130. How Predictive AI Is Transforming Senior Living with CarePredict

Matt Reiners sits down with Satish Movva, founder and CEO of CarePredict, to explore how predictive analytics, wearable technology, and AI are reshaping senior living. Inspired by his own experience caring for aging parents, Satish built CarePredict to provide deeper insights into resident wellbeing and help caregivers move from reactive responses to proactive interventions. 

Matt and Satish discuss practical applications of AI in senior living, the differences between wearable and ambient monitoring technologies, and how communities can evaluate technology partners beyond the hype. Satish shares real-world examples of how predictive insights can help reduce falls, identify health concerns earlier, optimize care plans, and even predict staff turnover. 

Discover more about CarePredict on their website.

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Transcript

Matt Reiners: [00:00:00] Welcome back to another episode of Raising Tech. Today, I am joined by the founder and CEO of CarePredict, Satish Moova. Um, and, you know, we're gonna g- go into it a little bit more, but CarePredict began in 2013 as a simple idea from Satish, who was searching for a better way to care for his parents from afar.

What started as one family's need has grown into a broader movement, transforming how seniors are supported in communities around the world. At its core, CarePredict believes better care starts with deeper insight. Drawing from personal experience, the company has built a platform that gives senior living operators the data and tools to enhance resident wellbeing, improve staff efficiency, and deliver proactive, personalized care.

By identifying meaningful patterns in the daily lives of older adults and translating those patterns into actionable [00:01:00] insights, which is always key, Satish, uh, as you know, uh, CarePredict helps communities recognize changes earlier and respond with better care. So Satish, thanks for joining me today. I'm excited to jump into it with you.

Satish Movva: Oh, absolutely, Matt. I'm looking forward to this, and again, thank you for the opportunity. Um, you know, half the time it's, uh, you have to shut me up, 'cause otherwise I can go on about this forever 'cause I'm really passionate about this stuff. 

Matt Reiners: Of course. Hey, I can tell, especially whenever we're trying to help our parents, right?

I find those companies that are built from, like, personal stories, like, you know, it becomes your own baby to help as many people as possible, so I love that. Um- 

Satish Movva: Yeah ... 

Matt Reiners: and, and I know we're gonna get into that, 'cause obviously I had mentioned in the intro that, you know, CarePredict was founded from that personal caregiving experience.

I'm wondering, like, how has that original mission continued to shape the company and your approach today? 

Satish Movva: Yeah. No, that's a great question, and as you mentioned, I really started this company to take care of my own parents. Um, you know, back in 2013, they were in their 80s, and they were going through a bad spell with their [00:02:00] health, and even though I lived close by and spoke to them every day, uh, when I showed up in person, I'd find these new and interesting things that caused me to, to either take them to the emergency room that day or to take them to specialist appointments the following week, and this unpredictability in their health was just, um, completely messing up my life, 'cause I'm trying to raise a family, juggling a career, and taking care of parents, and I'm not alone.

You know, for the first time in human history, we are the largest cohort of humans still taking care of parents while taking care of children, and I fit square into that. And I think, you know, that the m- the reason why I started the company then was really to get objective data about my parents to truly know how they are doing without me having to necessarily lay eyes on them, and that part of the mission hasn't changed.

Now, when I created this company, it was mainly for adult children of seniors to help them in, [00:03:00] you know, taking care of their parents. But very quickly in the life cycle of the company, we pivoted more towards senior group living and taking care of a larger number of people at one time, rather than having them spread all over.

And it really allowed us to scale faster and better by going into that, uh, into that, uh, uh, industry. In terms of, you know, today, you know, the, the, the arc that connects all of this, Matt, is that I started the company to take care of my parents, and my parents are ... You know, my dad is still here, but he lives with me.

He's 97 years old, and I'm his primary caregiver. So for me, the mission has never changed. The mission is ever present in the morning every day. When I wake up and see him, I know why I am doing this, and that part of it has not changed. I think where we have changed a little bit from that early ideas about learn the data, get the objective data, [00:04:00] we really started getting the insights, specific insights from the data that help senior living operators get the return on their investment in CarePredict.

