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Dynamic resource allocation to improve emergency department efficiency in real time

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  • Luscombe, Ruth
  • Kozan, Erhan

Abstract

A dynamic scheduling framework is proposed to provide real-time support for managing the scarce resources of the Emergency Department. The theory of parallel machine and flexible job shop environments are integrated to schedule patient-bed assignments and task-resource allocations. The solution method incorporates dispatch heuristics, disjunctive graph methods and meta-heuristic search in order to provide fast solutions that respond to unscheduled arrivals, competing priorities and heterogeneous patient care needs. The dynamic algorithm is compared against static solutions and is shown to achieve solutions within 5 percent of the best bound. The dynamic schedule updates are completed within 2 seconds of information updates. This level of decision support, when implemented within a patient management system, can reduce the clinicians’ workload by managing prioritized task lists such that the clinical staff is more free to focus on delivering clinical care.

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  • Luscombe, Ruth & Kozan, Erhan, 2016. "Dynamic resource allocation to improve emergency department efficiency in real time," European Journal of Operational Research, Elsevier, vol. 255(2), pages 593-603.
    • Handle: RePEc:eee:ejores:v:255:y:2016:i:2:p:593-603
    DOI: 10.1016/j.ejor.2016.05.039
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    Cited by:

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    2. Fattahi, Mohammad & Keyvanshokooh, Esmaeil & Kannan, Devika & Govindan, Kannan, 2023. "Resource planning strategies for healthcare systems during a pandemic," European Journal of Operational Research, Elsevier, vol. 304(1), pages 192-206.
    3. Agovino, Massimiliano & Musella, Gaetano & Scaletti, Alessandro, 2022. "Equilibrium and efficiency in the first aid services market: The case of the emergency department of Sorrento," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    4. Jaime González & Juan-Carlos Ferrer & Alejandro Cataldo & Luis Rojas, 2019. "A proactive transfer policy for critical patient flow management," Health Care Management Science, Springer, vol. 22(2), pages 287-303, June.
    5. Marynissen, Joren & Demeulemeester, Erik, 2019. "Literature review on multi-appointment scheduling problems in hospitals," European Journal of Operational Research, Elsevier, vol. 272(2), pages 407-419.
    6. Amir Elalouf & Guy Wachtel, 2022. "Queueing Problems in Emergency Departments: A Review of Practical Approaches and Research Methodologies," SN Operations Research Forum, Springer, vol. 3(1), pages 1-46, March.
    7. Burdett, Robert L. & Kozan, Erhan, 2018. "An integrated approach for scheduling health care activities in a hospital," European Journal of Operational Research, Elsevier, vol. 264(2), pages 756-773.
    8. Wu, Xiaodan & Li, Juan & Chu, Chao-Hsien, 2019. "Modeling multi-stage healthcare systems with service interactions under blocking for bed allocation," European Journal of Operational Research, Elsevier, vol. 278(3), pages 927-941.
    9. Niyirora, Jerome & Zhuang, Jun, 2017. "Fluid approximations and control of queues in emergency departments," European Journal of Operational Research, Elsevier, vol. 261(3), pages 1110-1124.
    10. Davide Duma & Roberto Aringhieri, 2020. "An ad hoc process mining approach to discover patient paths of an Emergency Department," Flexible Services and Manufacturing Journal, Springer, vol. 32(1), pages 6-34, March.
    11. Duma, Davide & Aringhieri, Roberto, 2023. "Real-time resource allocation in the emergency department: A case study," Omega, Elsevier, vol. 117(C).
    12. Bowen Guo & Wei Zhan, 2023. "Research on Integrated Scheduling of Multi-Mode Emergency Rescue for Flooding in Chemical Parks," Sustainability, MDPI, vol. 15(4), pages 1-18, February.

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