The Case Ordering Problem in Surgical Procedural Training through Process Mining Recently, process perspective has been incorporated into the surgical training. These approaches use Process Mining to provide instructors with procedural reports of each student. However, reviewing those reports in a random order could be unnecessarily tedious: each report could be totally different from the previous one, preventing the instructors of any comparative aid, and forcing them to change their mindset every time. Complementary to other approaches such as clustering or tacit knowledge, we propose an approach to optimize, from a process perspective, the ordering of the students executions, such that the procedural distance between an execution and the next one is the lowest possible. The contribution of this paper is twofold: first, the problem is formalized; second, a method for solving the problem tailored to surgical training is presented. Preliminary results are provided, and the preliminary validity of the approach has been corroborated by a medical expert.

Clinical Pathway Generation from Order Histories and Discharge Summaries This paper proposes construction of clinical process plan from nursing order histories and discharge summaries stored in hospital information system. First, the system extracts subgrouping from clinical cases with the same Diagnostic Procedure Combination code (DPC) by mixture model clustering. Subgroups give different types of diseases with different temporal evolution. Then, classification models of each subgroup are constructed by the analysis of discharge summaries to capture the meaning of each subgroup. Finally, cases are classified by using the classification model and a clinical pathway is generated for each new subgroup. The proposed method was evaluated on the data sets extracted hospital information system, whose results show that plausible clinical pathways were obtained, compared with previously introduced methods.