And the return on investment is, is amazing in terms of not dollars and cents, Matt. You know, everyone in senior living are doing it mainly because they are mission-focused people. So where we are getting the return on value, return on investment, the value, is in longer lengths of stay because operators are taking better care of these individuals 'cause they're anticipating issues, preventing issues.

So the person is staying within the building much longer and are having a better quality of lives. That part is what has changed, you know, in senior living. Now we actually have the value and the return on investment being demonstrated in a happier, safer resident that's living longer 

Matt Reiners: I love that. Yeah, and I find, you know, things like [00:05:00] CarePredict just allow staff to be smarter, right?

Like, I think when you're dealing in some of these outside industries- Yeah ... you know, right, you're selling widgets or this, you know, some of these other things, but when you're dealing with people, right, like how do you get smarter about people? And that's where you start to look of opportunities to predict their care.

There you go. Um, but I'm wondering- Yeah. ... 'cause obviously, you know, CarePredict started, uh, you know, over a decade ago, but, like, if you look at the last five years, you know, and I think 2021, it's crazy how fast time goes, uh, like how have you seen the senior living industry change both operationally and its openness to new technology?

Satish Movva: Uh, that's a great question. I think I would say, you know, uh, pre-COVID and post-COVID, it's a completely different market landscape. That really has been the inflection point where I think, to an extent, senior living suddenly woke up and found out, hey, we are not just a hospitality model or a social model, we are part of the healthcare model for our residents.[00:06:00] 

You know, our building is the residents' legal home. If they are gonna get care at home, that means they're getting care in our building, and we are part of that equation whether we like it or not. And I think that mind shift would not have happened, um, without COVID. That really brought it home to people.

And now there's a divergence the, in the industry. Vast majority are seeing, you know, we gotta do something clinical, we gotta be involved in the clinical side of things, whether that's directly with the operator involved in the clinical healthcare side of things, or if they're participating with a value-based care company that is bringing in those practitioners or primary care physicians through the building, uh, multiple times a day.

Um, either way, you have healthcare in our buildings today for certain, and that really w- would have never happened if it wasn't for COVID. Now, pre-COVID, the height of [00:07:00] technology in, in our buildings, and pardon me for saying this, was just nurse call stuff. You know, push a button on a pendant and get somebody to, a staff member to show up, or, uh, some kind of a light board outside your door to light up so people know you need assistance.

That literally was the peak of technology when I started CarePredict and we started selling it into senior living. And that has, you know, kind of changed to the point now people are expecting a lot more. People are expecting, and the market has caught up to what we pioneered back in 2013, which is getting all of these activity behavior signals from each individual, using that, using deep learning models and machine learning models, which by the way, we did those back in 2014.

So we are AI native right from day one, and not some new company saying AI. We've been doing AI since, uh, 2014 onwards. Uh, be able to actually show that the use of those facets of AI [00:08:00] actually results in predictive patterns and predictive results that you can actually take and use to prevent issues and retain your residents longer and, and healthier.

Um, I think the industry's kind of gotten to the point where now they're saying, "Okay, that should be the baseline of how we view technology." It's no longer about nurse calls and things like that, which are purely reactive, right? I mean, those were all reacting to an incident that already happened, and people are seeing that they really got to get ahead of stuff to be able to solve for things.

And I think going back to something you said earlier, um, uh, when you, when you asked just prior to this, uh, the preamble to this question, you talked about staff. You know, right from, you know, when I initially started this and we deployed into senior living in 2015, in pilots between 2015 and 2017, and commercially in 2017, we initially only looked at the resident side of [00:09:00] the equation.

We never looked at the staff side originally. But starting around 2016, in, in the very first pilots that we deployed to We had the executive directors come in and say, "Hey, you know, we really need to also get the data about staff as well. It's not just about the residents." And so right from 2016 onwards, we started measuring actual staff interaction time with each resident.

