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

초록

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 Overall statistical knowledge was moderate (mean = 85.56, 95% CI, 82.64-88.46). Gist knowledge lagged verbatim knowledge, especially about CIs. Nurses with advanced degrees had higher verbatim scores (P = .0108), while bachelor-level nurses performed better on discrete-choice tasks related to gist (P = .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. Hospitals are increasingly using artificial intelligence (AI) tools to support nurses' decision-making. For these tools to be safe and helpful, nurses need to understand what the AI results mean. In this study, we surveyed nurses at a large hospital to see how well they understood key ideas that explain AI "risk scores," such as sensitivity, specificity, and CIs. We tested 2 kinds of understanding. Verbatim understanding means remembering the exact numbers. Gist understanding means knowing the main message the numbers convey. Nurses scored about 86 out of 100 overall. They performed better at recalling exact numbers than at understanding their meaning, particularly for CIs. We also measured numeracy (comfort with numbers). Strong numeracy did not always mean nurses understood these statistics well. Nurses with advanced degrees were better at recalling exact facts, while nurses with bachelor's degrees did better on tasks that relied on gist understanding. After the AI tool was used in practice, some nurses asked how the model arrived at its predictions and what the "high risk" and "low risk" cutoffs meant. These findings suggest that nurses may require clearer training and simpler AI displays to use AI predictions with confidence in everyday patient care.

키워드