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Title: Automating the Generation of Antimicrobial Resistance Surveillance Reports: Proof-of-Concept Study Involving Seven Hospitals in Seven Countries
Authors: Cherry Lim
Thyl Miliya
Vilada Chansamouth
Myint Thazin Aung
Abhilasha Karkey
Prapit Teparrukkul
Batra Rahul
Nguyen Phu Huong Lan
John Stelling
Paul Turner
Elizabeth Ashley
H. Rogier van Doorn
Htet Naing Lin
Clare Ling
Soawapak Hinjoy
Sopon Iamsirithaworn
Susanna Dunachie
Tri Wangrangsimakul
Viriya Hantrakun
William Schilling
Lam Minh Yen
Le Van Tan
Htay Htay Hlaing
Mayfong Mayxay
Manivanh Vongsouvath
Buddha Basnyat
Jonathan Edgeworth
Sharon J. Peacock
Guy Thwaites
Nicholas Pj Day
Ben S. Cooper
Direk Limmathurotsakul
Oxford University Clinical Research Unit
Friends of Patan Hospital Nepal
Hospital for Tropical Diseases Vietnam
University of Cambridge
Brigham and Women's Hospital
Thailand Ministry of Public Health
Mahosot Hospital, Lao
Mahidol University
Nuffield Department of Medicine
Guy's and St Thomas' NHS Foundation Trust
University of Health Sciences
Myanmar Oxford Clinical Research Unit
Sunpasitthiprasong Hospital
North Okkalapa General Hospital
Angkor Hospital for Children
Keywords: Medicine
Issue Date: 2-Oct-2020
Citation: Journal of medical Internet research. Vol.22, No.10 (2020), e19762
Abstract: ┬ęCherry Lim, Thyl Miliya, Vilada Chansamouth, Myint Thazin Aung, Abhilasha Karkey, Prapit Teparrukkul, Batra Rahul, Nguyen Phu Huong Lan, John Stelling, Paul Turner, Elizabeth Ashley, H Rogier van Doorn, Htet Naing Lin, Clare Ling, Soawapak Hinjoy, Sopon Iamsirithaworn, Susanna Dunachie, Tri Wangrangsimakul, Viriya Hantrakun, William Schilling, Lam Minh Yen, Le Van Tan, Htay Htay Hlaing, Mayfong 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 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.
ISSN: 14388871
Appears in Collections:Scopus 2020

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