Patient Flow Analysis with a Custom Simulation Engine

Abstract

Patient flow simulation and analysis is one of the oldest IT -based methods used to optimize patient care processes and hospital management. During the pandemic, interest in this domain suddenly increased due to the various constraints and recommendations to reduce the likelihood of further infections in the hospital. Suddenly, metrics such as the number of patients waiting in the same area, the maximum time a patient could stay in a single room, and the minimum distance between patients became important issues to monitor and optimize. Using data and modelling concepts from various hospitals, our team developed a simulation tool that used bpmn models to define an emergency department. We then modified a single day's usual patient flow with various real-world inspired edge cases to evaluate how the simulated flow would change and which stations would become bottlenecks, where the quality of patient care would deteriorate and rooms would become overcrowded. To execute the models, we developed our own tool based on the open-source Camunda modeling tool and the Business Process Model Notation (BPMN) file format. To execute the generated models, we use our own Python-based execution environment based on the SpiffWorkflow library, which permits extensive logging and extensive customization of the attributes analysed. In addition, the modelling toolkit of Camunda was narrowed down and compiled so that it could be easily used by researchers who are not programmers. In the paper, we present both the modeling process and the scenario design process, as well as the results obtained through the runs, including the maximum waiting times during the model runs and the maximum number of patients waiting at once, which allowed us to validate the effectiveness of the framework.