Automated Generation of Patient Population for Discrete-Event Simulation Using Process Mining

Process mining is increasingly used to discover and analyze health care processes. It is especially powerful in the study and improvement of patient clinical pathways. Combining process mining results and discrete-event simulation is an interesting approach to discover, represent and assess clinical pathways and improve healthcare organizations. The objective of this work is to develop a framework to automate such studies from the data preprocessing stage to use in simulations. We describe the use of Python and the PM4PY package for formatting data and discovering processes. A generic discrete-event simulation model is developed to serve as a base for analyzing and improving the patient flow in a healthcare center. This type of framework enriches the classical simulation model with synthetic pathways based on real patients and should facilitate accessing aggregated patient data and transposing studies on third-party datasets.