共同设计人工智能干预:使用系统映射和变化建模理论进行参与式方法
Tabea Rambach1, Christophe Kunze1
1Care & Technology Lab, Furtwangen University, Furtwangen, Germany.
Studies in health technology and informatics
|May 17, 2025
概括
参与式系统映射和变化理论建模有助于分析临床工作流程和AI集成. 这些方法为将人工智能与医疗保健需求结合提供了宝贵的见解,尽管存在一些协作挑战.
科学领域:
- 医疗保健系统分析 医疗保健系统分析
- 人工智能在医学中的应用
- 临床工作流程优化 临床工作流程优化
背景情况:
- 临床工作流程是复杂的,整合新的技术,如AI预测系统提出了挑战.
- 了解系统依赖性和因果途径对于在医疗保健中成功实施AI至关重要.
研究的目的:
- 用参与式方法分析临床工作流程并评估基于人工智能的预测系统的整合.
- 在临床实践中可视化挑战,依赖和因果关系.
- 将医疗保健专业人士的见解纳入分析和人工智能集成过程.
主要方法:
- 员工参与系统绘制和变化理论建模在工作坊环境中.
- 利用视觉地图来表示临床过程和干预影响.
- 整合了医疗保健专业人员的直接投入和专业知识.
主要成果:
- 证明了这些方法在分析复杂的临床过程中的价值.
- 成功可视化了挑战,系统依赖和因果路径.
- 确定了可操作的观点,以使人工智能干预与实际医疗保健需求保持一致.
结论:
- 参与式系统映射和变化理论建模是临床工作流分析和AI整合评估的有效工具.
- 该方法为弥合人工智能能力与现实世界医疗需求之间的差距提供了有价值的见解.
- 解决诸如地图复杂性的挑战是优化医疗保健环境中的协作分析的关键.
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