检查接口设计和护士特征对机器学习决策支持解释满意度的影响
Ann Wieben1, Linsey Steege, Roger Brown
1Author Affiliations: University of Wisconsin-Madison School of Nursing (Drs Wieben, Steege, and Brown); and BerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison School of Medicine & Public Health (Dr Gilmore-Bykovskyi).
Computers, informatics, nursing : CIN
|May 13, 2025
概括
护士是一个护士.
科学领域:
- 临床信息学 临床信息学
- 人与计算机的交互
- 医疗保健中的人工智能
背景情况:
- 机器学习 (ML) 为临床决策支持 (CDS) 系统提供了潜力.
- 解释性信息对于临床医生信任和采用ML-CDS至关重要.
- 有限的研究存在于护士对ML解释信息显示的满意度.
研究的目的:
- 为了描述护士对ML-CDS的满意度,说明信息显示.
- 检查显示器设计 (格式,复杂性) 对用户满意度的影响.
- 调查护士特征 (年龄,算术,人工智能培训) 如何影响护士的满意度.
主要方法:
- 调查评估护士对ML-CDS解释信息的满意度.
- 分析显示特征与满意度之间的关联.
- 调查护士人口统计和识字程度的满意度.
主要成果:
- 基于局部特征的解释可能无法满足护士信息需求.
- 护士的年龄,人工智能培训水平和计算能力显著影响了满意度.
- 显示格式和复杂性并没有显著影响满意度.
结论:
- 护士的特点,而不是显示器设计,似乎是对ML-CDS解释满意度的关键驱动因素.
- 研究结果强调,需要将ML-CDS设计量身定制到个体护士的属性.
- 为护理实践设计更有效和可用的ML-CDS提供信息.
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