Assessing the Technical Efficiency of Public Hospitals in Cameroon: An Application of Data Envelopment Analysis (DEA)

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.

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

10.21203/rs.3.rs-36996/v1 ◽

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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Using Data Envelopment Analysis to Measure the Technical Efficiency of Public Hospitals in Turkey

Ege Akademik Bakis (Ege Academic Review) ◽

10.21121/eab.475514 ◽

2019 ◽

Vol 19 (3) ◽

pp. 373-387

Author(s):

Mehmet KARAHAN

Keyword(s):

Data Envelopment Analysis ◽

Technical Efficiency ◽

Public Hospitals ◽

Data Envelopment ◽

Using Data

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Hospital Efficiency Measurement in the West of Iran: Data Envelopment Analysis and Tobit Approach

10.21203/rs.3.rs-929618/v1 ◽

2021 ◽

Author(s):

Mohamad Yousefi Nayer ◽

Aliakbar Fazaeli ◽

Yadollah Hamidi

Keyword(s):

Data Envelopment Analysis ◽

Technical Efficiency ◽

Hospital Performance ◽

Hospital Efficiency ◽

Public Hospitals ◽

Data Envelopment ◽

The West ◽

Statistical Population ◽

Use Of Resources ◽

Number Of Patients

Abstract Objective The optimal hospital performance and optimal use of resources are among the goals of healthcare policymakers. This study aimed to assess the association between hospital size and hospital area population with technical efficiency in public hospitals . Methods In this descriptive-analytical study, the statistical population consisted of 15 public hospitals in the west of Iran. First, the data envelopment analysis (DEA) method was used to evaluate technical efficiency. Data inputs included staff and beds, and data outputs consisted of the number of surgeries, the number of patients, and the average length of stay. Then, according to the public ownership of all hospitals, their educational and therapeutic activities, as well as their size and population were considered as the environmental factor affecting efficiency. Thus, Tobit regression was applied to measure their effects on efficiency. Results The average technical efficiency of the studied hospitals, the average management efficiency, and the average efficiency of the scale were 0.933, 0.951, and 0.977, respectively. Out of the total evaluated hospitals, six and nine hospitals had an efficiency of less than one and one, respectively. Moreover, the size of the hospital and the population as the environment variable were significant in the Tobit model. Our regression demonstrated that although the size of the hospital is positively associated with its technical efficiency, the hospital population negatively affects hospital efficiency. Conclusion According to the size and area population of the hospitals, they decrease their inputs to maximize their efficacy by optimizing their surplus amounts. It would be possible for policy-makers to examine the least efficient hospitals to correct widespread inefficiency.

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Frontier efficiency of hospitals in United Arab Emirates: An application of data envelopment analysis

Journal of Hospital Administration ◽

10.5430/jha.v5n1p7 ◽

2015 ◽

Vol 5 (1) ◽

pp. 7 ◽

Cited By ~ 4

Author(s):

Ashraf Mahate ◽

Samer Hamidi

Keyword(s):

Data Envelopment Analysis ◽

Technical Efficiency ◽

United Arab Emirates ◽

Hospital Performance ◽

Public Hospitals ◽

Health Staff ◽

Data Envelopment ◽

Number Of Patients ◽

Private And Public ◽

Future Policy

Background: Over the past four decades the United Arab Emirates (UAE) has undertaken a series of initiatives to improve the efficiency of hospitals. This study aims to examine the efficiency of private and public hospitals in the UAE. A clearer understanding of the technical efficiency of private and public hospitals will be important in shaping future policy reforms as well as assisting private investors that play an important role in the provision of healthcare within the UAE.Methods: This study employs the Data Envelopment Analysis (DEA) technique to measure the efficiency of both private and public hospitals in the UAE. Efficiency scores are calculated using both Banker, Charnes, and Cooper (BCC) and Charnes, Cooper, and Rhodes (CCR) models. The inputs into the models are number of beds, numbers of doctors, dentists, nurses, pharmacists and allied health staff, and administrative staff, while the outputs are the number of treated inpatients, outpatients, and average length of stay.Results: We find that public hospitals represent about a third of the total number of facilities but treat about 60% of the total number of patients. On the positive side we find that a third of the hospitals in the UAE to be efficient. On the other extreme we find that half the hospitals are less than half as efficient as the top hospital. The average technical efficiency of 96 hospitals is 59% using BCC model and 48% using CCR model. The results show no difference in the average efficiency scores between public and private hospitals, nor between foreign and domestically managed hospitals. We find that there is an almost equal probability to be an efficient or inefficient hospital in any of the emirates.Conclusions: The study contributes to the existing body of literature by establishing baseline technical efficiency scores that could be used in monitoring the efficiency effects of future policy changes. About 41% to 52% of the production factors are wasted during the service delivery process in the hospitals. Using the existing amount of resources, the amount of delivered outputs can be doubled, which can significantly impact patient outcomes. This leads us to believe that the ownership itself and foreign management is not sufficient to bring about improvements in efficiency. Interventions to improve the quality of management in hospitals could help to improve efficiency. National and international benchmarking of hospital performance help to provide more insights on sources of hospital inefficiency.

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