Better antimicrobial resistance data analysis and reporting in less time

Christian F Luz, Matthijs S Berends*, Xuewei Zhou, Mariëtte Lokate, Alex W Friedrich, Bhanu Sinha, Corinna Glasner

*Corresponding author voor dit werk

OnderzoeksoutputAcademicpeer review

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Objectives: Insights about local antimicrobial resistance (AMR) levels and epidemiology are essential to guide decision-making processes in antimicrobial use. However, dedicated tools for reliable and reproducible AMR data analysis and reporting are often lacking. We aimed to compare traditional data analysis and reporting versus a new approach for reliable and reproducible AMR data analysis in a clinical setting.

Methods: Ten professionals who routinely work with AMR data were provided with blood culture test results including antimicrobial susceptibility results. Participants were asked to perform a detailed AMR data analysis in a two-round process: first using their software of choice and next using our newly developed software tool. Accuracy of the results and time spent were compared between both rounds. Finally, participants rated the usability using the System Usability Scale (SUS).

Results: The mean time spent on creating the AMR report reduced from 93.7 to 22.4 min (P 
Conclusions: This study demonstrated the significant improvement in efficiency and accuracy in standard AMR data analysis and reporting workflows through open-source software. Integrating these tools in clinical settings can democratize the access to fast and reliable insights about local microbial epidemiology and associated AMR levels. Thereby, our approach can support evidence-based decision-making processes in the use of antimicrobials.
Originele taal-2English
Aantal pagina's9
TijdschriftJAC-Antimicrobial Resistance
Nummer van het tijdschrift1
Vroegere onlinedatum18-jan.-2023
StatusPublished - feb.-2023


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