Better Antimicrobial Resistance Data Analysis & Reporting in Less Time

Abstract

AbstractIntroductionThe global challenge of antimicrobial resistances (AMR) requires the rational and responsible use of antimicrobials. Insights and knowledge about the local AMR levels and epidemiology are essential to guide optimal decision-making processes in antimicrobial use. However, dedicated tools for reliable and reproducible AMR data analysis and reporting are often lacking. Previously, we have developed a novel approach to AMR data analysis and reporting using open-source software tools. In this study, we aimed at comparing the effectiveness and efficiency of traditional analysis and reporting versus this new approach for reliable and reproducible AMR data analysis in a clinical setting.MethodsTen professionals in the field of AMR that routinely work with AMR data were recruited to participate and provided with one year’s blood culture test results from a tertiary care hospital results including antimicrobial susceptibility test results. Participants were asked to perform a detailed AMR data analysis in a two-step process: first (round 1) using their analysis software of choice and next (round 2) using the previously developed open-source software tools. Accuracy of the results and time spent were compared between the two rounds. Paired student’s t-tests were used to test for statistical significance. Finally, participants rated the usability of the tools using the systems usability scale.ResultsThe mean time spent on creating a comprehensive AMR report reduced from 93.7 (SD ±21.6) minutes to 22.4 (SD ±13.7) minutes (p < 0.001). Average task completion per round changed from 56% (SD: ±23%) to 96% (SD: ±5.5%) (p<0.05). The proportion of correct answers in the available results increased from 37.9% in the first round to 97.9% in the second round (p < 0.001). The usability of the new AMR reporting tool was rated with a median of 83.8 (out of 100) on the system usability scale.ConclusionThis study demonstrated the significant improvement in efficiency and accuracy in standard AMR data analysis and reporting workflows through the use of open-source software tools in a clinical setting. Integrating these tools in clinical settings can democratise 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.