Technology is transforming the way we care for our aging parents, in some amazing ways. Diane’s special guest this week, Satish Movva founder of Care Predict is here to tell how Artificial Intelligence is being used not only to deliver better care and keep our aging parents safe now, but it can also warn us about life-threatening medical emergencies BEFORE they even begin.
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Parents Are Hard To Raise S02 E78 Show Transcript
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Announcer: [00:00:37] Technology is transforming the way we care for our seniors in some amazing ways. Diane’s special guest this week, Satish Movva, founder of Care Predict tell how artificial intelligence is being used not only to deliver better care and keep our aging parents safe now, but to predict their future needs as well.
[00:00:59] Parents Are Hard To Raise is now available on Spotify and it’s 180 million monthly subscribers.
Diane: [00:01:17] Welcome to Parents Are Hard to Raise. Helping families grow older together without losing their minds. I’m elder care expert Diane Berardi.
[00:01:27] Those of us working in the clinical setting know firsthand that most healthcare crises don’t just happen out of the blue. There are subtle changes that take place, typically for quite some time before problems make themselves known.
[00:01:41] Many of these subtle changes in the patient’s daily activities and behavior patterns are so subtle that even a trained observer might miss them. Which is why the technology this week’s guest expert has developed is a literal game changer for the health and well-being of our aging parents, and for those of us who care for them.
[00:02:01] Satish Movva is the founder and CEO of Care Predict, the first wearable technology specifically designed for seniors aging independently.
[00:02:10] It looks like a stylish piece of jewelry, but it’s actually a very sophisticated smart device that sensors and analyzes a person’s routine activities, like bathing, brushing their teeth, washing their hands, eating, drinking and how well they’ve been sleeping. The device is so smart it even knows where the person is as they move throughout their home, and can automatically alert caregivers and family to any potential changes before they become emergencies. Like I said it’s a total game changer, and I for one am glad the technology is here.
[00:02:45] Satish Movva welcome to Parents Are Hard To Raise.
Satish Movva: [00:02:49] Diane, thank you very much.
[00:02:50] It’s a pleasure to be on your show.
Diane: [00:02:52] Well, thank you. So what inspired you to start Care Predict?
Satish Movva: [00:02:58] I started Care Predict even though I didn’t mean to. I started it because I had a personal problem that I was trying to deal with. My dad’s, now 91 and my mom’s 80. And back in 2013 they’re going through a rough patch where no matter how many times I called them during the week, when they showed up on that weekend, I would find something different, that was not at all prepared for.
[00:03:24] And more often than not, that particular issue would end up with me taking either mom or dad to the ER that day. Or, pretty much cancel all of my work schedules for Monday or Tuesday so I could take them to the physician’s office for a follow up visits and so on. And it became very difficult because I had no idea what my schedule would look like and it was always with a sense of trepedation that I would visit them on that Saturday, and not really knowing what was happening.
[00:03:55] So for example, I would show up on a Saturday and dad’s shuffling. And dads shuffling because he has all this of water in their ankles, called Edema. And in a 91-year-old guy with edema, he’s going to be in the cardiac unit in 24 hours with a cardiac issue, because the heart cannot keep pumping blood through all that fluid packed tissue. So these are all emergent type things that I had to handle. And that’s when it struck me that I could no longer rely on mom’s observational powers to keep Dad safe and vice versa, because she never even noticed that his the gate had changed from stepping to shuffling.
Diane: [00:04:34] [laughing] I know. We can all relate to that.
Satish Movva: [00:04:36] And that’s when I decided that I really needed some kind of a monitoring system to figure out what’s going on with them and give me some sort of a heads up, so I’ll have some certainty in my life.
[00:04:49] Raising parents is very difficult. And if you can get to a more predictable model of what’s going on and when, it’s just going to make your burden a little bit lighter.
[00:05:00] We all carry this huge burden. Anything that will help us do it with a little less stress is going to be welcome.
[00:05:08] And I looked and saw what was out there for this monitoring of people in their homes. And what I found was 40-year-old technology.
Diane: [00:05:16] Right.
Satish Movva: [00:05:17] People there using motion sensors to detect occupancy in their home. They were using switches to find out if the refrigerator door opened and closed, or the toilet flush handle went up and down. And the problem for me was that these technologies were not built for a situation where you had more than one person in a home.
Diane: [00:05:37] Right.
