Research Progress of Adverse Events Monitoring in Neonatal Ward

医疗相关不良事件(Adverse healthcare-related events, AEs)不仅影响医疗质量，增加医疗成本，更会给患者及其家属造成伤害。新生儿因其特殊的生理特性，成为AE的高发人群，可能引起严重后果，甚至危及生命。当前AE监测手段可分为主动上报和被动监测，但均存在一定局限性，随着人工智能(Artificial Intelligence, AI)技术的发展，在提升医疗安全上展现出巨大潜力。本文旨在总结新生儿AE的发生率、常见类型与分类方式，分析新生儿易发生AE的原因，探讨现有监测手段及未来AI在新生儿病房AE监测的应用前景。Adverse healthcare-related events (AEs) significantly impact healthcare quality, increase costs, and harm patients and their families. Neonates, due to their unique physiological characteristics, are particularly susceptible to AEs, which can lead to severe consequences, including life-threatening situations. Current AEs monitoring methods, including active reporting and passive surveillance, both have limitations. Nowadays, advancements in Artificial Intelligence (AI) throw light on enhancing healthcare safety. This article aims to summarize the incidence, common types, and classification of neonatal AEs, analyze the reasons for their high occurrence in neonates, and explore existing monitoring methods along with the future prospects of AI in neonatal AEs surveillance.