Accepted for/Published in: JMIR Nursing
Date Submitted: Apr 11, 2026
Date Accepted: Jun 15, 2026
Date Submitted to PubMed: Jun 15, 2026
Effectiveness of Artificial Intelligence-Based Nursing Interventions for Chronic Illnesses Care: An Umbrella Review
ABSTRACT
Background:
Background:
While AI-based nursing interventions are increasingly employed to manage chronic diseases, their definitive impact on clinical outcomes remains inconclusive, necessitating a comprehensive evidence synthesis.
Objective:
Aim/objective: This umbrella review aimed to synthesize the evidence regarding the effectiveness of artificial intelligence (AI)-based nursing interventions for chronic illness care and their subsequent impact on healthcare outcomes in clinical settings.
Methods:
Methods:
An umbrella review was conducted, and the protocol was prospectively registered. A systematic search of five electronic databases (PubMed, CINAHL, Cochrane Library, Scopus, and Web of Science) was performed for systematic reviews and meta-analyses published in English between 2021 and 2025. The methodological quality of included studies was evaluated using the Joanna Briggs Institute Critical Appraisal Checklist.
Results:
Results:
Eight high-quality systematic reviews were included, with machine learning identified as the predominant technology. Three primary outcome domains emerged: predictive, psychosocial, and hospital utilization. Due to measurement heterogeneity, results were synthesized narratively. Findings demonstrated that AI-based nursing interventions are effective in predicting adverse clinical events, unplanned hospital utilization, and healthcare costs. However, evidence regarding psychosocial outcomes remains insufficient.
Conclusions:
Conclusions:
This review provides systematic evidence supporting the utility of AI in chronic illness management, particularly for improving predictive and utilization outcomes. These findings offer actionable insights for nursing leaders to integrate AI into clinical practice and education. Future research should prioritize rigorous empirical designs to further strengthen the evidence base for AI-driven nursing care.
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.