Literature DB >> 33577467

Features Constituting Actionable COVID-19 Dashboards: Descriptive Assessment and Expert Appraisal of 158 Public Web-Based COVID-19 Dashboards.

Damir Ivanković1, Erica Barbazza1, Véronique Bos1, Óscar Brito Fernandes1,2, Kendall Jamieson Gilmore3, Tessa Jansen1, Pinar Kara4,5, Nicolas Larrain6,7, Shan Lu8, Bernardo Meza-Torres9,10, Joko Mulyanto1,11, Mircha Poldrugovac1, Alexandru Rotar1, Sophie Wang6,7, Claire Willmington3, Yuanhang Yang4,5, Zhamin Yelgezekova12, Sara Allin13, Niek Klazinga1, Dionne Kringos1.   

Abstract

BACKGROUND: Since the outbreak of COVID-19, the development of dashboards as dynamic, visual tools for communicating COVID-19 data has surged worldwide. Dashboards can inform decision-making and support behavior change. To do so, they must be actionable. The features that constitute an actionable dashboard in the context of the COVID-19 pandemic have not been rigorously assessed.
OBJECTIVE: The aim of this study is to explore the characteristics of public web-based COVID-19 dashboards by assessing their purpose and users ("why"), content and data ("what"), and analyses and displays ("how" they communicate COVID-19 data), and ultimately to appraise the common features of highly actionable dashboards.
METHODS: We conducted a descriptive assessment and scoring using nominal group technique with an international panel of experts (n=17) on a global sample of COVID-19 dashboards in July 2020. The sequence of steps included multimethod sampling of dashboards; development and piloting of an assessment tool; data extraction and an initial round of actionability scoring; a workshop based on a preliminary analysis of the results; and reconsideration of actionability scores followed by joint determination of common features of highly actionable dashboards. We used descriptive statistics and thematic analysis to explore the findings by research question.
RESULTS: A total of 158 dashboards from 53 countries were assessed. Dashboards were predominately developed by government authorities (100/158, 63.0%) and were national (93/158, 58.9%) in scope. We found that only 20 of the 158 dashboards (12.7%) stated both their primary purpose and intended audience. Nearly all dashboards reported epidemiological indicators (155/158, 98.1%), followed by health system management indicators (85/158, 53.8%), whereas indicators on social and economic impact and behavioral insights were the least reported (7/158, 4.4% and 2/158, 1.3%, respectively). Approximately a quarter of the dashboards (39/158, 24.7%) did not report their data sources. The dashboards predominately reported time trends and disaggregated data by two geographic levels and by age and sex. The dashboards used an average of 2.2 types of displays (SD 0.86); these were mostly graphs and maps, followed by tables. To support data interpretation, color-coding was common (93/158, 89.4%), although only one-fifth of the dashboards (31/158, 19.6%) included text explaining the quality and meaning of the data. In total, 20/158 dashboards (12.7%) were appraised as highly actionable, and seven common features were identified between them. Actionable COVID-19 dashboards (1) know their audience and information needs; (2) manage the type, volume, and flow of displayed information; (3) report data sources and methods clearly; (4) link time trends to policy decisions; (5) provide data that are "close to home"; (6) break down the population into relevant subgroups; and (7) use storytelling and visual cues.
CONCLUSIONS: COVID-19 dashboards are diverse in the why, what, and how by which they communicate insights on the pandemic and support data-driven decision-making. To leverage their full potential, dashboard developers should consider adopting the seven actionability features identified. ©Damir Ivanković, Erica Barbazza, Véronique Bos, Óscar Brito Fernandes, Kendall Jamieson Gilmore, Tessa Jansen, Pinar Kara, Nicolas Larrain, Shan Lu, Bernardo Meza-Torres, Joko Mulyanto, Mircha Poldrugovac, Alexandru Rotar, Sophie Wang, Claire Willmington, Yuanhang Yang, Zhamin Yelgezekova, Sara Allin, Niek Klazinga, Dionne Kringos. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 24.02.2021.

