Automating the Generation of Antimicrobial Resistance Surveillance Reports: Proof-of-Concept Study Involving Seven Hospitals in Seven Countries.
Lim C., Miliya T., Chansamouth V., Aung MT., Karkey A., Teparrukkul P., Rahul B., Lan NPH., Stelling J., Turner P., Ashley E., van Doorn HR., Lin HN., Ling C., Hinjoy S., Iamsirithaworn S., Dunachie S., Wangrangsimakul T., Hantrakun V., Schilling W., Yen LM., Tan LV., Hlaing HH., Mayxay M., Vongsouvath M., Basnyat B., Edgeworth J., Peacock SJ., Thwaites G., Day NP., Cooper BS., Limmathurotsakul D.
BACKGROUND: Reporting cumulative antimicrobial susceptibility testing data on a regular basis is crucial to inform antimicrobial resistance (AMR) action plans at local, national, and global levels. However, analyzing data and generating a report are time consuming and often require trained personnel. OBJECTIVE: This study aimed to develop and test an application that can support a local hospital to analyze routinely collected electronic data independently and generate AMR surveillance reports rapidly. METHODS: An offline application to generate standardized AMR surveillance reports from routinely available microbiology and hospital data files was written in the R programming language (R Project for Statistical Computing). The application can be run by double clicking on the application file without any further user input. The data analysis procedure and report content were developed based on the recommendations of the World Health Organization Global Antimicrobial Resistance Surveillance System (WHO GLASS). The application was tested on Microsoft Windows 10 and 7 using open access example data sets. We then independently tested the application in seven hospitals in Cambodia, Lao People's Democratic Republic, Myanmar, Nepal, Thailand, the United Kingdom, and Vietnam. RESULTS: We developed the AutoMated tool for Antimicrobial resistance Surveillance System (AMASS), which can support clinical microbiology laboratories to analyze their microbiology and hospital data files (in CSV or Excel format) onsite and promptly generate AMR surveillance reports (in PDF and CSV formats). The data files could be those exported from WHONET or other laboratory information systems. The automatically generated reports contain only summary data without patient identifiers. The AMASS application is downloadable from https://www.amass.website/. The participating hospitals tested the application and deposited their AMR surveillance reports in an open access data repository. CONCLUSIONS: The AMASS is a useful tool to support the generation and sharing of AMR surveillance reports.