Designing the Australian Cancer Atlas: visualizing geostatistical model uncertainty for multiple audiences.

Autor:	Goodwin S; Human-Centred Computing, Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia., Saunders T; ViseR, Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia., Aitken J; Viertel Cancer Research Centre, Cancer Council Queensland (CCQ), Fortitude Valley, QLD 4006, Australia., Baade P; Viertel Cancer Research Centre, Cancer Council Queensland (CCQ), Fortitude Valley, QLD 4006, Australia., Chandrasiri U; Viertel Cancer Research Centre, Cancer Council Queensland (CCQ), Fortitude Valley, QLD 4006, Australia., Cook D; Department of Econometrics and Business Statistics, Monash University, Clayton, VIC 3800, Australia., Cramb S; Australian Centre for Health Services Innovation, School of Public Health & Social Work, Queensland University of Technology (QUT), Kelvin Grove, QLD 4059, Australia., Duncan E; Health Workforce Data Intelligence Unit, Australian Government Department of Health and Aged Care, Philip, ACT 2606, Australia.; School of Mathematical Sciences, Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia., Kobakian S; School of Mathematical Sciences, Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia., Roberts J; School of Mathematical Sciences, Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia., Mengersen K; School of Mathematical Sciences, Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia.
Jazyk:	angličtina
Zdroj:	Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2024 Nov 01; Vol. 31 (11), pp. 2447-2454.
DOI:	10.1093/jamia/ocae212
Abstrakt:	Objective: The Australian Cancer Atlas (ACA) aims to provide small-area estimates of cancer incidence and survival in Australia to help identify and address geographical health disparities. We report on the 21-month user-centered design study to visualize the data, in particular, the visualization of the estimate uncertainty for multiple audiences. Materials and Methods: The preliminary phases included a scoping study, literature review, and target audience focus groups. Several methods were used to reach the wide target audience. The design and development stage included digital prototyping in parallel with Bayesian model development. Feedback was sought from multiple workshops, audience focus groups, and regular meetings throughout with an expert external advisory group. Results: The initial scoping identified 4 target audience groups: the general public, researchers, health practitioners, and policy makers. These target groups were consulted throughout the project to ensure the developed model and uncertainty visualizations were effective for communication. In this paper, we detail ACA features and design iterations, including the 3 complementary ways in which uncertainty is communicated: the wave plot, the v-plot, and color transparency. Discussion: We reflect on the methods, design iterations, decision-making process, and document lessons learned for future atlases. Conclusion: The ACA has been hugely successful since launching in 2018. It has received over 62 000 individual users from over 100 countries and across all target audiences. It has been replicated in other countries and the second version of the ACA was launched in May 2024. This paper provides rich documentation for future projects. (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)
Databáze:	MEDLINE
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