Mm. So we know how much time each staff member is spending with each resident anywhere in the community, not just their bedroom or bathroom, whether in the common area, wherever it is. And once we started getting that data, then it became very clear, you know, there are some people who consume a lot of care minutes, and there are some who don't.

And you really need to align your care plans correctly to make sure that those who are using up a lot of your staff time are at the appropriate care level [00:10:00] and care plan, and that otherwise you're, you're gonna have staff burnout because you're not staffing properly for the acuity of your level of your building.

And you also know who should be at a higher level care so that you're getting, uh, reimbursed, uh, commensurately to the care you are providing. And that is super important because if you step back and look at this industry, what is our product? Our product is care. You know, you put your mom or dad in assisted living, you're expecting care, you're buying care.

But our industry as a whole has been really poor at actually measuring care. How much care are we providing? How much care is somebody consuming? Is that commensurate with their level of care billing? All of that. For the first time with CarePredict, since 2016, you get down to the minute and second how much care a person consumed anywhere in the community, and [00:11:00] that lets you really right-size care plans and figure out, you know, is this person need to transition to the, to the next level?

So they start out in IL, they're in AL now, but then as you see the level of dependency increase and levels of care increase, and at some point you start seeing, you know, um, wandering and things like that, is it time to move them more into the, um, uh, you know, into the memory care side? So those care transitions, you can only navigate them smoothly if you have enough data to understand when that inflection is coming and react before that's there.

You know, way too often in senior care, we wait for a precipitating event to change, to shift Right? So you're taking, if somebody is living at home, when do they first suddenly think about going into an assisted living? Something happened. They fell, they break their bone, and then the family's like, "Oh my gosh, you can't take care of yourself.

You need to move into senior living." But even when they are within senior living, [00:12:00] there are levels and gradations, and they need to move through those, and you need to be able to track where they are at in their journey and be able to figure out that next level of dependency, and be able to transition them prior to that so that it's a very seamless, smooth flow and smooth arc to their aging cycle.

Matt Reiners: Yeah, I love that. And I know you had mentioned it before, but it's like, how do you take this reactive approach and just make it proactive, which obviously you guys are doing, and it's, it's always cool to talk to people who've been on the forefront of AI in this industry. You know, using AI before it was cool to use AI, right?

And, like, it's so funny now that I'm in this, uh, technology consultant role where I feel like every platform I look at is either talking about- Yeah ... how they're using AI or anticipate using it. Probably some, uh, you know- Mm-hmm ... building some sort of vaporware, but hey, that's on them. Um, but you know, it's all, it's all in the conversation right now, and I know you've touched on it a little bit, but, like, what are some of the most practical and valuable uses of AI in the senior living industry today?[00:13:00] 

Satish Movva: Yeah, absolutely. I think there are, there, there are a few. I mean, I'll start with what we do that's different so you can get an idea of what we do, and then what I'm seeing some of the other, uh, folks in the industry doing, um, which are also bringing a lot of value as well. So I think, you know, AI is a very broad term.

If you were to go back and define AI 10 years ago, people would have said, "That's machine learning. That's deep learning." And along the way, it became more large language models and generative systems and things like that, uh, which are the LLMs and things that people talk about today when they say AI. But I think in terms of AI, um, the, the things like machine learning and deep learning are incredibly powerful in consuming and ingesting vast amounts of data points and spitting out an insight that's buried in those data points.

That, I think, has the highest value today in senior living, mainly because of the [00:14:00] type of business we are in. We are in the business of taking care of older adults on a longitudinal or a long-term basis. For that kind of a model, you're going to be generating a lot of data points each day on them and on their behaviors, their activities, their physiological metrics.

And if you're taking all of that longitudinally, then you can actually start getting the trend lines and the predictions, predictive analytics based on those to see when the inflection points and when degradations in their quality of life are coming up. That, I think, is still the most valuable thing, uh, uh, valuable use of AI in senior living today.

