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Updated: Oct 20, 2025
Design and Analysis for Fall Detection System Simplification
Published on: April 6, 2020
Clinical prediction models for hospital falls: a scoping review protocol
Rex Parsons1, Susanna M Cramb2,3, Steven M McPhail2,4
1Australian Centre for Health Services Innovation and Centre for Healthcare Translation, School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, Queensland, Australia rex.parsons@hdr.qut.edu.au.
Introduction:
Falls remain one of the most prevalent adverse events in hospitals and are associated with substantial negative health impacts and costs. Approaches to assess patients' fall risk have been implemented in hospitals internationally, ranging from brief screening questions to multifactorial risk assessments and complex prediction models, despite a lack of clear evidence of effect in reducing falls in acute hospital environments. The increasing digitisation of hospital systems provides new opportunities to understand and predict falls using routinely recorded data, with potential to integrate fall prediction models into real-time or near-real-time computerised decision support for clinical teams seeking to mitigate fall risk. However, the use of non-traditional approaches to fall risk prediction, including machine learning using integrated electronic medical records, has not yet been reviewed relative to more traditional fall prediction models. This scoping review will summarise methodologies used to develop existing hospital fall prediction models, including reporting quality assessment.
Methods And Analysis:
This scoping review will follow the Arksey and O'Malley framework and its recent advances, and will be reported using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews recommendations. Four electronic databases (CINAHL via EBSCOhost, PubMed, IEEE Xplore and Embase) will be initially searched for studies up to 12 November 2020, and searches may be updated prior to final reporting. Additional studies will be identified by reference list review and citation analysis of included studies. No restriction will be placed on the date or language of identified studies. Screening of search results and extraction of data will be performed by two independent reviewers. Reporting quality will be assessed by the adherence to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis.
Ethics And Dissemination:
Ethical approval is not required for this study. Findings will be disseminated through peer-reviewed publication and scientific conferences.
