Nair Monika, Nygren Jens, Nilsen Per, Gama Fabio, Neher Margit, Larsson Ingrid, Svedberg Petra
School of Health and Welfare, Halmstad University, Halmstad, Sweden.
Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.
Front Digit Health. 2025 May 16;7:1550459. doi: 10.3389/fdgth.2025.1550459. eCollection 2025.
Absence of structured guidelines to navigate the complexities of implementing AI-based applications in healthcare is recognized by clinicians, healthcare leaders, and policy makers. AI implementation presents challenges beyond the technology development which necessitates standardized approaches to implementation. This study aims to explore the activities typical to implementation of AI-based systems to develop an AI implementation process framework intended to guide healthcare professionals. The Quality Implementation Framework (QIF) was considered as an initial reference framework.
This study employed a qualitative research design and included three components: (1) a review of 30 scientific articles describing differences empirical cases of real-world AI implementation in healthcare, (2) analysis of qualitative interviews with healthcare representatives possessing first-hand experience in planning, running, and sustaining AI implementation projects, (3) analysis of qualitative interviews with members of the research group´s network and purposively sampled for their AI literacy and academic, technical or managerial leadership roles.
The data were deductively mapped onto the steps of QIF using direct qualitative content analysis. All the phases and steps in QIF are relevant to AI implementation in healthcare, but there are specificities in the context of AI that require incorporation of additional activities and phases. To effectively support the AI implementations, the process frameworks should include a dedicated phase to implementation with specific activities that occur after planning, ensuring a smooth transition from AI's design to deployment, and a phase focused on governance and sustainability, aimed at maintaining the AI's long-term impact. The component of continuous engagement of diverse stakeholders should be incorporated throughout the lifecycle of the AI implementation.
The value of this study is the identified processual phases and activities specific and typical to AI implementations to be carried out by an adopting healthcare organization when AI systems are deployed. The study advances previous research by outlining the types of necessary comprehensive assessments and legal preparations located in the implementation planning phase. It also extends prior understanding of what the staff's training should focus on throughout different phases of implementation. Finally, the overall processual, phased structure is discussed in order to incorporate activities that lead to a successful deployment of AI systems in healthcare.
临床医生、医疗保健领导者和政策制定者都认识到，在医疗保健领域实施基于人工智能的应用程序面临着复杂性，却缺乏结构化的指导方针。人工智能的实施带来了技术开发之外的挑战，这就需要标准化的实施方法。本研究旨在探索基于人工智能的系统实施过程中的典型活动，以开发一个旨在指导医疗保健专业人员的人工智能实施过程框架。质量实施框架（QIF）被视为初始参考框架。
本研究采用定性研究设计，包括三个部分：（1）对30篇描述医疗保健领域实际人工智能实施经验案例差异的科学文章进行综述；（2）对具有规划、运行和维持人工智能实施项目第一手经验的医疗保健代表进行定性访谈分析；（3）对研究小组网络成员进行定性访谈分析，并根据他们的人工智能素养以及学术、技术或管理领导角色进行有目的的抽样。
使用直接定性内容分析，将数据演绎映射到QIF的步骤上。QIF中的所有阶段和步骤都与医疗保健领域的人工智能实施相关，但在人工智能背景下存在一些特殊性，需要纳入额外的活动和阶段。为了有效支持人工智能的实施，过程框架应包括一个专门的实施阶段，该阶段在规划之后进行特定活动，确保从人工智能设计到部署的平稳过渡，以及一个侧重于治理和可持续性的阶段，旨在维持人工智能的长期影响。不同利益相关者持续参与的部分应纳入人工智能实施的整个生命周期。
本研究的价值在于确定了采用人工智能系统的医疗保健组织在部署人工智能时实施过程中特定且典型的阶段和活动。该研究通过概述实施规划阶段所需的综合评估类型和法律准备，推进了先前的研究。它还扩展了之前对员工培训在实施的不同阶段应关注重点的理解。最后，讨论了整体的过程性、分阶段结构，以便纳入有助于在医疗保健领域成功部署人工智能系统的活动。
