As many as 400,000 Americans die each year because of medical errors, but many of these deaths could be prevented by using electronic sensors and artificial intelligence to help medical professionals monitor and treat vulnerable patients in ways that improve outcomes while respecting privacy.
The Fix: Invisible light guided by AI?
Haque, who compiled the 170 scientific papers cited in the Nature article, said the field is based largely on the convergence of two technological trends: the availability of infrared sensors that are inexpensive enough to build into high-risk care-giving environments, and the rise of machine learning systems as a way to use sensor input to train specialized AI applications in health care.
These alert systems are being tested to see if they can reduce the number of ICU patients who get nosocomial infections — potentially deadly illnesses contracted by patients due to failure of other people in the hospital to fully adhere to infection prevention protocols.
Constant monitoring by ambient intelligence systems in a home environment could also be used to detect clues of serious illness or potential accidents, and alert caregivers to make timely interventions. For instance, when frail seniors start moving more slowly or stop eating regularly, such behaviors can presage depression, a greater likelihood of a fall or the rapid onset of a dangerous health crisis. Researchers are developing activity recognition algorithms that can sift through infrared sensing data to detect changes in habitual behaviors, and help caregivers get a more holistic view of patient well-being.
Companies that structure themselves as location-independent have developed norms and practices that bridge the emotional and logistical distances. The same is true for their workers. For such companies, remote-only work can reduce costs, expand the talent pool and boost productivity. By contrast, being forced by a crisis to work remotely is likely to be disruptive and frustrating. It may be better than shutting down, but it will likely lead to a big drop in productivity.
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