Data Envelopment Analysis for Relative Efficiency Measurement of Chinese Hospitals: A Systematic Review

Dong, Shaoming; Zuo, Yuling; Guo, Shuyan; Li, Meng; Liu, Xinliang; Li, Hao

doi:10.22158/rhs.v2n2p79

Cited by 13 publications

(16 citation statements)

References 58 publications

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“…Therefore, it is necessary to investigate and select an appropriate index for using the DEA method. [ 37 ] Binder and Rudolph, in another study entitled “The systematic review of health care centers efficiency measurement” reviewed 21 studies and concluded that efficiency measurement of healthcare organizations has direct effect on their policy. They found that the difference in efficiency determined by DEA and SFA methods is due to various factors such as statistical errors, input and outputs definitions, and available data.…”

Section: Discussionmentioning

confidence: 99%

“…DEA can be easily used for calculating efficiency scores based on the proper selection of input and output indicators. [ 37 ] The DEA method and different input and output variables have been used in most studies conducted in Iran, and SFA has been less considered. In the present study, the DEA method in governmental educational hospitals showed a higher efficiency than SFA method in the hospitals under study.…”

Section: Discussionmentioning

confidence: 99%

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Hospitals' efficiency in Iran: A systematic review and meta-analysis

Mahdiyan

Dehghani

Tafti

et al. 2019

J Edu Health Promot

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Hospitals' efficiency in Iran: A systematic review and meta-analysis

Mahdiyan

Dehghani

Tafti

et al. 2019

J Edu Health Promot

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“…It is suggested in literature that in technical efficiency measurement, some monetary indicators such as total revenue, operational revenue, total expenditure, fixed assets, and so on should not be included so as to differentiate technical efficiency from allocative efficiency. [12][13][14] In this study, we only selected volume indicators as the input and output indicators. As CHSCs also provide public health services, some typical output indicators closely related to public health are also selected, such as number of physical examinations, number of discharged patients, number of health records, and so on.…”

Section: Selection Of Input and Output Indicatorsmentioning

confidence: 99%

“…[9][10][11] Moreover, it is suggested that the selection of input and output indicators among Chinese health care organizations needs to be standardized for more scientific estimation of efficiency scores. 12,13 The purpose of this study is to introduce Bootstrap-DEA method to estimate the technical efficiency of CHSCs in Wuhan city of China, to discuss possible policy implications for both Wuhan and other cities to further improve technical efficiency of CHSCs.…”

Section: Introductionmentioning

confidence: 99%

The Technical Efficiency of Community Health Service Centers in Wuhan, China: Estimation and Policy Implications

Liu

Wang

Barsanti

et al. 2018

INQUIRY

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This study seeks to measure the technical efficiency of Community Health Service Centers (CHSCs) in Wuhan City, China, to propose some policy recommendations to improve the efficiency of CHSCs. A total of 46 CHSCs in Wuhan City were included in the study as research subjects. Data of 11 selected input and output indicators were collected from Hubei Provincial Health and Family Planning Commission. R3.2.1 statistical software was used to estimate the efficiency scores of CHSCs using Bootstrap-Data Envelopment Analysis (DEA), and, on average, the bias-corrected technical efficiency score is found to be 0.7845. All the bias-corrected scores of CHSCs are lower than classical scores. At both local and regional level, large gaps exist in relative technical efficiency scores among the CHSCs, and 12 out of 46 CHSCs (26.09%) have efficiency scores at or below average levels. The results indicate big potential for the CHSCs in Wuhan to improve their technical efficiency, such as the optimization of health resources allocation, capacity building, information system strengthening, third party performance evaluation, and mechanisms design, and so on.

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“…Data Envelopment Analysis (DEA) has been widely applied to calculate the productivity and e ciency of healthcare institutions due to the characteristics of multiple inputs and multiple outputs (9). Furthermore, the Malmquist Productivity Index (MPI) model has been employed to indicate the dynamic productivity and e ciency changes of healthcare institutions or health systems (10,11).…”

Section: Introductionmentioning

confidence: 99%

Measuring Productivity and Efficiency of Traditional Chinese Medicine Hospitals From 2009 to 2016: A Bootstrap-Data Envelopment Analysis

Liu

Chen²,

et al. 2020

Preprint

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Background: Traditional Chinese Medicine (TCM) Hospitals are the dominant agents to provide TCM services in China. How to improve the productivity and efficiency of TCM hospitals is quite pivotal for the hospital managers and policy-makers to realize the optimal use of TCM resources. The purpose of this study was to estimate the productivity and efficiency of TCM hospitals with the Bootstrap-Malmquist-Data Envelopment Analysis (DEA) from 2009 to 2016 to provide empirical evidence for hospital managers and policy-makers to improve the management and quality of TCM service.Methods: The data of the individual tertiary public TCM hospitals were collected from official Yearbooks of Traditional Chinese Medicine of China (2010-2017). Bootstrap-Malmquist-DEA was employed to measure the productivity and efficiency of the TCM hospitals from 2009 to 2016. SPSS 23.0 version statistical software was used to conduct the descriptive analysis of the input and output indicators. R 3.2.1 version statistical software was applied to calculate the productivity and efficiency of the TCM hospitals with FEAR package. The statistical significant was set at P < 0.05.Results: The annual average growth rates for each input and output indicator were 6.61% (health professionals), 8.15% (actual open beds), 7.08% (outpatients and inpatients) and 12.50% (discharged patients) respectively from 2009 to 2016. Except the TFPC between 2014 and 2015, more than half of the TCM hospitals had TFPC scores over 1.000, indicating increased productivity. The overall annual geo-means of the scores of the TFPC and its decomposition index were 1.0379 (TFPC), 1.0167 (TEC), 1.0209 (TC), 1.0212 (PTEC), and 0.9955 (SEC) respectively from 2009 to 2016.Conclusions: The overall annual rate of the total factor productivity of the tertiary public TCM hospitals was slightly increased. The technological progress was the main driver to improve the total factor productivity. The decreased TE was more affected by the decreased SE. The TCM hospitals need to pay attention to the development and innovation of the TCM technology, thereby improving the competitiveness. The TCM hospitals managers should pursuit the high quality, high efficiency and low cost of the TCM medical services.

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Data Envelopment Analysis for Relative Efficiency Measurement of Chinese Hospitals: A Systematic Review

Abstract: The purpose was to explore the gap between China

Cited by 13 publications

References 58 publications

Hospitals' efficiency in Iran: A systematic review and meta-analysis

Hospitals' efficiency in Iran: A systematic review and meta-analysis

The Technical Efficiency of Community Health Service Centers in Wuhan, China: Estimation and Policy Implications

Measuring Productivity and Efficiency of Traditional Chinese Medicine Hospitals From 2009 to 2016: A Bootstrap-Data Envelopment Analysis

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