With the aim to improve care in the future, it is worth offering clinicians an objective view of their practices. The clustering of care pathways meets this objective of revealing homogeneous groups of patients. The difficulty in clustering care pathways, represented by sequences of timestamped events, lies in defining a semantically appropriate metric and clustering algorithms. In this article, we propose a method that combines the use of the Drop-DTW metric and the DBA approach for the construction of average time series. These approaches are adapted for sequences of timestamped events, and we derive the HIERASTISEQ algorithm for clustering time sequences. This approach is evaluated on synthetic and real data.