Evaluation of Sensor Technology to Detect Fall Risk and Prevent Falls in Acute Care.
| Autor: | Potter P, Allen K, Costantinou E, Klinkenberg WD, Malen J, Norris T, O'Connor E, Roney W, Tymkew HH, Wolf L |
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| Jazyk: | angličtina |
| Zdroj: | Joint Commission journal on quality and patient safety [Jt Comm J Qual Patient Saf] 2017 Aug; Vol. 43 (8), pp. 414-421. Date of Electronic Publication: 2017 Jun 22. |
| DOI: | 10.1016/j.jcjq.2017.05.003 |
| Abstrakt: | 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. (Copyright © 2017 The Joint Commission. Published by Elsevier Inc. All rights reserved.) |
| Databáze: | MEDLINE |
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