EHDViz: clinical dashboard development using open-source technologies

Abstract

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=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.

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.

Keywords: biomedical informatics; clinical dashboard; clinical decision systems; data visuzalization; early warning systems.

Figure 1

Client–server architecture of EHDViz. EHDViz, electronic healthcare data visualization.

Figure 2

A quantified-self, healthcare data visualisation dashboard developed using EHDViz. Different features of the dashboard are highlighted as (1) user management, (2) dynamic selection, (3) integration with data streams and (4) integration with manual data input. EHDViz, electronic healthcare data visualization.

Figure 3

Different scenarios of implementing a visual aid for MEWS using EHDViz framework. (A) Visualisation of a single patient; (B) visualisation of a single patient layered on patient admission, discharge and transfer data; (C) visualisation of trends of MEWS in different inpatient units; (D) visualisation of multiple patients in a same unit. EHDViz, electronic healthcare data visualization; MEWS, Modified Early Warning Score.

Figure 4

A customised, clinical evaluation dashboard developed using EHDViz that illustrates data in emergency department. Features of this dashboard include selection of specific clinical units using a drop-down menu, controlling for the layout and selecting patients that are tested for specific biomarkers. Different features of the dashboard are highlighted as (1) selection of individuals, (2) options to control visual layouts and (3) integration with ICD-9 codes. EHDViz, electronic healthcare data visualization; ICD-9, International Classification of Diseases, Ninth Revision.

Figure 5

A population health management visualisation dashboard implemented using EHDViz. Different features of the dashboard are highlighted as (1) visualisation of data from floor using admission, discharge transfer data, (2) dynamic control of visualisation and (3) real-time user interaction. EHDViz, electronic healthcare data visualization.