A Quest for Better Patient Outcomes Through AI and Compassion

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Saul Marquez:
Hey everybody! Welcome back to the Outcomes Rocket. Saul Marquez here. And today, I have a privilege of hosting a really awesome guest. His name is Narinder Singh. He’s the co-founder and CEO of Look Deep Health. Prior, he was co-founder and president of Appirio, a pioneer in cloud computing and first cloud investment of Salesforce.com and Sequoia Capital. The company grew over 1200 employees and was acquired in 2016 by Wipro. Previous to Narinder worked in the office of the CEO at SAP and ran product development at Web Methods. He’s just an incredible person. 40 under 40 in CNN, The Daily Show, just countless attention that he’s gotten. So we’re privileged to have time with him today. Narinder, thanks for joining us.

Narinder Singh:
Thanks, Saul. All that was before kids, so now I’m a slower-paced person.

Saul Marquez:
I love it, man. It’s a pleasure to have you here on the podcast. And before we dive into Look Deep Health and the work that you and the team do there, tell us a little bit about you and what inspires your work in healthcare.

Narinder Singh:
You covered the professional background. As we were ending and about to sell the company, I just was more and more enamored with two things. One, we had bought a million-person developer and algorithm data science community called TopCoder, and I became obsessed with AI. So that was like just inkling at me at a time when I was not commercially viable thing to sell to enterprises. The second was, I’d been very involved in philanthropy for many years and had this burning desire to bring my passions after work into my daytime experience. I decided to get into healthcare. My wife’s a professor. We moved back to California, I did a master’s in translational medicine with a bunch of 20-somethings at UCSF in Berkeley, and that was a wonderful experience. For your older listeners, it was like Rodney Dangerfield in Back to School, really transformative, but got my real education in healthcare after that. My mom had interstitial lung disease, she went into the hospital for a biopsy, which apparently went well, but then she developed ARDS and crashed while she was in the hospital. She immediately had to be treated. She had then, they said, She can’t come out. She’s got to get a lung transplant. So she had to qualify for a lung transplant while in this precarious situation, then eventually made it on the list, had to get ECMO to extend her time, had a successful bilateral lung transplant, multiple infections. So she was inpatient at UCSF for 6, for 12 weeks, and then we had another six weeks where we were in the city taking care of her own health facility. So I spent 1000 hours in the ICU on the floor of the hospital watching how every element of healthcare worked from the patient perspective and, frankly, amazed because for my mom to make it out, two grandkids, five years later, is because of a thousand things had to go right. But you notice the things that were, everything was critical. And for example, my favorite nurse was this woman named Meg. If she’s listening, I hope I get a chance to thank her, and she was such an inspiration through, but not because she was caring, compassionate, almost all the nurses were. The thing that Meg was very good at is, when she didn’t have another patient, she was very good at spotting problems, but before they became an issue. So I started thinking about why can’t Meg be with my mom all the time, and then why can’t Meg be with everyone’s mom all the time? And we’re not calling the AI Meg, don’t get me wrong, but we started thinking about AI and computer vision that could watch the patient at every instance, because on the average shift a nurse is in a patient’s room, an hour or two out of 12, a doctor’s there a few minutes a day. Most of the time, there’s not attention on the patient, and every clinical provider will say there is no substitute for seeing, feeling, and interacting with the patient. And so that really inspired me for healthcare, and really, what the company sought to do is use artificial intelligence and computer vision to help watch patients to help those guardians, those caregivers, provide the best care possible, even when they weren’t in the room with your loved one.

Saul Marquez:
Yeah, that’s a great story. And what a blessing that your mom made it, Narinder, like many people don’t, and especially going through ECMO and the transplant, and man, what a blessing that she’s still with us. So I’m just so grateful to hear that for you. And it sounds like you really pulled out a lot of gems and diamonds from that observation, that thousand hours of observation. Talk to us about Look Deep. How’s the business adding value to the healthcare ecosystem?

