LookDeep Health Intro & Platform Demo

Narinder Singh:

Hi, I’m Narinder Singh the CEO and co-founder of LookDeep Health and I’m here today with our Director of Nursing Tiffany Wyatt. To discuss AI telemedicine and the future of care in the hospital. Tiffany, I wanted to share some of the conversations we’ve had internally regarding the future of AI and hospitals. I thought we could look at this from the perspective of the provider hospitals and their operations it and then wrap up from the patient’s perspective. But first, why don’t we go and do a couple of quick introductions. So let me start with you.

Tiffany Wyatt

Thanks Surinder, I’m very happy to be here to discuss the future of care in the hospital setting. My name is Tiffany Wyatt. I have been in healthcare for 23 years and a registered nurse for almost 20 years. Most of my nursing experience was in hospital setting in specialty care units. I moved into the telehealth space about five years ago, where I built and managed a team of nurses taking care of patients remotely.

Narinder Singh:

Thanks, Tiffany. And my background starts in enterprise technology. I grew up in Cincinnati, Ohio, moved to the valley, and have been part of a number of fortunate trends related to enterprises in the internet, the founding of cloud computing. My last company, Appirio, was the first investment from salesforce.com. And we partnered with them Google Enterprise and workday to help enterprises use this new technology to transform their businesses. I decided to move into healthcare, went back to school and did a master’s in translational medicine at UCSF, because I cared about how innovation had occurred in hospitals. And I got a different kind of experience soon after that. My mom had interstitial lung disease. She went in for a biopsy, and she developed a complication. She was intubated two days later, all of a sudden, what was supposed to be a fairly routine procedure was a life and death struggle. So she had to qualify for a lung transplant had to receive ECMO to extend her time as she waited for lung and then eventually had a successful bilateral lung transplant. So I spent 1000 plus hours in the hospital over 12 weeks watching how care her care was delivered. And I was struck with how impactful my favorite nurse Meg was, especially when she didn’t have other patients and was able to focus on the acute changes that may be occurring with my mom. And so I started thinking about, well, why can’t Meg, that was her name, be with my mom all the time. And then I started thinking about why Meg couldn’t be with everyone’s mom all the time. So then we started the journey to LookDeep to really use AI and artificial intelligence, to watch patients in the hospital for every second of their journey to help nurses and doctors care for them when they couldn’t be with them in the room. And so this, to me is a very impactful and personal journey, that I want that kind of care and attention to be part of every patient’s journey, we were so fortunate with my mom. And I know that it was really a circumstance of the capacity of the day in unit. And so I’m really hoping that through the technology LookDeep develops that we can actually help hospitals be Ever Present for Every Patient. So Tiffany, with my long announcement, and maybe you can start by talking about the perspective of providers and most specifically the nurse talking about what got us to this point and what you’re seeing as the risks and opportunities of AI.

Tiffany Wyatt:

Sure, nursing has always been hard and historically nurses didn’t influence the decisions being made on technologies that impacted their workflow. COVID happens and it exacerbated the challenges already affecting nurses. And we saw historically high burnout and the massive nurse resignation. Hospitals were desperate to get nurses to take care of patients, the cost of hospital costs exploded. And suddenly the contribution of nurses were seen and valued. Because of this, the nurses had more of a voice. Now there’s a real interest in addressing that long term. And if we’re honest, it’s because hospitals have seen how expensive it is to not listen to an invest in their nurses. Part of the investment in nursing is the interest in pilots for that for Virtual Nursing has exploded. Interestingly, at the same time, we are seeing a real backlash against AI in the hospitals coming from nurses.

Narinder Singh:

And how do you resolve these two things this Virtual Nursing interest and the skepticism for AI?

Tiffany Wyatt:

Change is hard for nurses, especially when they have been burned by technology in the past. We need to remember the technologies that have made nurses’ lives easier. For instance, IV pumps. IV pumps have come a long way in assisting nurses with programming the right dosage and helping to keep air on the line. I discussed in a recent blog how AI could be an ally for nurses, but first, we have to explain it clearly in ways that are relevant. AI has not been explained well to nurses and some see it as taking away their expertise and decision making. It’s important to know that the AI is not taking over the nurse, it’s adding to the eyes they have on their patients. This combination of nurse plus AI will be accepted by the nurses if they can experience how it will help them. For example, AI can help ensure nurses are not in dangerous situations for potential violence, and AI can help watch to make sure the nurses’ patients are safe while they’re with another. However, it’s critical to have nursing input so the AI programs are adopted and successful,

Narinder Singh:

Good scenarios but kind of more concretely, can you talk about how we bring AI into the areas that nurses are going to most value.

