Institute for Patient Safety, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.
Institute for Patient Safety, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.
Appl Ergon. 2024 May;117:104243. doi: 10.1016/j.apergo.2024.104243. Epub 2024 Feb 1.
In healthcare, artificial intelligence (AI) is expected to improve work processes, yet most research focuses on the technical features of AI rather than its real-world clinical implementation. To evaluate the implementation process of an AI-based computer-aided detection system (AI-CAD) for prostate MRI readings, we interviewed German radiologists in a pre-post design. We embedded our findings in the Model of Workflow Integration and the Technology Acceptance Model to analyze workflow effects, facilitators, and barriers. The most prominent barriers were: (i) a time delay in the work process, (ii) additional work steps to be taken, and (iii) an unstable performance of the AI-CAD. Most frequently named facilitators were (i) good self-organization, and (ii) good usability of the software. Our results underline the importance of a holistic approach to AI implementation considering the sociotechnical work system and provide valuable insights into key factors of the successful adoption of AI technologies in work systems.
在医疗保健领域，人工智能（AI）有望改善工作流程，但大多数研究都侧重于 AI 的技术特征，而不是其实际的临床应用。为了评估基于人工智能的计算机辅助检测系统（AI-CAD）在前列腺 MRI 阅读中的实施过程，我们采用预后设计对德国放射科医生进行了采访。我们将研究结果嵌入到工作流程集成模型和技术接受模型中，以分析工作流程的影响、促进因素和障碍。最突出的障碍是：（i）工作流程中的时间延迟，（ii）需要采取额外的工作步骤，以及（iii）AI-CAD 的性能不稳定。最常被提及的促进因素是：（i）良好的自我组织能力，以及（ii）软件的良好可用性。我们的研究结果强调了在考虑社会技术工作系统的情况下，采用整体方法实施 AI 的重要性，并为成功将 AI 技术应用于工作系统提供了有价值的见解。
