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

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BACKGROUND 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%). OBJECTIVE 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. METHODS 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. RESULTS 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. CONCLUSIONS 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.

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