Peking University First Hospital, Beijing, China.
Clinical Research Institute, Institute of Advanced Clinical Medicine, Peking University, Beijing, China.
BMJ Open. 2024 Sep 10;14(9):e084398. doi: 10.1136/bmjopen-2024-084398.
To identify the barriers and facilitators to the successful implementation of imaging-based diagnostic artificial intelligence (AI)-assisted decision-making software in China, using the updated Consolidated Framework for Implementation Research (CFIR) as a theoretical basis to develop strategies that promote effective implementation.
This qualitative study involved semistructured interviews with key stakeholders from both clinical settings and industry. Interview guide development, coding, analysis and reporting of findings were thoroughly informed by the updated CFIR.
Four healthcare institutions in Beijing and Shanghai and two vendors of AI-assisted decision-making software for lung nodules detection and diabetic retinopathy screening were selected based on purposive sampling.
A total of 23 healthcare practitioners, 6 hospital informatics specialists, 4 hospital administrators and 7 vendors of the selected AI-assisted decision-making software were included in the study.
Within the 5 CFIR domains, 10 constructs were identified as barriers, 8 as facilitators and 3 as both barriers and facilitators. Major barriers included unsatisfactory clinical performance (Innovation); lack of collaborative network between primary and tertiary hospitals, lack of information security measures and certification (outer setting); suboptimal data quality, misalignment between software functions and goals of healthcare institutions (inner setting); unmet clinical needs (individuals). Key facilitators were strong empirical evidence of effectiveness, improved clinical efficiency (innovation); national guidelines related to AI, deployment of AI software in peer hospitals (outer setting); integration of AI software into existing hospital systems (inner setting) and involvement of clinicians (implementation process).
The study findings contributed to the ongoing exploration of AI integration in healthcare from the perspective of China, emphasising the need for a comprehensive approach considering both innovation-specific factors and the broader organisational and contextual dynamics. As China and other developing countries continue to advance in adopting AI technologies, the derived insights could further inform healthcare practitioners, industry stakeholders and policy-makers, guiding policies and practices that promote the successful implementation of imaging-based diagnostic AI-assisted decision-making software in healthcare for optimal patient care.
以更新的实施研究综合框架（CFIR）为理论基础，确定在中国成功实施基于成像的诊断人工智能（AI）辅助决策软件的障碍和促进因素，制定促进有效实施的策略。
本定性研究采用半结构式访谈，对象来自临床和行业的主要利益相关者。访谈指南的制定、编码、分析和结果报告都充分参考了更新的 CFIR。
根据目的抽样，在北京和上海的 4 家医疗机构和 2 家用于检测肺结节和糖尿病视网膜病变的 AI 辅助决策软件供应商中选择。
共纳入 23 名医疗保健从业者、6 名医院信息学专家、4 名医院管理人员和 7 名选定的 AI 辅助决策软件供应商。
在 5 个 CFIR 领域内，确定了 10 个障碍因素、8 个促进因素和 3 个既是障碍又是促进因素的因素。主要障碍包括临床性能不佳（创新）；初级和三级医院之间缺乏合作网络、缺乏信息安全措施和认证（外部环境）；数据质量不佳、软件功能与医疗机构目标不一致（内部环境）；未满足的临床需求（个人）。关键促进因素包括有效性的有力实证证据、提高临床效率（创新）；与 AI 相关的国家指南、在同行医院部署 AI 软件（外部环境）；将 AI 软件集成到现有医院系统中（内部环境）和临床医生的参与（实施过程）。
本研究结果从中国的角度对人工智能在医疗保健中的整合进行了探讨，强调需要综合考虑创新特定因素和更广泛的组织和背景动态。随着中国和其他发展中国家继续在采用人工智能技术方面取得进展，这些研究结果可以为医疗保健从业者、行业利益相关者和政策制定者提供进一步的信息，指导促进基于成像的诊断人工智能辅助决策软件在医疗保健中成功实施的政策和实践，以实现最佳的患者护理。
