ArticleJournal of medical Internet research2020
Automating the Generation of Antimicrobial Resistance Surveillance Reports: Proof-of-Concept Study Involving Seven Hospitals in Seven Countries.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
22 citing papers in PubMed.
- Mapping antimicrobial resistance interventions in ASEAN countries: a scoping review of implementation patterns, One Health integration, and evidence gaps.Tropical medicine and health · 2026Review
- Prevalence and mobile genetic element-mediated dissemination of antimicrobial resistance along the swine industry chain of Guizhou, China.BMC veterinary research · 2026Article
- eBIMIC: development of a web platform for real-time access to cumulative antimicrobial susceptibility reports and its potential impact on evidence-based antibiotic prescribing in bacteremia.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026Article
- Leveraging Artificial Intelligence to Advance Bioinformatics in Africa: Opportunities, Challenges, and Ethical Considerations in Combating Antimicrobial Resistance.Bioinformatics and biology insights · 2026Article
- How to utilize routine antimicrobial resistance surveillance data for local and national actions in an LMIC.JAC-antimicrobial resistance · 2025Review
- Tools and challenges in the use of routine clinical data for antimicrobial resistance surveillance.npj antimicrobials and resistance · 2025Review
- Antimicrobial resistance at a turning point: microbial drivers, one health, and global futures.Frontiers in microbiology · 2025Review
- The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance.Computational and structural biotechnology journal · 2025Review
- Article
- Epidemiology of Burkholderia pseudomallei, Streptococcus suis, Salmonella spp., Shigella spp. and Vibrio spp. infections in 111 hospitals in Thailand, 2022.PLOS global public health · 2025Article
- Frequency of antimicrobial-resistant bloodstream infections in 111 hospitals in Thailand, 2022.The Journal of infection · 2024Article
- Automating the Generation of Notifiable Bacterial Disease Reports: Proof-of-Concept Study and Implementation in Six Hospitals in Thailand.The American journal of tropical medicine and hygiene · 2024Article
- Article
- Local and timely antimicrobial resistance data for local and national actions: the early implementation of an automated tool for data analysis at local hospital level in Thailand.JAC-antimicrobial resistance · 2023Article
- A scoping review on laboratory surveillance in the WHO Southeast Asia Region: Past, present and the future.Journal of global health · 2023Article
- Integrating computational skills in undergraduate Microbiology curricula in developing countries.Biology methods & protocols · 2023Article
- Electronic information systems for One Health surveillance of antimicrobial resistance: a systematic scoping review.BMJ global health · 2022Article
- Excess mortality attributable to antimicrobial-resistant bacterial bloodstream infection at a tertiary-care hospital in Indonesia.PLOS global public health · 2022Article
- Surveillance strategies using routine microbiology for antimicrobial resistance in low- and middle-income countries.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2021Review
- Staphylococcal Biofilms: Challenges and Novel Therapeutic Perspectives.Antibiotics (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
32 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.