Optimizing Interhospital Patient Transfer Decisions: A Queueing Network Approach
Published Online:11 Mar 2026https://doi.org/10.1287/msom.2025.0497
References
- (2015) On patient flow in hospitals: A data-based queueing-science perspective. Stochastic Systems 5(1):146–194.Link, Google Scholar
- (2023) Modeling COVID-19 hospital admissions and occupancy in the Netherlands. Eur. J. Oper. Res. 304(1):207–218.Crossref, Google Scholar
- (2021) Noninvasive ventilatory support of patients with covid-19 outside the intensive care units (ward-covid). Ann. Amer. Thoracic Soc. 18(6):1020–1026.Crossref, Google Scholar
- (2021) Association of intensive care unit patient load and demand with mortality rates in US Department of Veterans Affairs hospitals during the COVID-19 pandemic. JAMA Network Open 4(1):e2034266.Crossref, Google Scholar
- (2010) A simple heuristic for load balancing in parallel processing networks with highly variable service time distributions. Queueing Systems 64(2):145–165.Crossref, Google Scholar
- CBC News (2021) Ontario hospitals hit by COVID-19 transferring record number of patients around province. Accessed February 22, 2026, https://tinyurl.com/2kz7j9tc.Google Scholar
- (2021) Dynamic server assignment in multiclass queues with shifts, with applications to nurse staffing in emergency departments. Oper. Res. 69(6):1936–1959.Link, Google Scholar
- (2012) Optimizing intensive care unit discharge decisions with patient readmissions. Oper. Res. 60(6):1323–1341.Link, Google Scholar
- (2024) Differentiable discrete event simulation for queuing network control. Preprint, submitted September 5, https://arxiv.org/abs/2409.03740.Google Scholar
- (2023) Optimal routing under demand surges: The value of future arrival rates. Oper. Res. 73(1):510–542.Link, Google Scholar
- (2014) Minimizing mortality in a mass casualty event: Fluid networks in support of modeling and staffing. IIE Trans. 46(7):728–741.Crossref, Google Scholar
- Crawley M (2020) Some of Ontario’s biggest hospitals are filled beyond capacity nearly every day, new data reveals. CBC News. Accessed February 22, 2026, https://tinyurl.com/3macuvts.Google Scholar
- (2022) Queueing network controls via deep reinforcement learning. Stochastic Systems 12(1):30–67.Link, Google Scholar
- (2019) Inpatient overflow: An approximate dynamic programming approach. Manufacturing Service Oper. Management 21(4):894–911.Link, Google Scholar
- (2022) Managing hospital capacity: Achievements and lessons from the covid-19 pandemic. Prehospital Disaster Medicine 37(5):600–608.Crossref, Google Scholar
- (2011) Centralized vs. decentralized ambulance diversion: A network perspective. Management Sci. 57(7):1300–1319.Link, Google Scholar
- (2023) Dynamic fair balancing of COVID-19 patients over hospitals based on forecasts of bed occupancy. Omega 116:102801.Crossref, Google Scholar
- (2020) Queueing models for patient-flow dynamics in inpatient wards. Oper. Res. 68(1):250–275.Link, Google Scholar
- (2006) Dynamic load balancing in parallel queueing systems: Stability and optimal control. Eur. J. Oper. Res. 168(2):509–519.Crossref, Google Scholar
- (2020) One hospital was besieged by the virus. Nearby was ‘plenty of space’. The New York Times Online. Accessed February 22, 2026, https://www.nytimes.com/2020/05/14/nyregion/coronavirus-ny-hospitals.html.Google Scholar
- (2020) Feasibility and clinical impact of out-of-ICU noninvasive respiratory support in patients with covid-19-related pneumonia. Eur. Respiratory J. 56(5):2002130.Crossref, Google Scholar
- (2023) Robustness of proactive intensive care unit transfer policies. Oper. Res. 71(5):1653–1688.Link, Google Scholar
- (2002) Two M/M/1 queues with transfers of customers. Queueing Systems 42(4):377–400.Crossref, Google Scholar
- (2024) Interfacility patient transfers during covid-19 pandemic: Mixed-methods study. Western J. Emergency Medicine 25(5):758–766.Crossref, Google Scholar
