Jeremy Weisz
see that. Hey, we have this thing. Yeah, right.
Gal Salomon
Yeah, I mean, I mean, guys, you want to play with technology, fine. I mean, like kids come and play in front of us. But after 1400 patient with you can see how models mathematical complex, more than like AI model can predict deterioration hours, before the team really understood that something is going to happen, then it’s not a tear. It’s not a story anymore. It’s reality.
Jeremy Weisz
What did you do at Mayo Clinic when you want to?
Gal Salomon
So we done a clinical study with a very gifted team, which is led by to Professor of Italian rocky wits. And Brian Pickering. I mean, those are wonderful guys. That it was pleasure to work with them. And we came to them and said, Guys, we have a way we have a method that we can predict one of the killer, one of the number one killer in the ICU, which is sepsis. sepsis is is his nickname for infections.
Jeremy Weisz
Widespread infection. Yeah.
Gal Salomon
And no one and no, everyone is talking about sepsis over the last 115 years, 16 years. No one has a solution for that. And people still dying because of sepsis. So we came to that with this idea. And they said, I mean, guys, I mean, many, many guys, many groups mean bigger than you more gifted, like you with more budget, have tried that so many times. I mean, what to bother. And we said, Guys, I mean, mean, give us a chance. I mean, I’ve done several things in my life, not in healthcare in other places. And now, I was successful. And you know, like other 20 iteration. So the guy came and said, Okay, you know what, I mean? Fine. I mean, let’s, let’s go with that. I can tell you that it’s not going to go it’s I mean, you’re wasting your time. And we have done a clinical trial with those guys. And we published that. And we showed clearly evidence around you know, there is no doubt that you can predict who’s going to be deteriorating due to sepsis from the septic to be septic shock. And that was the starting point. Over the time, we had more and more completed Every complication needs to come to a point that it’s need to go to the regulation pass, it’s FDA need to prove that. And it’s a long process, it’s taking a lot of time, a lot of energy, you need to have a very healthy budget for do that. Because it’s not just, you know, submitting papers, you need to do that over multiple institution, multiple demographics, multiple, multiple ethnic groups. And the FDA folks are very true. They’re very, very specific. And this is, this is their jell
Jeremy Weisz
Gal, how do you win? We’ll talk about COVID in a second. But, you know, from the standpoint of the sepsis, what are the doctors see on the screen, you know, so you know, you’re plugging away, you’re taking all these metrics and allowing the doctor to make a decision, a predictive, you know, what’s going to happen to the patient? What is the doctor sees a like an alert that comes on? What What do they see so that they can act on that data. So if anyone’s listening, we’re looking at a dashboard here. And this is what the doctor see.
Gal Salomon
So this is basically, this is the skeleton from one of the installation that we have, which is under IRB, in UMass Memorial at Boston, at Wooster in Boston. Yeah, this is a network of hospital, which is built from seven different dice use. So what you’re seeing is, obviously, the technology is behind the same. So what we’re seeing here is the different units that you can go and see how many patients what their their condition. So here, we’re in the six University units in one of the hospital mooster, you can see how many patients you can see that this patient is, is in good condition is probably going to be moved to step down units. But you can see that this patient is going to be deteriorating. And the type of the duration here is the hemodynamic instability can be hemodynamic can be respiratory failure, it can be any other. And and, and what you’re seeing here also, it’s the second one, a screen, which allowed you to understand exactly how many patient like that you have an in in which location they’re located. Also, everything that you’re running, you’re running from here. So this is a hemodynamic instability, this is a respiratory failure. Here, you can see all the protocols, which allows you basically to deal with best practices, which means that I can wean a patient from ventilator for medication for anything, everything is captured here, using the algorithm that we have. And the most important thing, this is the current condition of this patient, which you can see hundreds of different parameters that were manage and manipulate and recalculate every single minute that you can explain why we believe that this patient is under screen. So a solution like that would look exactly like that, that you will have the freedom that you’re putting the ordering, you have the MRI screen, you have the video that you can do zoom in, zoom out on every single patient. And we have the CLEW screen, we can describe you the network, the doctor and the patient patients. This is the sixth Screen button sticking up.
