Integration of simulation and DEA to determine the most efficient patient appointment scheduling model for a specific healthcare setting

摘要

Purpose: This study is to develop a systematic approach for determining the most efficient patient appointment scheduling (PAS) model for a specific healthcare setting with its multiple appointments requests characteristics in order to increase patients’ accessibility and resource utilization, and reduce operation cost. In this study, three general appointment scheduling models, centralized scheduling model (CSM), decentralized scheduling model (DSM) and hybrid scheduling model (HSM), are considered.\ud\udDesign/methodology/approach: The integration of discrete event simulation and data envelopment analysis (DEA) is applied to determine the most efficient PAS model. Simulation analysis is used to obtain the outputs of different configurations of PAS, and the DEA based on the simulation outputs is applied to select the best configuration in the presence of multiple and contrary performance measures. The best PAS configuration provides an optimal balance between patient satisfaction, schedulers’ utilization and the cost of the scheduling system and schedulers’ training.\ud\udFindings: In the presence of high proportion (more than 70%) of requests for multiple appointments, CSM is the best PAS model. If the proportion of requests for multiple appointments is medium (25%-50%), HSM is the best. Finally, if the proportion of requests for multiple appointments is low (less than 15%), DSM is the best. If the proportion is in the interval from 15% to 25% the selected PAS model could be either DSM or HSM based on expert idea. Similarly, if the proportion is in the interval from 50% to 70% the best PAS model could be either CSM or HSM.\ud\udOriginality/value: This is the first study that determines the best PAS model for a particular healthcare setting. The proposed approach can be used in a variety of the healthcare settings.\ud\udKeywords: data envelopment analysis, discrete event simulation, patient appointment scheduling, multiple appointments, centralized scheduling model, decentralized scheduling model, hybrid scheduling model