设计药物滥用数据仪表板用于过量致死性审查团队:以用户为中心的设计方法
Marie Pisani1, Madeline K Oguss2, Julia Dickson-Gomez3
1Department of Medicine, University of Wisconsin, Madison, WI, United States.
JMIR human factors
|January 15, 2026
概括
一个新的仪表板通过整合数据源来简化过量致死性审查 (OFR),减少公共卫生专业人员的手工工作量和认知压力. 该工具提高了识别过量预防策略的效率.
科学领域:
- 公共卫生信息学 公共卫生信息学
- 数据可视化 数据可视化
- 人类因素工程 人类因素工程
背景情况:
- 过量使用致死率审查 (OFR) 对于确定过量使用预防策略至关重要,但由于数据来源分散,需要大量的劳动力.
- 药物滥用数据共享 (SMDC) 平台整合了各种数据,但缺乏OFR的可视化工具.
- 目前的OFR流程涉及大量的手工工作和认知工作量,包括数据分析和案例准备.
研究的目的:
- 为SMDC平台开发一个全面的数据可视化仪表板.
- 应用人类因素设计原则来优化OFR流程.
- 提高过量致死性审查的效率和有效性.
主要方法:
- 调查了使用NASA任务负载指数来评估认知工作负载的OFR领导者.
- 进行半结构面试以确定工作流优化目标.
- 使用GPT-4合成数据开发了原型仪表板,随后进行了代设计,启发式评估以及通过调查和采访 (UTAUT,PUQ) 获得最终用户反.
主要成果:
- 目前的OFR任务显示了中度高的心理工作量 (NASA-TLX得分为12).
- 原型仪表板整合了多个数据源,生成了人口级别的可视化和患者级别的时间表.
- 最终用户预计效率和数据可访问性有所提高;仪表板显示了高的采用潜力 (UTAUT平均值4.07/5) 和中等的有用性 (PUQ平均值~3.6/5).
结论:
- 开发的仪表板提供了一个以用户为中心,基于信息学的解决方案,以简化OFR中的数据聚合和呈现.
- 实施这种仪表板可以显著减轻与OFR相关的手工工作量和认知负担.
- 该工具旨在为OFR领导人提供增强的能力,以开发干预措施来减少过量死亡.
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