University of Helsinki, Viikinkaari 5 E (P.O. Box 56), 00014, Helsingin yliopisto, Finland.
Ministry of Social Affairs and Health, P.O Box 33, 00023, Valtioneuvosto, Finland.
Res Social Adm Pharm. 2019 Jul;15(7):864-872. doi: 10.1016/j.sapharm.2018.11.013. Epub 2018 Nov 28.
Medication errors are common in healthcare. Medication error reporting systems can be established for learning from medication errors and risk prone processes, and their data can be analysed and used for improving medication processes in healthcare organisations. However, data reliability testing is crucial to avoid biases in data interpretation and misleading findings informing patient safety improvement.
To assess the inter-rater reliability of medication error classifications in a voluntary patient safety incident reporting system (HaiPro) widely used in Finland, and to explore reported medication errors and their contributing factors.
The data consisted of medication errors (n = 32 592), including near misses, reported by 36 Finnish healthcare organisations in 2007-2009. The reliability of the original classifications was tested by an independent researcher reclassifying a random sample of errors (1%, n = 288) based on narratives. The inter-rater reliability of agreement (κ) of the classifications was calculated to describe the degree of conformity between the researcher and the original data classifiers. Descriptive statistics were used to describe the medication errors.
The inter-rater reliability between the researcher and the original data classifiers was acceptable (κ ≥ 0.41) in 11 of 42 (26%) medication error classes. Thus, these errors could be pooled from different healthcare units for the exploration of medication errors at the level of all reporting organisations. Contributing factors were identified in 48% (n = 137) of the medication error narratives in the random sample (n = 288). The most commonly reported errors were dispensing errors (34%, n = 10 906), administration errors 25% (n = 7972), and documentation errors 17% (n = 5641).
The data classified by different classifiers can be pooled for some of the medication error classes. Consistency of the classification and the quality of narratives need improvement, as well as reporting and classification of contributing factors to provide high quality information on medication errors.
用药错误在医疗保健中很常见。可以建立用药错误报告系统，以便从用药错误和高风险流程中学习，并对其数据进行分析，用于改进医疗保健组织中的用药流程。但是，数据可靠性测试对于避免数据解释中的偏差和误导性发现至关重要，这些发现会影响患者安全的改善。
评估在芬兰广泛使用的自愿性患者安全事件报告系统（HaiPro）中用药错误分类的组内一致性，并探讨报告的用药错误及其促成因素。
该数据包含了 2007-2009 年 36 家芬兰医疗机构报告的 32592 例用药错误（包括接近差错），包括基于描述的随机错误样本（1%，n=288）的重新分类。使用独立研究者对原始分类进行重新分类，计算分类的组内一致性（κ），以描述研究人员与原始数据分类器之间的一致性程度。使用描述性统计来描述用药错误。
在 42 个用药错误类别中的 11 个类别中（26%），研究人员与原始数据分类器之间的组内一致性可以接受（κ≥0.41）。因此，可以将这些错误从不同的医疗机构中汇总起来，以便在所有报告机构的层面上探讨用药错误。在随机样本（n=288）中，48%（n=137）的用药错误描述中识别出促成因素。最常报告的错误是配药错误（34%，n=10906）、给药错误 25%（n=7972）和用药记录错误 17%（n=5641）。
对于某些用药错误类别，可以将不同分类器分类的数据汇总起来。需要改进分类的一致性和描述的质量，以及促成因素的报告和分类，以提供高质量的用药错误信息。
