Visualizing Infection Surveillance Data for Policymaking Using Open Source Dashboarding
| Autor: | Monika M. Wahi, Natasha Dukach |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Cross Infection
business.industry Computer science Process (engineering) media_common.quotation_subject Dashboard (business) Health Informatics Computer Science Applications Visualization User-Computer Interface Software Open source Health Information Management Public health surveillance Human–computer interaction Population Surveillance Humans InformationSystems_MISCELLANEOUS business Function (engineering) Policy Making Infection surveillance media_common |
| Zdroj: | Appl Clin Inform |
| ISSN: | 1869-0327 |
| Popis: | Background Health care-associated infections, specifically catheter-associated urinary tract infections (CAUTIs), can cause significant mortality and morbidity. However, the process of collecting CAUTI surveillance data, storing it, and visualizing the data to inform health policy has been fraught with challenges. Objectives No standard has been developed, so the objective of this article is to present a prototype solution for dashboarding public health surveillance data based on a real-life use-case for the purposes of enhancing clinical and policy-level decision-making. Methods The solution was developed in open source software R, which allows for the creation of dashboard applications using the integrated development environment developed for R called RStudio, and a package for R called Rshiny. How the surveillance system was designed, why R was chosen, how the dashboard was developed, and how the dashboard features were programmed and function will be described. Results The prototype dashboard includes multiple tabs for visualizing data, and allows the user to interact with the data by setting dynamic filters. Controls were used to facilitate the interaction between the user and application. Rshiny is reactive, in that when the user (e.g., clinician or policymaker) changes the parameters on the data, the application automatically updates the visualization as well as parameters available based on current filters. Conclusion The prototype dashboard has the potential to enhance clinical and policy-level decision-making because it facilitates interaction with the data that provides useful visualizations to provide such guidance. |
| Databáze: | OpenAIRE |
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