Predictive Analytics and AI Can Reduce Seniors’ UTIs and Sepsis, Help Nursing Staffs Monitor

November 14, 2018

SAN FRANCISCO–For a number of age-related reasons, urinary tract infections (UTIs) are common in seniors, and when left undiagnosed and untreated, can lead to the deadly and quickly-escalating condition called sepsis. New highly sensitive wearables and other devices can accurately predict and alert caregivers to sepsis and UTIs, greatly improving care outcomes for seniors.

For the thousands of seniors who live in a long-term care facility such as a nursing home, infections are the primary cause of death, and UTIs are the second most common infection threat to their health. In fact, UTI treatment accounts for 30-50 percent of antibiotic usage in long-term care facilities.

UTIs are prevalent in seniors for a number of reasons related to age and sometimes to catheterization—but the real danger involved with UTIs is the potential to develop sepsis. If not immediately treated, sepsis can severely disable or kill, and especially when nursing homes are understaffed, lack of sepsis discovery and diagnosis presents an enormous risk.

Philip Regenie, CEO of AI healthcare company Zanthion, urges care facilities to consider incorporating highly sensitive wearable devices and other sensors that monitor and report indicators of UTIs, accurately indicating the presence of infection and empowering caregivers to act before the situation escalates.

“AI can easily predict UTIs by measuring frequency of trips to the bathroom or by measuring increases in uric acid in adult briefs,” said Regenie. “This info allows for early intervention with antibiotics, which reduces the incidence of sepsis significantly.”

The elderly tend to experience weaker immune systems and are also subject to comorbidities (such as dementia, stroke, and Parkinson’s disease) that all make it easier for infections such as UTIs to emerge and develop.

Wheelchair-bound patients using catheters are also highly likely to develop these infections from time to time. UTI symptoms include frequent urination, pain while urinating, and pressure in the lower back. Patients whose activity and output are closely monitored by caregivers can often be caught in the early stages of a UTI, but if not, the infection can spread into the kidneys, causing sepsis to set in.

Sepsis is the body’s often deadly response to severe infection or injury, and it rapidly escalates. Patients often develop a fever, rapid heart rate, and—especially in the case of seniors—become confused. When this is the case, they have to be rushed to the hospital.

Caregivers at long-term care facilities are, of course, well aware that UTIs are a widespread problem, but staff shortages make it nearly impossible to monitor each resident as closely as is needed to ensure UTIs are caught and don’t progress. Shortages also make it difficult to ensure that residents who need bathroom assistance get it as often as necessary and that catheters are changed with optimum regularity, both of which are factors in UTI proliferation.

An estimated 3.5 million more healthcare workers are needed by 2030 just to maintain current care facility ratios. And many facilities cite as one of their main concerns an actual decrease in the number of people choosing to become certified nursing assistants (CNAs), meaning that it’s becoming even more difficult to fill out those staff rosters.

So the question becomes, how can existing nursing staff become more empowered to monitor, catch, and treat signs of senior UTIs?

The answer, according to Regenie, lies in leveraging the power of AI and predictive analytics to provide 24/7 monitoring and actionable info.

Zanthion has developed a line of highly sensitive wearables and environmental sensors that keep track of location, position, heart rate, temperature and more. The wearable sensors alone deliver 22 messages per second. These monitoring devices can aggregate information about all residents at a long-term care facility and display it on an easily accessible portal for caregivers.

These technologies make it easy to tell whether a resident has either been to the restroom too many times or not enough—meaning that it’s possible to get ahead of a UTI as soon as symptoms emerge and measure erratic behavior or distress that may point to an illness like sepsis.

In trials, real-time predictive analytics in long-term care settings have already produced promising results. One experiment conducted by Big Cloud Analytics captured one senior’s “unexplained variability” in heart rate a few weeks before doctors determined she needed a pacemaker; another resident, who was taken to the hospital for pneumonia, showed a rapid heart rate (despite a 70% activity decrease) that began days before she was displaying pneumonia symptoms.

“Our seniors deserve a safe, secure, peaceful life punctuated by happiness and health, not the life of indignity that is all too common within our current senior care system,” said Regenie. “Predictive analytics and AI can provide older adults with more independence and protect them from a traumatic, avoidable death by sepsis stemming from a simple UTI.”

Zanthion is an AI Digital Healthcare Company specializing in the integration of an extensible architecture of sensors and protective clothing and environmental equipment for both assisted living communities and the home—a cross between Uber, smart homes, fall detection and senior care. Its products track and detect possible issues and injuries. Open source, transparent, crowd-sourced platform and social processes accurately assess what happened, inform the correct resources, provide resources to the problem efficiently, and keep track of the efficiency of fixing the problem.

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