BackgroundSeveral 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.MethodsThis 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.ResultsFifty-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.ConclusionsOur 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.