Optimizing Interhospital Patient Transfer Decisions: A Queueing Network Approach

Problem definition: Geographical mismatch between demand for care and healthcare resources is a critical challenge for hospital networks, particularly during episodes of surge such as pandemics, mass casualty events, and natural disasters. In such contexts, interhospital patient transfers can play a vital role in balancing congestion and promoting equitable access to care. We propose and investigate interhospital patient transfer policies to alleviate hospital congestion and inequity in the distribution of patients across the health system. Methodology/results: We propose a queueing network model capturing salient features of patient flow between acute wards and intensive care units (ICUs) both within and across hospitals. We formulate a stochastic control problem to determine optimal transfer policies and develop a solution method by leveraging a deterministic fluid approximation. Using the COVID-19 pandemic as a case study, we estimate and validate our model with data from 17 hospitals in Ontario, Canada. Following our policy, the expected number of patient-days above 95% capacity can be reduced by up to 48.4% in wards and 26.4% in ICUs, improving inequity in COVID patient distribution by up to 31.1% compared with the no-transfer policy. Our approach also outperforms historical transfer decisions that focused primarily on addressing COVID load inequity. We show that minimizing COVID load inequity alone can exacerbate existing imbalances in overoccupancy. In contrast, jointly optimizing both objectives leads to a very small compromise in COVID load inequity for a significant improvement in overoccupancy costs. These benefits are achievable by transferring only COVID patients and exhibit diminishing returns as the number of transfers increases. Managerial implications: Guiding patient transfers using our approach can effectively address imbalanced congestion and inequity during episodes of surge. These benefits can be obtained with relatively few transfers.

Funding: The development of the GEMINI data platform used in this study has been supported with funding from the Canadian Cancer Society, the Canadian Frailty Network, the Canadian Institutes of Health Research, the Canadian Medical Protective Association, Green Shield Canada Foundation, the Natural Sciences and Engineering Research Council of Canada, Ontario Health, the St. Michael’s Hospital Association Innovation Fund, and the University of Toronto Department of Medicine with in-kind support from partner hospitals and the Vector Institute. We also acknowledge the support of the Government of Canada’s New Frontiers in Research Fund [Grant NFRFR-2022-00209].

Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2025.0497.