Tiffany Wyatt:

To live up to the expectations of Virtual Nursing, we must incorporate AI. Here you can see the progression of the sophistication of Virtual Nursing. Virtual care pilots started because it’s an investment in resources to support bedside nurses. A Virtual Nurse will take time consuming admits or discharges off the bedside nurse’s plate. From there we move to more general support, most commonly seen as mentorship, or preceptorship. For novice nurses, also ancillary support for rapid response or early warning functions. Ultimately, in this healthcare landscape, and less, it also helps hospitals do more with the same resources, Virtual Nursing pilots will fail to be deployed at scale. So we need AI that helps care to be everywhere at once, the AI acts as another set of eyes on the patients helping nurses know where they are needed.

Narinder Singh:

Tiffany, I want to drill into this part of the problem that you’ve described. I do believe you believe that AI is an industry, we’re pushing on these amazing new capabilities, without understanding and differentiating enough between automatically creating a website and a decision where someone’s life is on the line. This naturally doesn’t go over well with healthcare and nurses. And we already have a blueprint of how to do better, the self-driving car story. We started this journey 30 plus years ago, and we incrementally have gotten to that we’ve been almost a self-driving car for almost a decade now. And so what we found in self-driving cars, and we’re certainly going to find in healthcare is that for some time, human plus AI is better than just AI, a nurse plus AI supporting that nurse is going to be better than AI has taking over decision making. And so I believe that this is a fundamental way of getting that adoption from these nurses, because they’ll see that this is a partnership, not a new boss or replacing their judgment, as you alluded to. You’ve talked about how important workflow is can you talk through this workflow and walk us through how this might impact the Virtual Nurse and the bedside nurse?

Tiffany Wyatt:

Absolutely. I’m a nurse, so I prefer seeing it versus just videos. So let me bring up our virtual care platform live. And I’ll start by showing something important to patient care but adjacent to Virtual Nursing. This is our virtual sitting module, you can see videos of patients, you can also see that there is a safety zone on each patient. This virtual safety area is AI created. While created by AI. This can be managed by the Virtual Sitter and adjusted to each patient. Now you can see when a patient moves inside the safety zone region, nothing happens. But as they tried to leave the bed, the AI nudges a virtual center. This is powerful because it shows how the computer AI helps us care for patients. The Virtual Sitter is still the one deciding if they should intervene, talking to and redirecting the patients to keep them safe, or activating an integrated nurse call system. Most of the time Virtual Sitters can redirect the patient and reduce alarm fatigue for the bedside team.

Narinder Singh:

That is powerful Tiffany, I think now one of the things that we’ve looked at is this alignment with workflow and how we can take it even further. So with this capability called Infinity Watch, we’re now allowing a virtual center or virtual monitoring tech to go from watching 10 or 12 patients to watching 50, 70, 100 or even more, literally our vision AI capability can watch hundreds of patients and then surface the ones that need the attention of the Virtual Sitter. So now they can take action only when they’re needed and use their human judgment to adjudicate the final step, but they’re having the AI watch everyone. So this allows us to go from a niche Virtual Sitter program to a hospital wide fall prevention strategy, because we can cost effectively monitor every high risk patient for false.

Tiffany Wyatt:

That notion of becoming a program is critical, because if you look at the National fall rates over the last decade, we haven’t seen anything close to the claims of reducing falls with the one to one sitters at a macro level and that’s because they just watched too few patients. I want to apply this concept of AI that’s watching many patients to the Virtual Nursing discussion I opened with before. So here you can see, I am in the Virtual Nursing app, where the Virtual Nurse controls his or her day, I can work on my discharges and admits or respond to mentoring help. And let’s drill into this list of AI pressure risk. Here you can see it’s showing me all of the patients that are at high risk for pressure injury, the AI is watching everyone, and adding to this list those who are in bed, not moving very much and have not had care activity in the last two hours. What the AI is doing is very basic and easy to explain how it’s watching every patient in the hospital to get information is the power. If I drill into one of these patients, I can see the supporting information the AI is providing on their patterns, how they slept, if they’ve been spending most of their time in the bed, in the chair, or elsewhere, how they’ve been moving hour by hour or day over day. I can also open up the EHR and see if their medications have changed. Or if there’s a new lag values I should look at. And of course I can do a video visit with the patient and talk to them directly.

