Inpatient Fall Prediction Models: A Scoping Review.

Journal: Gerontology
PMID:

Abstract

INTRODUCTION: The digitization of hospital systems, including integrated electronic medical records, has provided opportunities to improve the prediction performance of inpatient fall risk models and their application to computerized clinical decision support systems. This review describes the data sources and scope of methods reported in studies that developed inpatient fall prediction models, including machine learning and more traditional approaches to inpatient fall risk prediction.

Authors

  • Rex Parsons
    Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Kelvin Grove, Queensland, Australia.
  • Robin D Blythe
    Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Kelvin Grove, Queensland, Australia.
  • Susanna M Cramb
    Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Kelvin Grove, Queensland, Australia.
  • Steven M McPhail
    Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Kelvin Grove, Queensland, Australia.