Shankar Ravi, Devi Fiona, Ang Emily, Er Joyce
National University Health System, Singapore
National University Health System, Singapore.
BMJ Open. 2025 Aug 21;15(8):e099875. doi: 10.1136/bmjopen-2025-099875.
Artificial 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.
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.
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.
CRD42024602808.
人工智能（AI）技术正越来越多地被开发和应用，以支持医疗保健中的临床决策、护理服务和患者监测。然而，护士作为医疗保健劳动力的最大组成部分且对患者护理至关重要，迄今为止，他们对人工智能驱动解决方案的采用一直有限。了解护士对采用人工智能的障碍和促进因素的看法对于在护理实践中成功整合人工智能至关重要。本系统评价旨在识别、评估和综合关于护士在临床实践中采用人工智能驱动解决方案时所感知的障碍和促进因素的定性证据。
我们将从数据库建立至2025年1月，对八个电子数据库（PubMed、科学网、Embase、护理学与健康领域数据库、医学在线数据库、考克兰图书馆、心理学文摘数据库和Scopus）进行系统检索，并辅以手工检索参考文献列表和灰色文献。将纳入探索注册护士在临床环境中对采用人工智能的看法的主要定性研究以及混合方法研究的定性部分。两名独立评审员将筛选研究，使用标准化表格提取数据，并使用批判性评估技能计划清单评估方法学质量。我们将采用元民族志来综合定性证据，包括对各项研究中的概念进行系统比较和转换，以形成总体主题和理论框架。将使用定性研究综述证据的推荐分级评估、发展和评价方法（GRADE-CERQual）来评估对综述结果的信心。该方案遵循系统评价与Meta分析方案的首选报告项目（PRISMA-P）指南以及定性研究综合报告的增强透明度声明（ENTREQ）。
由于本系统评价仅综合已发表研究的数据，因此无需伦理批准。研究结果将为医疗保健服务中以护士为导向的人工智能整合策略的制定、实施和评估提供有价值的见解。研究结果将通过同行评审出版物、会议报告和利益相关者参与活动进行传播。
PROSPERO注册号：CRD42024602808。
