在大规模的COVID-19 EHR数据集 (COVID-SPHERE) 中用于时间查询的交互式信息可视化系统:开发和定性评估
Yan Huang1,2, Shiqiang Tao1,2, Wei-Chun Chou1,2
1McGovern Medical School.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
概括
COVID-SPHERE通过为COVID-19研究提供电子健康记录 (EHR) 的轻松访问来简化临床研究. 该工具增强了数据分析和队列发现,加速了研究见解.
科学领域:
- 临床信息学 临床信息学
- 健康 数据科学 数据科学
- 计算流行病学计算流行病学
背景情况:
- 电子健康记录 (EHR) 的二次使用对COVID-19研究至关重要.
- 在访问和分析临床研究的大规模EHR数据方面存在挑战.
- 现有的信息学工具往往需要专门的数据库专业知识,为研究人员创造障碍.
研究的目的:
- 引入COVID-SPHERE,这是一个自助服务网络应用程序,用于COVID-19研究中二次使用EHR数据.
- 为临床研究人员提供复杂的时间查询构建和数据可视化.
- 减少信息技术障碍,提高临床和转化研究的效率.
主要方法:
- 开发了一个灵活的EHR概念框架,具有层次概念和本体学.
- 从单击界面实现动态,实时的MongoDB查询生成.
- 集成的交互式,面向可视化,用于纵向患者活动和健康记录分析.
- 在服务器上部署,管理超过5TB的数据,包括15年来来自880万患者的300亿份健康记录.
主要成果:
- 自2020年11月以来,COVID-SPHERE成功支持了多项已发表的关于COVID-19流行病学,治疗结果和长期后果的研究研究.
- 在各种与COVID相关的研究主题中展示了概括性.
- 促进了有效的队列发现和数据驱动的洞察力,用于监测和干预.
结论:
- COVID-SPHERE显著降低了信息学障碍,使研究人员能够更有效地利用EHR数据.
- 该应用程序提高了临床研究的效率,并促进了对公共卫生的数据驱动洞察力.
- 该架构作为未来公共卫生信息系统和大型临床研究数据仓库的可扩展模型.
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