Visualizing Infection Surveillance Data for Policymaking Using Open Source Dashboarding

Wahi, Monika M.; Dukach, Natasha

doi:10.1055/s-0039-1693649

Cited by 13 publications

(21 citation statements)

References 20 publications

(17 reference statements)

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“…As noted by Wahi and Dukach, 34 statistical software packages such as SAS, STATA, and SPSS have been traditionally used for health data analytics, but these products are limited in regard to visual capabilities and require knowledge of programming languages. Tableau uses VizQL, a visual query language that can convert drag-and-drop actions into data queries.…”

Section: Tableau Data Visualizationmentioning

confidence: 99%

Visualizing Opportunity Index Data Using a Dashboard Application: A Tool to Communicate Infant Mortality-Based Area Deprivation Index Information

et al. 2020

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Background An area deprivation index (ADI) is a geographical measure that accounts for socioeconomic factors (e.g., crime, health, and education). The state of Ohio developed an ADI associated with infant mortality: Ohio Opportunity Index (OOI). However, a powerful tool to present this information effectively to stakeholders was needed. Objectives We present a real use-case by documenting the design, development, deployment, and training processes associated with a dashboard solution visualizing ADI data. Methods The Opportunity Index Dashboard (OID) allows for interactive exploration of the OOI and its seven domains—transportation, education, employment, housing, health, access to services, and crime. We used a user-centered design approach involving feedback sessions with stakeholders, who included representatives from project sponsors and subject matter experts. We assessed the usability of the OID based on the effectiveness, efficiency, and satisfaction dimensions. The process of designing, developing, deploying, and training users in regard to the OID is described. Results We report feedback provided by stakeholders for the OID categorized by function, content, and aesthetics. The OID has multiple, interactive components: choropleth map displaying OOI scores for a specific census tract, graphs presenting OOI or domain scores between tracts to compare relative positions for tracts, and a sortable table to visualize scores for specific county and census tracts. Changes based on parameter and filter selections are described using a general use-case. In the usability evaluation, the median task completion success rate was 83% and the median system usability score was 68. Conclusion The OID could assist health care leaders in making decisions that enhance care delivery and policy decision making regarding infant mortality. The dashboard helps communicate deprivation data across domains in a clear and concise manner. Our experience building this dashboard presents a template for developing dashboards that can address other health priorities.

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Section: Tableau Data Visualizationmentioning

confidence: 99%

Visualizing Opportunity Index Data Using a Dashboard Application: A Tool to Communicate Infant Mortality-Based Area Deprivation Index Information

et al. 2020

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“…In clinical and translation research, designing and implementing dashboard-based data integration and analytic visualization has gained popularity, [26][27][28][29][30]. Application-based infrastructure helps clinicians to better inform the patient's behavior and medical history and guide them to make the best decision of the diagnosis, [26][27].…”

Section: Discussionmentioning

confidence: 99%

CONSENSUS: A Shiny application of Dementia evaluation and reporting for the KU ADC Longitudinal Clinical Cohort Database.

Sharma

Montgomery

Graves

et al. 2021

Preprint

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Background: The University of Kansas Alzheimer’s Disease Center (KU ADC) maintains several large databases to track participant recruitment, enrollment, and capture various research related activities. It is challenging to manage and coordinate all the research related activities. One of the crucial activities involves capturing the data and maintaining high data quality, which ensures data reusability and reproducibility.Methods: To effectively manage the cohort, the KU ADC utilizes a combination of open source Electronic Data Capture (EDC) (i.e. REDCap), along with other homegrown data management and visualization systems developed using R-studio and Shiny.Results: In this manuscript, we describe the method and utility of the user-friendly dashboard that was developed for the rapid reporting of dementia evaluations along with data visualization, which allows clinical researchers to summarize recruitment metrics, automatically generate letters to both participants and health care providers, and depict other key metrics, which ultimately help optimize workflows. Conclusions: We believe this general framework would be beneficial to any institution for capturing and maintaining similar longitudinal databases for reporting and summarizing key metrics pertaining to their research.

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“…Data visualization methods can be used to share information across a range of stakeholders and help users interact with data, interpret data, and inform decision-making. [1][2][3] Growing use of data visualization among health care systems, local and state governments, and the general public has helped data visualizations become integrated into daily life. Visualizations have proliferated within public health to display data about behavioral health, chronic disease risks, availability of care, communicable diseases, and environmental risks.…”

Section: Background and Significancementioning

confidence: 99%

U.S. COVID-19 State Government Public Dashboards: An Expert Review

et al. 2021

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Background In the United States, all 50 state governments deployed publicly viewable dashboards regarding the novel coronavirus disease 2019 (COVID-19) to track and respond to the pandemic. States dashboards, however, reflect idiosyncratic design practices based on their content, function, and visual design and platform. There has been little guidance for what state dashboards should look like or contain, leading to significant variation. Objectives The primary objective of our study was to catalog how information, system function, and user interface were deployed across the COVID-19 state dashboards. Our secondary objective was to group and characterize the dashboards based on the information we collected using clustering analysis. Methods For preliminary data collection, we developed a framework to first analyze two dashboards as a group and reach agreement on coding. We subsequently doubled coded the remaining 48 dashboards using the framework and reviewed the coding to reach total consensus. Results All state dashboards included maps and graphs, most frequently line charts, bar charts, and histograms. The most represented metrics were total deaths, total cases, new cases, laboratory tests, and hospitalization. Decisions on how metrics were aggregated and stratified greatly varied across dashboards. Overall, the dashboards were very interactive with 96% having at least some functionality including tooltips, zooming, or exporting capabilities. For visual design and platform, we noted that the software was dominated by a few major organizations. Our cluster analysis yielded a six-cluster solution, and each cluster provided additional insights about how groups of states engaged in specific practices in dashboard design. Conclusion Our study indicates that states engaged in dashboard practices that generally aligned with many of the goals set forth by the Centers for Disease Control and Prevention, Essential Public Health Services. We highlight areas where states fall short of these expectations and provide specific design recommendations to address these gaps.

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Visualizing Infection Surveillance Data for Policymaking Using Open Source Dashboarding

Cited by 13 publications

References 20 publications

Visualizing Opportunity Index Data Using a Dashboard Application: A Tool to Communicate Infant Mortality-Based Area Deprivation Index Information

Visualizing Opportunity Index Data Using a Dashboard Application: A Tool to Communicate Infant Mortality-Based Area Deprivation Index Information

CONSENSUS: A Shiny application of Dementia evaluation and reporting for the KU ADC Longitudinal Clinical Cohort Database.

U.S. COVID-19 State Government Public Dashboards: An Expert Review

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