Hamidzadeh Farhad, Pishvaee Mir Saman, Zarrinpoor Naeme
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
Department of Industrial Engineering, Shiraz University of Technology, Shiraz, Iran.
Health Care Manag Sci. 2024 Dec;27(4):555-579. doi: 10.1007/s10729-024-09683-6. Epub 2024 Oct 1.
Organ transplantation is one of the most complicated and challenging treatments in healthcare systems. Despite the significant medical advancements, many patients die while waiting for organ transplants because of the noticeable differences between organ supply and demand. In the organ transplantation supply chain, organ allocation is the most significant decision during the organ transplantation procedure, and kidney is the most widely transplanted organ. This research presents a novel method for assessing the efficiency and ranking of qualified organ-patient pairs as decision-making units (DMUs) for kidney allocation problem in the existence of COVID-19 pandemic and uncertain medical and logistical data. To achieve this goal, two-stage network data envelopment analysis (DEA) and credibility-based chance constraint programming (CCP) are utilized to develop a novel two-stage fuzzy network data envelopment analysis (TSFNDEA) method. The main benefits of the developed method can be summarized as follows: considering internal structures in kidney allocation system, investigating both medical and logistical aspects of the problem, the capability of expanding to other network structures, and unique efficiency decomposition under uncertainty. Moreover, in order to evaluate the validity and applicability of the proposed approach, a validation algorithm utilizing a real case study and different confidence levels is used. Finally, the numerical results indicate that the developed approach outperforms the existing kidney allocation system.
器官移植是医疗保健系统中最复杂、最具挑战性的治疗方法之一。尽管医学取得了重大进展，但由于器官供需之间存在明显差异，许多患者在等待器官移植的过程中死亡。在器官移植供应链中，器官分配是器官移植过程中最重要的决策，而肾脏是移植最为广泛的器官。本研究提出了一种新方法，用于在存在新冠疫情以及医疗和后勤数据不确定的情况下，评估合格器官-患者对作为肾脏分配问题决策单元（DMU）的效率并进行排名。为实现这一目标，采用两阶段网络数据包络分析（DEA）和基于可信度的机会约束规划（CCP）来开发一种新的两阶段模糊网络数据包络分析（TSFNDEA）方法。所开发方法的主要优点可概括如下：考虑肾脏分配系统的内部结构，研究问题的医疗和后勤两个方面，能够扩展到其他网络结构，以及在不确定性下进行独特的效率分解。此外，为了评估所提方法的有效性和适用性，使用了一种利用实际案例研究和不同置信水平的验证算法。最后，数值结果表明，所开发的方法优于现有的肾脏分配系统。
