Department of Anaesthesia and Intensive Care, Copenhagen University Hospital-Bispebjerg and Frederiksberg, 2400 Copenhagen, Denmark.
Department of Anaesthesia, Centre for Cancer and Organ Diseases, Copenhagen University Hospital-Rigshospitalet, 2100 Copenhagen, Denmark.
Sensors (Basel). 2024 Oct 9;24(19):6497. doi: 10.3390/s24196497.
Continuous vital sign monitoring (CVSM) with wireless sensors in general hospital wards can enhance patient care. An artificial intelligence (AI) layer is crucial to allow sensor data to be managed by clinical staff without over alerting from the sensors. With the aim of summarizing peer-reviewed evidence for AI support in CVSM sensors, we searched PubMed and Embase for studies on adult patients monitored with CVSM sensors in general wards. Peer-reviewed evidence and white papers on the official websites of CVSM solutions were also included. AI classification was based on standard definitions of simple AI, as systems with no memory or learning capabilities, and advanced AI, as systems with the ability to learn from past data to make decisions. Only studies evaluating CVSM algorithms for improving or predicting clinical outcomes (e.g., adverse events, intensive care unit admission, mortality) or optimizing alarm thresholds were included. We assessed the promised level of AI for each CVSM solution based on statements from the official product websites. In total, 467 studies were assessed; 113 were retrieved for full-text review, and 26 studies on four different CVSM solutions were included. Advanced AI levels were indicated on the websites of all four CVSM solutions. Five studies assessed algorithms with potential for applications as advanced AI algorithms in two of the CVSM solutions (50%), while 21 studies assessed algorithms with potential as simple AI in all four CVSM solutions (100%). Evidence on algorithms for advanced AI in CVSM is limited, revealing a discrepancy between promised AI levels and current algorithm capabilities.
在综合医院病房中使用无线传感器进行连续生命体征监测（CVSM）可以增强患者护理。人工智能（AI）层对于允许临床人员管理传感器数据而不会因传感器过度报警至关重要。我们旨在总结关于 CVSM 传感器中 AI 支持的同行评审证据，因此在 PubMed 和 Embase 上搜索了关于在普通病房中使用 CVSM 传感器监测的成年患者的研究。还包括 CVSM 解决方案官方网站上的同行评审证据和白皮书。AI 分类基于简单 AI 的标准定义，即没有记忆或学习能力的系统，以及高级 AI，即具有从过去数据中学习以做出决策的能力的系统。仅包括评估 CVSM 算法以改善或预测临床结果（例如不良事件、入住重症监护病房、死亡率）或优化报警阈值的研究。我们根据官方产品网站上的声明评估了每个 CVSM 解决方案的承诺 AI 水平。总共评估了 467 项研究；检索了 113 项进行全文审查，并纳入了四项不同 CVSM 解决方案的 26 项研究。四个 CVSM 解决方案的网站上都表示具有高级 AI 水平。五项研究评估了两项 CVSM 解决方案中具有潜在高级 AI 算法应用的算法（50%），而 21 项研究评估了四项 CVSM 解决方案中所有四个都具有潜在简单 AI 算法的算法（100%）。CVSM 中高级 AI 算法的证据有限，表明承诺的 AI 水平与当前算法能力之间存在差异。