Jeremy Weisz
Yeah, I want to point out, Gal that, um, you know, with the patient, that’s a high risk deteriorating, you know, I was I was watching one of the videos on CLEW. And by the way, if you want to check it out, you can go to CLEWmed.com And check out what they’re doing. But what’s interesting is the person or the patient may appear totally fine at the time. And that’s that’s a key it’s not like buzzers are going off and they’re going into some type of they’re having issues currently, they could look totally normal. And but it’s showing high risk meaning, like you said, it’s being proactive instead of reactive.
Gal Salomon
Dola dear here is to provide you an information that you don’t know, if I’m going to tell you that tomorrow morning you will have a rain and tomorrow morning you’re going out and you hit rain. I mean I didn’t tell you anything. But if you’re going outside of your home and you have a very clear skyline, the in the in, in, in, in everything look very normal and I will tell you the look in three hours you will have a massive rain here. You will come and said to me Are you joking me? I mean, what’s the matter with you? So this is the type of thing that we’re saying to because the monitor that you’re currently seeing on On on the patient doesn’t tell you anything because it’s capture like three minutes, right? I mean, three minutes crept here. And it will not indicate anything about the future. Because, you know, this is this is the nature of the prediction. I mean, you don’t have any clinical sign, every everything look normal your heart rate is okay. Your blood pressure was saturation, your pulse, your cardiac output, everything you know, is with with the normal. But we will tell you that storm is coming in the store when we come from this source. And this is how the trajectory, and this is what will happen here. So it’s better that you will start to act upon you know, what you’re seeing here and start to dealing with the patient. And you can save life, because you have time to make an intervention to save lives. And this is the heart of the technology that we’re bringing.
Jeremy Weisz
Yeah. And Gal, you know, you start off with, like, wanting to solve this big issue in intensive care, which is sepsis. Obviously, with COVID. Coming, was there any type of shift you need to make in the technology. So to to account for that, or was it built, you know, it would account for the same type of issues as sepsis to COVID.
Gal Salomon
So in the COVID space, I mean, most of the models that was played was the hemodynamic and the respiratory further, bear in mind that most of the covid patient have a respiratory issues. And we’re the only one that worked over many years and respiratory, and all those in order to predict when the patient we come to the situation if you need to be intubated. So, what we have done over there is we have created the system that can come and not boss right now that you have a situation you will have a situation a couple of hours. And therefore you need to be act upon what is the protocol is telling you to do. And, and in by doing that, you’re pretty much focused the medical team coming and said, Okay, in this patient, we need to do 1234 and this patient into the 659. And this one something depth, and what providing you a tools that we have, that we have developed internally, that you can put every single protocol around, you know, the different queries that you have, including what the nurse needs to do what the physician needs to do, you know, step by step in a way that you’re not going to miss anything. And the fact that it’s running from isolated environment, if you don’t really touch to it to the patient, or whether it’s give you a quiet zone that you can think and you can act and you can build the product. Or you can build the protocol around, you know, what you have thinking and and you know, even here, it’s a lot of cutting try. Because, you know, I mean, in COVID, we still have so many unknowns. And this is this is the one that we’re doing great. Yeah,
Jeremy Weisz
yeah, I want to talk about you get heat from people not telling enough people that it’s FDA cleared. So I was reading an article at the times of Israel about it was called FDA cleared Israeli tech to warn doctors before COVID-19 patients deteriorate. Um, talk about the FDA process. You know, how hard is it?
Gal Salomon
It’s hard. I mean, we got the FDA clearance in the UAE part. The UAE definitely helped us basically to expedite because the sense of urgency is exist. And we have started our dialogue with FDA team, I think, two years ago. And, and, and bear in mind that if VA started. I mean, to the best of my knowledge, again, I’m not like 20 years in health care, but you know, from talking to guys and read a lot of stuff. If they came from the medication from farm, this is how everything was started.
