ArticleBMJ open2025
Barriers and facilitators to nurses' adoption of artificial intelligence-driven solutions in clinical practice: a protocol for a systematic review of qualitative studies.
Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Barriers and Facilitators to AI Implementation in Intensive Care Units in China: Qualitative Study Among Nurse Managers.Journal of medical Internet research · 2026Article
- The Challenges of AI Applications in Health Care: Qualitative Study.Journal of medical Internet research · 2026Article
- Intelligent technologies in operating room nursing: A bibliometric analysis of research.International journal of nursing studies advances · 2026Article
- Heterogeneity in nurses' attitudes toward artificial intelligence: a latent profile analysis.BMC health services research · 2026Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionArtificial intelligence (AI) technologies are increasingly being developed and deployed to support clinical decision-making, care delivery and patient monitoring in healthcare. However, the adoption of AI-driven solutions by nurses, who comprise the largest segment of the healthcare workforce and are central to patient care, has been limited to date. Understanding nurses' perceptions of barriers and facilitators to AI adoption is critical for successful integration of AI in nursing practice. This systematic review aims to identify, appraise and synthesise qualitative evidence on nurses' perceived barriers and facilitators to adopting AI-driven solutions in their clinical practice. METHODS AND ANALYSIS: We will conduct systematic searches across eight electronic databases (PubMed, Web of Science, Embase, CINAHL, MEDLINE, The Cochrane Library, PsycINFO and Scopus) from inception to January 2025, supplemented by hand-searching reference lists and grey literature. Primary qualitative studies and qualitative components of mixed-methods studies exploring licensed/registered nurses' perceptions of AI adoption in clinical settings will be included. Two independent reviewers will screen studies, extract data using standardised forms and assess methodological quality using the Critical Appraisal Skills Programme checklist. We will employ meta-ethnography to synthesise the qualitative evidence, involving systematic comparison and translation of concepts across studies to develop overarching themes and a theoretical framework. The Grading of Recommendations Assessment, Development and Evaluation Confidence in the Evidence from Reviews of Qualitative Research (GRADE-CERQual) approach will be used to assess confidence in review findings. The protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines and the Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) statement. ETHICS AND DISSEMINATION: No ethical approval is required as this systematic review will synthesise data from published studies only. The findings will provide valuable insights to inform the development, implementation and evaluation of nurse-oriented strategies for AI integration in healthcare delivery. Results will be disseminated through peer-reviewed publication, conference presentations and stakeholder engagement activities. PROSPERO REGISTRATION NUMBER: CRD42024602808.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.