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. 2023 Mar 7:11:1091811.
doi: 10.3389/fpubh.2023.1091811. eCollection 2023.

Measuring the efficiency of public hospitals: A multistage data envelopment analysis in Fujian Province, China

Mengya Sun  1 , Yaojun Ye  1 , Guangdi Zhang  1 , Yuan Xue  2 , Xiuling Shang  3
Affiliations

Measuring the efficiency of public hospitals: A multistage data envelopment analysis in Fujian Province, China

Mengya Sun et al. Front Public Health. .

Abstract

Objective: The present study aimed to evaluate the operational efficiency of public hospitals in Fujian Province and the factors responsible for the inefficiency of these hospitals and provide relevant suggestions for health policymakers in allocating service resources.

Method: In the first stage of the research, the variables affecting the efficiency of hospitals were extracted by qualitative and quantitative methods, including literature optimization, gray related analysis and gray clustering evaluation. In the second stage, the data envelopment analysis (DEA) method was used to evaluate the operational efficiency of 49 hospitals of different levels and types selected by sampling in 2020. Finally, a Tobit regression model with introduced institutional factors and background factors was established to study the main influencing factors of hospital inefficiency.

Results: In the first stage, 10 input variables and 10 output variables necessary from the mangers' point of view were identified to test efficiency. In the second stage, the average comprehensive TE, PTE, and SE of 49 sample hospitals was 0.802, 0.888, and 0.902, respectively. 22.45% of these hospitals met the effective criteria, i.e., the overall effective rate was 22.45%. The low SE value of the hospital was the main reason hindering the improvement of the comprehensive efficiency value. The overall effective rate of secondary public hospitals (30.77%) was higher than that of tertiary public hospitals (19.44%), and the overall effective rate of public specialized hospitals (30%) was higher than that of general public hospitals (18.92%). Based on the third stage results, the bed occupancy rate (BOR) and the proportion of beds (POB) were major factors affecting the operation efficiency of grade III hospitals (p < 0.01). However, the operating efficiency of grade II hospitals was significantly affected by POB and regional per capita GDP(GDPPC) (p < 0.05). Moreover, the impact of BOR and GDPPC was positive, and POB was negatively correlated with hospital operation efficiency.

Conclusions: The study results indicated that the overall operation efficiency of public hospitals in Fujian Province is low. This study revealed that intervention should be strengthened from a policy and management perspective to improve the operation efficiency of public hospitals.

Keywords: Tobit regression; data envelopment analysis; gray clustering; gray relational analysis; hospital efficiency.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Scatterplot matrix of input and output indicators. NOP-PAE represents the number of physicians, nurses, beds, personnel expenditure, and public management expenditure. OV-TI represents the number of outpatient visits, the average cost of hospitalization, drug revenue, and total income.
Figure 2
Figure 2
Conceptual production structure diagram of hospitals.
Figure 3
Figure 3
DEA efficiency distribution of hospitals at different grades. The point at the bottom left of curve L1 represents the comprehensive TE of the hospital at <0.6. The point on curve L1 represents the comprehensive TE of the hospital as 0.6. The point between curves L1 and L2 represents the comprehensive TE of the hospital between 0.6 and 0.8, and the point on the upper right of curve L2 represents the comprehensive TE of the hospital >0.8. The point above line L3 represents SE greater than PTE. The point on line L3 represents the SE was equal to PTE, and the point below line L3 represents SE lower than PTE.
Figure 4
Figure 4
Comparison of the significance of environmental factors.
See this image and copyright information in PMC

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