A critical review examines AI and sociotechnical approaches to enhance medication safety in ICUs, suggesting improved outcomes.
Background/Objectives: Medication errors (MEs) in intensive care units (ICUs) remain a persistent threat to patient safety. A significant surveillance gap exists where traditional voluntary reporting detects as few as 0.02 MEs per patient-day, leaving approximately 98% of errors invisible to standard audits. This review critically examines how artificial intelligence (AI) and implementation science can bridge this gap through a proposed five-layer Intelligent Safety Stack. Methods: We conducted a critical narrative review of the peer-reviewed literature published between 2000 and 2025, synthesising evidence across medication safety, predictive analytics, generative AI, engineering controls, and sociotechnical frameworks. Results: Reported ME incidence varies widely (1.32% to 31.7%) due to the profound methodological heterogeneity. To achieve sustainable safety, we propose a five-layer framework: (1) Standardised Ontology (e.g., NCC MERP) to establish ground-truth data; (2) Intelligent Surveillance to identify and monitor high-risk patients; (3) Signal Optimisation to filter noise and reduce alert fatigue; (4) Generative Stewardship to automate reconciliation at transitions of care; and (5) Engineering Controls (smart pump interoperability and NRFit™), which have been shown to reduce administration errors by up to 54.8%. Conclusions: Isolated error counting is insufficient. Sustainable medication safety in the ICU involves a sociotechnical fusion of the Intelligent Safety Stack with success measured by rescue rates rather than error prevalence alone.
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Yun et al. (2026) studied this question.
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