Predicting In-Hospital Falls

Abstract

Falling is the most common hospital accident, and up to 15% of fallers sustain a serious injury. This study focused on developing a simple, practical fall risk screener using routine admission and daily in-hospital stay data. A case-control design was used. Logistic regression identified individual characteristics associated with an increased risk of a fall. Four variables were identified: history of falls, ambulation assistance, disoriented, and bowel control problems, creating a fall risk model with 70% sensitivity and 57% specificity.