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EHDViz: clinical dashboard development using open-source technologies

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 h...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4809078/
https://www.ncbi.nlm.nih.gov/pubmed/27013597
http://dx.doi.org/10.1136/bmjopen-2015-010579
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author 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
author_facet 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
author_sort Badgeley, Marcus A
collection PubMed
description 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.
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spelling pubmed-48090782016-04-01 EHDViz: clinical dashboard development using open-source technologies 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 BMJ Open Health Informatics 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. BMJ Publishing Group 2016-03-24 /pmc/articles/PMC4809078/ /pubmed/27013597 http://dx.doi.org/10.1136/bmjopen-2015-010579 Text en Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/ This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
spellingShingle Health Informatics
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
EHDViz: clinical dashboard development using open-source technologies
title EHDViz: clinical dashboard development using open-source technologies
title_full EHDViz: clinical dashboard development using open-source technologies
title_fullStr EHDViz: clinical dashboard development using open-source technologies
title_full_unstemmed EHDViz: clinical dashboard development using open-source technologies
title_short EHDViz: clinical dashboard development using open-source technologies
title_sort ehdviz: clinical dashboard development using open-source technologies
topic Health Informatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4809078/
https://www.ncbi.nlm.nih.gov/pubmed/27013597
http://dx.doi.org/10.1136/bmjopen-2015-010579
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