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DOI: 10.1055/s-0039-1693649
Visualizing Infection Surveillance Data for Policymaking Using Open Source Dashboarding
Funding None.Publication History
11 April 2019
12 June 2019
Publication Date:
24 July 2019 (online)
Abstract
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.
Keywords
system improvement - dashboard - user acceptance and resistance - organizational change managementProtection of Human and Animal Subjects
No human data was used in this prototype. All the data were generated for this project.
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References
- 1 Hassan KA, Fatima BK, Riffat M. Nosocomial infections: epidemiology, prevention, control and surveillance. Asian Pac J Trop Biomed 2017; 7 (05) 478-482
- 2 Kontula KSK, Skogberg K, Ollgren J, Järvinen A, Lyytikäinen O. Early deaths in bloodstream infections: a population-based case series. Infect Dis (Lond) 2016; 48 (05) 379-385
- 3 Rolnick J, Downing NL, Shepard J. , et al. Validation of test performance and clinical time zero for an electronic health record embedded severe sepsis alert. Appl Clin Inform 2016; 7 (02) 560-572
- 4 Westra BL, Landman S, Yadav P, Steinbach M. Secondary analysis of an electronic surveillance system combined with multi-focal interventions for early detection of sepsis. Appl Clin Inform 2017; 8 (01) 47-66
- 5 Tatham M, Macfarlane G, MacRae M, Tully V, Craig K. Development and implementation of a catheter associated urinary tract infection (CAUTI) ‘toolkit’. BMJ Qual Improv Rep 2015; 4 (01) u205441.w3668
- 6 Centers for Disease Control and Prevention. Urinary Tract Infection (Catheter-Associated Urinary Tract Infection [CAUTI] and Non-Catheter-Associated Urinary Tract Infection [UTI]) and Other Urinary System Infection [USI]) Events. National Healthcare Safety Network; 2017 . Available at: https://www.cdc.gov/nhsn/acute-care-hospital/cauti/index.html . Accessed June 30, 2019
- 7 Vincitorio D, Barbadoro P, Pennacchietti L. , et al. Risk factors for catheter-associated urinary tract infection in Italian elderly. Am J Infect Control 2014; 42 (08) 898-901
- 8 World Health Organization. Prevention of Catheter-Associated Urinary Tract Infection (CAUTI): Student Handbook; 2018 . Available at: https://www.who.int/infection-prevention/tools/core-components/CAUTI_student-handbook.pdf . Accessed June 30, 2019
- 9 Gulf Cooperation Council. Healthcare Associated Infections Surveillance Manual. Riyadh, Saudi Arabia: MInistry of National Guard Health Affairs; 2018
- 10 Centers for Disease Control and Prevention. About the National Healthcare Safety Network (NHSN). Published 2015. Available at: https://www.cdc.gov/nhsn/about-nhsn/index.html . Accessed July 30, 2018
- 11 Bordeianou L, Cauley CE, Antonelli D. , et al. Truth in reporting: how data capture methods obfuscate actual surgical site infection rates within a health care network system. Dis Colon Rectum 2017; 60 (01) 96-106
- 12 Neelakanta A, Sharma S, Kesani VP. , et al. Impact of changes in the NHSN catheter-associated urinary tract infection (CAUTI) surveillance criteria on the frequency and epidemiology of CAUTI in intensive care units (ICUs). Infect Control Hosp Epidemiol 2015; 36 (03) 346-349
- 13 Horwich-Scholefield S, Keller V, Kazerouni N, Janssen L. Validation of Hospital Healthcare-Associated Infections (HAI) Reporting via the National Healthcare Safety Network (NHSN) With a Focus on Improving Case-Finding, California 2014. Open Forum Infect Dis 2015; 2 (Suppl. 01) DOI: 10.1093/ofid/ofv133.182.
