Why Intensive Care Unit Doctors Need a Dashboard-Shane Cooke

Harry Glorikian: Right

Shane Cooke: All this data and looking at different patterns and outcomes, but then there’s the model-based approach that we do that actually helps clinicians in near real-time right at the bedside.

Harry Glorikian: And so, I mean, could you walk us through an example of how that would take place to give somebody, you know, the people listening, something real to sort of I’ve in their mind.

Shane Cooke: Sure, sure. So we have a number of case studies and that, that I could point to. So, so for instance, a patient comes out of cardiac surgery.

And they’re in the ICU. And you start to see our algorithm for essentially hypoxia it’s called inadequate delivery of oxygen is an FDA cleared out, algorithm the first one that we had FDA cleared going back a few years. And when that is trending red, that is the, the increasing probability that a patient has a mixed venous oxygenation below a certain level.

So. When a clinician looks at that and sees that that has been trending red and trending red for some time. What their first action is, is really to bring more attention to that patient and bring the doctors and nurses next, next to the bedside, look at all of the various trends for the patient and look at what may be out of normal range that they could perhaps intervene on.

So for instance, they may look at the algorithm and say, okay, the blood pressure is really low for this particular patient. So we have a few different options that we could. We could intervene on, we could give the patient intravenous fluids. We could give the patient of vasopressor et cetera, to, to impact those, those metrics and get them back online.

So that’s a, that’s a very common use. And what it’s often called is this escalation of care. As you see the algorithm trending in a particular direction, you are escalating care with the care team to make sure that that patient gets back on track.

Harry Glorikian: And so my assumption is being able to get ahead of this or being able to manage this better, theoretically should have two impacts, right?

One is somebody recovering faster or spending less time in an ICU, and then therefore, a decrease in costs. Moneyball Medicine is all about trying to achieve those two outcomes in my mind. So is that, are you guys seeing that happen with a with your platform?

Shane Cooke: We are, we have, we have evidence clinical evidence showing reduction in length of stay.

And we have clinical evidence showing reduction in cost per patient as well. There’s also some studies that show- that point towards a reduction readmission back to the ICU. So if you’re in the ICU for an extended period of time and leave to go to a. Stepdown unit or general care ward. Oftentimes those patients go back to the ICU.

So we’ve seen a pretty substantial decrease in readmissions back to the ICU in some of our clinical studies. So we continue to drive evidence. That’s one of the things that I’m most proud about the company is the, the level of evidence that, that we’re able to drive. And that’s frankly, that’s, that’s a key part of our system.

Being that it’s software being that the hospitals that we are involved with. We are collecting that data at the hospital level. They have access to that. They can mine through to drive that clinical evidence. So and frankly that the ability to collect that data for the hospital also points towards other potential utilizations of the technology as we move forward.

Harry Glorikian: Yeah. I mean, I’m just thinking, like, if you’ve got all this data on all these different patients, you, you, you can almost create simulations . Understanding, you know, basically based on how other patients have reacted to either a medication or an intervention, what typically happens next on one hand, you could almost help people move towards a more efficacious intervention.

But on the other hand, I think for a simulation on training, that would also be a, a big step up having the system react to what would probably happen in a, in a real patient.

Shane Cooke: Yeah. Yeah. And I think that’s a really good point. And that, that is certainly something that, that we have given the data that we have access to.

We of course get de-identified data. We don’t do anything with, with Phi, but you know, the hospitals collect the data at their, their level of, we get the de-identified data for future algorithm development, et cetera, as we continue to work on new algorithms. So our data science team works on that data.

And, and looks at that. And from a, from a perspective of looking at key trends and, and looking at exactly what you’re talking about here, you know, are there key trends that we can help hospitals point towards? We automate reports for hospitals as well, based on quality improvement initiatives.

So yeah, it’s, it’s a big part of what we’re about. It’s again, it’s, it’s really getting back to unlocking the full potential of patient data.

Harry Glorikian: That’s interesting. So I should, I should introduce you to a, another, you know CEO, I spoke to Charles Fisher from Unlearn.ai where they’re, they’re creating a digital twin for clinical trials, right?

So there might be some synergies that, that you guys can take advantage of because they’re trying to create the digital twin and you have actually the patterns that a patient would go through that might make that digital twin more real.

Shane Cooke: Yeah. And that that’s frankly, that’s the language that we use internally, as well as digital twin.

Yeah. Given that what separates us from a lot of companies out there and the way that they do data and analytics is we built a model of human physiology. So we have taken all of this data that we’ve collected all of the clinical literature that we have mined through and essentially looked at. All of the various functions in the body, cardiovascular mechanics, pulmonary mechanics, autonomic regulation, acid-base balance, essentially translated that to differential equations. So we have built a model, a mathematical model of human physiology that we essentially based on the data coming into our system is constantly updating to show what, what, and compared to that patient in the bed. So that’s, that’s very similar, frankly, the, the digital twin. I’m glad to hear that others, others using that, because I think it’s an important element of what we do.

