Increasing the performance of a hospital department with budget allocation model and machine learning assisted by simulation

Abstract

The COVID-19 pandemic highlighted the critical need for efficient resource management in healthcare. In this study, the internal medicine outpatient clinic in a hospital is modelled by simulation method. Appropriate statistical distributions of the parameters are derived from past data. The results of a limited number of simulation runs are used as training data for machine learning techniques and an estimation model is selected among them. The estimation results are considered as input to a mathematical model which determines the optimal budget allocation for improving the system performance. Analysis considers patient waiting times and system throughput under varied parameters. A significant amount of time is saved by using machine learning to predict the simulation model outcomes, which had previously taken a total of around 7 hours reduced to 30-40 minutes. Time savings through machine learning are projected to be notably greater for more complex simulations comparing to current case.