Better antimicrobial resistance data analysis and reporting in less time

Abstract

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 < 0.001). Average task completion per round changed from 56% to 96% (P < 0.05). The proportion of correct answers in the available results increased from 37.9% in the first to 97.9% in the second round (P < 0.001). Usability of the new tools was rated with a median of 83.8 (out of 100) on the SUS.

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.

Figure 1.

Study setup; the same AMR data were used along all steps and rounds.

Figure 2.

Interactive dashboard for AMR data analysis used in this study. This screenshot shows an overview of the tailored application built for this purpose. This web application is interactive and contains tabs and buttons to select, filter and present the underlying data.

Figure 3.

Usability framework based on ISO 9241-11.

Figure 4.

Data analysis software experience reported by study participants.

Figure 5.

Task completion in percentage by task number and round. The task rounds are plotted along the x-axis with their brief description. For each task the difference in task completion was compared between round 1 and round 2.

Figure 6.

Deviation from the correct result by task and round in absolute percent from correct result. Only completed tasks (n) are shown.

Figure 7.

Mean time spent per task in minutes in each round. Statistical significance was tested using two-sided paired t-tests. All results were included irrespective of correctness of the results.