Abstract
Healthcare management, in particular managing a hospital network, is a complex issue which requires taking into account the morbidity process dynamics, simultaneous administration of various therapies, preventive actions, drug supply and responding to emergencies, such as mass casualty traffic incidents, pandemics (e.g. SARS, MERS), etc. The overall objective is to provide appropriate, dedicated aid to patients and affected persons as quickly as possible. One of the aspects of key importance to achieving this objective is the issue of appropriate patient-to-hospital allocation within a specific area. Very often the allocation is determined by constrained resources which the healthcare facilities in the given area have at their disposal, i.e. the number of doctors with specific medical specialties, the number of nurses, the number of beds of specific types in individual hospitals, the number of available medical transportation vehicles, but most importantly, the time limits for initiating successful hospital treatment. Optimal use of constrained resources makes it possible/facilitates optimal allocation in patients, which is essential in emergency situations. The paper proposes a model of optimal patient-to-hospital allocation taking into account the constrained resources of the hospital network and emergency ambulance service which provides medical transportation. The proposed model is formulated using Binary Integer Programming (BIP).
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Sitek, P., Wikarek, J. (2020). Resource-Constrained Model of Optimizing Patient-to-Hospital Allocation During a Pandemic. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2020. Lecture Notes in Computer Science(), vol 12496. Springer, Cham. https://doi.org/10.1007/978-3-030-63007-2_14
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