ORIGINAL RESEARCH article
Front. Public Health
Sec. Health Economics
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1674348
This article is part of the Research TopicHealth Services and Economic Inequalities through the Lens of Sustainable DevelopmentView all articles
Analysis of the Efficiency and Equity of Resource Allocation in Healthcare Services: DEA and Concentration Indices Evidence from Hospital of Traditional Chinese Medicine in Gansu Province, China
Provisionally accepted
Yahui Ba1*
Zhonghua Luo2*- 1School of Health Management, Gansu University of Traditional Chinese Medicine, Lanzhou, China
- 2School of Marxism, Gansu University of Traditional Chinese Medicine, Lanzhou, China
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Background: Traditional Chinese medicine (TCM) hospitals are crucial to healthcare in China, but there are still concerns about the efficiency and fairness of resource allocation. Within the same province, cities with different levels of economic development have different resource allocation efficiencies. Many traditional Chinese medicine hospitals face challenges such as low technical efficiency and uneven resource allocation, which may hinder the provision of quality healthcare services and result in low fairness. Methods: The research is based on health statistics data from the official website of the Gansu Provincial Bureau of Statistics in China from 2018 to 2024. It uses a three-stage data envelopment analysis (DEA) model, the Malmquist index model, and health resources agglomeration degree (HRAD) to conduct a multi-angle analysis of the resource allocation efficiency and fairness of traditional Chinese medicine hospitals in 14 cities in Gansu Province. Results: In 2024, the comprehensive technical efficiency of healthcare services at Gansu Provincial Traditional Chinese Medicine Hospital was 0.961, with regions being DEA-effective, 1 region being weakly effective, and 3 regions being ineffective. From 2018 to 2024, the average total factor productivity of Gansu Provincial Traditional Chinese Medicine Hospital was 0.955, with an annual decline of 4.5%, indicating room for improvement in resource allocation efficiency. In 2024, the concentration ratio and population allocation equity ratio of healthcare services at Gansu Provincial Traditional Chinese Medicine Hospital were 0.078–8.898 and 0.016–3.599, respectively; from 2018 to 2023, the concentration ratio and population allocation equity ratio were 0.309–0.951 and 0.298–0.948, respectively. Conclusions: The efficiency and fairness of resource allocation for overall healthcare services at Gansu Provincial Hospital of Traditional Chinese Medicine need to be further improved, especially as insufficient resource allocation affects the efficiency of some hospitals. Technical efficiency is ahead of resource allocation efficiency, but the overall technical level still needs to be improved, and fairness in resource allocation, especially in terms of geographical distribution, is lacking. It is necessary to establish a provincial resource allocation mechanism, improve infrastructure in low-efficiency areas, and coordinate the efficiency of resource allocation for healthcare services in cities with different economic levels to enhance fairness.
Keywords: Traditional Chinese medicine hospital, Efficiency, health resourcesagglomeration degree, data envelopment analysis, Health management, healthinequalities
Received: 27 Jul 2025; Accepted: 04 Sep 2025.
Copyright: © 2025 Ba and Luo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yahui Ba, School of Health Management, Gansu University of Traditional Chinese Medicine, Lanzhou, China
Zhonghua Luo, School of Marxism, Gansu University of Traditional Chinese Medicine, Lanzhou, China
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