Division of Pharmacology and Pharmacotherapy, Faculty of Pharmacy, University of Helsinki, Viikinkaari 5 E, 00014, Helsinki, Finland.
HUS Pharmacy, Helsinki University Hospital, Stenbäckinkatu 9B, 00029 HUS, Helsinki, Finland.
BMC Health Serv Res. 2023 Jul 10;23(1):743. doi: 10.1186/s12913-023-09763-3.
Several classification systems for medication errors (MEs) have been established over time, but none of them apply optimally for classifying severe MEs. In severe MEs, recognizing the causes of the error is essential for error prevention and risk management. Therefore, this study focuses on exploring the applicability of a cause-based DRP classification system for classifying severe MEs and their causes.
This was a retrospective document analysis study on medication-related complaints and authoritative statements investigated by the Finnish National Supervisory Authority for Welfare and Health (Valvira) in 2013-2017. The data was classified by applying a previously developed aggregated DRP classification system by Basger et al. Error setting and harm to the patient were identified using qualitative content analysis to describe the characteristics of the MEs in the data. The systems approach to human error, error prevention, and risk management was used as a theoretical framework.
Fifty-eight of the complaints and authoritative statements concerned MEs, which had occurred in a wide range of social and healthcare settings. More than half of the ME cases (52%, n = 30) had caused the patient's death or severe harm. In total, 100 MEs were identified from the ME case reports. In 53% (n = 31) of the cases, more than one ME was identified, and the mean number of MEs identified was 1.7 per case. It was possible to classify all MEs according to aggregated DRP system, and only a small proportion (8%, n = 8) were classified in the category "Other," indicating that the cause of the ME could not be classified to specific cause-based category. MEs in the "Other" category included dispensing errors, documenting errors, prescribing error, and a near miss.
Our study provides promising preliminary results for using DRP classification system for classifying and analyzing especially severe MEs. With Basger et al.'s aggregated DRP classification system, we were able to categorize both the ME and its cause. More research is encouraged with other ME incident data from different reporting systems to confirm our results.
随着时间的推移，已经建立了几种药物错误（ME）分类系统，但没有一种系统能够最优地分类严重的 ME。在严重的 ME 中，识别错误的原因对于错误预防和风险管理至关重要。因此，本研究专注于探索基于原因的 DRP 分类系统在分类严重 ME 及其原因方面的适用性。
这是一项回顾性文件分析研究，对 2013-2017 年芬兰国家福利和健康监督局（Valvira）调查的药物相关投诉和权威声明进行了分析。应用 Basger 等人先前开发的聚合 DRP 分类系统对数据进行分类。使用定性内容分析识别错误设置和对患者的伤害，以描述数据中 ME 的特征。系统方法用于人为错误、错误预防和风险管理，作为理论框架。
58 份投诉和权威声明涉及 ME，这些 ME 发生在广泛的社会和医疗保健环境中。超过一半的 ME 病例（52%，n=30）导致患者死亡或严重伤害。从 ME 病例报告中共确定了 100 个 ME。在 53%（n=31）的病例中，确定了一个以上的 ME，每个病例的平均 ME 数量为 1.7 个。根据聚合 DRP 系统，可以对所有 ME 进行分类，只有一小部分（8%，n=8）被归类为“其他”，这表明 ME 的原因无法归类到特定的基于原因的类别中。“其他”类别的 ME 包括配药错误、记录错误、处方错误和险些失误。
我们的研究为使用 DRP 分类系统分类和分析特别是严重 ME 提供了有希望的初步结果。使用 Basger 等人的聚合 DRP 分类系统，我们能够对 ME 及其原因进行分类。鼓励使用来自不同报告系统的其他 ME 事件数据进行更多研究，以确认我们的结果。