With the newer technologies of AI, like large language models and stuff like that, there are two different, uh, areas where they bring value. One is summarization of large quantities of data into actual [00:15:00] actionable insights. Now, provided you have all of that data, you've done your predictive analytics, now you can feed those into LLMs and say, you know, very quickly ask it questions, um, related to the data or have it proactively present insights to you based on that data.

Now, this is all based on data, so you need to have that baseline data before you can do any of these other things. Uh, so that's one. I-- Because the, the way, uh, we-- it works in senior living with caregivers so driven, interrupt-driven to keep taking care of different things throughout the day- It's very hard to go sit in front of a screen and read analytics dashboards and stuff.

They need data presented to them at the point of care, at the point in time that they need it, and it has to be actionable to that individual. So one example of data, uh, you know, would be in a sense having somebody get a [00:16:00] summary of a resident or give you a summary of, of all of your residents in your building on any particular day and say, "These are the four that you need to focus on, and this is why," and give some, uh, contextual data so that you know what is important, what you need to act on immediately without running through reports and schedules and dashboards trying to figure out what to do.

I think there's a lot of value there. The other part of it is, um... And we are seeing this mainly in fall detection cameras by a lot of these new companies, um, you know, where they're using cameras with AI in the camera to detect a fall. Uh, again, it's reactive stuff, but still that's use of AI to detect something without necessarily having to have to employ a full-time set of eyes to keep watching somebody.

I think that's the other part where there's a lot of value with the newer, uh, AI models. 

Matt Reiners: Mm. Yeah, there's so much opportunity. It's so amazing, too, of just, like, going to all these different conferences. I feel like [00:17:00] sometimes half of the talks are about AI or all the talks are about AI, and, you know, how can really people use it?

And I think what always sticks out with me of how people can use it, you know, you mentioned it before of, like, what our industry delivers is care, and I go back to, like, how can we do that smarter? How can we do more of that? Because... You know, I always like to use the saying in my talks, because I'm one of those people that's talking about AI all the time like everybody else, of, like, automating the mundane to elevate the meaningful.

To each person- Yeah ... the mundane means something different. Each person, the meaningful means something different. And, you know, how do you just become more effective and more efficient and just provide better quality of life? Um- 

Satish Movva: That's, that's a terrific sound bite. I'm gonna, I'm gonna steal that from you, Matt.

Matt Reiners: Hey, I got it from ChatGPT, so you steal away, my friend. Steal away. I will give it, I will give it props. Um, and you know, I think we kind of, like, haven't directly said it, but, like, obviously I mentioned before, like, all these companies in here talking about AI. We're continuing to see more and more technology companies trying to get into the market.

I think everyone's finally reading some about those, uh, aging demographic shifts and are now looking [00:18:00] at B2B and senior living as an easy play. But, you know, I'm curious of your perspective. You've obviously worked with many operators, and when they're evaluating technology partners, how can they cut through the hype, being the providers themselves, and really just choose the right vendor?

Satish Movva: Yeah, and, and I think it's, uh, i- as in any other industry, I think you really need to look at the results more than anything else. If you're gonna evaluate a company, if you're gonna do a pilot, make sure you have a very defined timeframe, make sure you have an ROI, um, that you agreed on and, and you can see that.

And when you're evaluating new companies, you really need to look at is there truly a return on investment? You need to look at, uh, evidence-based studies. Um, so for example, if you have a peer-reviewed published study that shows over a number of months, and we've looked at a number of seniors, uh, uh, or se- uh, residents in senior living [00:19:00] buildings, and we've seen this kind of an outcome, then you can take that to the bank.

I think it's important, especially with all of the noise that's out there and so many companies claiming to do so many different things, that you really need to look at the results. And don't discount experience and maturity of a product when you're evaluating, um, the products that are on the market today or those that are coming on the market.

Um, somebody that's brand new may have really invented a new, uh, mousetrap. You know, n- never, never preclude that opportunity or that possibility, but also look and see those that are out there today that, uh, have the track record and are able to show the results, that that's something you can really count on.

