Clustering of Patients’ Trajectories with an Auto‐Stopped Bisecting K‐Medoids Algorithm

Nowadays, long wait, cancellations and resource overload frequently occur in healthcare, especially in those sectors related to the patients passing through the operating theatre in both United States and the European Union. Since more and more hospitals seek to develop the overall patient pathways instead of the effectiveness of ‘isolated’ departments, the most important work has been defining suitable patient groups for employing process management and simulation tools developed in the recent decades. In this study, we proposed a data mining method, an auto‐stopped Bisecting K‐Medoids clustering algorithm, to classify patients into groups with homogenous trajectories. This method classifies the patient trajectories with two stages. At the first stage, patients are classified by the complexity of outpatient visits; afterwards, the groups obtained at the first stage are further classified by the original information of the trajectories where all medical appointments including outpatient ones are taken into account. By using a real data set collected from a medium‐size Belgian hospital, we demonstrate how the proposed approach works and examine which kinds of trajectories are grouped into the same clusters. According to the experimental results, the proposed method can be used to classify patients into manageable groups with homogenous trajectories, which can be used as a base for the process modelling techniques and simulation tools.