Appl Clin Inform 2019; 10(03): 534-542
DOI: 10.1055/s-0039-1693649
Special Topic: Visual Analytics in Healthcare
Georg Thieme Verlag KG Stuttgart · New York

Visualizing Infection Surveillance Data for Policymaking Using Open Source Dashboarding

Monika Maya Wahi
1   General Education, Laboure College, Milton, Massachusetts, United States
2   DethWench Professional Services, Boston, Massachusetts, United States
Natasha Dukach
2   DethWench Professional Services, Boston, Massachusetts, United States
3   Biotechnology Program, Northeastern University, Boston, Massachusetts, United States
› Author Affiliations
Funding None.
Further Information

Publication History

11 April 2019

12 June 2019

Publication Date:
24 July 2019 (online)


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.

Protection of Human and Animal Subjects

No human data was used in this prototype. All the data were generated for this project.