In healthcare, documenting patients’ clinical interactions in medical records is essential. The information in medical records often needs to be shared between different healthcare units. When information is stored in varying formats across systems, data transfer becomes problematic and may result in data loss or the need for manual handling. To prevent such issues, data can be structured according to standardized models for clinical information, such as the openEHR framework. OpenEHR is based on archetypes, which have to be translated into the language of the country in which they are to be used. This study investigates challenges and needs in the translation process of openEHR archetypes, with a particular focus on semantic accuracy, linguistic consistency and organizational and technical aspects. To explore this, semi-structured interviews were conducted and analyzed using thematic analysis. The results indicate that semantic accuracy was perceived as crucial and that factors such as domain expertise and contextual knowledge were considered essential for ensuring it. Linguistic consistency was also emphasized, and respondents reported using glossaries and consulting previous translations to support it. In terms of workflow, the study highlights translation workflows between countries, suggesting that the processes vary significantly. Regarding technical aspects, the respondents were generally positive about future use of AI, particularly domain-specific models. However, they emphasized the importance of human review. In conclusion, the findings offer insights into challenges, needs and factors that influence quality and efficiency of openEHR archetype translation.