Cognitive readiness of nurses regarding artificial intelligence predictions: understanding through the dual lens of verbatim and gist knowledge

Abstract

Objectives: The expansion of artificial intelligence (AI)-enabled clinical decision support (CDS) requires nurses to interpret complex model outputs. However, their cognitive readiness remains underexplored, particularly in terms of their understanding of statistics. To assess nurses' understanding of key statistical concepts underlying AI predictions and their relationship to health numeracy. Materials and Methods: An organizational approach study involving 180 nurses from 6 medical-surgical units at a tertiary hospital, preparing to implement an AI fall-prediction model. Statistical knowledge was evaluated using a heuristic vignette based on fuzzy-trace theory, assessing both verbatim (literal) and gist (meaning-based) understanding of sensitivity, specificity, and CIs. Health numeracy was measured using the Lipkus Objective Numeracy Scale, Numeracy Understanding in Medicine Instrument: short form, and Subjective Numeracy Scale. Analyses included ANOVA and Kruskal-Wallis and Wilcoxon rank-sum tests, with thematic analyses applied to the qualitative concerns of nurses. Results: = .0124). Numeracy was not significantly associated with the understanding of statistics. Nurses overrode predictions due to cognitive mismatch, requesting greater model transparency, input rationale, and risk-threshold explanations. Conclusion: Despite displaying adequate numeracy, nurses' conceptual grasp of statistical concepts may hinder the safe application of AI CDS system outputs. These findings underscore the need for targeted education and a cognitive-fit-driven interface design to support the trustworthy use of AI in nursing practice.

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