Conformance Checking for a Medical Training Process Using Petri net Simulation and Sequence Alignment
This work addresses the need for objective conformance checking in healthcare training processes, offering a domain-specific and incremental improvement over existing methods.
The paper tackled the problem of objectively measuring deviations in a medical training process for Central Venous Catheter installation by analyzing event logs from ten students, using Petri net simulation and sequence alignment to provide performance measures and visual feedback.
Process Mining has recently gained popularity in healthcare due to its potential to provide a transparent, objective and data-based view on processes. Conformance checking is a sub-discipline of process mining that has the potential to answer how the actual process executions deviate from existing guidelines. In this work, we analyze a medical training process for a surgical procedure. Ten students were trained to install a Central Venous Catheters (CVC) with ultrasound. Event log data was collected directly after instruction by the supervisors during a first test run and additionally after a subsequent individual training phase. In order to provide objective performance measures, we formulate an optimal, global sequence alignment problem inspired by approaches in bioinformatics. Therefore, we use the Petri net model representation of the medical process guideline to simulate a representative set of guideline conform sequences. Next, we calculate the optimal, global sequence alignment of the recorded and simulated event logs. Finally, the output measures and visualization of aligned sequences are provided for objective feedback.