Narinder Singh:
Yeah, so, as I start, it’s interesting because we’ve learned a lot. You start with this kind of noble aspiration of my mom needs attention, and your mom needs attention, and everyone’s mom needs attention. Then you get into the reality of the business of healthcare, right? Our focus, we were like, We’re going to build artificial intelligence. So, we did research partnerships with Duke and UCSF to build that technology, and then we discovered, wait a second, the cost of deployment is so high because video in the hospital costs $10,000 or $15,000 a room. So we’re like, okay, I guess we have to integrate with hardware and build our own hardware simply so we can give it away for free so we can lower the cost of what’s there. And then we went through and said, Wait, there’s like 5 or 6 different solutions here for e-ICU, for virtual sitting. That doesn’t make any sense. So, we have to build a software platform that covers inpatient scenarios generically. So, Look Deep helps hospitals be present at every moment for every patient, and we do that through software that’s flexible, hardware that’s included without capital expense, and then, AI that helps nudge the attention of caregivers towards the patients that need them. And it’s really important because there’s so much hype around AI. Hospitals and healthcare are too critical to rely on AI alone today, just like full self-driving cars, been almost there for a decade, but driver assist has been something that’s saving lives for a decade. So that’s how our model is using AI to nudge the attention of caregivers towards the patients that need them. Zooming back for a second, the fundamental problem for hospitals for the next decade is going to be this imbalance between, patients are getting more acute, they’re getting older. The number of people over 65 is going to double in the next 50 years. The less acute patients are getting diverted to outpatient-centric care paradigms. The average staffing level is flat or declining. So if you’ve got flat or declining staffing, more acute patients in financial constraints, something’s got to give. And so our innovation is we’re not providing better care for more money, we’re trying to reduce cost and increase the scope of care at the same time, it’s the ultimate quadruple aim of healthcare. And so that’s our focus on the inpatient side, is really trying to commoditize cost of core inpatient telemedicine and use AI to rise the level of care and support these nurses and doctors who are caring for our loved ones.

Saul Marquez:
Well, that’s really great. And it sounds like rather than have something that’s capital heavy, you guys are integrating to existing OEM monitors, different vendors, providing the hardware at no cost, but providing the intelligence that rests on that hardware as part of what you guys do to enable more, more scalable care. What would you say makes what you do different or better than what’s out there? Is it a lot of what you already talked about, or is there more?

Narinder Singh:
I’d say yes, it’s what I talked about, but the real difference is everybody talks. And so we’re in the hype cycle of AI, and as I look out there, there’s so many people that have what I call as drawing boxes on pictures and saying we have AI too. So one of the things we’ve tried to do is approach this with seriousness. I mentioned our research partners. We’ve presented at the Society of Hospitals Medicine, the American Thoracic Society, Duke Grand Rounds. So we do that to expose ourselves, to talk about what we’ve done, where there’s potential, where there’s limitations. And so from that perspective on the side, we have, we believe, more training data than anybody in the industry, and we’re using that and sharing the results of that broadly and publicly in a way that we feel aligns with what we do for clinical interventions, not the tech hand-waving, where we try to do marketing. So I think the AI, the reality of our capability is definitely the most significant differentiator. And then, yes, reducing the cost of hardware, providing our own so that hospitals don’t have to. I can’t oversell how important that is because the first thing we get to is that, if you try to apply AI in every 10th room, imagine having a EHR in every 10th room. It doesn’t matter what the system does, you just can’t make it part of workflow. So that fundamental step of saying no capital expense, you can deploy broadly and count on the technology being there is really a minor but fundamental transformation that pairs with the AI to allow hospitals to see strategic change from this, not just another niche innovation that gets applied. We’re trying to be a platform for every patient in every room in the hospital.

Saul Marquez:
That’s fascinating. And now, you know, the front door to healthcare is not just one. Is there any applications of what you do here in the home?

Narinder Singh:
Yeah, so part two, I think, that makes us unique is focus. And the reason we’re so focused is because of the AI capabilities. Taking software and moving it from the hospital to assisted living facility is not very hard. Taking the AI models and having them translate is very difficult. And so I would say this, our focus is on acute care. So that means hospitals, …, some …, and potentially hospital-at-home because there it’s a room setting, it’s trying to replicate the hospital’s, like, capability in that room setting. There we’re very strong. Outside of that, I think our, a lot of our technology applies, but our differentiator is not. So we’re pretty focused on the acute care paradigm from home to hospital.

