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Automating the Generation of Antimicrobial Resistance Surveillance Reports: Proof-of-Concept Study Involving Seven Hospitals in Seven Countries

Authors :
Lim, Cherry
Miliya, Thyl
Chansamouth, Vilada
Aung, Myint Thazin
Karkey, Abhilasha
Teparrukkul, Prapit
Rahul, Batra
Lan, Nguyen Phu Huong
Stelling, John
Turner, Paul
Ashley, Elizabeth
van Doorn, H Rogier
Lin, Htet Naing
Ling, Clare
Hinjoy, Soawapak
Iamsirithaworn, Sopon
Dunachie, Susanna
Wangrangsimakul, Tri
Hantrakun, Viriya
Schilling, William
Yen, Lam Minh
Tan, Le Van
Hlaing, Htay Htay
Mayxay, Mayfong
Vongsouvath, Manivanh
Basnyat, Buddha
Edgeworth, Jonathan
Peacock, Sharon J
Thwaites, Guy
Day, Nicholas PJ
Cooper, Ben S
Limmathurotsakul, Direk
Source :
Journal of Medical Internet Research, Vol 22, Iss 10, p e19762 (2020)
Publication Year :
2020
Publisher :
JMIR Publications, 2020.

Abstract

BackgroundReporting 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. ObjectiveThis 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. MethodsAn 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. ResultsWe 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. ConclusionsThe AMASS is a useful tool to support the generation and sharing of AMR surveillance reports.

Details

Language :
English
ISSN :
14388871
Volume :
22
Issue :
10
Database :
Directory of Open Access Journals
Journal :
Journal of Medical Internet Research
Publication Type :
Academic Journal
Accession number :
edsdoj.77f48669e83f49d0950893557c2cb52d
Document Type :
article
Full Text :
https://doi.org/10.2196/19762