Predicting offer burden to optimize batch sizes in simultaneously expiring kidney offers

Our results demonstrate that survival-based predictive modeling can meaningfully improve the efficiency of simultaneously expiring offers in kidney allocation. By personalizing batch sizes based on expected offer burden, such models can reduce delays without overwhelming transplant professionals. These findings underscore the value of integrating real-time, data-driven tools into organ allocation systems to improve operational efficiency and facilitate practical implementation.