Evidence map›Paper›PMID 33577467›Full record

ArticleJournal of medical Internet research2021

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

Damir Ivanković, Erica Barbazza, Véronique Bos, Óscar Brito Fernandes, Kendall Jamieson Gilmore, Tessa Jansen, Pinar Kara, Nicolas Larrain, Shan Lu, Bernardo Meza-Torres and 10 more

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
54citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

54 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors.

Damir Ivanković *Department of Public and Occupational Health, Amsterdam UMC, Amsterdam Public Health Research Institute, University of Amsterdam, Amsterdam, Netherlands.ORCID 0000-0002-3501-5515
Erica Barbazza *Department of Public and Occupational Health, Amsterdam UMC, Amsterdam Public Health Research Institute, University of Amsterdam, Amsterdam, Netherlands.ORCID 0000-0001-7621-1638
Véronique BosDepartment of Public and Occupational Health, Amsterdam UMC, Amsterdam Public Health Research Institute, University of Amsterdam, Amsterdam, Netherlands.ORCID 0000-0002-7447-9662
Óscar Brito FernandesDepartment of Public and Occupational Health, Amsterdam UMC, Amsterdam Public Health Research Institute, University of Amsterdam, Amsterdam, Netherlands.ORCID 0000-0002-3212-373X
Kendall Jamieson GilmoreLaboratorio Management e Sanità, Institute of Management and Department EMbeDS, Scuola Superiore Sant'Anna, Pisa, Italy.ORCID 0000-0002-8012-8391
Tessa JansenDepartment of Public and Occupational Health, Amsterdam UMC, Amsterdam Public Health Research Institute, University of Amsterdam, Amsterdam, Netherlands.ORCID 0000-0002-2948-0533
Pinar KaraDanish Center for Clinical Health Services Research, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark.ORCID 0000-0002-0408-4112
Nicolas LarrainOptiMedis AG, Hamburg, Germany.ORCID 0000-0002-3324-3039
Shan LuSchool of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.ORCID 0000-0002-9339-762X
Bernardo Meza-TorresDepartment of Clinical and Experimental Medicine, University of Surrey, Surrey, United Kingdom.ORCID 0000-0001-6551-5484
Joko MulyantoDepartment of Public and Occupational Health, Amsterdam UMC, Amsterdam Public Health Research Institute, University of Amsterdam, Amsterdam, Netherlands.ORCID 0000-0002-4653-2470
Mircha PoldrugovacDepartment of Public and Occupational Health, Amsterdam UMC, Amsterdam Public Health Research Institute, University of Amsterdam, Amsterdam, Netherlands.ORCID 0000-0001-9872-7950
Alexandru RotarDepartment of Public and Occupational Health, Amsterdam UMC, Amsterdam Public Health Research Institute, University of Amsterdam, Amsterdam, Netherlands.ORCID 0000-0003-1382-7093
Sophie WangOptiMedis AG, Hamburg, Germany.ORCID 0000-0002-6770-4489
Claire WillmingtonLaboratorio Management e Sanità, Institute of Management and Department EMbeDS, Scuola Superiore Sant'Anna, Pisa, Italy.ORCID 0000-0001-6610-7634
Yuanhang YangDanish Center for Clinical Health Services Research, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark.ORCID 0000-0002-0208-344X
Zhamin YelgezekovaIndependent Researcher, Minneapolis, MN, United States.ORCID 0000-0002-1006-3270
Sara AllinInstitute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.ORCID 0000-0002-0579-8985
Niek KlazingaDepartment of Public and Occupational Health, Amsterdam UMC, Amsterdam Public Health Research Institute, University of Amsterdam, Amsterdam, Netherlands.ORCID 0000-0002-3937-8014
Dionne KringosDepartment of Public and Occupational Health, Amsterdam UMC, Amsterdam Public Health Research Institute, University of Amsterdam, Amsterdam, Netherlands.ORCID 0000-0003-2711-4713

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSince 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.

objectiveThe 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.

methodsWe 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.

resultsA 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.

conclusionsCOVID-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.

Indexed as

COVID-19Data DisplayInformation DisseminationInternetAdultComputer GraphicsDisease OutbreaksFemaleHumansInformation Storage and RetrievalMalePandemicsSARS-CoV-2Young AdultaccessibilitycommunicationCOVID-19dashboardexpertfeaturehealth information managementinternetonline toolpandemicperformance measurespublic healthpublic reporting of health care datasurveillance

Identifiers

PMID33577467
PMCPMC7906125

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.