Cooperative decision-making of county-level public hospitals based on generalized fuzzy data envelopment analysis_West China Medical Journal

Authors：

 RUAN Yilei ^1,2 , CHEN Caisen ² , LI Gang ³

1. Party Committee Office, Tiantan Hospital, Capital Medical University, Beijing 100070, P. R. China;
2. Military Exercise and Training Center, Army Academy of Armored Forces, Beijing 100072, P. R. China;
3. Scientific Research & Academic Department, Army Academy of Armored Forces, Beijing 100072, P. R. China;

Corresponding?author：

RUAN Yilei, Email: lgsjtu@126.com

Keywords：

County-level public hospitals; generalized fuzzy data envelopment analysis; cooperative decision-making; efficiency interval; fuzzy index

DOI：

10.7507/1002-0179.202102105

Video：

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Objective To establish a cooperative decision-making model of county-level public hospitals, so as to freely select the best partner in different decision-making units and promote the optimal allocation of medical resources. Methods The input and output data of 10 adjacent county-level public hospitals in Henan province from 2017 to 2019 was selected. Based on the traditional data envelopment analysis (DEA) model, a generalized fuzzy DEA cooperative decision-making model with better applicability to fuzzy indicators and optional decision-making units was constructed. By inputting index information such as total number of employees, number of beds, annual outpatient and emergency volume, number of discharged patients, total income and hospital grade evaluation, the cooperation efficiency intervals of different hospitals were calculated to scientifically select the best partner in different decision-making units.Results After substituting the data of 10 county-level public hospitals in H₁-H₁₀ into the model, taking H₂ hospital as an example to make cooperative decision, among the four hospitals in H₁, H₂, H₇ and H₁₀ of the same scale, under optimistic circumstances, the best partner of H₂ hospital was H₇ hospital, and the cooperation efficiency value was 1.97; in a pessimistic situation, the best partner of H₂ hospital was H₁₀ hospital, and the cooperation efficiency value was 0.98. The model had good applicability in the cooperative decision-making of county-level public hospitals. Conclusion The generalized fuzzy DEA model can better evaluate the cooperative decision-making analysis between county-level public hospitals.

Citation： RUAN Yilei, CHEN Caisen, LI Gang. Cooperative decision-making of county-level public hospitals based on generalized fuzzy data envelopment analysis. West China Medical Journal, 2022, 37(1): 87-91. doi: 10.7507/1002-0179.202102105 Copy

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3.	Chen XQ, Liu XW, Gong ZW. Extended DEA model under type-2 fuzzy environment: an application of rural poverty reduction in Hainan province. Kybernetes, 2019, 48(5): 1095-1133.
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11.	Chorfi Z, Berrado A, Benabbou L. An integrated DEA-based approach for evaluating and sizing health care supply chains. J Model Manag, 2020, 15(1): 201-231.
12.	Saen RF, Nia SSSH. Evaluating after-sales service units by developing inverse network data envelopment analysis mode. Benchmarking, 2020, 27(2): 695-707.
13.	Schmidt KW, Haz?r ?. A data envelopment analysis method for finding robust and cost-efficient schedules in multimode projects. IEEE Trans Eng Manag, 2020, 67(2): 414-429.
14.	Rashidi K. AHP versus DEA: a comparative analysis for the gradual improvement of unsustainable suppliers. Benchmarking, 2020, 27(8): 2283-2321.
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16.	陳宏明, 楊依旖. 基于超效率 DEA 模型的企業經營績效評價?以電力行業上市公司為例. 長沙理工大學學報(社會科學版), 2018, 33(1): 103-109.
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18.	焦軍, 張宇瑤, 董四平, 等. 醫療檢查控制機制的構建探究. 華西醫學, 2020, 35(12): 1496-1498.
19.	國家衛生健康委, 國家中醫藥管理局. 關于印發醫療聯合體管理辦法(試行)的通知. 中華人民共和國國家衛生健康委員會公報, 2020(7): 34-38.
20.	楊威, 李萌, 郭淑巖, 等. 醫院效率數據包絡分析模型有效性評價方法研究. 華西醫學, 2020, 35(12): 1435-1440.

