The Home as the Next Care Frontier: AI, Innovation & Human Support

The Home as the Next Care Frontier: AI, Innovation & Human Support

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Hello and welcome to CareSmartz360 On Air. I’m Dennis Giil, Senior Sales Consultant at CareSmartz360. The way people are receiving care is changing. As the aging population grows and the demand for home care services increases, it’s imperative to recognize that the future of care will depend on connected, proactive models centered on the home. The guest of this podcast has spent his career exploring what that future would look like. Robert Stanley is the CEO and Founder of Stay at Home Nursing Care Services and CHAH Technology

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He has a vision of building Comprehensive Healthcare at Home. A specific model designed to bring together technology, coordinated care & human expertise to help people age safely and independently at home. In this episode, Robert shares his perspective on why the home is becoming the next frontier of care delivery, the challenges of creating a more connected healthcare ecosystem, the role of predictive technology and AI – and what the future of aging at home could look like.

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Welcome to the podcast, Robert. >> Thanks so much, Dennis. It’s a pleasure to be here. >> No, we’re really thankful you were able to take out the time today for all our listeners. So without wasting any time, I’ll straight away go in with my first question for you. Uh so Robert, you spent years building technology uh companies in Asia-Pacific before returning to Canada. So what did you see in the care landscape that made you believe that home would become the next frontier of care delivery?

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>> Yeah, it’s um I think it’s something we all see if if we’re working in this industry. uh I came back and had a background as you mentioned in technology without knowing much about the health care industry. >> Okay. >> But wanting to build up my own company. And when you sit down and start looking at what are some of the areas of the greatest growth, the greatest need, the greatest chance of making an impact, >> healthcare is obviously uh something that is challenging right now that can

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can make a big difference and the home has to become the foundation of it. We look at especially here in Canada. I live in Ontario, Canada. >> Okay. >> Our hospitals are overcrowded. Our long-term care weight lists are increasing uh month by month, quarter by quarter. >> Okay. >> And it’s only going to get worse. You know, the demand for the care is only going to get more up until about 20. So, the next 25 years based upon the demographic populations we hit P82 year olds there. So the home has to become a

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place where most people want to get care. >> Okay. >> And simply because our institutions aren’t going to be able to handle it. >> Oh okay. Okay. Okay. And walk us through the idea behind comprehensive healthcare at home. What challenges in today’s care model uh were you trying to solve and what does a better model look like? So I I’m going to speak from the Canadian and specifically the Ontario experience, but I think it’s pretty common across >> uh most markets in North America and

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throughout most of the Western world as well. Europe is facing this, Asia is facing this. >> Mhm. >> We have an aging population. >> Um we deliver care in episodes. We we we go out and we do a visit. We do an appointment at the doctors. we do a a procedure at the the hospital. >> Mhm. >> But health care is happening in between all of that, right? So when somebody shows up at the ER uh because they’ve fallen and they’ve broken a hip, which is a terrible, terrible situation for an

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elderly person to be in, >> it’s not coming out of the blue. There’s weeks of information before that that is leading up to the fall. >> Okay. >> So the idea of comprehensive healthcare is built on two fairly simple pillars. simple to say but hard to implement. We have to shift more care into the home. One, >> two, we have to become more predictive and be able to see that fall and hip fracture before it occurs and take actions to prevent it. >> So, we’ve built a tool that does this.

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We we can analyze and watch in real time 247 what’s happening in the home. >> Okay? >> Uh and then we have AI that is doing two big things. We can detect adverse events. So we can detect that a fall has occurred and and get help out right away. >> Mhm. >> But even more, we can predict we can predict based upon mobility patterns that somebody is at a risk of fall and send an occupational therapist or physiotherapist out to try to change that trajectory before the fall lands

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you. uh we can detect that somebody is at risk of a urinary tract infection >> based upon frequency of washroom visits combined with um a mattress pad that we have that gives us temperature. So we know that slightly elevated temperature and increased washroom visits there’s a risk of a UTI um we can now send a nurse out because we have integrated care with this within an hour or so. >> Okay. >> Um do a dipstick test and diagnose whether it is an actual UTI uh or not. And the sooner you catch that, the much

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higher chance you have of preventing it escalating into a hospitalization. >> Wow. Uh, great to know about these things. And your model uses technology and predictive insights as was just telling to help care teams identify needs earlier. So, how do you see data and AI changing the way organizations support people aging at home? >> I mean, I think we have both an opportunity and a challenge. Um and again across most of North America this is is prevalent that the data is fragmented and broken into silos. So

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what the doctor in the you know your primary care doctor knows versus what your surgeon knows at the hospital versus what your home care company knows. >hm. >> They don’t necessarily talk very well to each other. But there is a lot of work being done and I think on our side with CHAH because we are watching in the home 24*7 we’re in one of the better positions to know what’s happening with the health changes that are occurring. >> Correct. >> And if you can predict I’ll go back to