Harry Glorikian: Well, you know, it’s, it’s also something the FDA is interested in is, you know, can we create a digital twin to help trials go forward. Cause sometimes you can’t get as many people as you like into a trial, but if you had a digital twin that might serve as, as a proxy for a real patient, I want to ask something like, you know, how many patients do you have like that you’ve monitored and trying to get an idea of like, how, how, cause at some point you get to a big enough data set that you really can simulate things.

With much more clarity than in other situations.

Shane Cooke: Yeah, I think the best way I could characterize it is, is we have millions of hours of collected physiology data. Human physiology. So we are, we are constantly seeking out more and more information around that data. So diagnosis, information medication, information, et cetera.

That’s, that’s always what, we’re, what we’re working on doing more of. So we can have more, a richer data set to be able to do some of the analysis that you’re speaking of, but we have quite a bit at our disposal that we continue to work through internally for, for algorithm development.

Harry Glorikian: So your main customer is, is our hospitals, but that data set you’ve got us has got to have, you know, data’s data.

I always tell people like the model is not just very singular and I sell a widget to, to someone and they use the widget, data is, has a lot more uses and can be manipulated and, or have value to a lot of other stakeholders. So, are there other groups that you think would, would be able to extract value from what you’ve created?

Shane Cooke: Yeah we’re really assessing that now, you know, as I came into the company coming up on a year ago one of the key goals was really to, to. There’s been so much fantastic technical and clinical work done over the last number of years. And, and really my goal was to build out the commercialization function for the company.

So to continue to drive, to grow the company on an annual basis, get into and engage with more hospitals, help more patients help more clinicians throughout all of this. But right now what we’re doing is, is assessing just that, which what you mentioned is what are some of the other areas. That could benefit from this data.

Anecdotally, I think that there is an angle with, with pharma and with clinical trials around. Automatically collecting data and potentially impacting some of the potential patient selection. You could even look at speed to inclusion or exclusion, and these are just, these are, these are all just hypotheses that we have as we’re assessing different markets that we could enter into with, with our technology.

But for the most part, what we are focused on, and I think it’s important for all startups to, to have that. That intense focus is on the ICU right now. There’s no doubt that, that the potential for this technology in the operating room, in the emergency department and other care settings throughout the hospital, that’s our long-term vision to essentially be that hospital safety net with our algorithms, with our data, with streamlining the care and driving efficiency.

But right now it’s, it’s primarily with the, with the ICU.

Harry Glorikian: That’s well, that was going to be one of my other questions is when you’re, you know, put this into a, a ICU, do you get that aha moment from people going? Damn, I wish I had this before. Like this would’ve made my life a whole lot easier and Oh, by the way, can we use this over here?

Because we could also use a monitoring system or a early warning platform over in this area.

Shane Cooke: That, tends to happen quite frequently, to be honest. Yeah. So the, for sure, getting the system in and seeing that visualization where you’re pulling all of this information together it’s, it’s, it’s kind of that immediate benefit that immediate aha to the efficiency and the workload that you have as a clinician.

It’s all right here in one place. And then of course, with the algorithms on top of that as an early warning sign and to bring more attention to that, patient, that’s been really vital aha moments for them. So. We have seen, and that’s, that’s a key part of our growth this year as a company is moving to other, other departments, other ICU’s within the same hospital, that’s a cornerstone of our, of our commercial strategy.

And, and that’s, that’s certainly took taken place this year.

Harry Glorikian: So have you seen it You know, getting a physician to do something different is not always a trivial exercise. But hopefully data sways someone’s decision-making. Have you, have you guys been able to sort of objectively measure a shift over time of how people might manage a patient based on the data that comes from the system?

Shane Cooke: Yeah I,  think it’s I think it’s. Probably anecdotal measurement right now, just based on feedback from customers. I, I, I’m very interested in driving adoption and assessing adoption on a, on a daily, weekly, monthly basis. So that’s, that’s definitely an undertaking that we are digging into with our customers to get more insight into that.

But there’s no doubt about it. Anytime you’re changing someone’s behavior, we’re all consumers, you know, think of yourself as you get a, get a new cell phone or something. Sometimes it takes a bit to get into to get into the flow with that. It’s the same thing with, with clinicians. What we, we focus on and pride ourselves on is, is reducing workload in driving efficiency.

So if you can show someone how you are taking time out of something, That might take it. Excuse me. Might’ve taken them longer before. That’s usually the, the path to driving adoption sooner. And when you can show them how easy to use it is that that’s been our path. So yeah. Having the web based platform as well.

