title: Predicting in-hospital falls: development of the Scott and White Falls Risk Screener.
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.
authors.affiliation: Scott and White Memorial Hospital and Clinic, Temple, Tex, USA. syauk@swmail.sw.org
authors.forename: Sheryl
authors.initials: S
authors.lastname: Yauk
authors.forename: Barbara A
authors.initials: BA
authors.lastname: Hopkins
authors.forename: Charles D
authors.initials: CD
authors.lastname: Phillips
authors.forename: Sandra
authors.initials: S
authors.lastname: Terrell
authors.forename: Joyce
authors.initials: J
authors.lastname: Bennion
authors.forename: Mark
authors.initials: M
authors.lastname: Riggs
mesh_terms: D000058:Accidental Falls / Q000517:prevention & control*; D000368:Aged; D006761:Hospitals; D006801:Humans; D016015:Logistic Models; D008403:Mass Screening / Q000379:methods*; D008962:Models, Theoretical; D012189:Retrospective Studies; D018570:Risk Assessment; D012308:Risk Management / Q000379:methods*; D012680:Sensitivity and Specificity; D013781:Texas