Efficiency and Productivity of County-level Public Hospitals Based on the Data Envelopment Analysis Model and Malmquist Index in Anhui, China

Abstract

Background:China began to implement the national medical and health system and public hospital reforms in 2009 and 2012, respectively. Anhui Province is one of the four pilot provinces, and the medical reform measures received wide attention nationwide. The effectiveness of the above reform needs to get attention. This study aimed to master the efficiency and productivity of county-level public hospitals based on the data envelopment analysis (DEA) model and Malmquist index in Anhui, China, and then provide improvement measures for the future hospital development.Methods:We chose 12 country-level hospitals based on geographical distribution and the economic development level in Anhui Province. Relevant data that were collected in the field and then sorted were provided by the administrative departments of the hospitals. DEA models were used to calculate the dynamic efficiency and Malmquist index factors for the 12 institutions.Results:During 2010–2015, the overall average relative service efficiency of 12 county-level public hospitals was 0.926, and the number of hospitals achieved an effective DEA for each year from 2010 to 2015 was 4, 6, 7, 7, 6, and 8, respectively, as measured using DEA. During this same period, the average overall production efficiency was 0.983, and the total productivity factor had declined. The overall production efficiency of five hospitals was >1, and the rest are <1 between 2010 and 2015.Conclusions:In 2010–2015, the relative service efficiency of 12 county-level public hospitals in Anhui Province showed a decreasing trend, and the service efficiency of each hospital changed. In the past 6 years, although some hospitals have been effective, the efficiency of the county-level public hospitals in Anhui Province has not improved significantly, and the total factor productivity has not been effectively improved. County-level public hospitals need to combine their own reality to find their own deficiencies.

Measuring Technical Efficiency of Hospitals Using Data Envelopment Analysis: A Review

“…In some studies, A study conducted in Ethiopia to measure technical efficiency for 12 public and private hospitals was employed BCC, VRS, output-oriented and the total combination number of inputs and outputs was 6 (Ali, Debela, & Bamud, 2017). Another study conducted in China to measure technical efficiency for 12 county level hospitals was used CCR, CRS, input-oriented and the total combination number of inputs and outputs was 7 (N. Li, Wang, Ni, & Wang, 2017), A study conducted in India to measure technical efficiency for 20 public hospitals was employed CCR and BCC, CRS and VRS, inputoriented and the total combination number of inputs and outputs was 4 (Surat, Dalbir, & Kamlesh, 2017).…”

“…In regard to return to scale, there were studies used VRS these studies were (Ali et al, 2017), (Kakeman et al, 2016), (Kalhor et al, 2016), (Mujasi et al, 2016), (H. Li & Dong, 2015), (Applanaidu et al, 2014), (J. P. Harrison & Meyer, 2014), (Jehu-Appiah et al, 2014), (Jat & Sebastian, 2013), (Yusefzadeh, Ghaderi, Bagherzade, & Barouni, 2013). For CRS model, the studies were shared in this model type were (N. Li, Wang, Ni, & Wang, 2017), , (Cheng et al, 2016). For both CRS and VRS, the studies were used this model were (Surat, Dalbir, & Kamlesh, 2017), (Mahate & Hamidi, 2016), (Kirigia & Asbu, 2013), (Cheng et al, 2015), (El-Seoud, 2013), (Kirigia & Asbu, 2013), (Kounetas & Papathanassopoulos, 2013), (Mitropoulos et al, 2013).…”

“…For input-oriented, the studies applied this orientation were (N. Li, Wang, Ni, & Wang, 2017), (Surat, Dalbir, & Kamlesh, 2017), , (Kakeman et al, 2016), (Kalhor et al, 2016), (Cheng et al, 2015), (Applanaidu et al, 2014), (J. P. Harrison & Meyer, 2014), (Kounetas & Papathanassopoulos, 2013), (Jat & Sebastian, 2013), (Yusefzadeh, Ghaderi, International Journal of Public Health and Clinical Sciences e-ISSN : 2289-7577. Vol.…”

“…The studies with combined number of 7 inputs and outputs were (N. Li, Wang, Ni, & Wang, 2017), (Applanaidu et al, 2014), (Kounetas & Papathanassopoulos, 2013). Some studies with combined number of 8 inputs and outputs were (Kakeman et al, 2016), (Mahate & Hamidi, 2016), (Jehu-Appiah et al, 2014), (El-Seoud, 2013), (Kirigia & Asbu, 2013), (Mitropoulos et al, 2013).…”

A Novel Method for Evaluating Dredging Productivity Using a Data Envelopment Analysis‐Based Technique

“…The DEA method was first proposed by Charnes et al [12] as a mathematical programming method to evaluate the relative efficiency of decision-making units (DMUs) with multiple inputs and outputs variables. Although some papers have used the DEA method to solve productivity-related issues, no research utilized this method to deal with the evaluation of dredging productivity [13][14][15][16][17]. This paper extends the DEA method to propose a novel dredging productivity evaluation method, solving the issues that are related to the evaluation of dredging productivity.…”