From Code to Critical Care Time: Implementing an AI-Driven ICU Length-of-Stay Clinical Decision Support System Under European Governance Constraints
This prospective implementer study demonstrates that while deploying an offline AI-driven ICU length-of-stay prediction system under European governance constraints incurs coordination costs, iterative model upgrades significantly reduce prediction errors and improve resident estimates, highlighting the necessity of embedding human factors and ethical oversight to bridge the translation gap between retrospective performance and bedside utility.