Using discrete-event simulation to assess an AHP-based dynamic patient prioritisation policy for elective surgery

Abstract

ABSTRACTPatient prioritisation in elective surgery is a critical multi-criteria decision-making issue. Elective patients should be ranked on the waiting list based on various criteria related to their health conditions and their evolution over time. This study suggests a dynamic prioritisation where elective patients are ranked considering their evolving health conditions, waiting time, and patient loss, i.e., the likelihood of patients abandoning the waiting list. We proposed deploying the Analytic Hierarchy Process (AHP) to weigh prioritisation criteria. The AHP-derived weights are integrated into a discrete-event simulation model, which periodically ranks patients considering their health status, waiting time, and patient loss. The proposed dynamic prioritisation is compared with the first-come, first-served (FCFS) policy and the static prioritisation, which ranks patients according to their initial priority. Results showed that dynamic prioritisation outperforms the FCFS and static prioritisation policies in preventing health deterioration, reducing weighted waiting time, and reducing the ratio of lost patients.