AI-Driven EHR Architectures for Safer, Smarter Clinical Handoff Systems

Abstract

Clinical handoff failures remain a leading source of preventable adverse events in acute healthcare, with communication breakdown at care transitions implicated in approximately 80% of serious sentinel events. Despite widespread adoption of structured protocols such as SBAR and the near-universal implementation of electronic health records (EHRs), handoff-related omission rates remain as high as 34.2% post-EHR implementation, and information transmission efficiency in unstructured verbal handoffs ranges from only 55–72%. This review argues that handoff failure is fundamentally an information architecture problem, not a communication behavior problem, and that existing EHR systems, designed for longitudinal documentation rather than transition-critical intelligence, are structurally incapable of resolving it. Through a synthesis of evidence across three domains, this paper establishes that transformer-based NLP models achieving F1-scores of 0.87–0.94, machine learning deterioration prediction models achieving AUROC values of 0.83–0.94, and event-driven microservices architectures processing clinical data streams at latencies of 120–340 milliseconds collectively provide the technical foundation for a deployable AI-integrated handoff system. A synthesised four-layer framework is proposed, incorporating federated learning governance, explainable AI dashboard design, and a composite handoff quality index. The framework demonstrates that structural redesign of clinical information architecture, rather than incremental protocol improvement, is the condition necessary for sustained patient safety gains at care transitions.