- 14 Farrell L, Williams K, Satchell L. Creating consensus: ensuring inter-rater reliability for reporting infections using NHSN surveillance criteria. Am J Infect Control 2018; 46 (06) S6
- 15 Allen-Bridson K, Pollock D, Gould CV. Promoting prevention through meaningful measures: improving the Centers for Disease Control and Prevention's National Healthcare Safety Network urinary tract infection surveillance definitions. Am J Infect Control 2015; 43 (10) 1096-1098
- 16 Talaat M, El-Shokry M, El-Kholy J. , et al. National surveillance of health care-associated infections in Egypt: developing a sustainable program in a resource-limited country. Am J Infect Control 2016; 44 (11) 1296-1301
- 17 Salama MF, Jamal W, Al Mousa H, Rotimi V. Implementation of central venous catheter bundle in an intensive care unit in Kuwait: effect on central line-associated bloodstream infections. J Infect Public Health 2016; 9 (01) 34-41
- 18 Jahani-Sherafat S, Razaghi M, Rosenthal VD. , et al. Device-associated infection rates and bacterial resistance in six academic teaching hospitals of Iran: findings from the International Nocosomial Infection Control Consortium (INICC). J Infect Public Health 2015; 8 (06) 553-561
- 19 Simpao AF, Ahumada LM, Larru Martinez B. , et al. Design and implementation of a visual analytics electronic antibiogram within an electronic health record system at a tertiary pediatric hospital. Appl Clin Inform 2018; 9 (01) 37-45
- 20 Isikgoz Tasbakan M, Durusoy R, Pullukcu H, Sipahi OR, Ulusoy S. ; 2011 Turkish Nosocomial Urinary Tract Infection Study Group. Hospital-acquired urinary tract infection point prevalence in Turkey: differences in risk factors among patient groups. Ann Clin Microbiol Antimicrob 2013; 12: 31
- 21 Weiner LM, Fridkin SK, Aponte-Torres Z. , et al. Vital signs: preventing antibiotic-resistant infections in hospitals - United States, 2014. MMWR Morb Mortal Wkly Rep 2016; 65 (09) 235-241
- 22 Riley L, Guthold R, Cowan M. , et al. The World Health Organization STEPwise approach to noncommunicable disease risk-factor surveillance: methods, challenges, and opportunities. Am J Public Health 2016; 106 (01) 74-78
- 23 Wahi MM, Seebach P. Analyzing Health Data in R for SAS Users. Boca Raton, FL: CRC Press; 2017
- 24 Tableau. R for Statistical Computing & Analysis. Tableau Software. Available at: https://www.tableau.com/solutions/r . Accessed May 29, 2019
- 25 Roosan D, Del Fiol G, Butler J. , et al. Feasibility of population health analytics and data visualization for decision support in the infectious diseases domain: a pilot study. Appl Clin Inform 2016; 7 (02) 604-623
- 26 RStudio. RStudio. Available at: https://www.rstudio.com/ . Accessed March 22, 2019
- 27 Chang W, Cheng J, Allaire JJ. , et al. Shiny: Web Application Framework for R; 2018 . Available at: https://CRAN.R-project.org/package=shiny . Accessed March 12, 2019
- 28 RStudio. Modularizing Shiny app code. Published 2017 . Available at: https://shiny.rstudio.com/articles/modules.html . Accessed March 22, 2019
- 29 Chang W, Ribeiro BB. . Shinydashboard: Create Dashboards with “Shiny”; 2018 . Available at: https://CRAN.R-project.org/package=shinydashboard . Accessed March 25, 2019
- 30 Wickham H, Chang W, Henry L. , et al. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics; 2018 . Available at: https://CRAN.R-project.org/package=ggplot2 . Accessed April 3, 2019
- 31 Conway J, Gehlenborg N. . UpSetR: A More Scalable Alternative to Venn and Euler Diagrams for Visualizing Intersecting Sets; 2017 . Available at: https://CRAN.R-project.org/package=UpSetR . Accessed April 3, 2019
- 32 RStudio. Shiny from RStudio. Available at: https://shiny.rstudio.com/ . Accessed March 13, 2019
- 33 RStudio. ShinyApps.io. Available at: https://www.shinyapps.io/ . Accessed March 22, 2019
- 34 Dukach N, Wahi MM. CAUTI Surveillance Shiny dashboard app. Published 2019 . Available at: https://cauti.shinyapps.io/CAUTI-dashboard/ . Accessed March 22, 2019
- 35 shinyWidgets.pdf. Available at: https://cran.r-project.org/web/packages/shinyWidgets/shinyWidgets.pdf . Accessed March 13, 2019
- 36 Shiny - How to understand reactivity in R. Available at: https://shiny.rstudio.com/articles/understanding-reactivity.html . Accessed March 18, 2019
- 37 Adding Reactive Expressions in Shiny Application and Customizing the Shiny Appearance | Analytics Profile. Available at: https://analyticsprofile.com/r-shiny-dashboard-tutorial/tutorial-3-adding-reactivity-and-customizing-appearance/ . Accessed March 22, 2019
- 38 Karavite DJ, Miller MW, Ramos MJ. , et al. User testing an information foraging tool for ambulatory surgical site infection surveillance. Appl Clin Inform 2018; 9 (04) 791-802