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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.
Evidence-based healthcare (EBHC) enables consistent and effective healthcare that prioritises patient safety. The competencies of advanced practice nurses (APNs) are essential for implementing EBHC because their professional duties include promoting EBHC.
To identify, critically appraise, and synthesise the best available evidence concerning the EBHC competence of APNs and associated factors.
A systematic review.
CINAHL, PubMed, Scopus, Medic, ProQuest, and MedNar.
Databases were searched for studies (until 19 September 2023) that examined the EBHC competence and associated factors of APNs were included. Quantitative studies published in English, Swedish and Finnish were included. We followed the JBI methodology for systematic review and performed a narrative synthesis.
The review included 12 quantitative studies, using 15 different instruments, and involved 3163 participants. The quality of the studies was fair. The APNs' EBHC competence areas were categorised into five segments according to the JBI EBHC model. The strongest areas of competencies were in global health as a goal, transferring and implementing evidence, while the weakest were generating and synthesising evidence. Evidence on factors influencing APNs' EBHC competencies was contradictory, but higher levels of education and the presence of an organisational research council may be positively associated with APNs' EBHC competencies.
The development of EBHC competencies for APNs should prioritise evidence generation and synthesis. Elevating the education level of APNs and establishing a Research Council within the organisation can potentially enhance the EBHC competence of APNs.
We should consider weaknesses in EBHC competence when developing education and practical exercises for APNs. This approach will promote the development of APNs' EBHC competence and EBHC implementation in nursing practice.
The review was registered in PROSPERO (CRD42021226578), and reporting followed the PRISMA checklist.
None.
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