TY - JOUR

T1 - The future of postoperative vital sign monitoring in general wards

T2 - Improving patient safety through continuous artificial intelligence-enabled alert formation and reduction

AU - Aasvang, Eske K.

AU - Meyhoff, Christian S.

N1 - Publisher Copyright: © 2023 Lippincott Williams and Wilkins. All rights reserved.

PY - 2023

Y1 - 2023

N2 - PurposeMonitoring 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 findingsCVSM 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.SummaryThe 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.

AB - PurposeMonitoring 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 findingsCVSM 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.SummaryThe 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.

KW - alert reduction

KW - artificial intelligence

KW - complications

KW - continuous monitoring

KW - vital signs

U2 - 10.1097/ACO.0000000000001319

DO - 10.1097/ACO.0000000000001319

M3 - Review

C2 - 37865847

AN - SCOPUS:85175432480

VL - 36

SP - 683

EP - 690

JO - Current Opinion in Anaesthesiology

JF - Current Opinion in Anaesthesiology

SN - 0952-7907

IS - 6

ER -