Systematic qualitative analysis of the designation of medicinal products in the Rote Liste® (Red List)

Medication errors are a serious problem in medicine and can lead to patient harm and entail high costs. To investigate the quality of brand names which we suggest can favor medication errors, this study looks at the designation of drugs (brand name). It analyses whether they are potentially a risk for medication errors by presenting a false image of the drug. The Rote Liste® (Red List) is one of the best-known drug directories in Germany; this drug directory was used for the categorization of medicinal products. The entries included in the Rote Liste ® dataset from 2020 were analyzed by their semantic appearance. By creating a new methodology, the entries were examined for an association between the designation of the medicinal product and their attributes, such as the RL-group, the active ingredients and the marketing authorization holder, and were divided into three main categories. Subsequently, correlations between the results of the analysis and the attributes were performed. To verify the quality of this method, the inter- and intra-rater-reliability was determined by using Cohen’s cappa. One thousand six hundred thirty-seven (22.36%) of the entries had a strictly generic designation. Three main categories were established, of which most entries (5454; 74.49%) were assigned to the category “coherent”. The other two categories are “incoherent” (1625; 22.19%) and “potentially confusing” (243; 3.32%). By looking at the distribution of the three main categories among the RL-groups, a few RL-groups condition for the most entries in the category “potentially confusing”. The inter- and intra-rater-reliability for this newly established analysis shows a moderate to substantial agreement. The results show that there are heterogenous linguistic forms of designation of medicinal products. A more transparent and uniform designation of medicinal products could lead to a reduction in medical errors. In this paper, we have provided specific suggestions on how drug naming could be improved.