Saul Marquez:
That’s fantastic. Thank you for sharing that. And folks, it’s something to think about, and I want to just pause here for a second to render, to just point out like when you’ve got a business, and you’re wanting to establish a niche, it’s important that you’re clear and vocal about what you don’t do because that is oftentimes more important. I was at Medtronic before, and when Omar Ishrak was the CEO, man, he just did this masterfully. He would sit in front of a crowd, and he’d be like, this is what we do, and this is what we don’t do, and let me expand, extend the list. So I think you’re doing a fantastic job of keeping us focused here, Narinder, on what it is you guys offer. It’s acute, it’s specific to that particular area, and no questions asked there. So how have, what you guys do, really, has it improved outcomes? Can you give us some stories there?

Narinder Singh:
Yeah, absolutely. So think about like our spectrum, if we get into acute care is we think about things that relate to safety, like virtual sitting. Like a lot of your listeners may not even be aware that in the average hospital, there’s 3% to 5% of patients that are watched with what’s called a sitter. That’s not a nurse, not an assistant, just somebody who sits in the room and says, Saul, please stay in bed right now, You’re in a hospital, trying to prevent falls and other kind of extreme activities. There’s been a market for virtual sitting for a decade where people will do that same thing over video. What we’re doing is expanding that dramatically because the AI is nudging the person so they can watch for more people. And so we’ve seen incredible outcomes on our virtual sitting capabilities, and what we’re really targeting is not just great outcomes on 5% of patients. How do we expand that to 15%, 20%, 30%, 50% of patients? So we’re seeing some of our hospitals deploy 20%, 30% of their senses through this approach, whereas in the past, they’ve only done 5%, and our goal is to get that to 100%. So safety is wave one, and now we’re engaging with several systems on virtual nursing. So taking the same way that we nudge a virtual sitter to say this person might be getting out of bed, we’ll nudge a virtual nurse to say, hey, this patient is not moving and is just static in bed, and nobody’s seen them for a couple hours. Should we talk to them about your pressure bundle? So nudging the nurse’s attention, so they can, a virtual nurse can support their bedside nurse by watching more patients, and that becomes the second wave. And then finally, transfers of care, virtual medicine, moving people from the ICU to the floor of the hospital. This is, we’re just now starting to get into, is seeing that can we provide overlaps so that you can move people at the appropriate time but still give them additional attention so that you can have ICU-like attention as they’re transitioning down. It’s somewhat odd, and if you’ve ever been in a hospital, you go from the ICU where you have one nurse for two patients to the floor, where you have one nurse for six, and you feel like you’ve been abandoned as a patient. And so it always struck me that there’s no smooth transition between those two, and some of the things that we’re working on the virtual side is providing that. And so, I would say virtual sitting, virtual nursing, virtual medicine, and in that order is where we’ve had, we’re turning those things into hospital-wide initiatives versus niche telemedicine solutions.

Saul Marquez:
That’s awesome. No, thank you for that. And then, I do want to take a moment to dig into the nudge, and really help the listeners understand better, and myself too, like, I’m curious. So when you say a nudge, a nudge could be a lot of things. A nudge could be a prompt on a EMR screen, it could be a vocera message, it could be a page. So talk to us a little bit about what that looks like to make it more real for everybody listening.

Narinder Singh:
Wonderful question. If you have it, like Richard Thaler, who won the Nobel Prize, has a wonderful book called Nudge that I would highly recommend. If he is the father of behavioral economics, and Nudge is like one of the paramount places on how you can, with small changes, drive larger changes in getting people to do the right thing for themselves. So in this context, here’s a couple things that are important. First is, you’ve got to respect the clinical work. You can’t try to go in and say, I’m going to change how all nurses do their job, and hey, I’m going to train you, AI, on this magical thing that you’re already little scared of because you saw the Watson stuff never go anywhere, etc. So you’ve got to respect the human change management aspect. So what we look at is Human + AI, as I mentioned. So in all of our scenarios, like the virtual nursing one, there’s a virtual nurse. And today, even before Look Deep, people are using virtual nurses to do things like admissions and discharges or try to provide support remotely. So for the bedside nurse, a virtual nurse is a good thing. It’s one of your people, one of your compatriots who’s helping and keeping an eye on things. So the first thing we said was, that’s the person we should nudge, because then the interface to the clinician on the ground, the bedside team, stays the same. It’s still one of your colleagues. And then for that virtual nurse, what we’re doing is saying, let’s say we’re watching several units, 100 patients. We’ll say, hey, here’s the patients that we think are right now at high risk. And in this case, an example, I gave a pressure, high risk. There’s usually a pretty defined protocol to turn people over a couple hours, and we can tell you that we’re nudging you based on something you understand. It’s not AI magic, the people who are in bed, not moving, and have not been visited, we’re going to escalate them to the top. And so, when the virtual nurse will see that, they’ll say already understand what that is. Oh, those are the people that are not moving in there. So we’re falling into things they understand, not saying there’s some black box algorithm. We’re trying to lean towards transparency of approach so we get more adoption, and now that virtual nurse can use our prompt of these are high to look in the EHR for what meds they’re on or things that might have changed. They can look at our data that shows day-over-day movement patterns. They can use our instant video to say, Mr. Smith, it looks like you’re a little lethargic this morning. Are you feeling the effects of the drug change we’ve made? And then, they can interface with their bedside team or their pressure bundle in their EHR, they can bring their whole context to make the decision. So all we did is take 100 patients and said, which are the first ten you should look to around this? So we’re simply getting them to use their expertise more efficiently. We’re not trying to take over the decision-making, certainly not now. And that makes it so that AI can be deployed right now, not in ten years.

