Zhou Sicheng, Kang Hong, Yao Bin, Gong Yang
University of Texas Health Science Center at Houston, Houston, TX, USA.
AMIA Annu Symp Proc. 2018 Dec 5;2018:1611-1620. eCollection 2018.
Medication error is a severe patient safety event in the United States. Medication error reports collected by Patient Safety Organizations provide an opportunity to analyze and learn from previous errors. However, the current workflow of analyzing the error reports is labor-intensive and time-consuming. To reduce the workloads for clinicians and save time, we developed a pipeline for medication error report pre-analysis by applying automated text classification techniques. The pipeline was proven functional in two tasks, i.e., identifying the error originated stages, error types and error causes from the medication error reports, and calculating the similarity scores between the error reports for re-organization. The proposed pipeline holds promise in helping clinicians understand the nature of medication error in an error report, and better manage the error reports, which could further facilitate the prevention of medication errors in healthcare settings.
用药错误在美国是一种严重的患者安全事件。患者安全组织收集的用药错误报告提供了一个分析和从以往错误中吸取教训的机会。然而,当前分析错误报告的工作流程既费力又耗时。为了减轻临床医生的工作量并节省时间,我们通过应用自动文本分类技术开发了一个用药错误报告预分析管道。该管道在两项任务中被证明是有效的,即从用药错误报告中识别错误发生阶段、错误类型和错误原因,以及计算错误报告之间的相似度分数以便重新组织。所提出的管道有望帮助临床医生理解错误报告中用药错误的本质,并更好地管理错误报告,这进而可以促进医疗环境中用药错误的预防。