{"title":"普通病房术后生命体征监测的未来:通过持续的人工智能提高患者安全,形成和减少警报。","authors":"Eske K Aasvang, Christian S Meyhoff","doi":"10.1097/ACO.0000000000001319","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Recent findings: </strong>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.</p><p><strong>Summary: </strong>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.</p>","PeriodicalId":50609,"journal":{"name":"Current Opinion in Anesthesiology","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The future of postoperative vital sign monitoring in general wards: improving patient safety through continuous artificial intelligence-enabled alert formation and reduction.\",\"authors\":\"Eske K Aasvang, Christian S Meyhoff\",\"doi\":\"10.1097/ACO.0000000000001319\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>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.</p><p><strong>Recent findings: </strong>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.</p><p><strong>Summary: </strong>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.</p>\",\"PeriodicalId\":50609,\"journal\":{\"name\":\"Current Opinion in Anesthesiology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2023-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Opinion in Anesthesiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1097/ACO.0000000000001319\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/10/5 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"ANESTHESIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Opinion in Anesthesiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/ACO.0000000000001319","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/10/5 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ANESTHESIOLOGY","Score":null,"Total":0}
The future of postoperative vital sign monitoring in general wards: improving patient safety through continuous artificial intelligence-enabled alert formation and reduction.
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.
期刊介绍:
Published bimonthly and offering a unique and wide ranging perspective on the key developments in the field, each issue of Current Opinion in Anesthesiology features hand-picked review articles from our team of expert editors. With fifteen disciplines published across the year – including cardiovascular anesthesiology, neuroanesthesia and pain medicine – every issue also contains annotated references detailing the merits of the most important papers.