MOUDAR Platform: A universal digital platform for real-time monitoring, automated alerts, and adaptive strategy recommendations in implementation research

Background: Despite significant advances in implementation science frameworks (CFIR, RE-AIM, EPIS, ERIC), a critical gap persists between theoretical knowledge and practical application. Existing tools lack real-time monitoring capabilities, automated alerts, and adaptive recommendation systems. Methods: We developed MOUDAR Platform, integrating four innovations: (1) a universal data metamodel with 12 entities across 4 layers applicable to any implementation study; (2) an Implementation Health Index (IHI) providing continuous 0-100 composite scores; (3) an automated alert system with hierarchical escalation; and (4) an adaptive recommendation engine based on the ERIC taxonomy. Results: The platform was designed for INNOV5-MH-MAROC, a 36-month cluster RCT integrating all five WHO-validated mental health innovations. The architecture enables real-time monitoring, early problem detection, and context-sensitive strategy recommendations. Conclusions: MOUDAR Platform represents the first comprehensive digital solution operationalizing AI integration with implementation science frameworks, addressing scalability challenges in resource-limited settings.