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Automating the Generation of Antimicrobial Resistance Surveillance Reports: Proof-of-Concept Study Involving Seven Hospitals in Seven Countries
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...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7568216/ https://www.ncbi.nlm.nih.gov/pubmed/33006570 http://dx.doi.org/10.2196/19762 |
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author | 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 |
author_facet | 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 |
author_sort | Lim, Cherry |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7568216 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-75682162020-11-02 Automating the Generation of Antimicrobial Resistance Surveillance Reports: Proof-of-Concept Study Involving Seven Hospitals in Seven Countries 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 J Med Internet Res Original Paper 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. JMIR Publications 2020-10-02 /pmc/articles/PMC7568216/ /pubmed/33006570 http://dx.doi.org/10.2196/19762 Text en ©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. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 02.10.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper 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 Automating the Generation of Antimicrobial Resistance Surveillance Reports: Proof-of-Concept Study Involving Seven Hospitals in Seven Countries |
title | Automating the Generation of Antimicrobial Resistance Surveillance Reports: Proof-of-Concept Study Involving Seven Hospitals in Seven Countries |
title_full | Automating the Generation of Antimicrobial Resistance Surveillance Reports: Proof-of-Concept Study Involving Seven Hospitals in Seven Countries |
title_fullStr | Automating the Generation of Antimicrobial Resistance Surveillance Reports: Proof-of-Concept Study Involving Seven Hospitals in Seven Countries |
title_full_unstemmed | Automating the Generation of Antimicrobial Resistance Surveillance Reports: Proof-of-Concept Study Involving Seven Hospitals in Seven Countries |
title_short | Automating the Generation of Antimicrobial Resistance Surveillance Reports: Proof-of-Concept Study Involving Seven Hospitals in Seven Countries |
title_sort | automating the generation of antimicrobial resistance surveillance reports: proof-of-concept study involving seven hospitals in seven countries |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7568216/ https://www.ncbi.nlm.nih.gov/pubmed/33006570 http://dx.doi.org/10.2196/19762 |
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