Liu Ying, Mai Lanxian, Huang Feng, Zeng Zhiyu
School Of Public Policy And Management., Guangxi University, Nanning, 530004, Guangxi, China.
School Of Information and Management, Guangxi Meidical University, Nanning, 530021, Guangxi, China.
Heliyon. 2024 Nov 12;10(23):e40312. doi: 10.1016/j.heliyon.2024.e40312. eCollection 2024 Dec 15.
This study addresses the challenge of achieving a more rational allocation of medical resources at the regional level, using Guangxi Province, China, as a case study. A three-stage super-efficiency Data Envelopment Analysis (DEA) model is employed to assess and analyze the effectiveness of resource allocation. The research methodology involves identifying input, output, and environmental variable indicators to construct a healthcare resource allocation index system. The indicator data are processed using Excel software. The three-stage super-efficiency DEA model is then applied to evaluate the healthcare system in Guangxi Province, focusing on Pure Technical Efficiency Change (PEC), Scale Efficiency Change (SEC), Efficiency Change (EC), Technological Change (TC), and Total Factor Productivity (TFP). Finally, the Malmquist index method is utilized to measure and dynamically analyze the efficiency of healthcare resource allocation. The study's results show that, from a static perspective, the average comprehensive efficiency is 1.067 before adjustment and 1.054 after adjustment, indicating relatively high overall efficiency in healthcare resource allocation in Guangxi Province. However, environmental factors and random errors have led to an overestimation of healthcare resource allocation efficiency, which the three-stage super-efficiency DEA model effectively corrects. Additionally, the average SEC and PEC values are 0.997 and 0.998, respectively, both below 1. This indicates that both scale efficiency and pure technical efficiency contribute to a decline in technical efficiency. Based on the results of the sensitivity analysis, the conclusions regarding the efficiency of healthcare resource allocation in Guangxi are deemed highly reliable. Despite the influence of uncertain factors, the model consistently provides stable and coherent assessment results in most scenarios. Therefore, special attention is needed to improve scale efficiency in healthcare resource allocation within the region, alongside enhancing management and technological capabilities in the healthcare sector. Overall, this study provides valuable reference and guidance for researchers and practitioners in related fields and offers scientific decision support for healthcare resource allocation.
本研究以中国广西壮族自治区为例，探讨了在区域层面实现医疗资源更合理配置的挑战。采用三阶段超效率数据包络分析（DEA）模型来评估和分析资源配置的有效性。研究方法包括确定投入、产出和环境变量指标，以构建医疗资源配置指标体系。指标数据使用Excel软件进行处理。然后应用三阶段超效率DEA模型对广西壮族自治区的医疗系统进行评估，重点关注纯技术效率变化（PEC）、规模效率变化（SEC）、效率变化（EC）、技术变化（TC）和全要素生产率（TFP）。最后，利用Malmquist指数法来衡量和动态分析医疗资源配置效率。研究结果表明，从静态角度看，调整前平均综合效率为1.067，调整后为1.054，表明广西壮族自治区医疗资源配置的总体效率相对较高。然而，环境因素和随机误差导致了医疗资源配置效率的高估，而三阶段超效率DEA模型有效地纠正了这一问题。此外，平均SEC值和PEC值分别为0.997和0.998，均低于1。这表明规模效率和纯技术效率都导致了技术效率的下降。基于敏感性分析的结果，关于广西医疗资源配置效率的结论被认为高度可靠。尽管存在不确定因素的影响，但该模型在大多数情况下始终提供稳定且连贯的评估结果。因此，需要特别关注提高该地区医疗资源配置中的规模效率，同时提升医疗部门的管理和技术能力。总体而言，本研究为相关领域的研究人员和从业者提供了有价值的参考和指导，并为医疗资源配置提供了科学的决策支持。