Saul Marquez:
That’s awesome. Thank you for diving into that a little bit deeper. And at the end of the day, folks, it’s about exception-based management. What are the exceptions out of all of the vitals and information that you’re getting, and those exceptions, the ones that are troubling, that need attention? That’s where we’re focused here with Narinder and what his team are doing. So I love that. I think about setbacks as opportunities personally as an entrepreneur, so let’s talk about one of those for you. What’s an experience you’ve had as a setback with the company and a key learning that’s come out of it?

Narinder Singh:
So we started the company a little over four years ago, which is before COVID. And so, certainly, COVID was, on one hand, a massive setback, but it did make people more accepting of video in the hospital. The real setback I’ve alluded to already, which is just understanding that the reality of, it doesn’t matter, you have to have a financial argument, and that financial argument plus better care, that’s great. Better care, long-term financial argument just doesn’t work, and that drove us to expand our solution much wider. We thought we were an AI-only company and really were a solution, right? We have to be a solution. We had, what does the customer need to succeed? What does the hospital need to succeed? So our setback was having to move from just AI to say we do care about hardware, we can’t defer that problem. We do care about software, we do care about integration. We have to care about clinical workflow on the floor. Those are all of our problem. We cannot be an AI-only company in healthcare, and we learned that lesson very early, and I think it’s been pretty profound for us, and it really affects every aspect of how we interact. Even our business model is we charge per patient day, not installed, not, if you use it, you pay us. If you don’t, you don’t pay us. And the reason we do that is to align our incentives with the hospitals so that if they put it on the shelf and it’s shelfware, we suffer. So it’s our responsibility to make them successful with the solution, no matter how small or big that is, how close or not it relates to other features that we provide, we’re in this together with them. And so that, to me, has been a profound release and uplifting that says, there is no excuses. It’s always our problem to make them be successful with this new technology because their plate is full. We need to make it so obvious that they want to go forward with this, and that they want to expand the usage.

Saul Marquez:
That’s great. It seems like a de-risked approach for anybody wanting to test out the platform and certainly a really smart way to approach the market, Narinder, so kudos to you and your team over there of thought leaders. And again, like the thing that you mentioned and double down on right now, is this idea of being a platform company, not a pipeline company. The platform company like Narinder’s takes a view of the entire market need and helps address it, so love that. We’ve been talking about this on several of our past podcasts, and you literally hit the sweet spot with it, so thank you. Narinder, what are you most excited about?

Narinder Singh:
So I think the things that I’m excited about is, I’ve never had an opportunity in my career where I feel this alignment between mission and opportunity. And so, a lot of, and maybe this is the naivete of only being in healthcare for 4 or 5 years, right? I haven’t gotten beaten down. I know it’s hard. I know sales cycles are long. I hear no a lot. I hear all the reasons people can’t change, but it feels like right now, and I’m sure many of the entrepreneurs said this, it feels like something’s got to give. What’s happening is there’s so much attention outside of the hospital, but I laid out all the facts of why hospital care has to change in the next decade, and COVID exposed many of those. So I think there’s a willingness to change that has not been there when the healthcare hospital system revenue was up into the right. So there’s a willingness to change. There’s all sorts of barriers to that change, but that willingness to change, I think, is a once-in-a-generation opportunity. I don’t think that’s existed. I think there’s been a lot of times where healthcare ten years later looks like healthcare ten years ago, and I think everybody is pretty, pretty clear that change is going to come. They may argue how fast it comes, but when we talk to people who are thought leaders in the industry, they’re like, this is going to happen. It’s just a question of when. I feel very blessed that we get an opportunity to have that in our backs as we get to try to help hospitals provide better care for patients.

