IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i17p11012-d905634.html
   My bibliography  Save this article

Informing the Design of Data Visualization Tools to Monitor the COVID-19 Pandemic in Portugal: A Web-Delphi Participatory Approach

Author

Listed:
  • Ekaterina Ignatenko

    (Centre for Management Studies of Instituto Superior Técnico (CEG-IST), Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal)

  • Manuel Ribeiro

    (Centro de Recursos Naturais e Ambiente (CERENA), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal)

  • Mónica D. Oliveira

    (Centre for Management Studies of Instituto Superior Técnico (CEG-IST), Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal
    iBB—Institute for Bioengineering and Biosciences and i4HB—Associate Laboratory Institute for Health and Bioeconomy, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal)

Abstract

Due to the large amount of data generated by new technologies and information systems in the health arena, health dashboards have become increasingly popular as data visualization tools which stimulate visual perception capabilities. Although the importance of involving users is recognized in dashboard design, a limited number of studies have combined participatory methods with visualization options. This study proposes a novel approach to inform the design of data visualization tools in the COVID-19 context. With the objective of understanding which visualization formats should be incorporated within dashboards for the COVID-19 pandemic, a specifically designed Web-Delphi process was developed to understand the preferences and views of the public in general regarding distinct data visualization formats. The design of the Delphi process aimed at considering not only the theory-based evidence regarding input data and visualization formats but also the perception of final users. The developed approach was implemented to select appropriate data visualization formats to present information commonly used in public web-based COVID-19 dashboards. Forty-seven individuals completed a two-round Web-Delphi process that was launched through a snowball approach. Most respondents were young and highly educated and expressed to prefer distinct visualisation formats for different types of indicators. The preferred visualization formats from the participants were used to build a redesigned version of the official DGS COVID-19 dashboard used in Portugal. This study provides insights into data visualization selection literature, as well as shows how a Delphi process can be implemented to assist the design of public health dashboards.

