一个计算框架来评估急诊室临床医生任务切换在电子健康记录使用事件日志
Amanda J Moy1, Kenrick D Cato2,3,4, Eugene Y Kim2
1Columbia University (CU) Department of Biomedical Informatics, NY, NY.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|January 15, 2024
概括
通过任务切换来衡量工作流的碎片化是紧急部门 (ED) 电子健康记录 (EHR) 文档负担的关键指标. 这项研究引入了一个计算框架来量化这种负担,揭示了ED医生之间高率的任务切换.
科学领域:
- 医疗信息学 医疗信息学
- 医疗信息学 医疗信息学
- 人与计算机的交互
背景情况:
- 工作流的碎片化,以任务切换为特征,是评估紧急部门 (ED) 电子健康记录 (EHR) 文档负担的潜在指标.
- 目前用于量化规模上的任务切换的现有方法是有限的.
- 基于时间的资源共享模型 (TBRSM) 假定任务切换与认知负载成正比.
研究的目的:
- 描述认知负载和文档负载结构之间的功能关系.
- 介绍一个计算框架,COMBINE,用于评估ED中的多层次任务切换.
- 对ED医生之间任务切换进行描述性分析.
主要方法:
- 开发了一个计算框架 (COMBINE) 来分析使用EHR事件日志的多层次任务切换.
- 从63名全职ED医生中提取和分析了2,068,605个EDHR事件,超过952个班次 (2021年4月至6月).
- 量化事件级,患者内部图表切换和患者间图表切换.
主要成果:
- 医生表现出高率的任务切换:事件级别的185.8事件/小时.
- 观察到患者内部显著的图表切换 (6.6例/图表) 和患者之间的图表切换 (27.5例/小时).
- 这些发现提供了关于ED环境中工作流碎片化的规模的经验数据.
结论:
- 通过任务切换来衡量工作流的碎片化,在ED中是相当大的.
- 组合框架提供了一个可扩展的方法来量化EHR文档负担.
- 了解任务切换对于解决认知负载和改善ED医生工作流程至关重要.
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