Bozkir Cem D C, Ozmemis Cagri, Kurbanzade Ali Kaan, Balcik Burcu, Gunes Evrim D, Tuglular Serhan
Industrial Engineering Department, Ozyegin University, Istanbul, Turkey.
Business Administration, College of Administrative Sciences and Economics, Koc University, Sariyer, Istanbul, Turkey.
Eur J Oper Res. 2023 Jan 1;304(1):276-291. doi: 10.1016/j.ejor.2021.10.039. Epub 2021 Oct 30.
Planning treatments of different types of patients have become challenging in hemodialysis clinics during the COVID-19 pandemic due to increased demands and uncertainties. In this study, we address capacity planning decisions of a hemodialysis clinic, located within a major public hospital in Istanbul, which serves both infected and uninfected patients during the COVID-19 pandemic with limited resources (i.e., dialysis machines). The clinic currently applies a 3-unit cohorting strategy to treat different types of patients (i.e., uninfected, infected, suspected) in separate units and at different times to mitigate the risk of infection spread risk. Accordingly, at the beginning of each week, the clinic needs to allocate the available dialysis machines to each unit that serves different patient cohorts. However, given the uncertainties in the number of different types of patients that will need dialysis each day, it is a challenge to determine which capacity configuration would minimize the overlapping treatment sessions of different cohorts over a week. We represent the uncertainties in the number of patients by a set of scenarios and present a stochastic programming approach to support capacity allocation decisions of the clinic. We present a case study based on the real-world patient data obtained from the hemodialysis clinic to illustrate the effectiveness of the proposed model. We also compare the performance of different cohorting strategies with three and two patient cohorts.
在新冠疫情期间，由于需求增加和不确定性，血液透析诊所为不同类型患者规划治疗方案变得具有挑战性。在本研究中，我们探讨了伊斯坦布尔一家大型公立医院内的血液透析诊所的容量规划决策，该诊所在资源有限（即透析机）的情况下，在新冠疫情期间为感染和未感染患者提供服务。该诊所目前采用三单元分组策略，在不同单元和不同时间治疗不同类型的患者（即未感染、感染、疑似），以降低感染传播风险。因此，每周开始时，诊所需要将可用的透析机分配到为不同患者群体服务的每个单元。然而，鉴于每天需要透析的不同类型患者数量存在不确定性，确定哪种容量配置能在一周内将不同群体的重叠治疗时段降至最低是一项挑战。我们通过一组情景来表示患者数量的不确定性，并提出一种随机规划方法来支持诊所的容量分配决策。我们基于从血液透析诊所获得的真实患者数据进行了案例研究，以说明所提出模型的有效性。我们还比较了三患者群体和两患者群体的不同分组策略的性能。