And being able to access that anywhere, especially this year of all years, this, this strangest years that we are the, you know, having a remote platform where you can access it away from the bedside or at home that that’s been really important. And that that’s really driven. A lot of adoption of our systems

Harry Glorikian: COVID has caused a big shift in.

I think adoption of technologies in a, in a way that we had had we planned this, I think I would have said, ah, some of this will take another five to 10 years. And COVID has sort of pulled everything closer, faster. How have you seen it affect what you guys are doing?

Shane Cooke:  I think the. Of course, it’s been a challenge for a lot of hospitals during this time, especially in the early days, everybody was, was, the clinicians were so overwhelmed with the influx of patients.

I remember chatting with clinicians that, that I’ve known for years and they, they, they were telling me, well, we spend most of our time running around looking for PPE early days, which was just. Pretty crazy, obviously. I think the hospitals are much better suited now, just given that we’ve been through that initial initial days, it’s for sure.

It’s still a challenge. Hospitals are still, still having challenges with that, but the way it’s affected us as a company we reached out to all of our customers and essentially worked with them to. Get our software wherever they needed it. You know, given the fact that it is remote given the fact that you can, you can hopefully, and potentially limit the viral exposure to clinicians by being able to remotely look at the patient and all of the key parameters and, and at times not, not need to be by the bedside.

That was really what we had spoken to customers about. I think you’ve seen many companies out there talk about how it’s been a challenging year medical device companies, and otherwise just given the lack of elective procedures, hospitals, budgets are there. They’re worried about budgets. Of course.

So I think that’s affected every company out there. We have been able to. And we’re, we’re really proud of this help more and more hospitals and patients this year than ever before. And that’s partly due to COVID for sure. Just, just given that I think if, if COVID has shown anything, it’s shown that if you can be efficient in your care, There’s a huge plus for that.

And ICU care is, a challenge to drive efficiency. And especially when you have this influx of patients, so anything that can make things faster and impact decision-making quicker, which is where we live with our technology. There’s that there’s always going to be a positive for that.

Harry Glorikian: Well, and I, I do believe like, you know, if there’s more adoption, it means there’s more data coming in, which means analytics get better.

I mean, data has a way of, of getting, you know, giving you more and more to play with all the time. So it just gets richer and richer and its ability to, to tease out interesting, you know, trends or identify like what works over, what doesn’t work.

Shane Cooke: Sure. Agreed. Yeah.

Harry Glorikian: So what’s what, what do you see next for the company?

Shane Cooke: So we, we are, are right now focusing on some key technical advancements of the platform. We have a couple of key things with our algorithms that will, when they’re, when they’re launched, provide more insight, not just about what is happening with the algorithm, but why, why an algorithm is elevated, which can hopefully inter.

Impact the interventions that take place with the clinicians. So again, we, we really focus on going deeper and deeper into the why. Not just what may happen, but why is what has happened? Why is the patient in the condition that they’re in right now? What can you, what can you hopefully do about it by, by assessing that information?

So we’re really excited about those. We are really pushing forward on a number of clinical studies as well, which Continuing to advance the science in this space and around using data most effectively is really what we’re all about. And then of course, growth. We, we are really in our growth phase interacting with more and more hospitals on a daily basis.

That that’s really what we’re focused on for, for the short term.

Harry Glorikian: Well, if you if you actually know why, which would be, you know, just from a human physiology and. Understanding the mechanism of action. You can much clearer figure out what to do next. And then you can actually measure when you do something next, like which one gives you the best outcome.

I mean, it’s so I just look at this as a giant figure, eight feedback loop, right. That, that just gets better over time. You just have to have the data sciences, you know, can sit there and, and crunch through all this data.

Shane Cooke: Yeah. Agreed. And that’s, that’s really what we’re focused on. That’s where the name of the company comes from.

Etiometry we’re, we’re looking at the etiology. We’re trying to figure out the etiologies of what is causing a particular condition to better inform the decision-making around the intervention on the other side.

Harry Glorikian: Awesome. Well, it was great to talk to you today. I know we’re COVID secluded, but. Hopefully, you know, by next summer we’ll, you know, start to get back to normal.

Shane Cooke: Yeah. Let’s let’s hope. Let’s. Let’s hope we get it under, under control here pretty soon. And yeah, it was a pleasure speaking with you and thanks for having me on and Look forward to chatting with you again soon. Hopefully.

Harry Glorikian: Yeah. And I hopefully one of these days we’ll meet in person.

Shane Cooke: Yeah, absolutely.

All right. Excellent. Thanks Shane. Thanks. Thank you.

Harry Glorikian: That’s it for this week’s show, we’ve made almost 50 episodes of Moneyball medicine, and you can find all of them at  dot com forward slash podcast. You can follow me on Twitter at H Glorikian. If you liked the show, please do us a favor and leave us a rating and review at Apple podcasts.

Thanks. And we’ll be back soon with our next interview.