Abstract Background Preparatory, written plans for mass casualty incidents are designed to help hospitals deliver an effective response. However, addressing the frequently observed mismatch between planning and delivery of effective responses to mass casualty incidents is a key challenge. We aimed to use simulation-based iterative learning to bridge this gap. Methods We used Normalisation Process Theory as the framework for iterative learning from mass casualty incident simulations.
