As medical databases grow larger and larger, medical experts oftenlack appropriate and accessible tools to make the best of the datasetsavailable and transform data into actionable information. Manyknowledge extraction algorithms provide relevant results but failto provide explainable and transparent results. Accountability isparamount in healthcare, and hospital staff cannot rely on black boxtools when it comes to taking informed decisions. To address thissituation we propose an algorithm able to structure thousands ofelectronic medical records by similarity and typicality. Using a rank-based approach suitable for high-dimensional data, we associateeach patient's record to a very similar yet more typical record.This provides a structure suitable for data visualization, allowingfor both a high-level summary of a cohort and its representativepatients, and a detailed representation of similarities and relationsin each cluster. We applied this method to electronic medical recordsof diabetic patients, providing an easy tool for visualization andexploration of these data with the added benefit of explainability.