Entities:  

Keywords:  COVID-19; accessibility; communication; dashboard; expert; feature; health information management; internet; online tool; pandemic; performance measures; public health; public reporting of health care data; surveillance

Year:  2021        PMID: 33577467     DOI: 10.2196/25682

Source DB:  PubMed          Journal:  J Med Internet Res        ISSN: 1438-8871            Impact factor:   5.428


  13 in total

1.  Comparing COVID-19 in the antipodes: Insights from pandemic containment strategies on both sides of the Pacific.

Authors:  Francisco Benita; Luis Fuentes; Luis A Guzmán; Rafael Martínez; Juan Carlos Muñoz; Harvey Neo; Sebastian Rodríguez-Leiva; Jaime Soza-Parra
Journal:  Transp Res Interdiscip Perspect       Date:  2022-07-19

2.  Integration of an Intensive Care Unit Visualization Dashboard (i-Dashboard) as a Platform to Facilitate Multidisciplinary Rounds: Cluster-Randomized Controlled Trial.

Authors:  Chao-Han Lai; Kai-Wen Li; Fang-Wen Hu; Pei-Fang Su; I-Lin Hsu; Min-Hsin Huang; Yen-Ta Huang; Ping-Yen Liu; Meng-Ru Shen
Journal:  J Med Internet Res       Date:  2022-05-13       Impact factor: 7.076

3.  Comparison of prediction accuracies between mathematical models to make projections of confirmed cases during the COVID-19 pandamic by country/region.

Authors:  Kang-Ting Tsai; Tsair-Wei Chien; Ju-Kuo Lin; Yu-Tsen Yeh; Willy Chou
Journal:  Medicine (Baltimore)       Date:  2021-12-17       Impact factor: 1.817

4.  Analysis of a Web-Based Dashboard to Support the Use of National Audit Data in Quality Improvement: Realist Evaluation.

Authors:  Natasha Alvarado; Lynn McVey; Mai Elshehaly; Joanne Greenhalgh; Dawn Dowding; Roy Ruddle; Chris P Gale; Mamas Mamas; Patrick Doherty; Robert West; Richard Feltbower; Rebecca Randell
Journal:  J Med Internet Res       Date:  2021-11-23       Impact factor: 5.428

5.  A hybrid Shewhart chart for visualizing and learning from epidemic data.

Authors:  Gareth Parry; Lloyd P Provost; Shannon M Provost; Kevin Little; Rocco J Perla
Journal:  Int J Qual Health Care       Date:  2021-12-04       Impact factor: 2.038

6.  Applications, features and key indicators for the development of Covid-19 dashboards: A systematic review study.

Authors:  Akram Vahedi; Hamid Moghaddasi; Farkhondeh Asadi; Azam Sadat Hosseini; Eslam Nazemi
Journal:  Inform Med Unlocked       Date:  2022-03-18

7.  COVID-19 data reporting systems in Africa reveal insights for future pandemics.

Authors:  Seth D Judson; Judith Torimiro; David M Pigott; Apollo Maima; Ahmed Mostafa; Ahmed Samy; Peter Rabinowitz; Kevin Njabo
Journal:  Epidemiol Infect       Date:  2022-06-16       Impact factor: 4.434

8.  Environmental scan of COVID-19 infection dashboards in the Florida public school system.

Authors:  Hye Ryeon Jang; Jordan Quinones-Marrero; Juan M Hincapie-Castillo
Journal:  Front Public Health       Date:  2022-07-29

9.  The experiences of 33 national COVID-19 dashboard teams during the first year of the pandemic in the World Health Organization European Region: A qualitative study.

Authors:  Erica Barbazza; Damir Ivanković; Karapet Davtyan; Mircha Poldrugovac; Zhamin Yelgezekova; Claire Willmington; Bernardo Meza-Torres; Véronique L L C Bos; Óscar Brito Fernandes; Alexandru Rotar; Sabina Nuti; Milena Vainieri; Fabrizio Carinci; Natasha Azzopardi-Muscat; Oliver Groene; David Novillo-Ortiz; Niek Klazinga; Dionne Kringos
Journal:  Digit Health       Date:  2022-08-29

10.  Using the absolute advantage coefficient (AAC) to measure the strength of damage hit by COVID-19 in India on a growth-share matrix.

Authors:  Daw-Hsin Yang; Tsair-Wei Chien; Yu-Tsen Yeh; Ting-Ya Yang; Willy Chou; Ju-Kuo Lin
Journal:  Eur J Med Res       Date:  2021-06-24       Impact factor: 2.175

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