BackgroundRare Diseases (RDs) are difficult to diagnose. Clinical Decision Support Systems (CDSS) could support the diagnosis for RDs. The MIRACUM (Medical Informatics in Research and Medicine) consortium developed a CDSS for RDs based on distributed clinical data from ten German university hospitals. To support the diagnosis for difficult patient cases, the CDSS uses data from the different hospitals to perform a patient similarity analysis in order to obtain an indication of a diagnosis. To optimize our CDSS, we conducted this qualitative study to investigate the usability of the CDSS with its functionality and information included. Methods A Thinking Aloud Test (TA-Test) was performed with RDs experts recruited from Rare Diseases Centres (RDCs) at the MIRACUM locations which were specialized in the diagnosis and treatment of RDs.An instruction sheet with tasks was prepared that the participants should perform with the CDSS during the study. Participants were asked to share any thoughts about the CDSS. The TA-Test was recorded on audio and video. A questionnaire was handed out at the end of the study including the System Usability Scale (SUS). Afterwards, the data was analysed with the qualitative content analysis according to Mayring, which includes a category-guided deductive approach.ResultsA total of eight experts were included in the study since eight MIRACUM locations have established an RDC.The results show that more detailed information about the patients, such as descriptive attributes or findings, are needed. The given functionality of the CDSS was rated positively, such as the function for the overview of similar patients and medical history. However, there is a lack of transparency regarding the results of the CDSS patient similarity analysis. The participants stated that the system should present exactly which symptoms, diagnosis etc. have matched. Regarding usability, the CDSS received a score of 73.21 points according to the SUS, which is ranked as a good usability.ConclusionsThis qualitative study investigated the usability of a CDSS of RDs. Despite the promising results, the CDSS still needs some revisions before use in clinical practice, e.g. by improving the transparency of the patient similarity analysis.