Healthcare Quality Service, Clínica Universidad de Navarra, Pamplona, Spain.
Research Support Service, Central Clinical Trials Unit, Clínica Universidad de Navarra, Pamplona, Spain.
J Adv Nurs. 2021 Jul;77(7):3168-3175. doi: 10.1111/jan.14779. Epub 2021 Feb 23.
To identify and prioritize the root causes of adverse drug events (ADEs) in hospitals and to assess the ability of artificial intelligence (AI) capabilities to prevent ADEs.
A mixed method design was used.
A cross-sectional study for hospitals in Spain was carried out between February and April 2019 to identify and prioritize the root causes of ADEs. A nominal group technique was also used to assess the ability of AI capabilities to prevent ADEs.
The main root cause of ADEs was a lack of adherence to safety protocols (64.8%), followed by identification errors (57.4%), and fragile and polymedicated patients (44.4%). An analysis of the AI capabilities to prevent the root causes of ADEs showed that identification and reading are two potentially useful capabilities.
Identification error is one of the main root causes of drug adverse events and AI capabilities could potentially prevent drug adverse events.
This study highlights the role of AI capabilities in safely identifying both patients and drugs, which is a crucial part of the medication administration process, and how this can prevent ADEs in hospitals.
确定并确定医院中药物不良事件（ADE）的根本原因，并评估人工智能（AI）功能预防 ADE 的能力。
采用混合方法设计。
2019 年 2 月至 4 月，在西班牙的医院中进行了一项横断面研究，以确定和确定 ADE 的根本原因。还使用名义小组技术评估了 AI 功能预防 ADE 的能力。
ADE 的主要根本原因是缺乏对安全协议的遵守（64.8％），其次是识别错误（57.4％）和脆弱且多药治疗的患者（44.4％）。对预防 ADE 根本原因的 AI 功能进行分析后发现，识别和读取是两个潜在有用的功能。
识别错误是药物不良事件的主要根本原因之一，人工智能功能有可能预防药物不良事件。
本研究强调了 AI 功能在安全识别患者和药物方面的作用，这是药物管理过程中的关键部分，以及如何预防医院中的 ADE。
