A data envelopment analysis approach to evaluate efficiencies in organ allocation problem: A case study

Data envelopment analysis (DEA) is a data-oriented approach to assess the performance of a set of entities known as decision-making units (DMUs), which transform multiple inputs into multiple outputs. On the other hand, the transplantation of organs is one of the most complex and challenging treatments in medicine, and organ allocation is the most important decision throughout the organ transplantation operation. Due to the enormous disparity between organ availability and demand, many individuals die while waiting for organ transplants despite major medical and technological improvements. Furthermore, kidney is the most commonly transplanted organ in the transplantation supply chain all over the world which is investigated in this paper. This research presents a two-stage network DEA model for assessing the efficiency of related DMUs. The main advantage of this study is considering network DEA with internal structures instead of black box DEA models in organ allocation problems. It should be noted that black box DEA models fail to present sufficient data for identifying the inefficiency of DMUs. In addition, it is unclear what occurs within the black box DEA models, and internal relations are not investigated. Finally, a real case study related to the organ allocation problem is presented, and the findings indicate that the proposed method in this study is strongly effective and outperforms the current kidney allocation system in Iran.