1. Emrouznejad A, Yang GL. A survey and analysis of the first 40 years of scholarly literature in DEA: 1978-2016. Socioecon Plann Sci, 2018, 61(1): 4-8.
2. Zhou Z, Zhao L, Lui S, et al. A generalized fuzzy DEA/AR performance assessment moded. Math Comput Model, 2012, 55(11/12): 2117-2128.
3. Chen XQ, Liu XW, Gong ZW. Extended DEA model under type-2 fuzzy environment: an application of rural poverty reduction in Hainan province. Kybernetes, 2019, 48(5): 1095-1133.
4. Ashrafi A, Kaleibar MM. Cost, revenue and profit efficiency models in generalized fuzzy data ecvelopment analysis. Fuzzy Information Eng, 2017, 9(2): 237-246.
5. 畢景濤, 王中華. 江蘇省三級醫院效率評價的數據包絡分析. 南京醫科大學學報(社會科學版), 2018, 18(3): 230-233.
6. 林凱, 袁波英, 孟雪暉. 基于三階段 DEA 的浙江省三級公立醫院運行效率分析. 中國醫院管理, 2017, 37(11): 34-36.
7. 陳聚祥, 黎映靜. 基于數據包絡分析的福建三級甲等醫院運營效率評價. 中國衛生統計, 2016, 33(5): 749-751.
8. 天津曠維. 河南省醫療衛生機構具體情況 3 年數據分析報告 2020 版. (2021-01-22)[2021-02-01]. https://wenku.baidu.com/view/f09824d8fe4ffe4733687e21af45b307e971f9ea.html.
9. 劉向莉. 公立醫院協同合作策略與合作博弈模型研究. 武漢: 華中科技大學, 2015.
10. 張太芳, 楊靜. 基于 DEA 模型的醫院運行效率評價研究. 中國醫院統計, 2016, 23(4): 275-276.
11. Chorfi Z, Berrado A, Benabbou L. An integrated DEA-based approach for evaluating and sizing health care supply chains. J Model Manag, 2020, 15(1): 201-231.
12. Saen RF, Nia SSSH. Evaluating after-sales service units by developing inverse network data envelopment analysis mode. Benchmarking, 2020, 27(2): 695-707.
13. Schmidt KW, Haz?r ?. A data envelopment analysis method for finding robust and cost-efficient schedules in multimode projects. IEEE Trans Eng Manag, 2020, 67(2): 414-429.
14. Rashidi K. AHP versus DEA: a comparative analysis for the gradual improvement of unsustainable suppliers. Benchmarking, 2020, 27(8): 2283-2321.
15. 趙春英, 馬占新. 具有偏好錐的面向輸出的超效率 DEA 模型分析. 運籌學學報, 2020, 24(1): 57-72.
16. 陳宏明, 楊依旖. 基于超效率 DEA 模型的企業經營績效評價?以電力行業上市公司為例. 長沙理工大學學報(社會科學版), 2018, 33(1): 103-109.
17. 國務院辦公廳. 國務院辦公廳關于推進醫療聯合體建設和發展的指導意見. 中華人民共和國國務院公報, 2017(13): 14-18.
18. 焦軍, 張宇瑤, 董四平, 等. 醫療檢查控制機制的構建探究. 華西醫學, 2020, 35(12): 1496-1498.
19. 國家衛生健康委, 國家中醫藥管理局. 關于印發醫療聯合體管理辦法(試行)的通知. 中華人民共和國國家衛生健康委員會公報, 2020(7): 34-38.
20. 楊威, 李萌, 郭淑巖, 等. 醫院效率數據包絡分析模型有效性評價方法研究. 華西醫學, 2020, 35(12): 1435-1440.

West China Medical Journal

Cooperative decision-making of county-level public hospitals based on generalized fuzzy data envelopment analysis

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