The future of postoperative vital sign monitoring in general wards: improving patient safety through continuous artificial intelligence-enabled alert formation and reduction

Abstract

PURPOSE: Monitoring of vital signs at the general ward with continuous assessments aided by artificial intelligence (AI) is increasingly being explored in the clinical setting. This review aims to describe current evidence for continuous vital sign monitoring (CVSM) with AI-based alerts - from sensor technology, through alert reduction, impact on complications, and to user-experience during implementation. RECENT FINDINGS: CVSM identifies significantly more vital sign deviations than manual intermittent monitoring. This results in high alert generation without AI-evaluation, both in patients with and without complications. Current AI is at the rule-based level, and this potentially reduces irrelevant alerts and identifies patients at need. AI-aided CVSM identifies complications earlier with reduced staff workload and a potential reduction of severe complications. SUMMARY: The current evidence for AI-aided CSVM suggest a significant role for the technology in reducing the constant 10-30% in-hospital risk of severe postoperative complications. However, large, randomized trials documenting the benefit for patient improvements are still sparse. And the clinical uptake of explainable AI to improve implementation needs investigation.

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