And if at all possible, use out- you know, rely on outside third-party type, uh, [00:20:00] studies and things like that that truly objectively show you that the ROI is there and the technology is there, and that it's not vaporware. And I think it's important to do that in any industry because anytime a fad becomes widespread, you're gonna get a lot of people that are aspiring, uh, to be in, to be in your building, to be in your community.

You want to make sure that what they are offering you is real, and that it works, and that it has, um, the, the longevity behind it, and it will withstand the, you know, the, all of the things that change in, in the future in senior living 

Matt Reiners: Yeah, I think, I think those are, there's some good call-outs there. Um, you know, I think too many times I've seen it, you know, even in the decade I've been in this, I've seen startups, companies come and go, and, like, to your point of making sure that they're built to be around, um, you know, just making sure that you're set up for the long term 'cause, you know, nothing's worse than going deep down on a pilot with somebody and then they lose funding and then they're not there the next [00:21:00] day, and time, effort, energy, money all wasted in one way or another.

Um, and I, and I'm wondering, like, when we think about, uh, some of the things out there, because I know, like, so there's, like, passive and ambient monitoring solutions. Like, those are getting a lot of attention right now. There's a lot of investment being, uh, driven to companies like that. I'm wondering from your perspective, like, what do you see as the biggest pros and cons of that category, and where does it fall short or succeed?

Satish Movva: Yeah, that's a... I mean, that's an argument that's as old as, um, monitoring in senior living, right? I mean, do you, do you, um... Ambient monitoring was all the rage back in, uh, 2000. For example, Intel owns most of the patents around 2000 about putting cameras in senior living to try and track resident behavior, staff behavior, all of that stuff.

Um, where they, where they found, um, certain shortcomings in that model is if you're monitoring ambiently [00:22:00] using a camera or something, it becomes very hard to know exactly what is happening with that resident elsewhere in the community, outside the community. So you have ambient monitoring, which is a fixed device in a fixed location.

You have passive monitoring that could be on the wrist, a wearable or some other, uh, kind of patch or something like that. Those are the two things that I look at. And if you are monitoring ambiently, the burden increases tremendously to try and identify what's happening to that individual at that location.

So I'll give an example. If somebody were to put a camera in the bedroom, you only know about the resident and what's happening to them in that bedroom, whether they fell, whether they're standing, sitting, sleeping, whatever it is. But the minute they walk into the bathroom, you have no idea. The minute they walk out of that door, of their apartment door, you have no idea what's happening with them.

And the person's life is not contained within just that bedroom. It's all over the community. So you're missing [00:23:00] literally 80 to 90% of that person's life because you're concentrating only on one room. Now, you can start putting cameras in different parts of the building and all of that, but then your burden increases.

You're trying to do video recognition of the person, facial recognition, trying to see who they are, what they are doing, and it's-- it becomes a very capital-intensive effort at that point because you're putting these wired devices in all these different parts of the community. Whereas on the flip side, if you're putting something on a person's body, whether it's a patch or wearable or wrist wearable or whatever, then you're following that person no matter where they are at, and you know what they're doing and all of that stuff.

So it becomes much easier. But of course, there's a downside, right? Ambient monitoring, you're, you know, you're on the-- sitting on the wall, you're a camera, you're plugged in, you ran electrical wires to that camera, you run networking wires or whatever else. So you-- all that is there and running. But if it's a wearable [00:24:00] device, then you have to look at, okay, does the wearable need to be taken off and charged, right?

You know, you look at the Apple Watch, it's, it, it needs to be taken off and charged every day for it to operate Um, and so there's pros and cons, but then you have to look at it and say, you know, like the way we approached it, for example, with Care Predict, we, our very first version back in 2015 was, had a battery inside and it ran for whatever, five days, and it needed to be taken off the wrist at that point and charged for two hours and then put back on.

And what we learned very quickly was that that- Time when you take it off and you're charging it, you know, it's the time you miss a lot about the individual, and if something happens to that individual or if they forget to put it back on, then it becomes an issue. So for ex-- So when we encountered those issues, we decided that we need a wearable that never leaves the wrist.

