Nurses’ Fall Risk Judgements - Cognitive Biases and Contextual Factors Explaining Variability: A multi-centre cross-sectional study

Background Assessing fall risk is a complex process requiring the integration of diverse information and cognitive strategies. Despite this complexity, few studies have explored how nurses make these judgements. Moreover, existing research suggests variability in nurses’ fall risk assessments, but the reasons for this variation and its appropriateness remain unclear. Objective This study aimed to investigate how nurses judge fall risk, and the factors associated with their judgements. Methods Using purposive sampling, 335 nurses from six hospitals in western Japan participated in an online survey. The participants rated the likelihood of falls in 18 patient scenarios and completed measures of base-rate neglect, belief bias, and availability bias. A linear mixed-effects regression tree was used to identify factors related to their judgements, and a linear mixed-effects regression model examined associations between judgement variability, cognitive biases, and clinical specialty. Results Nurses’ fall risk assessments were primarily influenced by whether patients called for assistance, followed by the use of sleeping pills, the presence of a tube or drain, and patient mobility status. Judgement variability was linked to nurses’ gender, education, clinical specialty, and susceptibility to availability bias. Conclusion Variability in clinical judgement may be justified when reflecting personalised, context-specific care. However, inconsistencies arising from cognitive biases are problematic. Healthcare organisations should offer targeted training to enhance contextual expertise and reduce the influence of cognitive biases on fall risk assessments.