Events related to medication errors and related factors involving nurses’ behavior to reduce medication errors in Japan: a Bayesian network modeling-based factor analysis and scenario analysis

This study aimed to identify the relationships between medication errors and the factors affecting nurses’ knowledge and behavior in Japan using Bayesian network modeling. It also aimed to identify important factors through scenario analysis with consideration of nursing students’ and nurses’ education regarding patient safety and medications. We used mixed methods. First, error events related to medications and related factors were qualitatively extracted from 119 actual incident reports in 2022 from the database of the Japan Council for Quality Health Care. These events and factors were then quantitatively evaluated in a flow model using Bayesian network, and a scenario analysis was conducted to estimate the posterior probabilities of events when the prior probabilities of some factors were 0%.

本研究旨在采用贝叶斯网络（Bayesian network）建模方法，明确日本地区用药差错与影响护士知识及执业行为的相关因素之间的关联。此外，本研究还将结合护理学生与护士的患者安全及用药相关教育培训背景，通过情景分析识别关键影响因素。本研究采用混合研究范式。首先，从日本医疗质量评价委员会（Japan Council for Quality Health Care）2022年的数据库中，对119份实际事件报告开展定性提取，获取与用药差错相关的事件及关联因素。随后，基于贝叶斯网络构建流模型，对上述提取的事件与因素进行定量评估，并通过情景分析估算当部分因素的先验概率设定为0%时，相关事件的后验概率。