Yair Perez-Tezoco Jaime, Alfonso Aguilar-Lasserre Alberto, Gerardo Moras-Sánchez Constantino, Francisco Vázquez-Rodríguez Carlos, Azzaro-Pantel Catherine
Division of Research and Postgraduate Studies, Tecnológico Nacional de México/Instituto Tecnológico de Orizaba, Av. Oriente 9, 852. Col. Emiliano Zapata, Orizaba 94320, México.
Instituto Mexicano del Seguro Social, Poniente 7 Num. 1350, Col. Centro, Orizaba, 94300, Veracruz, México.
Comput Ind Eng. 2023 Jun 28:109408. doi: 10.1016/j.cie.2023.109408.
With the outbreak of the novel coronavirus SARS-CoV2, many countries have faced problems because of their available hospital capacity. Health systems must be prepared to restructure their facilities and meet the requirements of the pandemic while keeping their services and specialties active. This process, known as hospital reconversion, contributes to minimizing the risk of contagion between hospital staff and patients and optimizing the efficient treatment and disposal of healthcare wastes that represent a risk of nosocomial infection contagion. A methodology based upon simulation and mathematical optimization with genetic algorithms is proposed to address the hospital reconversion problem. Firstly, a discrete event simulation model is developed to study the flow of patients within the hospital system. Subsequently, the hospital reconversion problem is formulated through a mathematical model seeking to maximize the proximity relationships between departments and minimize the costs due to the flow of agents within the system. Finally, the results obtained from the optimization process are evaluated through the simulation model. The proposed framework is validated by assessing the hospital reconversion process in a COVID-19 Hospital in Mexico. The results show the mathematical model's effectiveness by incorporating the medical personnel's expertise in decisions regarding the use of elevators, departments' location, structural dimensions, use of corridors, and the floors to which the departments are assigned when facing a pandemic. The contribution of this approach can be replicated during the hospital reconversion process in other hospitals with different characteristics.
随着新型冠状病毒SARS-CoV2的爆发，许多国家因现有医院容量而面临问题。卫生系统必须做好准备，对其设施进行重组，并在保持其服务和专科业务正常运转的同时，满足疫情的要求。这个过程，即医院改建，有助于将医院工作人员与患者之间的传染风险降至最低，并优化对存在医院感染传染风险的医疗废物的高效处理。提出了一种基于模拟和遗传算法的数学优化方法来解决医院改建问题。首先，开发了一个离散事件模拟模型来研究医院系统内患者的流动情况。随后，通过一个数学模型来阐述医院改建问题，该模型旨在最大化各科室之间的邻近关系，并最小化系统内人员流动所产生的成本。最后，通过模拟模型对优化过程得到的结果进行评估。通过评估墨西哥一家新冠肺炎医院的医院改建过程，验证了所提出的框架。结果表明，该数学模型通过在面对疫情时将医务人员的专业知识纳入有关电梯使用、科室位置、结构尺寸、走廊使用以及科室所分配楼层的决策中，从而具有有效性。这种方法的贡献可以在其他具有不同特点的医院的医院改建过程中得到复制。
