From Code to Critical Care Time: Implementing an AI-Driven ICU Length-of-Stay Clinical Decision Support System Under European Governance Constraints

Abstract

Abstract Background ML models in critical care often perform well retrospectively but deliver limited bedside value once deployed under European governance constraints. Objective Evaluate feasibility and early sociotechnical lessons from an offline Clinical Decission Support System (CDSS) predicting ICU LoS on a surgical ICU in practice Methods We conducted a prospective implementer study. Residents used AI and recorded estimates (n=136), consultants provided blinded estimates (n=162), AI outputs were logged (n=221). Version 1 showed LoS prediction, Version 2 updated the model and added a compact feature importance panel by using TreeSHAP. Human factors were assessed with Psychological Assessment of AI-based Decision Support (PAAI) and an embedded ethicist informed design and onboarding. Ethics Projekt.Nr 24-0336-KB, registry: DRKS00037851. Results Offline deployment was feasible but caused coordination burden. Version 2 reduced MAE for AI (5.80→4.92) and resident+AI estimates (6.18→3.84; both p<0.05). PAAI identified user groups. Conclusions Early embedding exposed governance-driven costs and highlighted iterative upgrading to address the translation gap.

Ähnliche Arbeiten