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the UTI example I just gave. >> Yeah. >> Um in in Canada uh 325,000 people were hospitalized for UTI back in 2013. >> Oh. The cost of a hospitalization for a UTI is $20,000. So it’s like 10 days in hospitalization for uh antibiotics and and uh IV antibiotics and rehydration. So that’s $6.5 billion and 325,000 people that are hospitalized. But there’s a worse statistic on that. >> One out of every 12 people, 8.1% uh don’t exit the hospital. It’s an 8.1%

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mortality. So about 28,000 of those 325,000 people passed away in hospital because of the UTI. Uh >> oh. >> If you can catch this early enough, >> yeah, >> you can prevent the hospitalization with about $20 worth of oral antibiotics. So, when I’m talking to our team, our team of nurses and care providers out in the field, >> if we can if we can identify 12 people, every 12 people we identify that have a UTI that we keep out of hospital, >> we’ve just saved the Canadian health

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care system a quarter of a million dollars for 12 people staying out of hospital. >> And we’ve also statistically at least prevented an avoidable death. >> Wow. Great. Really, really great work. Really great work, Robert. And uh in home monitoring raises important questions about privacy and trust. I was also thinking about this. So how did you approach designing CHAH’s technology model so that people feel supported rather than surveilled? >> So I I I think it comes down to a few

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different answers to that question. It depends on who we’re talking to and how we’re talking to them. Um privacy is built into the system from the ground up. So the way we work is we have various sensors and detectors in the home >> and we have what’s called a child support hub that gathers that information. >> Most of the detections and predictions are going on in the home. We have a fairly the support hub is a an advanced AI machine that sits on top of a bookshelf. >> Okay.

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>> The entire system is ambient. So the elderly person doesn’t have to interact with it. Um it’s very helpful with people with dementia. it it just monitors >> and the only thing that comes out of the home then is if an alert occurs. >> Okay, >> that goes straight to our uh EHR tool that we use for managing all of our clients and schedules. >> And because I I operate both a home care company, stay at home nursing, >> and Shaw Technologies, which is this

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advanced AI solution we’re talking about, we fully integrated the care. So when an alert comes out that somebody is at risk of a follow fall, it shows up on the dashboard for my care designers who are the nurses who oversee care and they schedule a visit, you know, that day to go out and see what is changing about the mobility patterns that might lead to a fall >> and maybe we recommend installing some grab bars or change from a cane to a walker >> um to try to avoid that. So the the

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privacy is is built in that most of the detections happening at the home. No human is actually looking at it. It’s just a machine >> and the only time that we’re interacting from the human level >> is to provide preventative care. Uh so what we’re finding we’ve we’ve got a very limited number of clients right now we we’ve built it and deployed it. It is live. >> Um but everybody that’s using it right now loves it. uh there there haven’t been privacy concerns expressed to us

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>> because it provides 24/7 peace of mind for the families and and sometimes for the clients depending upon their cognitive state. >> Yeah, correct. Correct. And Robert, you have drawn inspirations from models like butorg and neither lands if I’m pronouncing it right and pace in the US. So what lessons from these approaches influenced CHA and what needed to be adapted for the Canadian healthcare environment? >> That’s a it’s a great question and it it steps away from the technology for a

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second. >> Okay, >> I’ve talked about, you know, being able to send a a nurse out if we detect that there’s a risk of a UTI within an hour. >> Um, we also detect a dementia patient that is at risk of exit seeking. Mhm. >> And so within our model, if we detect that a dementia patient has gone out on a cold winter night, um that becomes an alert that comes to a 24/7 monitoring team I have >> and they’ll call 911. The person’s exited, they’re out in the winter, we’re

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going to get emergency services looking at them. >> Mhm. But if the person is just showing agitation and is at risk of exiting, instead of taxing our public system, which is already over overburdened, uh we send one of our PSWs out at 2 a.m. on a Sunday night >> to be able to settle the person in bed before they end up exit seeking. So, we’ll sit with him. >> So, what I’m talking about now is a 24/7 response model that when we get these alerts or predictions, we can respond.

