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ABSTRACTBackgroundReporting 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, analysing data and generating a report are time-consuming and often require trained personnel. We illustrate the development and utility of an offline, open-access and automated tool that can support the generation of AMR surveillance reports promptly at the local level.MethodsAn offline application to generate standardized AMR surveillance reports from routinely available microbiology and hospital data files was written in the R programming language. The application can be run by a double-click 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 in 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 (PDR), Myanmar, Nepal, Thailand, the United Kingdom, and Vietnam.FindingsWe developed the AutoMated tool for Antimicrobial resistance Surveillance System (AMASS), which can support clinical microbiology laboratories to analyse their microbiology and hospital data files (in CSV or Excel format) onsite and promptly generate AMR surveillance reports (in PDF and Excel formats). The data files could be those exported from WHONET and/or other laboratory information systems. The automatically generated reports contain only summary data without patient identifiers. The AMASS application is downloadable from The participating hospitals tested the application and deposited their AMR surveillance reports in an open-access data repository.InterpretationThe AMASS application can be a useful tool to support the generation and sharing of AMR surveillance reports.FundingMahidol Oxford Tropical Medicine Research Unit (MORU) is funded by the Wellcome Trust (Grant no. 106698/Z/14/Z). Oxford University Clinical Research Unit (OUCRU) is funded by the Wellcome Trust (Grant no. 106680/B/14/Z). The investigators are funded by the Wellcome Trust (CL is funded by a Training Research Fellowship [Grant no. 206736] and DL is funded by an Intermediate Training Fellowship [Grant no. 101103]). BSC is funded by the UK Medical Research Council and Department for International Development (Grant no. MR/K006924/1). The funder has no role in the design and conduct of the study, data collection, or analysis and interpretation of the data.

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