Saul Marquez:
Yeah, no, I think that’s a great call. And now is the time. And look, there’s the shift, the shifts that are happening in healthcare, the new entrance into healthcare, people are making moves, and it’s a $4 trillion, $4.3 trillion pie in the US alone annually, so there’s definitely incentive to make change happen.

Narinder Singh:
And sort of that 4.3 trillion, about a trillion and a half, is in hospitals. And our argument is that the number of startups and innovation, new innovations in that section is 1 to 100 less in hospitals. So we might be crazy and going after the highest hill, but we think there’s such opportunity for innovation there because, in the past, people have focused so much on the outpatient side that I think there’s an appetite for hospitals who want to change with new innovative solutions.

Saul Marquez:
Hey, so give me that stat again. It’s 1 in 100? Give me that one again.

Narinder Singh:
So, out of the 4.3 trillion, about 25% to 35% of that spend is in hospitals.

Saul Marquez:
Okay.

Narinder Singh:
If we go and look at the funding of healthcare investors, the last ten years in startups, in seed and series especially, that has been 50 to 1, 100 to 1 outpatient. And even when it’s inpatient, it’s like revenue cycle management, barely ever the care pieces. The care innovation has only been the Medtronics of the world or medical devices that a lot of things we’ve seen have really neglected those hospitals because they’d be like, oh, hospitals, that’s different. And I think that truism is true until it’s not, and I think we’re seeing with COVID that it’s not. Like they we’re seeing the opportunity for change to happen, and just like a lot of exponential changes, they feel like they drip along, and then they’re everywhere. And I think we’ve seen that with AI, and the problem with AI in hospitals is how do you bring it in pragmatically. And I think we’ve at least taken a good crack at solving that, so I think we’re going to see this kind of change in hospitals look like it’s nowhere and then everywhere all at once.

Saul Marquez:
Love that. Thanks for double-clicking on that one for me, Narinder, definitely wanted to dig deep into that stat. And yeah, look, I’m a fan of going the opposite way. It oftentimes has helped me in my career and in my moves, so I think it’s a great call out and an interesting fact, so definitely one we’ll call out in the show notes. And folks, by the way, the show notes have everything you need, links to things we’ve talked about, information about Look Deep Health, information about Narinder. So make sure you check that out and find ways to engage with him and the team there. We’re here at the end, Narinder, so I’d love if you could just close us out with a closing thought and where you want people to get in touch with you and your team.

Narinder Singh:
My closing thought is this. It’s my favorite quote of all time. It’s by this guy, you might have heard of Albert Einstein. As simple as possible, but no simpler. And I think that covers so much of what we’re looking at is that we don’t need magical treatments that have this incredible complexity, but we also cannot oversimplify the problems and the reality of tensions we have in the healthcare system, especially in hospitals. So, to me, that’s our guiding principle and should be for hospitals: as simple as possible, but no simpler. We’d love to engage with you. As you mentioned, our website is LookDeep.Health. My personal Twitter is @SinghNS. LookDeep.Health is on Twitter. We’re happy to engage folks. And I think you said something in the middle of the conversation, we’re all about proving. You shouldn’t believe what I said, you should force us to prove it. Our business model, our entire approach, is on that. So, we’re happy to step up to the challenge of comparing against current standards of care in any of these areas. Because if we’re not better, people shouldn’t use us. And we think that’s a driving force for this industry of change, and we’d like to lean into that. So I hope your listeners will hold us to account on that.

Saul Marquez:
I love that. Narinder, thanks for that bold close there. And for anybody wanting to take a challenge to really try out the technology that Narinder and his team are doing, it seems pretty de-risked to me. So, take a shot. These podcasts, we have them so that you could actually get after it and make a difference with the stuff that we do. The rocket is about 10x, not 10%. So, if you’re looking for that type of growth, link up with Narinder. Such a pleasure to have you on, and looking forward to staying in touch, my friend.

Narinder Singh:
I love the engagement. Thank you so much, Saul.