Let’s consider: What if Ellen Ripley, famously played by Sigourney Weaver in the 1979 sci-fi classic “Alien,” had looked at a database of previous sci-fi horror films, noted the obvious similarities and said, “Nope, we’re not going to investigate that creepy, derelict spaceship”? With her foresight, she could have prevented the whole messy episode and saved everyone a lot of heartache and slime. Admittedly, that would have made for a far less entertaining film, but that’s not the point.
The point is that predicting the future saves lives is not only valid in science fiction.
But perhaps fictional horror isn’t the best argument for prediction and prevention. Try instead this compelling piece by Anna Gorman in which she outlines just how damaging a visit to the hospital can be for seniors, citing a study in the Journal of the American Medical Association. Following Janet Prochazka, a once-independent 75-year-old woman, the article goes on to describe how elderly patients sent to the hospital for one reason often leave with new, worse problems wholly unrelated to the original.
This article and cited study are altogether surprising. Ingrained into our subconscious, hospitals are a place of healing. For most patients that is still the case, but seniors are a unique case. They often require special treatment not available, or, as Gorman uncovers, often not considered – such as providing quieter settings, tracking memory loss, or comparatively measuring ADLs (activities of daily living).
Some hospitals recognize the need for particular care for seniors, and a few have started programs specifically to combat the unintended side effects of a stay. Unfortunately, they are few and far between.
What if we look for a solution to this problem from the other end? How can we eliminate some of those visits? What if we could predict an impending fall and prevent it from happening? Or recognize changes in behavior and activity sooner, potentially preventing exacerbated health problems resultant of a late diagnosis?
Predictive technology is a fascinating subject because it often starts with the discovery of unexpected patterns within large sets of data. Fitbit, a wristband notably famous for tracking how many steps you take in a day, has the unintended side effect of capturing stressful life events that may lead to depression or even heart attacks. Similarly, a building management company, intending to use motion sensors simply for security, instead discovered how inefficient or unused entire sections of their buildings were – leading to redesigns for future buildings and huge savings.
Silversphere explores predictive technology intended to keep seniors healthy, independent, and out of the hospital as long as possible. Through the use of various sensors, millions of collected events, and decades of experience, it dives through data to create predictive and proactive reporting to help operators see potential problems on the horizon and avoid them.
The human machine, across all races and genders, works the same way in more cases than not – a fact that can be used to everyone’s advantage. Once a pattern of if-this-than-that is established, technology can safely predict what will happen if “this” happens and “that” is on the horizon. Prevent “that,” and you’ve avoided the whole problem.
Let’s take a page out of Shakespeare’s book: If you prick us, do we not bleed? If you tickle us, do we not laugh? Both easily answered “if-this-than-that” questions. But soon, with access to so many more data points and patterns, it might be possible to predict scenarios like: What happens if the lighting is low, the hour is late, the resident has established a pattern of waking up around this time, and she’s taking this particular prescription. Meeting these criteria – there’s a high risk of a fall, does she not fall?
And our answer will be she does not – because we’ll see it coming, and let the right people know.