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

BACKGROUND: Sensor technology that dynamically identifies hospitalized patients' fall risk and detects and alerts nurses of high-risk patients' early exits out of bed has potential for reducing fall rates and preventing patient harm. During Phase 1 (August 2014-January 2015) of a previously reported performance improvement project, an innovative depth sensor was evaluated on two inpatient medical units to study fall characteristics. In Phase 2 (April 2015-January 2016), a combined depth and bed sensor system designed to assign patient fall probability, detect patient bed exits, and subsequently prevent falls was evaluated. METHODS: Fall detection depth sensors remained in place on two medicine units; bed sensors used to detect patient bed exits were added on only one of the medicine units. Fall rates and fall with injury rates were evaluated on both units. RESULTS: During Phase 2, the designated evaluation unit had 14 falls, for a fall rate of 2.22 per 1,000 patient-days-a 54.1% reduction compared with the Phase 1 fall rate. The difference in rates from Phase 1 to Phase 2 was statistically significant (z = 2.20; p = 0.0297). The comparison medicine unit had 30 falls-a fall rate of 4.69 per 1,000 patient-days, representing a 57.9% increase as compared with Phase 1. CONCLUSION: A fall detection sensor system affords a level of surveillance that standard fall alert systems do not have. Fall prevention remains a complex issue, but sensor technology is a viable fall prevention option.

Anatomy of Inpatient Falls: Examining Fall Events Captured by Depth-Sensor Technology.

BACKGROUND: Sensor technology offers a new way to identify patient movement, detect falls, and automatically alert health care staff when falls occur. The information gained from analyzing actual fall events can be beneficial in developing individualized fall prevention strategies, informing nursing staff about the nature of falls, and identifying opportunities to make the patient care environment safer. METHODS: A six-month performance improvement pilot was conducted at Barnes-Jewish Hospital (St. Louis) to assess the ability of a depth-sensor system to capture inpatient fall events within patient hospital rooms. Depth sensors were installed on two inpatient medicine units with a history of high fall rates. The depth sensors captured actual fall events on video. Video clips were reviewed and analyzed to identify the characteristics of patient falls, staff response times, and environmental conditions contributing to falls. RESULTS: A total of 16 falls involving 13 patients were recorded by depth sensors. Six of the 13 patients who fell were classified as high risk on the basis of the hospital's fall rating tool. Common contributing factors included difficulty rising from their bed, weakened lower extremities, and unsteady or slow gait. Eleven of the falls involved patients reaching for objects in their path in an effort to achieve stability. Nurses had less than two minutes from the time a patient began to exit a bed to the time a fall occurred. Patients expressed few complaints with depth sensors installed in rooms. CONCLUSION: Fall-detection sensor systems offer valuable data for analyzing the nature of patient falls, with the potential promise of prescribing specific fall interventions for patients and to identify staff development opportunities. Hospitals should understand these devices' benefits and limitations and how they affect nursing practice.