It should always be on the wrist, never comes off, and if it needs to [00:25:00] be recharged, then the battery just slides out, goes in the charger, and the freshly charged battery slides on, less than fi- five seconds. If you do it that way, then it's there on the wrist all the time and you solve that problem. And what we found was getting that longitudinal data without any breaks, um, was incredibly helpful in cr- in creating that data profile to be able to predict and prevent things.

Otherwise, you'd have gaps in their, in their activity behavior signals that would cause you to lose resolution and granularity on what you can predict and prevent. So the way I view this ambient versus passive is really, you know, the accuracy of the data and how much of the data is going to be worth it.

So one quick analogy on, on, um, on wearables there. You know, we used to have Fitbit. Fitbit used to go for seven, 10 days without requiring a recharge, but it was a very simple device. All it did was count steps, nothing else. And then along came Apple Watch. This thing needs to be recharged every [00:26:00] day, but it does a lot of stuff, right?

And the reason most people prefer an Apple Watch to a Fitbit these days is it's not just steps that matter, it's everything else. And that's very analogous even to where we are in senior living. You know, our wearable, you know, people swap the battery out every, every, you know, 24 to 44 hours, 48 hours when the, when the thing tells them to, or even every day if they feel like it to get into a habit.

But what it captures, you know, all of the activity behavior signals down to tracking the fork to the mouth and knowing how much you're eating. Are you bathing, sleeping, cooking? All of those things. Are you interacting with another individual? That is a completely quantumly different level of data collection versus, you know, a wearable that sits on the wrist and just a button to call for help.

That's no different than a pendant around the neck. So even within passive, with ultra-sophisticated wearables like ours, and then literally there are just pendants [00:27:00] on wrists that do nothing but push a button and call for help. So even there, there's a spectrum. So similarly, on the ambient side, you might have door and window sensors that tell you a door or a window is opening, but then you have sophisticated cameras that are telling if somebody fell.

So both sides, you have those two, two kinds of spectrums. 

Matt Reiners: Yeah. I remember when I first got my Apple Watch, I was blown away how it died within a day and I was like, "Oh my gosh, like how often do I gotta charge this thing?" But, you know, to your point, it's just become a part of the habit now because the, the pros outweigh that little amount of time I have to take to charge it, you know, for an hour or two per day.

It's gotten better though with the newer ones. Yeah. Um, um, and I know... And I'm curious from your take, because I know earlier on we were talking about the ROI, and of course, like people's looking at technology, want to look at that. Obviously you've had the ROI conversation with your clients and, you know, other industry providers over the years.

And like when communities are implementing CarePredict, like what kind of real ROI are they seeing? You know, [00:28:00] staffing, resident outcomes, risk reduction, operational efficiency, all of the above, something I didn't even mention here? Like what are you guys seeing in terms of that impact within the, the community itself?

Satish Movva: Yeah. I think that's one area where we excel because we have data since 2015 in senior living So we actually have such a large corpus of data that we have a lot of ROI, proven ROI from that amount of data and that longevity in the space. So for example, there was a peer-reviewed published study done that's available at the National Institute of Health, where 500, nearly 500, um, uh, seniors in senior living were monitored for 24 months.

Um, and, and those communities that were using CarePredict saw 39% reduction in hospitalization, which essentially means reduction in ER visits. So which is really, you know, something that every senior living [00:29:00] operator tries to minimize, because if somebody goes into an ER, there's a good chance they're not coming back to senior living.

They're probably going into a rehab unit, a step-down unit, or skilled nursing, and so on. So if you can prevent hospitalizations, that's a big deal. And preventing hospitalizations re- literally means preventing UTIs and preventing falls, which are the primary two things that cause a person to be hospitalized.

And within those, we are seeing, uh, that same study saw a 69% reduction in falls in communities that use CarePredict because it's predicting falls. It's not about catching falls after they already happened. It's really about predicting them and enough to be able to bring in PT, OT, or whatever to increase the balance of this individual so they don't fall, so you're preventing the fall.

