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arXiv:2605.19139 · v2

Managing Shared Hospital Capacity for Medical Tourism: Patient Behavior and Hybrid Simulation

首次提交 2026-05-19 05:38
版本更新 2026-10-26 04:16
雷达观测 2026-10-29 00:36
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摘要

Hospitals that serve medical tourists alongside local patients share beds, specialists, and appointment slots between two groups for whom waiting has different consequences. Operations research models are widely used for hospital capacity and scheduling, but often do not explicitly capture patient behavior; agent-based simulation can represent such behavior alongside discrete-event patient flow, while medical-tourism research still mainly treats traveling patients as cases to be scheduled. We develop a hybrid simulation that represents channel choice, physician switching, treatment abandonment, and medication-related deterioration in a multi-specialty international patient department. A 256-run fractional factorial experiment screens 16 controllable factors across six performance measures, followed by a replicated comparison with a discrete-event-only version without these behaviors. At a bed-constrained configuration, mean inpatient admission waiting time is 42% lower in the hybrid model (14.8 versus 25.8 days in the discrete-event-only model, p < 0.001) and 25% to 64% lower under two further conditions. Lacking these behaviors, the simpler model shows no abandonment and fewer emergency escalations. The comparison shows how results depend on model structure; neither variant is validated against observed waiting times. A bed-sharing routine in both variants adds no beds as implemented, so bed sharing is not evaluated. The screening identifies bed capacity as the most consistent driver of waiting; priority rules, channel design, and appointment timing also matter, while specialist capacity has less effect on waiting. Managers should review bed capacity and admission rules before adding specialists or tightening schedules.

作者备注:19 pages, 6 figures, 7 tables; Online Resource 1 (24 pages) attached as an ancillary file. v2: revised and retitled version prepared for journal submission; replaces the May 2026 preprint titled "Reducing Waiting Time for Medical Tourists Through Hybrid Agent-Based and Discrete-Event Simulation: A Hospital Case Study". Code and model: https://doi.org/10.5281/zenodo.22646799

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v2 · 2026-10-26 04:16 · 最新观测版本

Managing Shared Hospital Capacity for Medical Tourism: Patient Behavior and Hybrid Simulation

Hospitals that serve medical tourists alongside local patients share beds, specialists, and appointment slots between two groups for whom waiting has different consequences. Operations research models are widely used for hospital capacity and scheduling, but often do not explicitly capture patient behavior; agent-based simulation can represent such behavior alongside discrete-event patient flow, while medical-tourism research still mainly treats traveling patients as cases to be scheduled. We develop a hybrid simulation that represents channel choice, physician switching, treatment abandonment, and medication-related deterioration in a multi-specialty international patient department. A 256-run fractional factorial experiment screens 16 controllable factors across six performance measures, followed by a replicated comparison with a discrete-event-only version without these behaviors. At a bed-constrained configuration, mean inpatient admission waiting time is 42% lower in the hybrid model (14.8 versus 25.8 days in the discrete-event-only model, p < 0.001) and 25% to 64% lower under two further conditions. Lacking these behaviors, the simpler model shows no abandonment and fewer emergency escalations. The comparison shows how results depend on model structure; neither variant is validated against observed waiting times. A bed-sharing routine in both variants adds no beds as implemented, so bed sharing is not evaluated. The screening identifies bed capacity as the most consistent driver of waiting; priority rules, channel design, and appointment timing also matter, while specialist capacity has less effect on waiting. Managers should review bed capacity and admission rules before adding specialists or tightening schedules.

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