A Simulation-Based Framework for Evaluation of Healthcare Systems with Interacting Factors and Correlated Performance Measures

All multi-criteria decision making (MCDM) techniques used with discrete event simulation (DES) in previous literature in the analysis of healthcare systems assume that performance measures are independent, while in fact many are correlated. In this study, correlations between performance measures are taken into consideration using modified TOPSIS. A framework is developed in which DES is used to model multiple experiments and factor levels to estimate various performance measures. Design of experiments is used to study the effects of factors and their interactions. Experiments are input to modified TOPSIS as alternatives to be compared based on multiple correlated performance measures. A case study is presented at Educational Dentistry Clinics in Jordan. This study was designed to improve overall clinic performance. Results show that when using appropriate appointment scheduling rules and orthodontist working schedules, and exploiting the resources effectively, the best scenario obtained shows an effective decrease in orthodontic patient waiting times and length of stay (LOS), and primary diagnosis patients LOS by 29.44%, 37.74% and 7.12%, respectively. It also shows an increase in orthodontist and receptionist utilizations, and number of patients served by 11.22%, 65.82% and 69.39%, respectively. The results obtained from modified TOPSIS were more accurate as compared to traditional TOPSIS. As a future work, other appointment rules can be tested and other MCDM techniques that take criteria correlations into consideration can be used to rank the experiments such as the analytic network process.