Nurses' Experiences With Artificial Intelligence and the Association Between Clinical Decision-Making and Missed Nursing Care: A Convergent Mixed-Methods Study.
Artificial intelligence (AI) is increasingly being incorporated into clinical nursing practice, but evidence remains limited on how nurses experience AI-supported systems and how decision-making orientation relates to missed nursing care (MNC).
To explore nurses' experiences with AI-supported clinical decision support (CDS) and examine associations among clinical decision-making orientation, professional characteristics, and MNC.
A convergent parallel mixed-methods study.
The study was conducted in six hospitals in Beni Suef Governorate, Egypt. The quantitative sample included 210 intensive care unit (ICU) nurses who completed the Nursing Decision-Making Instrument-Revised 2014 (NDMI-R) and the Missed Intensive Nursing Care Scale (MINCS). Fourteen nurses completed semistructured interviews, and all transcripts were included in the thematic analysis.
NDMI-R scores showed a small negative bivariate correlation with MNC (r = -0.178, p < 0.01), but the adjusted association was not statistically significant (β = -0.124, p = 0.063). The regression model explained 16.9% of the variance in MNC (adjusted R2 = 14.5%). Years of experience (β = 0.198, p = 0.004) and bachelor's degree or above compared with a technical nursing diploma (β = -0.255, p < 0.001) were significant predictors. The other education contrast, AI training/support and MISSCARE Reasons were not statistically significant. In interviews, nurses described AI-related tools as potentially helpful for alerts, information review, workflow, and communication while also raising concerns about trust, overreliance, empathy, and training.
Nurses perceived potential benefits from AI-supported tools, but the quantitative findings did not establish an independent effect of AI or a statistically significant adjusted association between decision-making orientation and MNC. These findings support cautious, nurse-centered implementation and further longitudinal evaluation.
Managers should pair any AI-supported CDS implementation with role-specific training, clear accountability, usability monitoring, and safeguards that preserve clinical judgment and humanistic care.
To explore nurses' experiences with AI-supported clinical decision support (CDS) and examine associations among clinical decision-making orientation, professional characteristics, and MNC.
A convergent parallel mixed-methods study.
The study was conducted in six hospitals in Beni Suef Governorate, Egypt. The quantitative sample included 210 intensive care unit (ICU) nurses who completed the Nursing Decision-Making Instrument-Revised 2014 (NDMI-R) and the Missed Intensive Nursing Care Scale (MINCS). Fourteen nurses completed semistructured interviews, and all transcripts were included in the thematic analysis.
NDMI-R scores showed a small negative bivariate correlation with MNC (r = -0.178, p < 0.01), but the adjusted association was not statistically significant (β = -0.124, p = 0.063). The regression model explained 16.9% of the variance in MNC (adjusted R2 = 14.5%). Years of experience (β = 0.198, p = 0.004) and bachelor's degree or above compared with a technical nursing diploma (β = -0.255, p < 0.001) were significant predictors. The other education contrast, AI training/support and MISSCARE Reasons were not statistically significant. In interviews, nurses described AI-related tools as potentially helpful for alerts, information review, workflow, and communication while also raising concerns about trust, overreliance, empathy, and training.
Nurses perceived potential benefits from AI-supported tools, but the quantitative findings did not establish an independent effect of AI or a statistically significant adjusted association between decision-making orientation and MNC. These findings support cautious, nurse-centered implementation and further longitudinal evaluation.
Managers should pair any AI-supported CDS implementation with role-specific training, clear accountability, usability monitoring, and safeguards that preserve clinical judgment and humanistic care.
Authors
Alanazi Alanazi, Tawfik Tawfik, Mahgoub Mahgoub, Alenezi Alenezi, Atta Atta, El-Ashry El-Ashry, Lotfy Lotfy, Yehia Yehia
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