This project investigates challenges in implementing AI-based sequential decision-making in intensive care, using intracranial pressure prediction as a case study. A machine-learning model generates minute-by-minute risk scores within simulated patient trajectories and is compared with clinical interventions to assess alert burden, causal relevance, and practical usability. The study aims to support the translation of predictive models toward clinically deployable decision-support systems.