通过患者数据的视觉总结和呈现来支持临床推理:系统性审查
Hao Fan1, Angela Hardi2, Po-Yin Yen1,3
1Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, St Louis, MO 63110, United States.
概括
本综述探讨了电子健康记录 (EHR) 数据可视化如何帮助临床推理. 它确定了关键的可视化策略和数据到信息流,以改善决策和患者护理.
科学领域:
- 医疗信息学 医疗信息学
- 人与计算机的交互
- 临床决策支持 临床决策支持
背景情况:
- 临床医生使用电子健康记录 (EHR) 数据进行临床推理和决策.
- 有效的EHR数据总结和可视化可以显著提高这一过程.
- 本次审查的重点是将电子病历数据可视化与核心临床推理任务结合起来.
研究的目的:
- 系统地审查如何总结和可视化EHR数据.
- 检查这些方法与7个关键临床推理和决策任务的一致性.
- 识别有效的可视化策略和数据-信息-可视化 (数据-信息-视) 流.
主要方法:
- 在7个数据库中搜索了关于电子病历数据可视化,临床决策支持和患者摘要的研究.
- 提取了关于EHR数据类型,总结方法,可视化策略和评估的数据.
- 综合证据来定义代表性的数据-信息-视频流.
主要成果:
- 包括112项研究,62.5%报告了可用性评估.
- 发现了从EHR数据中获得可操作的见解的漏洞,特别是在数据质量报告中.
- 确定了三个代表性的数据信息流:时间模式,抽象的微型图表和高级视觉隐喻.
结论:
- 出现了两个主要的可视化策略:基于时间线的时间趋势和基于快照的状态概述.
- 识别的设计方法和数据-信息-视频流量是根据特定的临床推理任务量身定制的.
- 研究结果为开发基于先进可视化的临床决策支持工具提供了洞察力.
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