Department of Health Information Science, University of Victoria, Victoria, BC, Canada.
JMIR Hum Factors. 2024 Aug 29;11:e48633. doi: 10.2196/48633.
Artificial intelligence (AI) use cases in health care are on the rise, with the potential to improve operational efficiency and care outcomes. However, the translation of AI into practical, everyday use has been limited, as its effectiveness relies on successful implementation and adoption by clinicians, patients, and other health care stakeholders.
As adoption is a key factor in the successful proliferation of an innovation, this scoping review aimed at presenting an overview of the barriers to and facilitators of AI adoption in health care.
A scoping review was conducted using the guidance provided by the Joanna Briggs Institute and the framework proposed by Arksey and O'Malley. MEDLINE, IEEE Xplore, and ScienceDirect databases were searched to identify publications in English that reported on the barriers to or facilitators of AI adoption in health care. This review focused on articles published between January 2011 and December 2023. The review did not have any limitations regarding the health care setting (hospital or community) or the population (patients, clinicians, physicians, or health care administrators). A thematic analysis was conducted on the selected articles to map factors associated with the barriers to and facilitators of AI adoption in health care.
A total of 2514 articles were identified in the initial search. After title and abstract reviews, 50 (1.99%) articles were included in the final analysis. These articles were reviewed for the barriers to and facilitators of AI adoption in health care. Most articles were empirical studies, literature reviews, reports, and thought articles. Approximately 18 categories of barriers and facilitators were identified. These were organized sequentially to provide considerations for AI development, implementation, and the overall structure needed to facilitate adoption.
The literature review revealed that trust is a significant catalyst of adoption, and it was found to be impacted by several barriers identified in this review. A governance structure can be a key facilitator, among others, in ensuring all the elements identified as barriers are addressed appropriately. The findings demonstrate that the implementation of AI in health care is still, in many ways, dependent on the establishment of regulatory and legal frameworks. Further research into a combination of governance and implementation frameworks, models, or theories to enhance trust that would specifically enable adoption is needed to provide the necessary guidance to those translating AI research into practice. Future research could also be expanded to include attempts at understanding patients' perspectives on complex, high-risk AI use cases and how the use of AI applications affects clinical practice and patient care, including sociotechnical considerations, as more algorithms are implemented in actual clinical environments.
人工智能（AI）在医疗保健中的应用案例正在增加，有可能提高运营效率和护理效果。然而，由于其有效性依赖于临床医生、患者和其他医疗保健利益相关者的成功实施和采用，因此将 AI 转化为实际的日常使用一直受到限制。
由于采用是创新成功传播的关键因素，因此本范围界定综述旨在介绍医疗保健中采用 AI 的障碍和促进因素概述。
本研究使用 Joanna Briggs 研究所提供的指南和 Arksey 和 O'Malley 提出的框架进行范围界定综述。检索了 MEDLINE、IEEE Xplore 和 ScienceDirect 数据库，以确定以英文发表的报告医疗保健中 AI 采用障碍或促进因素的出版物。本综述重点关注 2011 年 1 月至 2023 年 12 月期间发表的文章。本综述对医疗保健环境（医院或社区）或人群（患者、临床医生、医生或医疗保健管理人员）没有任何限制。对选定文章进行了主题分析，以绘制与医疗保健中 AI 采用障碍和促进因素相关的因素图。
在最初的搜索中确定了 2514 篇文章。经过标题和摘要审查，最终有 50 篇（1.99%）文章被纳入最终分析。这些文章回顾了医疗保健中 AI 采用的障碍和促进因素。大多数文章是实证研究、文献综述、报告和思想文章。确定了大约 18 个类别的障碍和促进因素。这些因素按顺序排列，以提供 AI 开发、实施和促进采用所需的整体结构方面的考虑因素。
文献综述表明，信任是采用的重要催化剂，并且发现它受到本综述中确定的几个障碍的影响。治理结构可以是一个关键的促进因素，除此之外，还可以确保适当解决确定的所有障碍。研究结果表明，在许多方面，医疗保健中 AI 的实施仍然依赖于建立监管和法律框架。需要进一步研究治理和实施框架、模型或理论的组合，以增强信任，这将专门为将 AI 研究转化为实践的人提供必要的指导。未来的研究还可以扩展到尝试了解患者对复杂、高风险 AI 使用案例的看法，以及 AI 应用的使用如何影响临床实践和患者护理，包括社会技术考虑因素，因为越来越多的算法在实际临床环境中实施。
