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Collaborative Visual Analytics: A Health Analytics Approach to Injury Prevention

Background: Accurate understanding of complex health data is critical in order to deal with wicked health problems and make timely decisions. Wicked problems refer to ill-structured and dynamic problems that combine multidimensional elements, which often preclude the conventional problem solving app...

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Autores principales: Al-Hajj, Samar, Fisher, Brian, Smith, Jennifer, Pike, Ian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5615593/
https://www.ncbi.nlm.nih.gov/pubmed/28895928
http://dx.doi.org/10.3390/ijerph14091056
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author Al-Hajj, Samar
Fisher, Brian
Smith, Jennifer
Pike, Ian
author_facet Al-Hajj, Samar
Fisher, Brian
Smith, Jennifer
Pike, Ian
author_sort Al-Hajj, Samar
collection PubMed
description Background: Accurate understanding of complex health data is critical in order to deal with wicked health problems and make timely decisions. Wicked problems refer to ill-structured and dynamic problems that combine multidimensional elements, which often preclude the conventional problem solving approach. This pilot study introduces visual analytics (VA) methods to multi-stakeholder decision-making sessions about child injury prevention; Methods: Inspired by the Delphi method, we introduced a novel methodology—group analytics (GA). GA was pilot-tested to evaluate the impact of collaborative visual analytics on facilitating problem solving and supporting decision-making. We conducted two GA sessions. Collected data included stakeholders’ observations, audio and video recordings, questionnaires, and follow up interviews. The GA sessions were analyzed using the Joint Activity Theory protocol analysis methods; Results: The GA methodology triggered the emergence of ‘common ground’ among stakeholders. This common ground evolved throughout the sessions to enhance stakeholders’ verbal and non-verbal communication, as well as coordination of joint activities and ultimately collaboration on problem solving and decision-making; Conclusions: Understanding complex health data is necessary for informed decisions. Equally important, in this case, is the use of the group analytics methodology to achieve ‘common ground’ among diverse stakeholders about health data and their implications.
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spelling pubmed-56155932017-09-30 Collaborative Visual Analytics: A Health Analytics Approach to Injury Prevention Al-Hajj, Samar Fisher, Brian Smith, Jennifer Pike, Ian Int J Environ Res Public Health Article Background: Accurate understanding of complex health data is critical in order to deal with wicked health problems and make timely decisions. Wicked problems refer to ill-structured and dynamic problems that combine multidimensional elements, which often preclude the conventional problem solving approach. This pilot study introduces visual analytics (VA) methods to multi-stakeholder decision-making sessions about child injury prevention; Methods: Inspired by the Delphi method, we introduced a novel methodology—group analytics (GA). GA was pilot-tested to evaluate the impact of collaborative visual analytics on facilitating problem solving and supporting decision-making. We conducted two GA sessions. Collected data included stakeholders’ observations, audio and video recordings, questionnaires, and follow up interviews. The GA sessions were analyzed using the Joint Activity Theory protocol analysis methods; Results: The GA methodology triggered the emergence of ‘common ground’ among stakeholders. This common ground evolved throughout the sessions to enhance stakeholders’ verbal and non-verbal communication, as well as coordination of joint activities and ultimately collaboration on problem solving and decision-making; Conclusions: Understanding complex health data is necessary for informed decisions. Equally important, in this case, is the use of the group analytics methodology to achieve ‘common ground’ among diverse stakeholders about health data and their implications. MDPI 2017-09-12 2017-09 /pmc/articles/PMC5615593/ /pubmed/28895928 http://dx.doi.org/10.3390/ijerph14091056 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Al-Hajj, Samar
Fisher, Brian
Smith, Jennifer
Pike, Ian
Collaborative Visual Analytics: A Health Analytics Approach to Injury Prevention
title Collaborative Visual Analytics: A Health Analytics Approach to Injury Prevention
title_full Collaborative Visual Analytics: A Health Analytics Approach to Injury Prevention
title_fullStr Collaborative Visual Analytics: A Health Analytics Approach to Injury Prevention
title_full_unstemmed Collaborative Visual Analytics: A Health Analytics Approach to Injury Prevention
title_short Collaborative Visual Analytics: A Health Analytics Approach to Injury Prevention
title_sort collaborative visual analytics: a health analytics approach to injury prevention
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5615593/
https://www.ncbi.nlm.nih.gov/pubmed/28895928
http://dx.doi.org/10.3390/ijerph14091056
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