Introduction: Artificial Intelligence (AI) is increasingly being integrated into healthcare, particularly through predictive analytics that can enhance patient care and operational efficiency. Nursing leaders play a crucial role in the successful adoption of these technologies.
Aim: This study aims to assess the readiness of nursing leaders for AI integration and evaluate their perceptions of the benefits of AI-driven predictive analytics in healthcare.
Methods: A descriptive cross-sectional study was conducted among 187 nurse leaders across nine private hospitals in Cairo. The sample was selected using a combination of simple random sampling and non-probability convenience sampling methods to ensure a diverse representation of nursing leadership. Data collection took place from March to May 2024, utilizing a structured questionnaire specifically designed to assess nurse leaders' readiness for AI integration and their perceptions of AI-driven predictive analytics The data were analyzed using IBM SPSS Statistics, version 26.0. Exploratory Factor Analysis (EFA) was employed to identify underlying factors related to readiness and perceived benefits. Confirmatory Factor Analysis (CFA) was subsequently performed to validate the factor structure. Multiple linear regression analysis was conducted to identify significant predictors of AI readiness and perceived benefits.
Results: The study revealed that over one-third of nurse leaders exhibited high readiness for AI integration. Significant predictors of readiness included age, educational attainment, and employment status. Positive correlations were found between readiness and perceived benefits of AI, particularly in areas such as care planning and decision-making.
Conclusion: The findings suggest that nursing leaders are generally prepared to integrate AI into their workflows, especially those with advanced education and experience. However, further training and policy development are necessary to fully realize the benefits of AI in nursing practice.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11737245 | PMC |
| http://dx.doi.org/10.1186/s12912-024-02653-x | DOI Listing |
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