Research progress on in-hospital clinical early warning systems:from traditional models to artificial intelligence
Early identification of high-risk patients and timely intervention can prevent serious adverse events such as cardio-respiratory arrest during hospitalization.Patients often show abnormalities in vital signs or physiological indicators before in-hospital cardiorespiratory arrest.The clinical early warning system is based on monitoring these critical indicators to achieve early identification and intervention of high-risk patients,thereby reducing the incidence of adverse events.This article reviews the development process of clinical early warning systems in hospitals,from traditional single parameter systems,multi-parameter systems and comprehensive weighted early warning systems,to automated early warning systems based on information technology,and to new clinical early warning systems based on machine learning and artificial intelligence.Additionally,this article evaluates the effectiveness of these systems in real clinical settings and their potential to improve patient safety and outcomes.
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