Suggested Citation

  • Ekaterina Ignatenko & Manuel Ribeiro & Mónica D. Oliveira, 2022. "Informing the Design of Data Visualization Tools to Monitor the COVID-19 Pandemic in Portugal: A Web-Delphi Participatory Approach," IJERPH, MDPI, vol. 19(17), pages 1-18, September.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:11012-:d:905634
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/17/11012/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/17/11012/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nuno Marques da Costa & Nelson Mileu & André Alves, 2021. "Dashboard COMPRIME_COMPRI_MOv: Multiscalar Spatio-Temporal Monitoring of the COVID-19 Pandemic in Portugal," Future Internet, MDPI, vol. 13(2), pages 1-17, February.
    2. Alba Gracia-Sánchez & Adriana López-Pineda & Esther Chicharro-Luna & Vicente F. Gil-Guillén, 2021. "A Delphi Study Protocol to Identify Recommendations on Physical Activity and Exercise in Patients with Diabetes and Risk of Foot Ulcerations," IJERPH, MDPI, vol. 18(20), pages 1-10, October.
    3. Yigitbasioglu, Ogan M. & Velcu, Oana, 2012. "A review of dashboards in performance management: Implications for design and research," International Journal of Accounting Information Systems, Elsevier, vol. 13(1), pages 41-59.
    4. Marta Salgado & Ana C. L. Vieira & Anália Torres & Mónica D. Oliveira, 2020. "Selecting Indicators to Monitor and Assess Environmental Health in a Portuguese Urban Setting: A Participatory Approach," IJERPH, MDPI, vol. 17(22), pages 1-16, November.
    5. Vieira, Ana C.L. & Oliveira, Mónica D. & Bana e Costa, Carlos A., 2020. "Enhancing knowledge construction processes within multicriteria decision analysis: The Collaborative Value Modelling framework," Omega, Elsevier, vol. 94(C).
    6. Maren Hintermeier & Andreas W. Gold & Stella Erdmann & Clara Perplies & Kayvan Bozorgmehr & Louise Biddle, 2022. "From Research into Practice: Converting Epidemiological Data into Relevant Information for Planning of Regional Health Services for Refugees in Germany," IJERPH, MDPI, vol. 19(13), pages 1-17, June.
    7. Daniele Pala & Enea Parimbelli & Cristiana Larizza & Cindy Cheng & Manuel Ottaviano & Andrea Pogliaghi & Goran Đukić & Aleksandar Jovanović & Ognjen Milićević & Vladimir Urošević & Paola Cerchiello & , 2022. "A New Interactive Tool to Visualize and Analyze COVID-19 Data: The PERISCOPE Atlas," IJERPH, MDPI, vol. 19(15), pages 1-16, July.
    8. Kyent-Yon Yie & Tsair-Wei Chien & Yu-Tsen Yeh & Willy Chou & Shih-Bin Su, 2021. "Using Social Network Analysis to Identify Spatiotemporal Spread Patterns of COVID-19 around the World: Online Dashboard Development," IJERPH, MDPI, vol. 18(5), pages 1-15, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Petr Iakovlevitch Ekel & Sandro Laudares & Adriano José de Barros & Douglas Alexandre Gomes Vieira & Carlos Augusto Paiva da Silva Martins & Matheus Pereira Libório, 2023. "Geovisualization: A Practical Approach for COVID-19 Spatial Analysis," Geographies, MDPI, vol. 3(4), pages 1-16, December.
    2. Peters, Matt D. & Wieder, Bernhard & Sutton, Steve G. & Wakefield, James, 2016. "Business intelligence systems use in performance measurement capabilities: Implications for enhanced competitive advantage," International Journal of Accounting Information Systems, Elsevier, vol. 21(C), pages 1-17.
    3. Dilla, William N. & Raschke, Robyn L., 2015. "Data visualization for fraud detection: Practice implications and a call for future research," International Journal of Accounting Information Systems, Elsevier, vol. 16(C), pages 1-22.
    4. Kocsis, David, 2019. "A conceptual foundation of design and implementation research in accounting information systems," International Journal of Accounting Information Systems, Elsevier, vol. 34(C), pages 1-1.
    5. Hutchison, Paul D. & Daigle, Ronald J. & George, Benjamin, 2018. "Application of latent semantic analysis in AIS academic research," International Journal of Accounting Information Systems, Elsevier, vol. 31(C), pages 83-96.
    6. Somnath Chaudhuri & Gerard Giménez-Adsuar & Marc Saez & Maria A. Barceló, 2022. "PandemonCAT: Monitoring the COVID-19 Pandemic in Catalonia, Spain," IJERPH, MDPI, vol. 19(8), pages 1-22, April.
    7. Reinking, Jeff & Arnold, Vicky & Sutton, Steve G., 2020. "Synthesizing enterprise data through digital dashboards to strategically align performance: Why do operational managers use dashboards?," International Journal of Accounting Information Systems, Elsevier, vol. 37(C).
    8. Lisa Perkhofer & Conny Walchshofer & Peter Hofer, 2020. "Does design matter when visualizing Big Data? An empirical study to investigate the effect of visualization type and interaction use," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 31(1), pages 55-95, April.
    9. Ionuț NICA & Nora CHIRIȚĂ & Ștefan IONESCU, 2021. "Using of KPIs and Dashboard in the analysis of Nike company's performance management," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(1(626), S), pages 61-84, Spring.
    10. Nuno Marques da Costa & Nelson Mileu & André Alves, 2021. "Dashboard COMPRIME_COMPRI_MOv: Multiscalar Spatio-Temporal Monitoring of the COVID-19 Pandemic in Portugal," Future Internet, MDPI, vol. 13(2), pages 1-17, February.
    11. Janvrin, Diane J. & Raschke, Robyn L. & Dilla, William N., 2014. "Making sense of complex data using interactive data visualization," Journal of Accounting Education, Elsevier, vol. 32(4), pages 31-48.
    12. Marta Salgado & Ana C. L. Vieira & Anália Torres & Mónica D. Oliveira, 2020. "Selecting Indicators to Monitor and Assess Environmental Health in a Portuguese Urban Setting: A Participatory Approach," IJERPH, MDPI, vol. 17(22), pages 1-16, November.
    13. Selim, Hassan & Eid, Riyad & Agag, Gomaa & Shehawy, Yasser Moustafa, 2022. "Cross-national differences in travelers’ continuance of knowledge sharing in online travel communities," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    14. Miranda, Rafael & Oliveira, Mónica Duarte & Baptista, Filipa Matos & Albuquerque, Isabel, 2023. "Telemonitoring in Portugal: where do we stand and which way forward?," Health Policy, Elsevier, vol. 131(C).
    15. Vincenzo Sforza & Pietro Marigonda & Bruno Bernardi & Alberto Domenicali, 2019. "L?impiego del controllo di gestione negli atenei: i risultati di un?indagine AssoController," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2019(3), pages 99-122.
    16. Nathaniel R. Geyer & Eugene J. Lengerich, 2023. "LionVu: A Data-Driven Geographical Web-GIS Tool for Community Health and Decision-Making in a Catchment Area," Geographies, MDPI, vol. 3(2), pages 1-17, April.
    17. Martínez, Ricardo & Sánchez-Soriano, Joaquín & Llorca, Natividad, 2022. "Assessments in public procurement procedures," Omega, Elsevier, vol. 111(C).
    18. Aubert, Alice H. & Schmid, Sara & Lienert, Judit, 2024. "Can online interfaces enhance learning for public decision-making? Eliciting citizens’ preferences for multicriteria decision analysis," European Journal of Operational Research, Elsevier, vol. 314(2), pages 760-775.
    19. Cheng, Cindy & Messerschmidt, Luca & Bravo, Isaac & Waldbauer, Marco & Bhavikatti, Rohan & Schenk, Caress & Grujic, Vanja & Model, Tim & Kubinec, Robert & Barceló, Joan, 2023. "A General Primer for Data Harmonization," OSF Preprints baf2j_v1, Center for Open Science.
    20. Amorim-Lopes, Mário & Oliveira, Mónica & Raposo, Mariana & Cardoso-Grilo, Teresa & Alvarenga, António & Barbas, Marta & Alves, Marco & Vieira, Ana & Barbosa-Póvoa, Ana, 2021. "Enhancing optimization planning models for health human resources management with foresight," Omega, Elsevier, vol. 103(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:11012-:d:905634. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.