Digital Infrastructure for Antimicrobial Susceptibility Testing and Surveillance: A CLSI and EUCAST-Based Model for Resource-Limited Settings (Preprint)

<sec> <title>BACKGROUND</title> Antimicrobial resistance (AMR) poses a significant global health threat, requiring effective antimicrobial susceptibility testing (AST) and surveillance systems. At the University Teaching Hospital of Butare (CHUB) in Rwanda, a baseline Laboratory Assessment of Antibiotic Resistance Testing Capacity (LAARC) identified critical gaps in the Laboratory Information System (LIS), including low capture rates for culture observation (60%) and AST data (25%), no standardization of AST panels (0%), and limited cumulative antibiogram generation (17%). </sec> <sec> <title>OBJECTIVE</title> This study aimed to develop an enhanced LIS to improve AST reliability and enable real-time Antimicrobial Resistance (AMR) surveillance at CHUB, addressing challenges in resource-limited settings to support antimicrobial stewardship and improve patient care. </sec> <sec> <title>METHODS</title> We developed an enhanced LIS using the OpenClinic GA open-source hospital information system, integrating Clinical and Laboratory Standards Institute (CLSI) and European Committee on Antimicrobial Susceptibility Testing (EUCAST) guidelines, and leveraging metadata from the AMR for R package and EUCAST Expert Rules. An agile development approach was employed, incorporating a custom database schema, Java-based application programming interfaces (APIs), and web-based user interfaces. The system was designed to support Minimum Inhibitory Concentration (MIC) and Disk Diffusion (DD) methods, automate result interpretation with color-coded outputs, prioritize WHO AWaRe “Access” antibiotics, and enable data export to WHONet for global surveillance. </sec> <sec> <title>RESULTS</title> The enhanced LIS improved AST data capture and standardization, providing reliable, automated result interpretation and real-time AMR surveillance capabilities. The system’s user-friendly interface and compatibility with WHONet facilitated seamless data integration and reporting, addressing previous deficiencies in data capture and antibiogram generation. </sec> <sec> <title>CONCLUSIONS</title> This scalable, open-source LIS model enhances antimicrobial stewardship by improving AST reliability and surveillance in resource-limited settings. By addressing critical gaps at CHUB, the system supports better patient outcomes and contributes to global AMR monitoring efforts. </sec>