Satish Movva: [00:05:37] And I had two. So mom… It could tell me mom was in this room or it could tell me people were in this room or that room, but not who was there. It couldn’t attribute activity to an individual. And then the biggest issue for me was that, the leap of faith it asked me to make. Just because the refrigerator door opened and closed, I have to assume that they ate. And that’s a huge leap of faith. Especially if you see you know our parents aging at home, it’s a low stimulus environment. They tend to be very bored. They tend to have aimless wandering and walk around. And they open and close things. Doesn’t really mean that you know that they’ve actually performed the activity correlating with that particular switch or refrigerator door or what have you.
Diane: [00:06:25] Right. So, how does the whole system work? I know there is like a bracelet you wear around your wrist and could you take us through that and tell us.
Satish Movva: [00:06:37] So the system works to… The system works by having a wearable device that goes on the wrist of the dominant arm.
[00:06:46] So one of the failings of the older technology was there was nothing to attribute activity to an individual. It was rooms and places and things that were being monitored but not the person. So I knew that I had to make our device something that’s on person, on body type device. So we chose a wrist device an on-wrist device that looks very pretty, it looks like a piece of jewelry. And it goes on the wrist of the dominant arm.
[00:07:12] And the reason it needs to go on the wrist of the dominant arm is because it actually tracts the activities that the arm performs. Is it brushing teeth? Is it brushing hair? Is it lifting a fork of food or a chopstick stick to the mouth or is it a drink set? And those are really important things to know and measure.
Diane: [00:07:34] Right.
Satish Movva: [00:07:34] And It also tracks where they are in the home. So are they in the bedroom? The bathroom? The kitchen? And so on. And the reason we do that is the kind of room you’re in defines the kind of activities you normally engage in.
[00:07:49] So for example, if we see that you are in the dining room and we are seeing arm move in a pattern that’s indicative of lifting food to the to the mouth, we can then have a very high confidence that we’ve tracked that activity correctly and we know that you eating. And so it’s important to know what kind of a room you’re in. Now, you could eat in the kitchen, you could eat in the dining room, but it’s highly unusual that you would eat in the bathroom. So it helps in really giving us more confidence in our algorithms and tracking abilities.
[00:08:22] And so we take all of this data and we put it into what is called a deep learning neural nets, which is an artificial intelligence technique that collects all these data points and understands what the person’s normal baselines are and then it looks for anomalies.
[00:08:40] So for example, if the normal activity pattern is, mom wakes up at 7:00 in the morning in the bedroom, goes to the bathroom for 10 minutes, and goes to a kitchen and then the living room. But today she woke up like normal, went to the bathroom like normal, but then came back and lay down on the bed again. Our system can send a soft ping to me saying hey mom might not be feeling well, why don’t you call her and see what’s going on.
[00:09:01] So, it gives you that peace of mind, without you necessarily invading your parent’s privacy, and saying you know how many times did mom go to the bathroom? That’s not the point. The point is really the system is monitoring all of that. And it’s telling you when you need to engage with your parents and then, you know, and then figure out what’s going on by talking to them and communicating to them. So it’s closing that gap between you not knowing anything at all, to giving you that little heads up that says, now might be a good time to reach out to mom and find out how things are.
Diane: [00:09:39] Now, are there privacy issues with this technology.
Satish Movva: [00:09:43] Yes, there could potentially be privacy issues. And we spent a lot of time thinking through this. So, when you’re tracking somebody in the home and you’re tracking which room they are in, and then things like that, you know you want to make sure that data is protected, that it doesn’t go to people who doesn’t need it. And we really built the system not for an individual to keep looking at the dashboard and seeing you know how many bathroom visits happened or how much time was spent in the bathroom, but for the computer algorithms to watch and monitor that. There is no human in the loop. And so the computer algorithms are the ones looking at that and then they come back and they say, you know, you’re mom now has very unusual bathroom patterns and these patterns are indicative of an approaching urinary tract infection. And so, you can then just react to that prediction that there’s potentially something going on without having to know about mom’s particular bathroom patterns. So that’s how we built it. We were, you know, I built this for my own parents. And so privacy was top of my mind.
[00:10:48] And that’s how we approached it. We let the computer algorithms be the ones that look at the private data but only serves us the insights that that allow you to intervene, as you need.
Diane: [00:11:00] We’ll be back with Satish Movva, CEO of Care Predict. But if you’re a woman, or there’s a woman in your life, I’m going to tell you about something that you absolutely need to know.
[00:11:12] I Want to tell you about my friend Katie. Katie is a nurse and she was attacked on her way home from work. She was totally taken by surprise. And although Katie is only 5 feet tall and 106 pounds she was easily able to drop her 6 foot 4, 250-pound attacker to his knees and get away unharmed.
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Announcer: [00:13:15] You’re listening to Parents Are Hard To Raise. Now, thanks to you, the number one eldercare talk show on planet earth. Listen to this and other episodes on demand, using the iHeart Radio app. iPhone users can listen on Apple Podcasts and Android users on Google Podcasts.
