Process mining based modeling and analysis of workflows in clinical care - A case study in a chicago outpatient clinic

Abstract

The United States currently spends over 17% of its gross domestic product on healthcare and this expenditure will continue to rise in the next few decades. Improving the performance and the efficiency of the United States healthcare system is of practical value to lowering such expenditure. Discrete event modeling, which can capture the complex behaviors of healthcare systems and provide statistical estimations of `what if' scenarios, is one of the most powerful and cost-effective methods for healthcare system improvement. It involves creating an abstract-level workflow model with an accurate view of the patient flow while considering the dynamic nature of healthcare processes. In this paper, an outpatient clinic in Chicago, Illinois, USA, is used as a case study to illustrate a process mining based method for healthcare processes management and improvement. This method is able to discover meaningful knowledge, i.e., the workflow, of the clinical care processes by mining event logs. Based on the results from process mining, a discrete event simulation model is proposed to quantitatively analyze the clinical center. Sensitivity analyses have also been carried out to investigate the care activities with limited resources such as doctors and nurses. The results suggest that this methodology is a useful and flexible tool for healthcare process performance improvement.