search.noResults

search.searching

note.createNoteMessage

search.noResults

search.searching

orderForm.title

orderForm.productCode
orderForm.description
orderForm.quantity
orderForm.itemPrice
orderForm.price
orderForm.totalPrice
orderForm.deliveryDetails.billingAddress
orderForm.deliveryDetails.deliveryAddress
orderForm.noItems
QUALITY IMPROVEMENT


John Sanders, a resident of Front Porch’s


Carlsbad By the Sea retirement community, demonstrated how even just having an Alexa-enabled Echo speaker in the home can make life for residents more social. Sanders told the Aging2.0 audience that one of the community’s centenarians has Alexa read her the Bible. Alexa has the capability of starting again where she last left off. The woman, Grace, is so enamored with the capability that she tells everyone she knows about it. The retirement community has also founded an Alexa Club, where seniors get together to discuss its capabilities. “It’s important that us old folks who don’t


want to adopt technology don’t realize that this is technology because we’re so infatuat- ed with what it does,” Sanders said. AI isn’t just about sci-fi-sounding robots.


Entrepreneurs are trying to use it to predict, prevent, and reduce the chances of falls— one of the biggest risks for seniors and a ma- jor issue for senior living providers. George Netscher, a doctoral student in computer vision on leave from the University of Cali- fornia, Berkeley, for example, started a new company called SafelyYou in 2016 that uses


computer vision to analyze the risks associ- ated with falls of seniors in memory care. The goal is to help senior living providers update their operations and minimize those risks in light of the information provided by the images. Carlton Senior Living is one of several providers in California that is currently beta testing the system with SafelyYou. SafelyYou has published results showing an 80 percent reduction in the fall rate, which they hope to validate at a larger scale over the next six months. They’re also using it to better coordinate


care. CarePredict, with offices in Florida and Silicon Valley, for example, is trying to use AI and sensors to collect informa- tion about the habits of seniors and detect patterns of change that might indicate precursors to a decline in health. The system is currently in use at five locations with 600 residents. Four more companies have signed up for the service this year, said Satish Movva, CarePredict’s founder and chief executive officer. Other startups at the conference worth


a mention: Mentia, a company that has created a tablet-based virtual reality world


for those with moderate to severe demen- tia; Deva, an app featuring a digital world, was specifically designed for a tablet so that someone suffering from dementia could explore and engage with it alongside a care- giver; and Unforgettable.org, a UK startup that has created a shopping portal and community with products and resources for those with dementia and their families and caregivers. The company won Aging2.0’s best global startup business award for 2017. OPTIMIZE didn’t just focus on technol-


ogy. One big form of meaningful engage- ment is work. Former hotelier and Airbnb advisor Chip Conley ended the conference with this provocative idea: Experienced workers over the age of 50 can serve a new role in 21st century companies that are increasingly emulating Silicon Valley ones led by engineers and businesspeople in their twenties. Older workers can serve as both interns and mentors—roles he played at Airbnb. This idea is the seed of a new retreat he’s creating called the Modern Elder Academy in Baja California. It’s also the basis of a forthcoming book titled “The Making of a Modern Elder.”


32 SENIOR LIVING EXECUTIVE / ISSUE 6 2017


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68