Graduate School of Public Health, St. Luke's International University, Tokyo, Japan.
The University of Tokyo, Tokyo, Japan.
Stud Health Technol Inform. 2022 Jun 6;290:354-358. doi: 10.3233/SHTI220095.
At present no adequate annotation guidelines exists for incident report learning. This study aims at utilizing multiple quantitative and qualitative evidence to validate annotation guidelines for incident reporting of medication errors. Through multiple approaches via annotator training, annotation performance evaluation, exit surveys, and user and expert interviews, a mixed methods explanatory sequential design was utilized to collect 2-stage evidence for validation. We recruited two patient safety experts to participate in piloting, three annotators to receive annotation training and provide user feedback, and two incident report system designers to offer expert comments. Regarding the annotation performance evaluation, the overall accuracy reached 97% and 90% for named entity identification and attribute identification respectively. Participants provided invaluable comments and opinions towards improving the annotation methods. The mixed methods approach created a significant evidential basis for the use of annotation guidelines for incident report of medication errors. Further expansion of the guidelines and external validity present options for future research.
目前,事件报告学习没有足够的注释指南。本研究旨在利用多种定量和定性证据来验证药物错误事件报告的注释指南。通过注释者培训、注释性能评估、退出调查以及用户和专家访谈等多种方法,采用混合方法解释性顺序设计来收集 2 阶段的验证证据。我们招募了两名患者安全专家参与试点研究,三名注释者接受注释培训并提供用户反馈,两名事件报告系统设计人员提供专家意见。关于注释性能评估,命名实体识别和属性识别的整体准确率分别达到 97%和 90%。参与者提供了宝贵的意见和建议,以改进注释方法。混合方法为使用药物错误事件报告注释指南提供了重要的证据基础。进一步扩展指南和提高外部有效性为未来的研究提供了选择。