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Experts’ perceptions on the use of visual analytics for complex mental healthcare planning: an exploratory study

BACKGROUND: Health experts including planners and policy-makers face complex decisions in diverse and constantly changing healthcare systems. Visual analytics may play a critical role in supporting analysis of complex healthcare data and decision-making. The purpose of this study was to examine the...

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Autores principales: Walsh, Erin I., Chung, Younjin, Cherbuin, Nicolas, Salvador-Carulla, Luis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206783/
https://www.ncbi.nlm.nih.gov/pubmed/32380946
http://dx.doi.org/10.1186/s12874-020-00986-0
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author Walsh, Erin I.
Chung, Younjin
Cherbuin, Nicolas
Salvador-Carulla, Luis
author_facet Walsh, Erin I.
Chung, Younjin
Cherbuin, Nicolas
Salvador-Carulla, Luis
author_sort Walsh, Erin I.
collection PubMed
description BACKGROUND: Health experts including planners and policy-makers face complex decisions in diverse and constantly changing healthcare systems. Visual analytics may play a critical role in supporting analysis of complex healthcare data and decision-making. The purpose of this study was to examine the real-world experience that experts in mental healthcare planning have with visual analytics tools, investigate how well current visualisation techniques meet their needs, and suggest priorities for the future development of visual analytics tools of practical benefit to mental healthcare policy and decision-making. METHODS: Health expert experience was assessed by an online exploratory survey consisting of a mix of multiple choice and open-ended questions. Health experts were sampled from an international pool of policy-makers, health agency directors, and researchers with extensive and direct experience of using visual analytics tools for complex mental healthcare systems planning. We invited them to the survey, and the experts’ responses were analysed using statistical and text mining approaches. RESULTS: The forty respondents who took part in the study recognised the complexity of healthcare systems data, but had most experience with and preference for relatively simple and familiar visualisations such as bar charts, scatter plots, and geographical maps. Sixty-five percent rated visual analytics as important to their field for evidence-informed decision-making processes. Fifty-five percent indicated that more advanced visual analytics tools were needed for their data analysis, and 67.5% stated their willingness to learn new tools. This was reflected in text mining and qualitative synthesis of open-ended responses. CONCLUSIONS: This exploratory research provides readers with the first self-report insight into expert experience with visual analytics in mental healthcare systems research and policy. In spite of the awareness of their importance for complex healthcare planning, the majority of experts use simple, readily available visualisation tools. We conclude that co-creation and co-development strategies will be required to support advanced visual analytics tools and skills, which will become essential in the future of healthcare. GRAPHICAL ABSTRACT: [Image: see text]
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spelling pubmed-72067832020-05-14 Experts’ perceptions on the use of visual analytics for complex mental healthcare planning: an exploratory study Walsh, Erin I. Chung, Younjin Cherbuin, Nicolas Salvador-Carulla, Luis BMC Med Res Methodol Research Article BACKGROUND: Health experts including planners and policy-makers face complex decisions in diverse and constantly changing healthcare systems. Visual analytics may play a critical role in supporting analysis of complex healthcare data and decision-making. The purpose of this study was to examine the real-world experience that experts in mental healthcare planning have with visual analytics tools, investigate how well current visualisation techniques meet their needs, and suggest priorities for the future development of visual analytics tools of practical benefit to mental healthcare policy and decision-making. METHODS: Health expert experience was assessed by an online exploratory survey consisting of a mix of multiple choice and open-ended questions. Health experts were sampled from an international pool of policy-makers, health agency directors, and researchers with extensive and direct experience of using visual analytics tools for complex mental healthcare systems planning. We invited them to the survey, and the experts’ responses were analysed using statistical and text mining approaches. RESULTS: The forty respondents who took part in the study recognised the complexity of healthcare systems data, but had most experience with and preference for relatively simple and familiar visualisations such as bar charts, scatter plots, and geographical maps. Sixty-five percent rated visual analytics as important to their field for evidence-informed decision-making processes. Fifty-five percent indicated that more advanced visual analytics tools were needed for their data analysis, and 67.5% stated their willingness to learn new tools. This was reflected in text mining and qualitative synthesis of open-ended responses. CONCLUSIONS: This exploratory research provides readers with the first self-report insight into expert experience with visual analytics in mental healthcare systems research and policy. In spite of the awareness of their importance for complex healthcare planning, the majority of experts use simple, readily available visualisation tools. We conclude that co-creation and co-development strategies will be required to support advanced visual analytics tools and skills, which will become essential in the future of healthcare. GRAPHICAL ABSTRACT: [Image: see text] BioMed Central 2020-05-07 /pmc/articles/PMC7206783/ /pubmed/32380946 http://dx.doi.org/10.1186/s12874-020-00986-0 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Walsh, Erin I.
Chung, Younjin
Cherbuin, Nicolas
Salvador-Carulla, Luis
Experts’ perceptions on the use of visual analytics for complex mental healthcare planning: an exploratory study
title Experts’ perceptions on the use of visual analytics for complex mental healthcare planning: an exploratory study
title_full Experts’ perceptions on the use of visual analytics for complex mental healthcare planning: an exploratory study
title_fullStr Experts’ perceptions on the use of visual analytics for complex mental healthcare planning: an exploratory study
title_full_unstemmed Experts’ perceptions on the use of visual analytics for complex mental healthcare planning: an exploratory study
title_short Experts’ perceptions on the use of visual analytics for complex mental healthcare planning: an exploratory study
title_sort experts’ perceptions on the use of visual analytics for complex mental healthcare planning: an exploratory study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206783/
https://www.ncbi.nlm.nih.gov/pubmed/32380946
http://dx.doi.org/10.1186/s12874-020-00986-0
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