Innovation

How tech can help seniors 'age in place,' save money, and be independent

At the Louisville Innovation Summit, health and tech experts explored how big data, artificial intelligence, and the Internet of Things are helping older adults stay healthy and independent.


"Aging care is one of the most aggressive industries in innovation," said Keisha Deonarine, economic development manager for the city of Louisville, Kentucky. Deonarine, speaking at the third annual Louisville Innovation Summit on Monday, hit on a key point: The rise of big data, IoT, and AI have enormous implications for the improvement of the US healthcare system.

It's a system ripe for innovation—already this year, $6.5 billion has been invested in digital health startups.

And it's an industry with critical implications for seniors. In the US, there are 46 million people over the age of 65, and 20 million over the age of 75, said industry analyst and former CIO, Laurie Orlov. Where do they all live? Almost half (46%) of women over 75, live alone.

So how can we use technology to help those at home live well? It's becoming a "crisis," said Orlov. According to a 2013 AARP report, the caregiver gap is growing, and by 2030, there will be a 4:1 ratio of caregivers to older adults—a drop from today's 7:1 ratio.

And a major obstacle is that the assisted care industry is still private, said Orlov, and many aging Boomers don't have the funds to cover assisted living.

The telehealth market, which Orlov said is "hot, hot, hot," will grow to $1.9 billion by 2018—a 56% increase. "Everything is changing," she said, "and fast." Orlov talked about how tools like sensors, GPS, Skype, and voice activation power "tech-enabled homecare"—systems through which older adults can order, monitor, and track their homecare through mobile devices. Half of the population over 75 now has access to the internet, she said, making this possible. Still, Orlov said, it is a small piece of the overall homecare industry.

Some of the biggest challenges around aging are transitions, transportation for seniors from home to hospital, from assisted care to hospital. Especially for those with dementia, Orlov said, many doctors are unaware that a patient they are seeing may not have the faculties to communicate their health care needs or understand how to medicate themselves.

Orlov sees tools like Amazon Echo, with voice recognition, as the "paradigm for the future."

"We have been far too focused on hardware," she said. "The future will all be software."

SEE: AI app uses social media to spot public health outbreaks (TechRepublic)

This is a point that was echoed by Mark Cavicchia, chief innovation officer at RC1X.

"From all of our technologies, there's a tremendous amount of data being generated," said Cavicchia. "But now, information is being kept in siloes. "What will happen over the next 10 years is data integration, AI, and predictive analysis," he said, "so data can be used to help you in real-time through applications and things that don't necessarily involve you going to a clinic."

Nadia Morris, head of innovation at the AT&T Connected Health Foundry, expanded on this idea.

"We have all this data," said Morris, a computer scientist by training. "Now what?"

Harnessing the data, Morris said, is a major challenge in healthcare. She works with all the major players in healthcare to understand how to use the data, which she said is often "overwhelming." Doctors and caregivers are struggling, said Morris.

"We're throwing spaghetti at the wall to see what sticks," she said.

SEE: Machine learning: The smart person's guide (TechRepublic)

Morris talked about the scale problem. And sees AI as the key to addressing the challenge. "Tech is at a place where it can really have an impact, but we need to do it right," she said.

What kind of data can we use for AI? Morris sees many areas for collecting information about older adults, including: Electronic health records, clinical research, insurance claims, lab tests, IoT, wearables, and biometrics.

But machine learning, said Morris, is only as good as its training—and the correct data entry is essential. Unlike AT&T's machine learning that looks at making wireless networks more efficient, healthcare machine learning relies on "real data from real experts," she said.

Also see

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Laurie Orlov, principal analyst, aging in place, for Technology Watch, spoke at the Louisville Innovation Summit.

About Hope Reese

Hope Reese is a Staff Writer for TechRepublic. She covers the intersection of technology and society, examining the people and ideas that transform how we live today.

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