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We’re not 911 fast, but within an hour or so, >> we’ll call 911 if it needs something more urgent. But if if it’s not urgent, the person fell, but they got back up, we’ll send one of our staff out. >> To do that, we needed an entirely new working model for the staff, which is full-time employment, where we have what we call small care teams, >> okay, >> that work 40 hours a week, five people on a care team. um that covers the full 247 rotation and allows us to be able to

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send people out uh into the community at any time. The the Bertzog reference that you mentioned there is something that’s been going on for years in the Netherlands and it’s about building these small empowered teams that I’m talking about our five person team >> that get to know the clients better and and actually feel more responsibility towards them. So it it changes kind of the the level of interaction and the quality of the care quite quite substantially. >> And is it the same the pace in US is

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that’s the same or it’s a bit different. >> Pace in the US is a little bit different. Both both models have influenced us and we’ve taken a look at them. Pace is about a holistic approach to health care >> in the home and providing kind of a range of services. So Bertzog is more about the workforce model and empowering people that have more reliability in their jobs, more empowerment and and as a result deliver better care. >> Um PACE is about delivering a wide range

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of services which we do through the predictive technologies to say >> you know one of the things we monitor is nutrition. Um, so are people eating regularly? And if we detect that >> their meal patterns have changed significantly, again, that becomes an alert that we might work with the family to offer or suggest uh that we bring in another partner that would do meal services for them, for example. And that’s what PACE does a really good job of already. >> Okay, what PACE does. And your model

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combines predictive AI with trained care coordinators responsible for a group of clients. So, how did this combination change? what care teams can identify, prioritize and act on before a situation becomes a crisis. >> This is, I think, one of the biggest challenges in technology and healthcare. Our our nurses and our PSWs and people out in the field uh are incredibly dedicated, incredibly talented, incredibly caring people, but they spend most of their days dealing with, you know, for example, our nurses doing

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wound care can debride a wound. Mhm. >> Uh they don’t spend hours sitting in front of a computer. So bringing advanced technology in, we have to fit it into the workflows that they already have. >> Um and that’s that’s what we’ve been able to do because we have both sides of that that coin, right? We have the home care operation that’s been running for 10 years. >> Yeah. >> So we we built the entire CHAW solution, the technology side to fit straight into

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the existing clinical workflows. Um, we do daily standups on our clients to identify risks. We’ve been doing it for years. >> Uh, if we if we have a progress note that suggests, uh, you know, Mr. Smith is showing a little bit of cognitive decline, you know, what are we going to do about it? How are we going to coordinate with the family? Now, we’re just adding to that with an insertion from CHA saying Mr. Smith’s mobility has significantly declined. His nutrition has declined. and and this becomes an

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automated alert that our teams are already used to dealing with similar kinds of alerts. Uh and that you know it just helps surface things that we might not notice through the busy demands that home care and healthcare generally provide to us. >> Generally, does. Okay. Okay. And as predictive, AI becomes more common in care delivery. So where do you believe technology should lead and where should human judgment always remain central? I I it’s a great question. I I think that humans are the heart of care

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entirely >> and that AI always has to be just an assistive tool to help manage our time and our focus. Um I’ll give you a practical example. Sure. in in long-term care or in in retirement homes. Um if we have a number of clients in a you know a 300 bedew facility that are incontinent, the nurses and PSWs kind of walk around to each room throughout the night to check to see if if there needs to be a a brief change or or something else. Um, with technology now we can detect moisture in the bed with the bed

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mattress pad and we can instead of going around to a hundred different clients that are at risk of doing this, we can send our resources out much more efficiently, >> but it will be still the human going out to do that. We’re just getting them smarter about where they go and where they spend their time. >> Okay. And uh finally, if CHA scales the way you envision through 2035, how will the experience of aging and receiving care at home look different from what people experience today?

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>> So remember, CHA stands for comprehensive healthcare at home and uh we do have a white paper on our website www.cha.ai/witaper I/W white paper >> okay >> that goes through our vision and it’s not a vision for us it’s a vision of transformation for Canadian healthcare where the home becomes a central place for delivering health care as much as your doctor’s office or even a hospital the idea in 2035 of having a fully robust hospital at home >> uh we we have a lot of programs in

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Ontario and across Canada right now for hospital to home I’m discharged from hospital and I get a certain amount of personal care and support from nurses for the 8 weeks or 16 weeks after I return home from hospital. >> Okay. >> But in the UK for example, they have over 10,000 hospital at home beds. >> Okay. >> And this is you know with the vision of what CHAW is and not just us as a company but the broader vision of being able to deliver healthcare at home. I I think this idea of making the home a

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central location for care is is inevitable. We we just don’t have enough beds for the for the demands and the population that are there. So, we have to figure out a way of delivering truly comprehensive health care, primary care, specialist care, acute care, and long-term care in the place where almost, you know, 99% of people would prefer it in their home that they’ve lived in. >> Wow. Great. It was really a very wonderful session. I would say I really liked it and I hope our listeners will

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also love it when they go through this and it was lovely lovely talking to you Robert. I would love to have more conversation with you in future too. >> Thank you so much Dennis. It’s been a pleasure and uh we’re we’re all in this together. So I appreciate everything you guys are doing as well to um help support us and and and the world out there for delivering better care. >> Yeah. >> Thank you. Thank you for your kind words and thank you everyone for your time today. Thank you my listeners and I’ll

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be back shortly with you with my next podcast. Thank you. >> Thanks so much. Take care. >> Take care. Bye.