Jeremy Weisz
Was that sort of the Food and Drug Administration? Yeah.
Gal Salomon
All right. And then it was like, many years ago, you started to have medical devices came and then they need to spread and really understand what these medical devices because their core expertise was formed. And then, you know, a couple of years ago, they have coming the new boy to the neighbor, which is saying digital health Within digital health, you AI, and right now wait for, you know, infinite money to understand what this mathematical model look like. So we met a very mean, very, very good teams over there. And we we move and work with multiple teams in the FDA. Because cardiovascular is one thing. Respiratory is another thing. You know, role risk is another thing, glue everything. I mean, I mean, you’re not working with a single point of contact, though we have one single point of contact, but he need to coordinate everything. And, and it’s took some time and took some time because you have a learning curve. And, and it’s a complex kind of things. When you have hundreds of data points, which is entered to some big system. And by the end of the day, you have a black box, that doesn’t tell you anything, you will have only results, then you’re kind of coming and said, What’s the matter? What is the trick here? So it took a lot of time to really explain what is AI? What is machine learning what is deep learning what is classifier is a what is a normally detections that was part was done in our physician, and we’re employed here, a very big team of medical doctors. And the second one is coming with a very gifted team that we have included, which is mathematicians, this is how we’re building everything. And the glue layer is the software guide that we have here. But it’s not enough to go and show the guys from the FDA dashboard, they want to understand what you need for them. And how they can save money because you know, you need to remember that by the end of the day, FDA here is to protect the patient, is to make sure that you’re not compromised anything around the patients. And and this isn’t the process, it’s long process. It’s a lot of time, it’s a lot of you know, going back and forth, then they have a lot of question which you need to answer. And it’s a very iterative kind of process. But the UAE definitely gave us a very fast road to start moving. And, and and you know, for good and bad that was to our benefits.
Jeremy Weisz
Yeah. I want to talk about adoption, right. You know, if anyone listening has a company, their founder that entrepreneur adoption, you know, is not always easy. And now we’re talking about adoption in major medical facilities. I’m curious, like, you know, Mayo Clinics, like, yeah, go play around, you’re probably not going to solve anything. Good luck. I’m going to the next facilities, what were some of the objections that you get, that you had to push through, before they would actually come in and implement it?
Gal Salomon
So, you know, I was a little bit frustrated, you know, six months ago. And part of the frustration that I have, you know, I came from totally different things on a totally different industry, which was consumer electronics, this is what I’ve done over the last 20 something years. The beauty about the consumer electronics business, that you have an owners, you have a sense of urgency, you have, you know, clear timelines. This is a very, very fast environment, which is very competitive. Healthcare is working differently. And to some extent, it’s very, very slow, the opposite. Yeah. doesn’t move so fast. Very mean, they’re not very thrilled about technology, they’re looking for service, they’re looking for patient safety. They’re not really you know, they’re not going to bind, you know, buzzword. The wrong API’s, or anything good is some less they can see prove,
Jeremy Weisz
like how many clinical studies This is, does this house or something like that, right,
Gal Salomon
make a run, and we’re continue to run more and more clinical study. So it’s probably never will end. I mean, and you need to have millions of millions of dollars to spend around those clinical trials. Because every time that you have a model, you need to you need to build it you need to qualify the need to test and bear in mind that FDA is not allowed us basically to continue to To improve the model, unlike other industries, and so models around self learning are not going to be part of this kind of the country, simply because of the basic fact that FDA wants to approve every single model, which means that major part of our elements is going to be attached to every version that we have, we need to pre submit, submit, get approval installed. And this has taken time. So going back to your question, you were talking about Yeah, rejections? Yeah. Not the most fast adaptation kind of industries or I mean, healthcare by itself, they have their concern. They have their own, you know, obstacles about, you know, adopting new technology.
