To assist caregivers in assessing the risk of seniors to diseases of the urinary tract, the company Zanthion has designed a new wearable device. The device is designed to assess, predict and alert about the risk of sepsis and urinary tract infections in relation to seniors.
Seniors are subject to infections that increase in likelihood for age-related reasons. These infections include diseases of the urinary tract and these, if untreated, can lead to sepsis. Infections can affect different parts of your urinary tract, including your bladder (cystitis), urethra (urethritis) or kidneys (kidney infection). Furthermore, in the U.S. infections relating to seniors who live in nursing homes is a major cause of death.
While urinary tract infections can be treated with antibiotics, there are situations that arise where certain infective bacteria are resistant to antimicrobials. Moreover, the risk of sepsis can arise. The elderly are also more susceptible to infections due to weaker immune systems.
To help to address this, Zanthion is recommending that companies incorporate highly sensitive wearable sensors designed to monitor and report indicators of infections. An early alert allows for appropriate treatment to be administered.
Zanthion’s CEO Philip Regenie says that utilizing the power of artificial intelligence and predictive analytics can provide 24/7 monitoring and actionable information relating to infection risk.
Regenie states: “AI can easily predict urinary tract infections by measuring frequency of trips to the bathroom or by measuring increases in uric acid in adult briefs. This info allows for early intervention with antibiotics, which reduces the incidence of sepsis significantly.”
Zanthion have produced highly sensitive wearables and environmental sensors that are designed to keep track of location, position, heart rate, temperature and other parameters. The information collected from the devices is delivered at a rate of 22 messages per second). The technology enables caregivers to assess whether or not a resident has been to the toilet too many times or on a insufficient number of occasions. Such data analysis enables a prediction to be made about the risk of urinary tract infections.
The success of the devices has been measured in trials, with one study conducted by Big Cloud Analytics. The devices can also make a contribution towards those who live in assisted living communities, the home, or in a healthcare setting.
