A Smarter Approach to the Traditional Fall Alert System

Falls are among the most costly, and also the most preventable, medical events in the senior housing setting. They’re costly to the health system, as falls are a frequent cause of hospitalization, and costly to the housing operator. Falls may create the need for extra staff or they may trigger unnecessary vacancies.

All this may be preventable, though not by the present systems, especially the pervasive Timed Up and GO (TUG) assessment. In this test of falls likelihood:

  • The senior sits with his or her back against a chair back;
  • On the “go” mark the individual rises, walks about 10 feet at a comfortable pace, turns, returns to the chair and sits down;
  • Timing begins at “go” and stops when the individual is seated; and
  • Those who take longer than 12 seconds to complete circuit are estimated to be at high risk for falling.

This technique is adequate but hardly perfect. Senior care has long needed a smarter approach to fall alerts, and now that technology has become available.

A better way

Sensor-based monitoring, powered by the analytic muscle of artificial intelligence (AI) software, has been shown to prevent falls reliably, using passive alerts to notify caregivers of a likely negative event.

It works like this. Using an advanced network of pressure, motion, and depth sensors, such a system will “learn” a senior’s baseline patterns of motion, building up a picture of behavior over time. Should a resident appear to deviate from the norm, automated detection and alert would engage care providers of a potential negative event impending.

Sensor-based prevention does not abandon the TUG principle entirely, but rather incorporates and automates the fundamentals of TUG testing, taking the best of an existing approach and pairing with the power of a new technology. Automation is a key to the puzzle, as it gives caregivers predictive power, while freeing them from the time-consuming (and often subjective) task of frequent monitoring.

One of the most powerful elements of the sensor-and-AI based approach lies in the ability to record and rewind. No two falls are the same, and the nature of a fall may have a profound impact in the care needed after the fact. The ability to rewind and review an incident gives caregivers the opportunity to analyze a fall, to understand which area of the body was affected and to explore what factors might be altered to prevent future risk.

It’s worth noting that such a passive alert system can deliver a range of other metrics, including the monitoring of vital signs and the observation of a potential pressure ulcer situation. Just as will potential falls, early detection of potential negative health situations can help to ensure resident health while also helping operators to reduce overtime expenses, minimize vacancies and generally run a community that is both safer and more cost effective.

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