评估用于COVID-19决策支持的EsteR工具包:灵敏度分析和可用性研究
Rieke Alpers1, Lisa Kühne2, Hong-Phuc Truong3
1Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.
JMIR formative research
|June 27, 2023
概括
这就是EsteR工具包.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 医疗信息学 医疗信息学
背景情况:
- 当地卫生当局在德国管理了COVID-19病例.
- 接触者追踪和监测对于制至关重要.
- EsteR项目开发了统计模型,以帮助卫生当局.
研究的目的:
- 验证EsteR工具包的统计模型和Web应用程序.
- 评估模型参数稳定性 (后端).
- 评估Web应用程序的可用性和适用性 (前端).
主要方法:
- 使用基于文献的COVID-19参数的5种统计模型的灵敏度分析.
- 不相似度指标和轮图用于模型比较.
- 认知步行和焦点小组与6个封闭侦察员进行可用性测试.
主要成果:
- 模型对参数变化的灵敏度有所不同;在一次性使用的情况下确定了稳定的参数区域.
- 集团使用案例的稳定性取决于用户输入,缺乏总体稳定性.
- 用户反表明需要UI简化和更多指导;该工具被认为是有用的,特别是对于新员工.
结论:
- 基于评估结果,EsteR工具包得到了改进.
- 灵敏度分析提供了合适的模型参数和稳定性的信息.
- 可用性测试导致了网络应用程序的用户友好性的改进.
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