Badgeley Marcus A, Shameer Khader, Glicksberg Benjamin S, Tomlinson Max S, Levin Matthew A, McCormick Patrick J, Kasarskis Andrew, Reich David L, Dudley Joel T
Harris Center for Precision Wellness, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York City, New York, USA Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York City, New York, USA.
Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York City, New York, USA Department of Anesthesiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York City, New York, USA.
BMJ Open. 2016 Mar 24;6(3):e010579. doi: 10.1136/bmjopen-2015-010579.
To design, develop and prototype clinical dashboards to integrate high-frequency health and wellness data streams using interactive and real-time data visualisation and analytics modalities.
We developed a clinical dashboard development framework called electronic healthcare data visualization (EHDViz) toolkit for generating web-based, real-time clinical dashboards for visualising heterogeneous biomedical, healthcare and wellness data. The EHDViz is an extensible toolkit that uses R packages for data management, normalisation and producing high-quality visualisations over the web using R/Shiny web server architecture. We have developed use cases to illustrate utility of EHDViz in different scenarios of clinical and wellness setting as a visualisation aid for improving healthcare delivery.
Using EHDViz, we prototyped clinical dashboards to demonstrate the contextual versatility of EHDViz toolkit. An outpatient cohort was used to visualise population health management tasks (n=14,221), and an inpatient cohort was used to visualise real-time acuity risk in a clinical unit (n=445), and a quantified-self example using wellness data from a fitness activity monitor worn by a single individual was also discussed (n-of-1). The back-end system retrieves relevant data from data source, populates the main panel of the application and integrates user-defined data features in real-time and renders output using modern web browsers. The visualisation elements can be customised using health features, disease names, procedure names or medical codes to populate the visualisations. The source code of EHDViz and various prototypes developed using EHDViz are available in the public domain at http://ehdviz.dudleylab.org.
Collaborative data visualisations, wellness trend predictions, risk estimation, proactive acuity status monitoring and knowledge of complex disease indicators are essential components of implementing data-driven precision medicine. As an open-source visualisation framework capable of integrating health assessment, EHDViz aims to be a valuable toolkit for rapid design, development and implementation of scalable clinical data visualisation dashboards.
设计、开发并制作临床仪表盘原型，以使用交互式实时数据可视化和分析模式整合高频健康与保健数据流。
我们开发了一个名为电子医疗数据可视化（EHDViz）工具包的临床仪表盘开发框架，用于生成基于网络的实时临床仪表盘，以可视化异构生物医学、医疗保健和健康数据。EHDViz是一个可扩展的工具包，它使用R包进行数据管理、规范化，并使用R/Shiny网络服务器架构在网络上生成高质量可视化效果。我们开发了用例，以说明EHDViz在临床和健康环境的不同场景中作为改善医疗服务可视化辅助工具的效用。
使用EHDViz，我们制作了临床仪表盘原型，以展示EHDViz工具包在不同场景中的通用性。一个门诊队列用于可视化人群健康管理任务（n = 14221），一个住院队列用于可视化临床科室的实时急性风险（n = 445），还讨论了一个使用单个人佩戴的健身活动监测器的健康数据的个体量化自我示例（n-of-1）。后端系统从数据源检索相关数据，填充应用程序的主面板，并实时集成用户定义的数据特征，然后使用现代网络浏览器呈现输出。可视化元素可以使用健康特征、疾病名称、手术名称或医学代码进行定制，以填充可视化内容。EHDViz的源代码以及使用EHDViz开发的各种原型可在http://ehdviz.dudleylab.org的公共领域获取。
协作式数据可视化、健康趋势预测、风险估计、主动急性状态监测以及对复杂疾病指标的了解是实施数据驱动的精准医学的重要组成部分。作为一个能够整合健康评估的开源可视化框架，EHDViz旨在成为快速设计、开发和实施可扩展临床数据可视化仪表盘的有价值工具包。
