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    科学领域:

    • 医疗保健信息学 医疗保健信息学
    • 临床决策支持 临床决策支持
    • 人与计算机的交互

    背景情况:

    • 医疗保健中的人工智能 (AI) 模型通常显示出强大的预测准确性,但由于与临床工作流程的整合不佳,无法改善患者的结果.
    • 人工智能模型的技术性能与其现实世界的临床实用性之间存在差距.

    研究的目的:

    • 展示以人为中心的设计方法来开发医疗保健人工智能模型.
    • 确保AI预测目标与可行的临床干预措施保持一致,并改善患者的治疗结果.

    主要方法:

    • 使用了儿科急性损伤的案例研究.
    • 一个多学科的工作组采用了用户故事,人,环境,技术和任务 (PETT) 扫描和流程映射.
    • 在AI模型开发之前,分析了社会技术因素和工作流杆点.

    主要成果:

    • 针对不同的临床角色 (医院医生,科医生,重症治疗师) 确定了不同的预测目标.
    • 关键障碍包括监测不足,风险患者的可见性差,以及损伤进展不清楚.
    • 定义了具有高影响力的预测目标,以支持可行的干预措施.

    结论:

    • 在AI开发之前,整合临床背景和以人为中心的设计至关重要.
    • 这种方法弥合了人工智能模型性能和临床实用性之间的差距.
    • 该方法可以提高AI在改善患者护理方面的有效性.