Narinder Singh:

Thanks, Tiffany, that’s helpful to see through the eyes of a nurse, I want to switch to something that’s very personal to me, I know you as well, the patient’s experience. With my mom’s journey, part of the reason that I was there all the time that if I wasn’t I was cut off from all the information, I couldn’t see what was happening, I couldn’t get a sense of how she was doing. I couldn’t translate her cues on things like was she in pain or was that just anxiety. And because English was her second language, these were harder to determine. But even more critical because her moving around was the difference between her being strong enough to get a transplant or not being able to qualify.

Tiffany Wyatt:

It’s such an important powerful point. When I was a nurse at the bedside, I saw this same scenario often with patients and their family. And personally, at the beginning of this year, my mom became suddenly ill and ended up spending a month in the ICU. Having family connect while she was in the hospital multiple states away would have made it much easier for me and my family to support her and be part of the care by attending daily rounds for example.

Narinder Singh:

We’ve hit on just a couple of aspects Tiffany of our platform, we talked about virtual sitting a little bit about Virtual Nursing, about connecting families. Overall, we want to be able to serve any aspect of acute care, because we want it to be something that’s available for every nurse, every doctor, every therapist without thinking about it. And that’s how we can actually impact the workflow of a hospital versus creating yet another one-off exception case. Now LookDeep, we believe that we’ve got the most flexible and future proof platform to do this the strongest AI support and we’re most aligned with the hospital itself and what they’re trying to do for your patients. I want you to talk a little bit about how we do that and how it relates to your current and past work as a nurse.

Tiffany Wyatt:

I believe flexibility is critical so that we can match the workflows at the hospital and evolve with them over time, instead of making them adapt to us. As I’ve said today, the AI component as I see it can be a massive benefit to nurses. It really can be a win win if the AI is real and aligns with the workflows. But the last and most important point for me as a nurse is we are taught to put patient safety and care above all else. And it’s critical that all parties involved want that as well. Even though everyone says that’s the case, creating long term locking in contracts and only succeeding if the solution is adopted is not what is best for patients. And I am proud of how we are structured and hope everyone in the market copies us.

Narinder Singh:

Me too, but I hope they’re a little slower to do it. Seriously, I couldn’t agree more. There’s a massive opportunity to help patients, providers, and hospitals create a better and more sustainable platform for care. And anybody who’s doing that we can commend, even if we’re competing, I want to open it up to questions and walk through some of the things that we might have interest on. The first one is for me.

Q&A

We are doing a smart hospital initiative, how do I support that? Or do I have to replace what I have with your technology?

You can absolutely leverage what you have, we provide a solution that can be an all in one solution that without capital expense, you can deploy in days, but it can also augment what you have already. Or if you have a full infrastructure, we can deploy just our AI engine and leverage everything that you have. Over time, we expect this is going to be more and more what happens is hospitals build up the infrastructure as they construct new hospitals. And we think that in the future cameras are going to get better and cheaper and faster. So we’re constantly going to have to change what we’re connecting to, because of those capabilities advancing so fast. And so we want to be able to leverage what you have today. And anything that you might leverage in the future.

Your technology looks very compelling. But what about the resourcing in my hospital we have are challenged to actually even get nurses?

Hospitals are varied. Some will need a ton of support for resourcing and others will not. You want to look for a partner that can understand your hospital’s unique needs. That may be supported with a permanent placement of Virtual Nurses or On Demand Virtual Nurses, perhaps you need help only with discharges on weekday mornings from eight to one. We believe hospitals need the flexibility to have staffing models that meet them where they are at and can evolve with them over time.

Everyone is talking about AI? How do I know what’s real? And what’s just hype? 

I’ll tell a little story to start with. I came over from enterprise technology where there’s tons of marketing around software that can make you thinner or sell more market better and expected a bit more literacy in healthcare where the FDA is the panel, the ultimate guide for medical devices. I expected that to trickle down. And to be frank, we haven’t seen that. So we think transparency is the key. There’s two things to help start with that. The first is can you see the AI that someone’s claiming work in their actual product as a customer uses? Not in a video of it? Not in some internal sandbox instance they have. But can a customer show you what it’s like to use some aspect of that? The second is have they publicly shared information in a peer reviewed session, a peer reviewed conference, poster, publication, somewhere where it’s not just you paying to speak and talk about how great your solution is. There’s a very famous company that claimed incredible technology but refused to share any of their data in peer reviewed formats. And wouldn’t even expose that to customers expect under an NDA. That company was Theranos. We’ve seen similar examples of that with things like IBM Watson, there has to be evidence and evidence of intent to transparency, or else we have a real risk in the industry of over optimism or bad actors, souring nurses and doctors on the technology at a time when we already have concerns. So we’re huge believers in that transparency, and being able to share the direction that we’re going so that we can all advance together.