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[00:14:13] A big shout out to Lorin from Anderson, South Carolina, who listens to the show on Alexa. That’s great Lorin. Just tell Alexa play the show for you. And Giovanna a medical doctor from the city of Arezzo in the beautiful Tuscan countryside. I love Arezzo. “Arezzo” there goes my New Jersey accent. Giovanna listens on her iPhone through Apple Podcasts. And she showed a colleague how to do the same. Prego, Giovanna.
[00:14:44] So we were talking to Satish Movva, the founder and CEO of Care Predict. This is such great technology, Satish. What are some of the hurdles though, you find with technology in the older population?
Satish Movva: [00:15:01] That’s a great question, Diane. I think some of them are the expected ones, and some are the unexpected.
[00:15:07] So for example, we expect that the older generation who has not had experience with this level of technology right from childhood would have difficulty using it.
[00:15:20] So for example, a smartwatch. It becomes very hard to use it as we age, due to manual dexterity issues or visual acuity issues. But I think the other part of it is also that the apps and things like that today, require so much more familiarity right from the beginning with technology, that we find that it takes longer for them to understand how to use it.
Diane: [00:15:49] Yes.
Satish Movva: [00:15:49] So in all of them that we cannot bring in just the generic consumer type of technology because it’s really geared and targeted for a different generation, that has a much deeper fundamental familiarity with technology.
[00:16:05] I think we continue to see that. Which is why you see a lot of products for seniors tend to be things that are very specific to a Senior, whether it’s a Jitterbug phone with the large numbers and the large screen. Or whether it’s a pendant with the life alert. Just push one larger button and it works. I think those are the typical problems we see that the hurdles in technology.
[00:16:28] The other part of it is also that technology has progressed to a great extent. So that some of the things that are out there such as AI, deep learning and artificial intelligence and so on, are coming up with some really incredible advances that seem almost like magic. And those are things that take a lot of acceptance to accept, and take them at face value and find the utility and value.
Diane: [00:16:59] I know my mom… I was just talking to, and you know I give her a lot of credit. She has an iPhone. And everyone has showed her and she’s gone to the Apple store, to be shown. And she called me yesterday and someone rear-ended her car. She was driving somewhere and she said, you know I had to go into a store because I had to call the insurance company. But you know you have to press there’s no keypad it says you have to press “one.” She says, and you can’t do that when you’re talking on this phone. So, and the poor thing, she’s eighty-nine and she’s really done pretty good. And I said, well there is a way mom.
[00:17:47] “Oh there is. Well… Can You show me? Can you tell me? And I’m thinking, oh my gosh. It will take me four hours to explain it on the phone. And you know, she probably won’t remember. So, I’m like, “Next time I see you, I will show you how to do that.”
[00:18:02] And if you’ve listened to my show you know her sidekick “Joe.” She’s like. ” Joe. See there is a way. And my father’s like knew there was no way, but I just didn’t have the time to try to figure it out.
[00:18:18] So I mean…
Satish Movva: [00:18:21] I could relate to that. My parents are very much like your mom and Joe in that sense. We gave my mom an iPhone. And it’s that old scarcity mentality of folks coming out of the depression and so on.
[00:18:39] They want to conserve the phones so they can turn it off most of the time.
Diane: [00:18:43] [laughing] Yes!
Satish Movva: [00:18:43] They only turn it on when they want to use it. And it’s inside their Handbag, and I’m like… “Mom, you need to put it on you, all the time.”
[00:18:52] [laughing] I know.
[00:18:52] My Mom was going somewhere and I said to her– because I know, cell phone will be off, in her purse– and I’m like Mom… She goes, “call me. Call me…”
[00:19:02] I go, “you have to keep that cell phone on and handy.”
[00:19:06] “Oh I will. I will. I’m going to hold it.”.
[00:19:08] Of course. No. Did that happen? No I can’t get her.
[00:19:14] What can we do. You know it’s like the key fobs. You know, my dad he doesn’t use it. Because he doesn’t want the battery to wear out.
Satish Movva: [00:19:27] Exactly. It’s that Post Depression scarcity mentality.
[00:19:32] This generation has been through World Wars and whatnot. They know how to conserve.
Diane: [00:19:37] Right.
Satish Movva: [00:19:38] They still bring it. Yeah. Yeah. Absolutely.
Diane: [00:19:42] Now, Care Predict… is it just in the US?
Satish Movva: [00:19:48] Yes. Actually up to now we are just in the US and Canada. We just started expanding into Asia. Our first installations in assisted living facilities and Memory Care are going live in Japan, January 1st.
