scholarly journals Technical Efficiency of Maternal Health Services Provision in Public Hospitals of Northwest Ethiopia: A Two-Stage Data Envelopment Analysis

2020 ◽  
Vol Volume 13 ◽  
pp. 3135-3146
Author(s):  
Tsegaw Amare ◽  
Mezgebu Yitayal ◽  
Getasew Amare
Keyword(s):  
Data Envelopment Analysis ◽  
Maternal Health ◽  
Health Services ◽  
Technical Efficiency ◽  
Maternal Health Services ◽  
Northwest Ethiopia ◽  
Public Hospitals ◽  
Data Envelopment ◽  
Two Stage

scholarly journals Technical efficiency of public health centers in three districts in Ethiopia: two-stage data envelopment analysis

2018 ◽  
Vol 11 (1) ◽  
Author(s):  
Firew Tekle Bobo ◽  
Mirkuzie Woldie ◽  
Muluemebet Abera Wordofa ◽  
Gebeyehu Tsega ◽  
Tesfamichael Alaro Agago ◽  
...  
Keyword(s):  
Public Health ◽  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Health Centers ◽  
Data Envelopment ◽  
Two Stage

scholarly journals Efficiency evaluation of public hospitals in Saudi Arabia: an application of data envelopment analysis

BMJ Open ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. e031924 ◽  
Author(s):  
Ahmed D Alatawi ◽  
Louis Wilhelmus Niessen ◽  
Jahangir A M Khan
Keyword(s):  
Saudi Arabia ◽  
Data Envelopment Analysis ◽  
Health Services ◽  
Production Capacity ◽  
Efficiency Score ◽  
Current Level ◽  
Public Hospitals ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Ministry Of Health

ObjectiveIn this study, we assess the performance of public hospitals in Saudi Arabia. We detect the sources of inefficiency and estimate the optimal levels of the resources that provide the current level of health services. We enrich our analysis by employing locations and capacities of the hospitals.DesignWe employ data envelopment analysis (DEA) to measure the technical efficiency of 91 public hospitals. We apply the input-oriented Charnes, Cooper and Rhodes, and Banker, Charne, Cooper models under Constant and Variable Returns-to-Scale. The assessment includes four inputs, and six output variables taken from the Ministry of Health databases for 2017. We conducted the assessment via PIM-DEA V.3.2 software.SettingMinistry of health-affiliated hospitals in the Kingdom of Saudi Arabia.ResultsFindings identified 75.8% (69 of 91) of public hospitals as technically inefficient. The average efficiency score was 0.76, indicating that hospitals could have reduced their inputs by 24% without reduction in health service provision. Small hospitals (efficiency score 0.79) were more efficient than medium-sized and large hospitals. Hospitals in the central region were more efficient (efficiency score 0.83), than those located in other geographical locations. More than half of the hospitals (62.6%) were operating suboptimally in terms of the scale efficiency, implying that to improve efficiency, they need to alter their production capacity. Performance analysis identified overuse of physician’s numbers and shortage of health services production, as major causes of inefficiency.ConclusionMost hospitals were technically inefficient and operating at suboptimal scale size and indicate that many hospitals may improve their performance through efficient utilisation of health resources to provide the current level of health services. Changes in the production capacity are required, to facilitate optimal use of medical capacity. The inefficient hospitals could benefit from these findings to benchmarking their system and performance in light of the efficient hospital within their capacity and geographical location.

scholarly journals Technical Efficiency in Public Hospitals in Kenya: A Two –Stage Data Envelopment Analysis

2018 ◽  
Vol 10 (6) ◽  
pp. 141
Author(s):  
Francis Kimani Mwihia ◽  
James Machoki M’ Imunya ◽  
Germano Mwabu ◽  
Urbanus M. Kioko ◽  
Benson B. A. Estambale
Keyword(s):  
Regression Analysis ◽  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Public Hospitals ◽  
Capital City ◽  
Data Envelopment ◽  
Tobit Regression ◽  
Hospital Variation ◽  
Dea Analysis ◽  
Tobit Regression Analysis

The paper uses the DEA technique to estimate efficiency scores in Kenyan public hospitals and then applies the Tobit regression to study inter-hospital variation in the scores. The DEA analysis reveals that small hospitals are more efficient than large hospitals, with efficiency levels ranging from 74-91% in small DMUs and from 57-78% in large DMUs. Tobit regression analysis shows efficiency scores are negatively correlated with the hospital’s distance from the manager’s residence and from the capital city. Internal and external supervisions are suggested as mechanisms for increasing performance of hospitals.

scholarly journals Measuring Efficiency of Public Hospitals in Iran: A Comparative Study Using Extended Data Envelopment Analysis, 2012-2016

2020 ◽  
Author(s):  
Efat Mohamadi ◽  
Amirhossein Takian ◽  
Alireza Olyaee Manesh ◽  
Reza Majdzadeh ◽  
Farhad Hosseinzadeh Lotfi ◽  
...  
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Public Hospitals ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Data Envelopment ◽  
Use Of Resources ◽  
District Hospitals ◽  
Geographical Regions ◽  
Efficiency And Productivity

Abstract Background: Aiming to enhance quality of care and increase efficiency, public hospitals have undergone several reforms in the course of last two decades in Iran. This paper reports the result of a national research that aimed to measure the technical efficiency and productivity change of public hospitals during 2012-2016 in Iran. Methods: We used Extended Data Envelopment Analysis (Extended-DEA) (an innovative modification to conventional DEA) to measure technical efficiency and productivity of 568 public hospitals. Nationally representative data were extracted from the official annual health reports. Data were analysed using GAMS software 24.3. Results: The average efficiency score of all hospitals was 0.733. 10.1% of all hospitals were efficient while 2.68% of them were under 0.2. The Malmquist Productivity Index (MPI) progressed in 49.3% of hospitals, remained constant in 2.3%, while 48.2% of hospitals regressed during 2015-2016. The average of MPI was 1.07 over the period of analysis. Conclusions: Extra efforts seem to be essential to enhance the efficient use of resources and develop appropriate policy solutions and tools. In particular, to increase the return to scale, we advocate the merger of small-size district hospitals towards establishing bigger efficient hospitals in various geographical regions across the country.

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