This shiny app was developed in the context of a 1339-bed academic tertiary referral hospital to handle data of more than 180,000 admissions. Users can filter patient groups by 17 different criteria and investigate antimicrobial use, microbiological diagnostic use and results including antimicrobial resistance, and outcome in length of stay. Results can easily be stratified and grouped to compare defined patient groups based on individual patient features.

All graphs and tables as well as the filtered dataset can be downloaded in various formats. AMS teams can use RadaR to identify areas within their institutions that might benefit from increased support and targeted interventions. Diagnostic and therapeutic procedures can be assessed and analyses can easily be visualized and communicated.

Exploring large hospital data for better use of antimicrobials

by Christian Luz

This shiny app was developed in the context of a 1339-bed academic tertiary referral hospital to handle data of more than 180,000 admissions. Users can filter patient groups by 17 different criteria and investigate antimicrobial use, microbiological diagnostic use and results including antimicrobial resistance, and outcome in length of stay. Results can easily be stratified and grouped to compare defined patient groups based on individual patient features.

All graphs and tables as well as the filtered dataset can be downloaded in various formats. AMS teams can use RadaR to identify areas within their institutions that might benefit from increased support and targeted interventions. Diagnostic and therapeutic procedures can be assessed and analyses can easily be visualized and communicated.

View app
View code
Try it on RStudio Cloud
dashboard, hospital