It also saw, because of those two first things, the, the hospitalization and the, and the fall reduction, it saw a 67% increase in length of [00:30:00] stay. Those are the kind of ROIs that you really should be looking at and saying, "Is the product I'm choosing providing me that? And is there independent, objective, peer-reviewed published studies that are proving that ROI?"

I think that's important. The other parts that we're really seeing is, you know, we, we pioneered this entire care plan optimization by looking at actual resident and staff interaction time, exactly how many minutes of care, uh, was consumed by each individual. And we actually did a retrospective study for a very large operator, uh, who's been using us, and we found that on average, prior to CarePredict, between tw- they were, uh, undercounting the care that was being provided by anywhere from 25% to 155%.

Which is astounding when care is your product and your s- and your staff is there to provide care, you had that much of a miss between, you know, what you were [00:31:00] charging the individuals and h- actual amount of care you were providing. So that was another, uh, ROI that really resonates within our customer base, that they're actually being able to right-size care by, personalized by individual.

'Cause for example, it might take somebody to bathe me, because I'm extremely frail, it might take them 35 minutes, and to bathe another individual might only take them 10 minutes. And, and really, you need to be able to say that what you bill me is different than what you're billing the other individual, because my frailty is higher, I'm consuming more individual care of a caregiver, and you're paying the salary of that caregiver, and when they're occupied with me, they can't be serving someone else, so you need to staff up, so you need to actually get more from me to be able to subsidize that.

Um, so that's another ROI. And then some of the other big ones, and we had some of our customers actually issue case studies completely on their own, is about UTIs, how many UTIs [00:32:00] we are catching. Because we have the power of predictive analytics, we typically can help predict, um, the probability of a UTI 3.9 days ahead of time And if UTIs and falls are the big things that are driving these hospitalizations, ER visits, and, and falls, and all these other things, the ability to do that is astounding, and we have several case studies by our customers where they found UTIs three, four days ahead of time, did the predictive preventive things, and stopped stuff, uh, bad stuff from happening to these individuals.

And that's the kinda stuff that keeps us going in this, uh, business, Matt, the fact that we are making that much of a difference to our, to our customers, the building operators. 

Matt Reiners: I love that. Yeah. It's so cool when you're able to see some of that ROI come out in things, like, you didn't necessarily think would come out of it, and then you hear some of these stories of, you know, just improving that quality of care, improving that quality of life, and, like, those are the feel-good [00:33:00] moments as I'm-- obviously I'm preaching to the choir- Without a doubt

on that 

Satish Movva: one. Yeah. And, uh, just as a corollary to that, um, one thing I would add is the, the other side is the staff side of the equation. Very often that's kind of forgotten in the sense that, you know, you're not really looking at the staff side and wondering, um, you know, are we, are we correctly staffed at the right time of the day, the right days of the week, all of that?

Because in our, um, product, in our s- uh, system, the, the staff, um, location is observed and recorded as well. That's how we track our staff, uh, resident interaction times. We also know, um, by using real-time sentiment surveys from the staff all the time, constantly in real time, um, we actually have the industry first and, you know, staff turnover predictor.

Never been done before, and we did this for a bunch of our customers recently, and we shared the data we found with them. [00:34:00] We were predicting 60% of their staff turnovers before they happened. 

Matt Reiners: Wow. 

Satish Movva: Uh, which is astounding because our industry is beset by staff turnover. I think 60 to 80% of staff turnover is common in our industry.

Now, within that staff you have, you know, you have your MVPs, those you want to pull all the plugs out to try and save. With our data now, and with our MVP scoring of the staff, you know, you can now look and see, hey, these are the folks that we really need to go and help retain. For the first time, again, predict and prevent that turnover, not just react to it after they've left.

That's where we are, and that's what we do, and it's in our name. CarePredict, but now we're also predicting staff turnover as well, with astounding results. 

Matt Reiners: Wow, that's so powerful. Now you just need to get it to talk to some of those, [00:35:00] uh, unhappy family members, and, like, you're hitting on all the boxes there.