Jeremy Weisz
There’s so many obstacles, Gal, because First, you have to get it in to the hospitals, then you have to get the staff to use it. I mean, that’s a whole nother
Gal Salomon
thing, then. And then you have another elements, which are becoming very, very important, specially those days, which is, you know, okay, you prove, you prove to me that clinically, you’re working fine. But then, you know, the decision maker is becoming right now, the CFO, and the CFO coming in said to you, okay, you’re clinically okay, but how you can help me to either save money, or to generate more revenue. So as you’re probably today, you know, you can’t come with a solution like that, unless you can have an initial equation, very clear, very describe in a way that the CFO can come and said, Okay, I have this checkmark that I need to feel it, but I need to prove my financial results, especially those days with health care provider, losing millions of millions of dollars. I mean, the equation need to be not just the clinical part of the equation, you need to also the financial part.
Jeremy Weisz
So how do you include that part? Is it then have to do something with Well, it’s more likely to get approved by insurance? Because we’ve proved with this data, or what how do you, you know, incorporate the financial piece because that that makes perfect sense. And I wasn’t even including that piece.
Gal Salomon
So the financial one is, is is very, very well described from one hand, but it’s complicated from the other hand, because you need to remember that the ICU intensive care units are not stained by itself, it’s either part of the or part of the surgical activities are part of any other, which means that you need to prove is that given your dear, giving your your bundle payment that your gig that you’re getting from the payer, you need to make sure that you’re that you’re within the limits that that bear is providing, if you want to improve, you need to perform much better. Because if your dear g covered like, four days in the hospital, but after three days, the patient is stable enough to go home, that means you have saved one day that you can set that you can sell the same bed for more patients, right? This is what we’re trying to do. We’re trying to here to increase efficiency, increasing throughput time. That’s on a given resource of that you have the same amount of that same amount of nurses, the same amount of doctor, you will see more patients. Yes, how it’s gonna be
Jeremy Weisz
gone. It’s interesting. It’s not enough to save lives anymore. You gotta have a financial incentive. reason to,
Gal Salomon
you must have I mean, let’s not hiding between the bushes. I mean, the furnishing, waging is important as the clinical part. Yeah.
Jeremy Weisz
You know, it’s interesting. Yeah, thanks for sharing that. It’s kind of like, it’s not the best analogy. galva when when I was researching CLEW, I was kind of thinking, it’s kind of when I first started using Waze. I was like, I know a better way. Okay, I know the shortcuts. And I would plug in Waze and Wazes was always right. If I went against Waze, it didn’t matter. I was wrong, even though I thought I knew the shortcuts. And I feel like it’s the same with CLEW. So like, Listen, I can see the doctor like I see everything. I know what CLEW says. But listen, here’s my experience. And you have this other data that you sometimes as a human can’t see, because they’re not making a million permutations of these calculations. So it’s kind of when I start researching. That’s the what I thought of like Now never go against Waze. You know, because of that.
