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The use of technology to support nurses' decision-making is increasing in response to growing healthcare demands. AI, a global trend, holds great potential to enhance nurses' daily work if implemented systematically, paving the way for a promising future in healthcare.
To identify and describe AI technologies for nurses' clinical decision-making in healthcare settings.
A systematic literature review.
CINAHL, PubMed, Scopus, ProQuest, and Medic were searched for studies with experimental design published between 2005 and 2024.
JBI guidelines guided the review. At least two researchers independently assessed the eligibility of the studies based on title, abstract, and full text, as well as the methodological quality of the studies. Narrative analysis of the study findings was performed.
Eight studies showed AI tools improved decision-making, patient care, and staff performance. A discharge support system reduced 30-day readmissions from 22.2% to 9.4% (p = 0.015); a deterioration algorithm cut time to contact senior staff (p = 0.040) and order tests (p = 0.049). Neonatal resuscitation accuracy rose to 94%–95% versus 55%–80% (p < 0.001); seizure assessment confidence improved (p = 0.01); pressure ulcer prevention (p = 0.002) and visual differentiation (p < 0.001) improved. Documentation quality increased (p < 0.001).
AI integration in nursing has the potential to optimise decision-making, improve patient care quality, and enhance workflow efficiency. Ethical considerations must address transparency, bias mitigation, data privacy, and accountability in AI-driven decisions, ensuring patient safety and trust while supporting equitable, evidence-based care delivery.
The findings underline the transformative role of AI in addressing pressing nursing challenges such as staffing shortages, workload management, and error reduction. By supporting clinical decision-making and workflow efficiency, AI can enhance patient safety, care quality, and nurses' capacity to focus on direct patient care. A stronger emphasis on research and implementation will help bridge usability and scalability gaps, ensuring sustainable integration of AI across diverse healthcare settings.
To summarise the effect of mentoring within mentoring programmes on the retention and turnover of newly graduated nurses in healthcare settings.
An umbrella review.
Two independent reviewers screened the titles, abstracts and full texts for eligibility and critically appraised the included reviews using the JBI critical appraisal. The findings were tabulated and synthesised.
The search was conducted in five electronic databases (CINAHL, OvidMedline, ProQuest, Scopus, Cochrane and Medic) in November 2023.
Out of 450 Papers, 13 systematic and integrative reviews were included. Thirteen mentoring programmes were identified and categorised into three groups based on their content: didactic mentoring programmes, interaction-based mentoring programmes and combined mentoring programmes. Across these programme types, retention among newly graduated nurses ranged from 72% to 100% at the 1-year mark and 70% to 98% at 2 years. Turnover rates showed consistent reductions, with post-intervention rates ranging from 3.5% to 20% compared to pre-intervention rates of up to 50%. Several studies reported statistically significant improvements in retention and turnover, particularly in programmes integrating structured education and preceptorship models.
Several different mentoring programmes have been developed to support the transition of newly graduated nurses. Mentoring programmes that provide ongoing support and structured guidance increase retention and reduce turnover among newly graduated nurses.
Effective mentoring programmes are key to ensuring high-quality patient care and a sufficient supply of qualified nurses in the future.
The findings can provide information for developing transition support and mentoring programmes for newly graduated nurses.
This umbrella review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement.
No patient or public contribution.
The umbrella review protocol was registered in PROSPERO: CRD42023478044.
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