title: Development and evaluation of an automated fall risk assessment system.
abstract: OBJECTIVE
  Fall risk assessment is the first step toward prevention, and a risk assessment tool with high validity should be used. This study aimed to develop and validate an automated fall risk assessment system (Auto-FallRAS) to assess fall risks based on electronic medical records (EMRs) without additional data collected or entered by nurses.
  
  
  METHODS
  This study was conducted in a 1335-bed university hospital in Seoul, South Korea. The Auto-FallRAS was developed using 4211 fall-related clinical data extracted from EMRs. Participants included fall patients and non-fall patients (868 and 3472 for the development study; 752 and 3008 for the validation study; and 58 and 232 for validation after clinical application, respectively). The system was evaluated for predictive validity and concurrent validity.
  
  
  RESULTS
  The final 10 predictors were included in the logistic regression model for the risk-scoring algorithm. The results of the Auto-FallRAS were shown as high/moderate/low risk on the EMR screen. The predictive validity analyzed after clinical application of the Auto-FallRAS was as follows: sensitivity = 0.95, NPV = 0.97 and Youden index = 0.44. The validity of the Morse Fall Scale assessed by nurses was as follows: sensitivity = 0.68, NPV = 0.88 and Youden index = 0.28.
  
  
  CONCLUSIONS
  This study found that the Auto-FallRAS results were better than were the nurses' predictions. The advantage of the Auto-FallRAS is that it automatically analyzes information and shows patients' fall risk assessment results without requiring additional time from nurses.
authors: Seoul St Mary's Hospital, The Catholic University of Korea, Seoul, Korea.
authors: Ju Young
authors: JY
authors: Lee
authors: College of Nursing, The Catholic University of Korea, Seoul, Korea.
authors: Yinji
authors: Y
authors: Jin
authors: College of Nursing, The Catholic University of Korea, Seoul, Korea.
authors: Jinshi
authors: J
authors: Piao
authors: College of Nursing, The Catholic University of Korea, Seoul, Korea.
authors: Sun-Mi
authors: SM
authors: Lee
mesh_terms: D000058:Accidental Falls / Q000517:prevention & control* / Q000706:statistics & numerical data; D000465:Algorithms; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D015203:Reproducibility of Results; D056910:Republic of Korea; D018570:Risk Assessment / Q000379:methods*
keywords: accidental falls; prevention; risk assessment; validity