Diane: [00:20:07] That’s great! Because we have a huge audience in Japan, in Asia so that’s wonderful.
Satish Movva: [00:20:14] Yeah. Absolutely. What we were surprised with, Diane, was that aging is a much bigger and much urgent issue, much more urgent issue in the Asia area because they have had a negative population growth for a very long time. They have a huge number of aging folks and not enough caregivers. They’re all looking for a force multiplier like Care Predict there that can help the fewer take care of the many.
Diane: [00:20:41] That’s wonderful. Now, is the system just available like an assisted living, in a you know continuing care retirement communities? Or is it available directly to the consumer?
[00:20:58] So when I started the company, I started it with people like myself in mind. Adult child of a senior. But along the way, we gravitated more towards assisted living and Alzheimer dementia care facilities, only because it would be much more easier to go outfit an entire facility and get a 150 seniors or so on the system and start better refining the product and making it better suitable.
[00:21:27] But we’ve come to the point now where we are going to be launching our first pilot direct to consumer starting October 1st.
Diane: [00:21:35] Oh wow.
Satish Movva: [00:21:37] Yeah. And we are doing that in two markets. In California and Florida. To see what we need to learn to make it that much more smooth and easy to operate device for folks living at home. It will be a much more simpler application. Very easy to set up. More consumer friendly mobile and smart apps for the caregivers and the family members to look at. And they’ll be much more easier to self install the system as well.
[00:22:02] And we hope to take that learning by the beginning of next year and take it again throughout the country for B-to-C as well as partner with others– big box retailers and things like that– to get it out on the store shelves or get it out into the channel, by the middle of next year. And we’ll be doing the same in Japan as well, as well as China and then South Korea, all in the first couple of quarters of next year.
Diane: [00:22:30] That’s fantastic. Because I’m thinking you know people to remain home, that is wonderful.
[00:22:43] Just this week I had a poor client. He lived alone, and you know he had a care in the home, but he was alone most the time. He never married, never had children. And so his sisters actually and brother in law, they all kind of tried to take turns you know helping him, in conjunction with the care he was getting in the home. And his home health aide went in on Saturday evening, as she normally does. And she found him… Unfortunately he had fallen and hit his head. And he fell face down and he… He had already died. You know he was alone.
[00:23:31] And so, this is fantastic for people, because he didn’t have… He needed 24 hour care. But he didn’t have the funds to do that. And you know, the family paid for what they could and they tried to take turns. But you know, people can be there all the time.
Satish Movva: [00:23:53] Absolutely. Absolutely.
Diane: [00:23:53] And this is a perfect…
Satish Movva: [00:24:00] That’s a heartbreaking story. And that’s exactly the kind of situation we are geared to provide our services through. So for example, with Care Predict, not only can we predict the likelihood of somebody falling in the next couple of weeks, but if they do fall, and we can trigger that false alarm automatically, so it goes out to the care circle, whether it’s family or neighbors or whatnot, so they can do a quick wellness check. So even if you’re incapacitated and cannot push the button to call for help, we can do that. We can predict things like you know what is the likelihood of somebody going into a urinary tract infection over the next four days. Or what is the likelihood of somebody heading into a depression seven days from now.
[00:24:43] Those are the kind of things that you can predict, so you have enough time to intervene, so you can prevent those from happening. And you know it it breaks my heart every time I hear a story like that, Diane. Because that’s the kind of stuff we were built to try to prevent.
[00:25:00] This is fantastic. How can people contact you? Please tell us. .
Satish Movva: [00:25:11] Certainly from our Web site www.CarePredict.com and through our sales lines as well, 800-608-7188.
Diane: [00:25:19] Satish, thank you so much for telling us about Care Predict, for inventing it and making it available to people who do need it. Thank you so much for being here.
Satish Movva: [00:25:39] Diane, thank you so much for this opportunity, and for being able to tell the world our story.
Diane: [00:25:43] I hope you got something out of this episode. I know I certainly did.
[00:25:51] I love getting your emails and questions, so please keep sending them in. You can reach me at Diane@ParentsAreHardToRaise dot org. Or just click the green button on our homepage.
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[00:26:31] Parents Are Hard To Raise CounterThink Media production. The music used in this broadcast was managed by Cosmo Music, New York, New York. Our New York producer is Joshua Green. Our Broadcast producer is Well Gambino. And from our London studios, the melodic voice of “Miss Dolly D.”.
[00:26:50] Thank you so much for listening. Till next time… May you forget everything you don’t want to remember, and remember everything you don’t want to forget.
[00:26:59] See you again next week!
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