TY - JOUR
T1 - Experts' perceptions on the use of visual analytics for complex mental healthcare planning
T2 - An exploratory study
AU - Walsh, Erin I.
AU - Chung, Younjin
AU - Cherbuin, Nicolas
AU - Salvador-Carulla, Luis
N1 - Funding Information:
We thank the following respondents, who provided their names for attribution and valuable responses to the survey. Alexander Lim, Amber Shuhyta, Anna Brooks, Annette Erlangsen, Bianca Calabria, Carlos Pereira Rodriguez, Clara Ha, Denise Riordan, Elizabeth Moore, Federico Alonso-Trujillo, Harry Lovelock, Helen Benassi, Helen Killaspy, Ilaria Montagni, John Acs, John Mendoza, Jose A. Salinas-Perez, Judit Simon, Kerry Hawkins, Kinley Wangdi, Lilisbeth Perestelo Perez, Maria Luisa Rodero Cosano, Maria Rubio-Valera, Nasser Bagheri, Paul Mayers, Phil Batterham, Pilar Campoy, Ruben Drost, Sarah Pollock, Scott Henderson, Simon Viereck, Sue Lukersmith, and Wei Du. We also thank another seven participants, who anonymously provided precious responses to the survey. We specially thank Mencia R. Gutierrez-Colosia for coordinating the submission of this survey in Europe, and the contribution of other members of the PECUNIA Group. The PECUNIA project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement (No. 779292). The authors would also like to acknowledge Dr. Richard Terrett for proof reading assistance.
Publisher Copyright:
© 2020 The Author(s).
PY - 2020/5/7
Y1 - 2020/5/7
N2 - 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: [Figure not available: see fulltext.]
AB - 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: [Figure not available: see fulltext.]
KW - Co-development
KW - Complex data analysis
KW - Evidence-informed decision-making
KW - Expert experience
KW - Mental healthcare systems
KW - Visual analytics
UR - http://www.scopus.com/inward/record.url?scp=85084399533&partnerID=8YFLogxK
U2 - 10.1186/s12874-020-00986-0
DO - 10.1186/s12874-020-00986-0
M3 - Article
C2 - 32380946
AN - SCOPUS:85084399533
SN - 1471-2288
VL - 20
SP - 1
EP - 9
JO - BMC Medical Research Methodology
JF - BMC Medical Research Methodology
IS - 1
M1 - 110
ER -