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The purpose of this study was to identify predictors of falls that result in serious injury in hospitalized patients. The study involved secondary data analysis of 1,438 patient falls in a community hospital system between 2008 and 2010. The analysis included demographics, severity of illness, diagnosis-related group (surgical vs. medical), event type (bathroom, bed, chair, transfer, ambulating), risk factors identified by the Hendrich II fall risk assessment prior to the fall (confusion, depression, altered elimination, dizziness, antiepileptic or benzodiazepine medications), and contributing factors identified through an online event reporting system post-fall (incontinence, confusion, history of falls, alteration in mobility, and medication-related). Logistic regression results indicated that the overall model was a good fit and two predictors, age greater than 64 and male gender, were statistically reliable in predicting which patient falls would result in serious injury.

作者:Chrys, Anderson;Mary, Dolansky;Elizabeth G, Damato;Katherine R, Jones

来源:Clinical nursing research 2015 年 24卷 3期

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作者:
Chrys, Anderson;Mary, Dolansky;Elizabeth G, Damato;Katherine R, Jones
来源:
Clinical nursing research 2015 年 24卷 3期
标签:
acute care setting assessment clinical research areas fall injuries falls health care settings nursing interventions
The purpose of this study was to identify predictors of falls that result in serious injury in hospitalized patients. The study involved secondary data analysis of 1,438 patient falls in a community hospital system between 2008 and 2010. The analysis included demographics, severity of illness, diagnosis-related group (surgical vs. medical), event type (bathroom, bed, chair, transfer, ambulating), risk factors identified by the Hendrich II fall risk assessment prior to the fall (confusion, depression, altered elimination, dizziness, antiepileptic or benzodiazepine medications), and contributing factors identified through an online event reporting system post-fall (incontinence, confusion, history of falls, alteration in mobility, and medication-related). Logistic regression results indicated that the overall model was a good fit and two predictors, age greater than 64 and male gender, were statistically reliable in predicting which patient falls would result in serious injury.