- (2018) An examination of early transfers to the ICU based on a physiologic risk score. Manufacturing Service Oper. Management 20(3):531–549.Link, Google Scholar
- (2021) Covid-19: Comparing the applicability of shared room and single room occupancy. Transboundary Emerging Diseases 68(4):2059–2065.Crossref, Google Scholar
- (2020) Do hospitals value everyone? This winter, they have a chance to prove it. The New York Times Online. Accessed February 22, 2026, https://www.nytimes.com/2020/10/30/opinion/coronavirus-treatment-hospitals.html.Google Scholar
- (2014) Are call center and hospital arrivals well modeled by nonhomogeneous poisson processes? Manufacturing Service Oper. Management 16(3):464–480.Link, Google Scholar
- (2015) ICU admission control: An empirical study of capacity allocation and its implication for patient outcomes. Management Sci. 61(1):19–38.Link, Google Scholar
- (1990) Load balancing in a multi-server queuing system. Comput. Oper. Res. 17(1):17–25.Crossref, Google Scholar
- (2022) The value of health information technology interoperability: Evidence from interhospital transfer of heart attack patients. Manufacturing Service Oper. Management 24(2):827–845.Link, Google Scholar
- (2009) Pediatric and neonatal interfacility transport medicine after mass casualty incidents. J. Trauma 67(2):S168–S171.Google Scholar
- (1998) Strong approximations for Markovian service networks. Queueing Systems 30(1):149–201.Crossref, Google Scholar
- (2022) Regional transfer coordination and hospital load balancing during covid-19 surges. JAMA Health Forum 3(2):e215048.Crossref, Google Scholar
- (2019) Inter-hospital transfer and patient outcomes: A retrospective cohort study. BMJ Quality Safety 28(11):e1.Crossref, Google Scholar
- (2020) Optimal resource and demand redistribution for healthcare systems under stress from covid-19. Preprint, submitted November 6, https://arxiv.org/abs/2011.03528.Google Scholar
- (2007) Approximate Dynamic Programming: Solving the Curses of Dimensionality. Wiley Interscience, Hoboken, NJ.Crossref, Google Scholar
- (2023) An update to the Kaiser Permanente inpatient risk adjustment methodology accurately predicts in-hospital mortality: A retrospective cohort study. J. General Internal Medicine 38(15):3303–3312.Crossref, Google Scholar
- (2020) Why surviving the virus might come down to which hospital admits you. The New York Times Online. Accessed February 22, 2026, https://tinyurl.com/37ktp3z9.Google Scholar
- (2024) Trends in patient transfers from overall and caseload-strained US hospitals during the covid-19 pandemic. JAMA Network Open 7(2):e2356174.Crossref, Google Scholar
- (2016) Constructing minimum-width confidence bands. Econom. Lett. 145:182–185.Crossref, Google Scholar
- (2016) Models and insights for hospital inpatient operations: Time-dependent ED boarding time. Management Sci. 62(1):1–28.Link, Google Scholar
- (1993) Analytic models of adaptive load sharing schemes in distributed real-time systems. IEEE Trans. Parallel Distributed Systems 4(7):740–761.Crossref, Google Scholar
- (2006) Capacity-related interfacility patient transports: Patients affected, wait times involved and associated morbidity. Canadian J. Emergency Medicine 8(4):262–268.Crossref, Google Scholar
- (2017) Patient characteristics, resource use and outcomes associated with general internal medicine hospital care: The general medicine inpatient initiative (GEMINI) retrospective cohort study. CMAJ Open 5(4):E842–E849.Crossref, Google Scholar
- (2021) Characteristics and outcomes of hospital admissions for covid-19 and influenza in the Toronto area. CMAJ 193(12):E410–E418.Crossref, Google Scholar
- (2023) Applications of fluid models in service operations management. Queueing Systems 103(1–2):161–185.Crossref, Google Scholar
- (2020) Bed blocking in hospitals due to scarce capacity in geriatric institutions—Cost minimization via fluid models. Manufacturing Service Oper. Management 22(2):396–411.Link, Google Scholar

