Effectiveness of Artificial Intelligence-Based Nursing Interventions for Chronic Illness Care: Umbrella Review

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not reported — narrative synthesis only; no pooled effect sizes calculated

• Primary Outcome: AI-based nursing interventions demonstrated effectiveness across three outcome domains: (1) predictive outcomes — improved prediction of adverse clinical events; (2) hospital utilisation outcomes — reduction in unplanned hospital utilisation and healthcare costs; (3) psychosocial outcomes — evidence remains insufficient to draw conclusions

• Nnt Or Sensitivity: Not reported — no NNT, sensitivity, specificity, or hazard ratios provided at the umbrella review level; individual included reviews may contain such data but are not synthesised quantitatively

• Confidence Interval: Not reported

Clinical Application

Feasibility of AI-based nursing interventions varies substantially by healthcare setting, institutional infrastructure, data governance frameworks, and nursing workforce digital literacy. The review does not address implementation barriers, costs of AI system deployment, or required training. Clinical adoption requires institutional investment in interoperable electronic health record systems, validated AI algorithms, and robust governance frameworks for clinical decision support. In the Australian context, integration of AI-based nursing tools into chronic illness management is an emerging priority. The Australian Digital Health Agency's National Digital Health Strategy and the My Health Record system provide infrastructure for data-driven care, but AI-specific clinical governance frameworks remain underdeveloped. The Therapeutic Goods Administration (TGA) regulates software as a medical device (SaMD), and AI-based clinical decision support tools meeting the definition of SaMD require TGA approval prior to clinical deployment — a regulatory consideration absent from this review. The RACGP and Australian College of Nursing have not yet issued specific guidelines on AI-assisted nursing interventions for chronic disease. PBS implications are indirect; however, if AI tools demonstrably reduce unplanned hospitalisations (as suggested by this review), there may be health economic arguments for public funding models. The review's findings regarding hospital utilisation outcomes are particularly relevant to Australian healthcare given the burden of chronic disease and pressure on public hospital systems. Adults with chronic illnesses (specific conditions not delineated in the abstract) receiving nursing care in clinical settings where AI-based tools, predominantly machine learning systems, are deployed or under consideration for deployment

Abstract

BACKGROUND: Artificial intelligence (AI)-based nursing interventions are increasingly being used to manage chronic illnesses; however, their definitive impact on clinical outcomes remains inconclusive, necessitating a comprehensive evidence synthesis. OBJECTIVE: This umbrella review aimed to synthesize the evidence regarding the effectiveness of AI-based nursing interventions for chronic illness care and their subsequent impact on health care outcomes in clinical settings. METHODS: We conducted an umbrella review and prospectively registered the protocol. A systematic search of 5 electronic databases (PubMed, CINAHL, Cochrane Library, Scopus, and Web of Science) was performed to identify systematic reviews and meta-analyses published in English between 2021 and 2025. The methodological quality of the included studies was evaluated using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist. 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, the results were synthesized narratively. Our findings demonstrated that AI-based nursing interventions are effective in predicting adverse clinical events, unplanned hospital utilization, and health care costs. However, evidence regarding psychosocial outcomes remains insufficient. 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.

References

1. 1.Joo, J. Y., Liu, M., Cho, Y., & Cho, H. (2026). Effectiveness of artificial intelligence-based nursing interventions for chronic illness care: Umbrella review. JMIR Nursing. https://doi.org/10.2196/97905