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Predicting falls: considerations for screening tool selection vs. screening tool development
Author(s) -
McKechnie Duncan,
Pryor Julie,
Fisher Murray J.
Publication year - 2016
Publication title -
journal of advanced nursing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.948
H-Index - 155
eISSN - 1365-2648
pISSN - 0309-2402
DOI - 10.1111/jan.12977
Subject(s) - context (archaeology) , selection (genetic algorithm) , risk assessment , medicine , risk analysis (engineering) , human factors and ergonomics , poison control , computer science , medical emergency , machine learning , paleontology , computer security , biology
Aims This paper discusses considerations for falls risk screening tool selection vs. the need to develop new tools. Background Inpatient falls are a complex patient safety issue that represent a significant burden for the healthcare system. In the inpatient context, falls risk screening tools are most often used for predicting falls, but in some populations assessment tools are more suited, however in others, a clinician's clinical judgment may be just as effective. Limited external validity is a central issue with falls risk screening tools when used in different populations than the original study. There is clinical need for guidance regarding screening tool selection vs. the need to development new tools and how to effect change in relation to the prediction of falls. Design Discussion paper. Data sources This discussion paper is based on our own experiences and research and is supported by literature. Implications for nursing This paper provides clinicians with a better understanding of considerations for falls risk screening tool selection vs. the need to develop new tools. In doing so, it provides clinicians guidance on how to critique the efficacy and utility of their falls risk screening tool. This paper equips clinicians for effecting change in relation to the prediction of falls. Conclusion Falls risk prediction is a particularly complex patient safety issue. Clinicians need to be aware of the limitations of their tool used to predict falls.