Peykani Pejman, Pishvaee Mir Saman
School of Industrial Engineering, Iran University of Science and Technology, Tehran 1684613114, Iran.
Healthcare (Basel). 2024 Mar 7;12(6):611. doi: 10.3390/healthcare12060611.
In the context of healthcare systems, the performance evaluation of hospitals plays a crucial role in assessing the quality of healthcare systems and facilitating informed decision-making processes. However, the presence of data uncertainty poses significant challenges to accurate performance measurement. This paper presents a novel uncertain common-weights data envelopment analysis (UCWDEA) approach for evaluating the performance of hospitals under uncertain environments. The proposed UCWDEA approach addresses the limitations of traditional data envelopment analysis (DEA) models by incorporating the uncertainty theory (UT) to model the inherent uncertainty in input and output data. Also, by utilizing a common set of weights (CSW) technique, the UCWDEA method provides a more robust and reliable assessment of hospital performance. The main advantages of the proposed UCWDEA approach can be succinctly summarized as follows. Firstly, it allows for the comparison of all hospitals on a consistent basis to calculate a realistic efficiency score, rather than an overly optimistic efficiency score. Secondly, the uncertain common-weights DEA approach exhibits linearity, enhancing its applicability. Thirdly, it possesses the capability to extend its utility under various other prevalent uncertainty distributions. Moreover, it enhances the discriminatory power of results, facilitates the ranking of hospitals in the presence of data uncertainty, and aids in identifying the sensitivity and stability levels of hospitals towards data uncertainty. Notably, in order to showcase the pragmatic application and efficacy of the uncertain common-weights DEA model, a genuine dataset has been utilized to evaluate the efficiency of 20 public hospitals in Tehran, all of which are affiliated with the Iran University of Medical Sciences. The results of the experiment demonstrate the efficacy of the UCWDEA approach in assessing and ranking hospitals amidst uncertain conditions. In summary, the research outcomes can offer policymakers valuable insights regarding hospital performance amidst data uncertainty. Additionally, it can provide practical recommendations on optimizing resource allocation, benchmarking performance, and formulating effective policies to augment the overall efficiency and effectiveness of healthcare services.
在医疗保健系统的背景下，医院的绩效评估在评估医疗保健系统质量和促进明智的决策过程中起着至关重要的作用。然而，数据不确定性的存在给准确的绩效衡量带来了重大挑战。本文提出了一种新颖的不确定共同权重数据包络分析（UCWDEA）方法，用于评估不确定环境下医院的绩效。所提出的UCWDEA方法通过纳入不确定性理论（UT）来对输入和输出数据中的固有不确定性进行建模，从而解决了传统数据包络分析（DEA）模型的局限性。此外，通过使用一组共同权重（CSW）技术，UCWDEA方法对医院绩效提供了更稳健和可靠的评估。所提出的UCWDEA方法的主要优点可以简要概括如下。首先，它允许在一致的基础上比较所有医院，以计算现实的效率得分，而不是过于乐观的效率得分。其次，不确定共同权重DEA方法具有线性，增强了其适用性。第三，它具有在各种其他普遍的不确定性分布下扩展其效用的能力。此外，它增强了结果的区分能力，有助于在存在数据不确定性的情况下对医院进行排名，并有助于识别医院对数据不确定性的敏感性和稳定性水平。值得注意的是，为了展示不确定共同权重DEA模型的实际应用和功效，使用了一个真实数据集来评估德黑兰20家公立医院的效率，所有这些医院都隶属于伊朗医科大学。实验结果证明了UCWDEA方法在不确定条件下评估和排名医院方面的有效性。总之，研究结果可以为政策制定者提供有关数据不确定性下医院绩效的宝贵见解。此外，它可以就优化资源分配、对标绩效以及制定有效政策以提高医疗服务的整体效率和效果提供实际建议。
