Reducing Waiting Time for Medical Tourists Through Hybrid Agent-Based and Discrete-Event Simulation: A Hospital Case Study

TL;DR

This paper presents a hybrid simulation model combining agent-based and discrete-event approaches to reduce medical tourists' waiting times in a Tehran hospital, providing a decision-support tool.

Contribution

It introduces a novel hybrid simulation framework specifically designed for medical tourism operations, integrating behavioral and procedural hospital processes.

Findings

Hybrid model reduced waiting time from 13.666 to 2.416 days.

Bed capacity and patient-priority rules are highly influential.

Model reveals dropout and emergency patterns not seen in traditional models.

Abstract

Medical tourists face a scheduling problem that differs from that of local patients. Treatment delays extend not just care delivery time, but also accommodation and travel costs. This study develops a hybrid agent-based and discrete-event simulation model for an international patient department in a Tehran hospital case study. The model represents registration, consultation, admission, bed allocation, and discharge through discrete-event simulation, while patient, physician, and ward behaviours are represented through agent-based logic. A 256-run two-level fractional factorial design over 16 controllable factors is used to evaluate bed capacity, specialist counts, online consultation shares, bed-scheduling rules, patient-priority policy, and clinic slot interval across six performance measures. The primary outcome is the average waiting time of medical tourists in the hospital queue. In the case study, the hybrid model reduces this measure from 13.666 days in a DES-only counterpart to 2.416 days. It also reveals dropout and emergency-escalation patterns that a purely procedural representation suppresses. The results indicate that bed capacity, patient-priority rules, channel design, and clinic slot interval are the most influential levers for managing tourist waiting time. The study contributes a case-grounded decision-support model for hospital managers balancing shared capacity and service differentiation in medical tourism operations.