EHDViz: clinical dashboard development using open-source technologies

被引:33
|
作者
Badgeley, Marcus A. [1 ,2 ]
Shameer, Khader [1 ,2 ]
Glicksberg, Benjamin S. [1 ,2 ]
Tomlinson, Max S. [1 ,2 ]
Levin, Matthew A. [2 ,3 ]
McCormick, Patrick J. [3 ]
Kasarskis, Andrew [2 ]
Reich, David L. [3 ]
Dudley, Joel T. [1 ,2 ,4 ]
机构
[1] Icahn Sch Med Mt Sinai, Mt Sinai Hlth Syst, Harris Ctr Precis Wellness, New York, NY 10029 USA
[2] Icahn Sch Med Mt Sinai, Icahn Inst Genom & Multiscale Biol, Mt Sinai Hlth Syst, Dept Genet & Genom Sci, New York, NY 10029 USA
[3] Icahn Sch Med Mt Sinai, Dept Anesthesiol, Mt Sinai Hlth Syst, New York, NY 10029 USA
[4] Icahn Sch Med Mt Sinai, Mt Sinai Hlth Syst, Dept Populat Hlth Sci & Policy, New York, NY 10029 USA
来源
BMJ OPEN | 2016年 / 6卷 / 03期
基金
美国国家卫生研究院;
关键词
early warning systems; biomedical informatics; data visuzalization; clinical decision systems; clinical dashboard; ELECTRONIC HEALTH RECORDS; EARLY WARNING SCORE; DECISION AID; BIG-DATA; PHYSIOLOGICAL DETERIORATION; RISK STRATIFICATION; VISUALIZATION; INFORMATION; DESIGN; TRIAL;
D O I
10.1136/bmjopen-2015-010579
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Objective 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. Materials and methods 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. Results 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=14221), 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. Conclusions 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.
引用
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页数:11
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