title: Effect of Automatic Inpatient Fall Prediction Using Routinely Captured EMR Data: Preliminary Results.
abstract: The increasing adoption of electronic medical record (EMR) systems including nursing documentation worldwide provides opportunities for improving patient safety by the automatic prediction of adverse events using EMR data. An inpatient fall is a preventable adverse event that can be managed more effectively and efficiently through a data-driven predictive approach. This study implemented a new approach and explored its effects in neurologic inpatient units. The results suggest that integrating an automatic fall prediction system with the EMR system could reduce inpatient falls.
authors.affiliation: Department of Nursing, Inha University, Incheon, South Korea.
authors.forename: Insook
authors.initials: I
authors.lastname: Cho
authors.affiliation: Seoul National University, Bundang Hospital, Gyeonggi-Do, South Korea.
authors.forename: Eunja
authors.initials: E
authors.lastname: Chung
mesh_terms: D000058:Accidental Falls / Q000517:prevention & control* / Q000706:statistics & numerical data*; D000465:Algorithms; D003627:Data Interpretation, Statistical; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D011159:Population Surveillance / Q000379:methods; D015995:Prevalence; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D056910:Republic of Korea / Q000453:epidemiology; D018570:Risk Assessment / Q000379:methods*; D012680:Sensitivity and Specificity