Defining AI and machine learning terminology isn’t academic, but can influence your business. In reading a straightforward interview about the CarePredict wearable sensor for behavioral modeling and monitoring in an AI-titled publication, this Editor realized that AI–artificial intelligence–as a descriptor is creeping into all sorts of predictive systems which are actually based on machine learning. As TTA has written about previously [TTA 21 Aug], there are many considerations around AI, including the quality of the data being fed into the system, the control over the systems, and the ability to judge the output. Using the AI term sounds so much more ‘techie’–but it’s not accurate.
Artificial intelligence is defined as the broader application of machines being able to carry out tasks in a ‘smart’ way. Machine learning is tactical. It’s an application that assumes that we give the machine access to data and let the machine ‘learn’ on its own. Neural networks in computer design have made this possible. “Essentially it works on a system of probability – based on data fed to it, it is able to make statements, decisions or predictions with a degree of certainty.”, as stated in this Forbes article by Bernard Marr.
CarePredict has been incorporating many aspects of machine learning, particularly in its interface with the wrist-worn wearable and its interaction with sensors in a residence. It gathers more over time than older systems like QuietCare (this Editor was marketing head) and with more data, CarePredict does more and progressed beyond the relatively simple algorithms that created baselines in QuietCare. They now claim effective fall detection, patterns of grooming and feeding, and environment. (Disclosure: this Editor did freelance writing for the company in 2017)
In wishing CEO Satish Movva much success, this Editor believes that using AI to describe his system should be used cautiously. It makes it sound more complicated than it is to a primarily non-techie, senior community administrative and clinical audience. Say what you do in plain language, and you won’t go wrong. AI for Healthcare: Interview with Satish Movva, Founder & CEO of CarePredict
Our July profile on the innovative ‘Green Houses’ model for older adults as a designed-from-ground-up alternative to nursing homes mentioned the considerable support that the Robert Wood Johnson Foundation (RWJF) has given to the Green House Project with an initial 10-year, $10 million low-interest credit facility in 2011. RWJF has added a $2.77 million grant for a three-year acceleration of the model to include an updated financial model and a stronger marketing plan to create demand. All good things for this care model for high acuity residents. Grant on the RWJF website. MedCityNews.
LeadingAge, the main association for non-profit ‘aging services’ providers, hosted a ‘hackathon’ of sorts called HackFest at its annual convention last week. Eight international teams of students were given a 24 hour challenge to come up with an idea and create a prototype application, device or website. The winner was Team Global EngAge who developed a platform for retirement communities to offer their activities–book clubs, religious services and clubs–online so that home-bound elderly can participate via video conferencing. The purpose of the hackathon was to focus on technology needs in senior services and was sponsored by investor Ziegler and Asbury Communities. Unfortunately neither McKnights or LeadingAge list or explore the seven other concepts, which would have been interesting as all these teams can look to further develop and fund their ideas.
Long-time reader and now guest contributor John Boden of ElderIssues LLC and developer of the LifeLedger, reasons that if young children can use tablets fairly meaningfully, so can older adults at home or in senior communities. This is adapted from one of his series of ‘Caregiver Tips’ available via opt-in at email@example.com.
Socialize With Technology: Tablets and iPads
If babies can use iPads, so can the very old.
Tablets and iPads are everywhere – EXCEPT – with nursing home patients.
This must be the season for me to have “aha!” moments. Last month it was while reading “Still Alice” and this month it was while visiting a nursing home where Sue told me she liked playing Scrabble but it was hard to find people to play with.
I am sure it is hard in a nursing home where you have to find another patient (more…)