Ye Yong, Huang Lizhen, Wang Jie, Chuang Yen-Ching, Pan Lingle
Institute of Public Health and Emergency Management, Taizhou University, Taizhou, 318000, Zhejiang, China.
Business College, Taizhou University, Taizhou, 318000, Zhejiang, China.
BMC Med Inform Decis Mak. 2022 Dec 15;22(1):331. doi: 10.1186/s12911-022-02074-3.
Patients are classified according to the severity of their condition and graded according to the diagnosis and treatment capacity of medical institutions. This study aims to correctly assign patients to medical institutions for treatment and develop patient allocation and medical resource expansion schemes among hospitals in the medical network.
Illness severity, hospital level, allocation matching benefit, distance traveled, and emergency medical resource fairness were considered. A multi-objective planning method was used to construct a patient allocation model during major epidemics. A simulation study was carried out in two scenarios to test the proposed method.
(1) The single-objective model obtains an unbalanced solution in contrast to the multi-objective model. The proposed model considers multi-objective problems and balances the degree of patient allocation matching, distance traveled, and fairness. (2) The non-hierarchical model has crowded resources, and the hierarchical model assigns patients to matched medical institutions. (3) In the "demand exceeds supply" situation, the patient allocation model identified additional resources needed by each hospital.
Results verify the maneuverability and effectiveness of the proposed model. It can generate schemes for specific patient allocation and medical resource amplification and can serve as a quantitative decision-making tool in the context of major epidemics.
根据患者病情严重程度进行分类，并根据医疗机构的诊疗能力进行分级。本研究旨在将患者正确分配至医疗机构进行治疗，并制定医疗网络中医院间的患者分配及医疗资源扩充方案。
考虑疾病严重程度、医院级别、分配匹配效益、行程距离及急诊医疗资源公平性。采用多目标规划方法构建重大疫情期间的患者分配模型。在两种场景下进行模拟研究以检验所提方法。
（1）与多目标模型相比，单目标模型得到的是不平衡解。所提模型考虑多目标问题，平衡了患者分配匹配度、行程距离和公平性。（2）非分层模型资源拥挤，而分层模型将患者分配至匹配的医疗机构。（3）在“供不应求”的情况下，患者分配模型确定了各医院所需的额外资源。
结果验证了所提模型的可操作性和有效性。它能够生成特定的患者分配和医疗资源扩充方案，可作为重大疫情背景下的定量决策工具。
