title: Construction and evaluation of FiND, a fall risk prediction model of inpatients from nursing data.
abstract: OBJECTIVE
  To construct and evaluate an easy-to-use fall risk prediction model based on the daily condition of inpatients from secondary use electronic medical record system data.
  
  
  METHODS
  The present authors scrutinized electronic medical record system data and created a dataset for analysis by including inpatient fall report data and Intensity of Nursing Care Needs data. The authors divided the analysis dataset into training data and testing data, then constructed the fall risk prediction model FiND from the training data, and tested the model using the testing data.
  
  
  RESULTS
  The dataset for analysis contained 1,230,604 records from 46,241 patients. The sensitivity of the model constructed from the training data was 71.3% and the specificity was 66.0%. The verification result from the testing dataset was almost equivalent to the theoretical value.
  
  
  CONCLUSIONS
  Although the model's accuracy did not surpass that of models developed in previous research, the authors believe FiND will be useful in medical institutions all over Japan because it is composed of few variables (only age, sex, and the Intensity of Nursing Care Needs items), and the accuracy for unknown data was clear.
authors.affiliation: Department of Planning, Information and Management, The University of Tokyo Hospital, Tokyo, Japan.
authors.forename: Shinichiroh
authors.initials: S
authors.lastname: Yokota
authors.affiliation: Department of Medical Informatics and Economics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
authors.forename: Kazuhiko
authors.initials: K
authors.lastname: Ohe
mesh_terms: D000058:Accidental Falls / Q000706:statistics & numerical data*; D000328:Adult; D005260:Female; D006801:Humans; D007297:Inpatients; D008297:Male; D008875:Middle Aged; D009729:Nursing; D012307:Risk Factors
keywords: accident prevention; accidental falls; decision support techniques; projections and predictions; risk management