A Solution Framework Based on Process Mining, Optimization, and Discrete-Event Simulation to Improve Queue Performance in an Emergency Department

Long waiting lines are a frequent problem in hospitals' Emergency Departments and can be critical to the patient's health and experience. This study proposes a three-stage solution framework to address this issue: Process Identification, Process Optimization, and Process Simulation. In the first, we use descriptive statistics to understand the data and obtain indicators as well as Process Mining techniques to identify the main process flow; the Optimization phase is composed of a mathematical model to provide an optimal physician schedule that reduces waiting times; and, finally, Simulation is performed to compare the original process flow and scheduling with the optimized solution. We applied the proposed solution framework to a case study in a Brazilian private hospital. Final data comprised of 65,407 emergency cases which corresponded to 399,631 event log registries in a 13-month period. The main metrics observed were the waiting time before the First General Assessment of a physician and the volume of patients within the system per hour and day of the week. When simulated, the optimal physician scheduling resulted in more than 40% reduction in waiting times and queue length, a 29.3% decrease of queue occurrences, and 54.2% less frequency of large queues.