Uh, I, I kid, I kid. But, you know, my, my last question here for you, Satish, you know, and I, I want you to look into your crystal ball. You know, if you could look ahead in the next, like, 10 years, like, where do you see the senior living industry going? What role will data, AI, predictive insights really play in that future?

Satish Movva: That's a great question. Um, I think if you look back with all of the incredible work senior living operators have been doing in their building to keep their residents safe, happier, living longer there, Matt, if you stop to think about it, who's been the number one beneficiary of all of that good work?

It's the healthcare insurance company. It's Medicare. Do they care? Yes. They care that you save them a ton of money. Are they contributing anything at all? To date, zero, right? Because it's private pay [00:36:00] model. Families are paying for you to be taken care of properly and well within senior living communities and, and the communities are doing that, but the healthcare beneficiary has not contributed a dollar to this.

That's gonna change in the future because this is the person's home, you're taking care of them at home and keeping them safe and happy and, and healthier longer. The health insurance companies are gonna get involved, and the future is going to be value-based care where these insurance companies are gonna come and pay the senior living operators additional monies for the great work they are doing in keeping people safe.

We are starting to see that already with value-based care operating in senior living buildings, risk-bearing entities coming in, primary care practices with nurse practitioners coming in and doing, you know, three, four, five visits a week to the building, rounding on all their members, looking at data like Care Predict that predicts and [00:37:00] prevents, and saying, "Who are the people that we need to look at, and what are the things we need to prevent?"

And that is saving the healthcare, um, uh, insurance companies and the healthcare payers a lot of money, so they're willing to subsidize the operators. And that has to be the future, where you're starting to get healthcare dollars flowing into senior living communities to help subsidize the care of those residents.

That's absolutely the future. Now, for that to work, you need data and a lot of data, and you need longitudinal, highly precise data. You need data like Care Predict that's tracking activity and behavior signals 24/7 throughout the day about the individual, that has the predictive analytic models that can predict and prevent things, UTIs, risk of a fall in the next 24 hours, depression, nutrition risk.

All of these things that can be predicted from the data streams will mean that everybody will have to contribute in [00:38:00] providing the data. That, I think- The only precise and granular way to do that has to be some kind of an on-body device, a wrist-worn device like CarePredict or some other kind of device that some future companies may invent, that's gonna track all of these signals, contribute all of those signals to those same AI models like we're doing now, that can help predict and prevent.

And I think it's going to become part of the expectation, especially as the newer, uh, wave of aging boomers comes through the pipeline now in s- into senior living, that they expect to be fully involved in their own aging process. They want to see all of the data about themselves, the quantified self, but for the senior segment, and they would want to know, if I'm wearing this Apple Watch and all the data it's giving me, I'm looking

and, and how it's h- helping me inform how I should improve my life. That's going to be the same for the future, uh, residents in senior living. They're gonna be looking at their CarePredict w- wrist- wearable data [00:39:00] and saying, "Okay, th- this is what it's telling me about how I am aging, and, and, uh, you know, how can I be more empowered to age more gracefully, more healthy, and, and live longer?"

I think that's going to be a, a big necessity going forward, is that stream of data, and I believe, uh, that it's going to be from a wrist-worn or an o- on-body device of some kind. 

Matt Reiners: I love that. Well, Satish, I, I appreciate you, uh, dropping some knowledge and sharing a little bit more about CarePredict, AI, and all the, all the fun stuff here.

Um, keep up the great work with CarePredict. You know, I know it's been going on, I think, 2013, so 13 years now, but hey, there's ... the industry's better because of people like you, so thank you, my friend. 

Satish Movva: No, thank you very much. Like I said, this is a mission for us, Matt. This is not a get rich quick, get some VCs to fund us kind of deal.

This is we are in it for the long haul. We are here to transform, help transform the industry and help take care of seniors, and I live with this every day, my friend. I see my 97-year-old [00:40:00] dad every morning. This is, uh, that's a reminder of why I do this every day. 

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