Evaluation of Sensor Technology to Detect Fall Risk and Prevent Falls in Acute Care.

Area of Science:

• Medical device technology
• Patient safety systems
• Clinical informatics

Background:

• Hospital-acquired patient falls are a significant safety concern.
• Existing fall prevention methods have limitations in real-time detection.
• Sensor technology offers a novel approach to monitor patient mobility and predict fall risk.

Purpose of the Study:

• To evaluate the effectiveness of a combined depth and bed sensor system in reducing patient falls.
• To assess the system's ability to dynamically identify fall risk and detect early exits from bed.
• To compare fall rates before and after the implementation of the sensor system.

Main Methods:

• A phased performance improvement project was conducted on two inpatient medical units.
• Phase 1 involved evaluating depth sensors for fall characteristics.
• Phase 2 implemented a combined depth and bed sensor system on one unit for fall prevention, with fall rates compared to a control unit and Phase 1 data.

Main Results:

• The unit with the combined sensor system experienced a 54.1% reduction in fall rates (2.22 falls per 1,000 patient-days) compared to Phase 1.
• This reduction was statistically significant (p=0.0297).
• The comparison unit, without bed sensors, saw a 57.9% increase in fall rates (4.69 falls per 1,000 patient-days).

Conclusions:

• Sensor technology, specifically combined depth and bed sensors, is a viable option for fall prevention.
• This system provides enhanced surveillance beyond standard fall alert systems.
• Implementing advanced sensor technology can significantly reduce patient falls and associated harm.