcare by visiting our podcast page@beckershospitalreview.com com thank you so much for tuning in.