Gal Salomon
That’s exactly what we thought. And, you know, we had a lot of discussion about CLEW is the medical part of ways, right? I guess what do you think we should do in keeping one couple of things. One, you have a very, very gifted doctors, which are devoted their life to dealing with patients. But those guys, as we said, they’re under pressure, they have a lot of priority, they have enough patient, but more so I mean, you know, you have weekends, you have nights, you have holidays, that you don’t have the best team 24 hour, you don’t have 24 seven. And this is basically the combination that we’re providing, because you have the safety net, which allowed you to use you know, even students even you know, medical teams, which are not really a, you know, have tons of experience, because you have this type of technology that can help you to understand what’s going on. But besides that, it will help you basically to navigate where to go,
Jeremy Weisz
Well, I’m excited about this got this technology, not even for intensive care, but I’m hoping that you you prove this out which you have with intensive care, but for actual everyday human beings walking around, because we hear of people, friends who just dropped out of a heart attack people who dropped out of a stroke, and there was no warning sign. So I’m hoping at some point, you have some technology that will warn people of those things, so that they don’t just drop in the middle of their bathroom. And no one had a CLEW. You know what I mean? So I guess that’s why you name it CLEW that, but, um, so I’m hoping for those days, but you mentioned the, the team, you know, everyone’s function as a team and assembling your team, your team, when I was doing research looks outstanding. And I’m wondering, you know, all these people probably have tons of opportunities, they could they could do a million different things. You have professors you have, you know, MDs, you have an amazing team. And you have to come in and sell the vision to them to work on this new thing. years ago. CLEW who was the hardest to convince
Gal Salomon
Doctors,
Jeremy Weisz
doctors, yeah, medical doctors are, you know, who specifically Are you from your team that you had to go to? And basically,
Gal Salomon
I mean, it was starting from john halamka, which is great guy. I mean, it’s really friend, john was the CIO of Beth Israel in Boston. Right now. He’s the president of mayo digital, and, but I think every every single person was, you know, we came with the story. And I said, Fine, guys, I’d heard you know, many, many stories are on that I’m not going to spend more time on another fairy third. And the third jaded. I think, the level of how to put it this way. They’re not really, you know, technology is nice, but you know, guys, we have went to medical school, we have like, 30 years of experience, how come? You know, software algorithm can tell us something that we don’t know. It’s impossible. And and I think think that thanks to technology, we can we can show you what we can bring them, you know, by the end of the day, we have amazing thing. And they’re basically it’s not a very easy audience. They’re not taking no for an answer that ask them question, show me the proof. Explain me. Tell me how it’s gonna work. I don’t believe in those numbers. Show me how to work. Look, it’s a it’s a very, very, very tough job. Because when you’re coming from, from the point is guys, I don’t think technology can help in this kind of situation, which which is very complex, then you need to work very hard to convince them.
Jeremy Weisz
Mm hmm. First of all, God wanna I have two last questions before I ask them, I just want to thank you. It’s what you’ve created is truly amazing. Everyone should check out CLEWmed which is CL e. w. med.com. Check out what they’re doing. Gal two things I was asking is inspired Insider. What’s been a tough point, low moment challenge that you have to really push through. And on the flip side, what’s been especially proud moment with the journey so far. What’s been a
Gal Salomon
tough time. So the tough time is right now what is what’s going on right now. We have the technology, it was proven was installed. And guys, we are here and we can do so many steps, so many kind of things. I mean, I mean, it’s kind of amazing to see what is the boundary, what is the limit of technology. And right now, what we’re facing right now that this happened in the US that you don’t have the time to listen, I mean, COVID is very, very aggressively right now in the states that no one have the time to listen no and have the time to make a any progress. And given the fact that you know, the skies closed, and you can’t really fly. And healthcare providers don’t allow, you know, vendors to come in even you know, going inside of the facility. This is this is bad, because, you know, we have worked in this solution for five years. It’s ready to deploy it. We have in the UAE FDA approval, we can start shipping product. But we need to have a willing partner that’s coming said yes, it’s a bad time. Right now, everyone is panic mode. But we need this type of mode.
Jeremy Weisz
It’s tough. It’s like a very time someone needs it is when they like we need to wait. So, um, what’s been a proud moment from the journey?
Gal Salomon
The problem more than, you know, when we when we started, you know, the installation here. Usually, when we got the phone call from the two largest healthcare providers here at Sheba Medical Center, intelligent Medical Center. And we know the guys here when we stopped everything that we said, Yes, we are focusing only in the US market. But let’s put everything on hold. Let’s help. Let’s create and we hadae wonderful thing. We have great team here in CLEW that put in work like 20 hours every day for three for almost three weeks to bring the solution. And when you can see that the system is really up and running. And it’s helping to save lives. I was trialed, and the team was very trained about it. And you know, and when you’re talking to the doctor, you’re talking with the medical team when they’re coming to said that. This is so amazing. This is you know what we wanted to be here. That’s it. We need more. Yeah.
Jeremy Weisz
Gal, I wanna be the first one that Thank you, everyone. Check out CLEWmed.com always a pleasure. Thanks, Gal
Gal Salomon
Thank you very, very much.
