Measuring the Efficiency of a Hospital based on the Econometric Stochastic Frontier Analysis (SFA) Method

Rezaei, Satar; Zandian, Hamed; Baniasadi, Akram; Tz, Moghadam; Delavari, Sajad

doi:10.19082/2025

Cited by 16 publications

(12 citation statements)

References 6 publications

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“…It is noteworthy that more advanced methods should be used since a hospital is an organization that encounters some inputs and outputs [ 12 ]. Extensive studies have focused on assessing the hospital, including Mahfoozpour et al [ 13 ], Kiadaliri et al [ 14 ], Rezaei et al [ 15 , 16 ], Mosadeghrad et al [ 17 ], Hatam et al [ 18 ], Goudarzi et al [ 19 ] in Iran as well as Mujasi [ 20 ], Cheng, [ 21 ], and Ali [ 22 ] in Uganda, China, and Ethiopia, respectively. Therefore, there is now a great interest in data envelopment analysis as a dynamic, capable, and progressive method for measuring efficiency and productivity.…”

Section: Introductionmentioning

confidence: 99%

Hospital efficiency measurement in the west of Iran: data envelopment analysis and econometric approach

Nayer

Fazaeli

Hamidi

2022

Cost Eff Resour Alloc

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Objective Measuring hospital efficiency is one of the way how to use resources.The optimal hospital performance is the goals of healthcare policymakers. This study aimed to the current study was conducted to evaluate the efficiency the current study was conducted to evaluate the efficiency and 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. inputs included staff and beds, and 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, 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 0.935, 0.961, and 0.987, 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. Tobit regression analysis concludes that hospital size and population covered by the hospital significant effect on hospitals' efficiency.

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Section: Introductionmentioning

confidence: 99%

Hospital efficiency measurement in the west of Iran: data envelopment analysis and econometric approach

Nayer

Fazaeli

Hamidi

2022

Cost Eff Resour Alloc

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“…That is, possibly the inefficiency is not in the scarcity of public hospitals, but in the scale of operation at the existing ones. Different aspects associate economies of scale and scope with higher performance and better outcomes in health services: the learning curve resulting from increased volumes of activity may lead to higher productivity and less wastage of resources, and bigger hospitals may decrease the proportion of fixed costs per health service and increase the bargaining power with suppliers (Lindlbauer and Schreyögg, 2014;Narcı et al, 2015;Rezaei et al, 2016). Also, in Brazil a significant number of private hospitals sign agreements with the SUS to provide beds and assistance to the population, decreasing the demand pressure on public services, as mentioned by Araujo et al (2013).…”

Section: Discussionmentioning

confidence: 99%

“…The healthcare literature on performance measurement has increased significantly in the last decades, providing valuable information for policy makers (Hollingsworth, 2008). The analyses are conducted at different levels of aggregation, such as healthcare units (Ketabi, 2011;Kirigia et al, 2007) and hospitals (Chowdhury et al, 2011;Narcı et al, 2015;Rezaei et al, 2016;van Ineveld et al, 2016). The health services are also aggregated at municipality (Halkos and Tzeremes, 2011;Varela et al, 2012), state and country levels (Hadad et al, 2013).…”

Section: Related Literaturementioning

confidence: 99%

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A performance analysis of Brazilian public health: TOPSIS and neural networks application

Araújo

Siqueira

2018

IJPPM

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Purpose The purpose of this paper is to estimate the performance of Brazilian hospitals’ services and to examine contextual variables in the socioeconomic, demographic and institutional domains as predictors of the performance levels attained. Design/methodology/approach The paper applied a two-stage approach of the technique for order preference by similarity to the ideal solution (TOPSIS) in public hospitals in 92 Rio de Janeiro municipalities, covering the 2008–2013 period. First, TOPSIS is used to estimate the relative performance of hospitals in each municipality. Next, TOPSIS results are combined with neural networks in an effort to originate a performance model with predictive ability. Data refer to hospitals’ outpatient and inpatient services, based on frequent indicators adopted by the healthcare literature. Findings Despite a slight performance increase over the period, substantial room for improvement is observed. The most important performance predictors were related to the demographic and socioeconomic status (area in square feet and GDP per capita) and to the juridical nature and type of ownership of the healthcare facilities (number of federal and private hospitals). Practical implications The results provide managerial insights regarding the performance of public hospitals and opportunities for better resource allocation in the healthcare sector. The paper also considers the impact of external socioeconomic, demographic and institutional factors on hospitals’ performance, indicating the importance of integrative public health policies. Originality/value This study displays an innovative context for applying the two-stage TOPSIS technique, with similar efforts not having been identified in the healthcare literature.

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“…The stochastic frontier production model was developed simultaneously by Meeusen and van den Broeck (1977) and Aigner et al (1977). The Cobb-Douglas function is commonly used with the following form (Rezaei et al, 2016):…”

Section: Phase 1: Sfa As a Technical Efficiency Testmentioning

confidence: 99%

Examining Epira and Its Effect on Technical and Operational Efficiencies of the Top Five Private Distribution Utilities in the Philippines: Sfa and Dea Approaches

Acebedo

Bool

Garcia

2020

HSSR

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Electric Industry. After two decades of EPIRA’s enactment, it is essential to assess where in the Distribution Sector, the reform’s trajectory has stalled. The study aimed to examine and assess the technical and operational efficiencies of the top five electric private distribution utilities (PDUs) in the Philippines under the regulatory policy. Methodology: This is a causal-comparative study using a panel data structure on technical and operational efficiencies of the top five PDUs in the Philippines from 1995 to 2014, employing quantitative measurement. This study used Data Envelopment Analysis (DEA) as the non-parametric test while Stochastic Frontier Analysis (SFA) as the parametric approach. Main Findings: Using the Stochastic Frontier Analysis (SFA) model showed that the implementation of EPIRA (post-EPIRA: 2001 to 2014) made the top five electric private distribution utilities in the Philippines more technically efficient in their operations than without the reform (pre-EPIRA: 1995 to 2000). It was also noted that PDUs with bigger franchise areas and older in existence were technically efficient in their operations than their smaller and younger PDUs counterpart, respectively. Applications: This study will enhance public awareness in the various assessment of regulatory policy arenas in PEI. Moreover, this study will also render useful insights into the management of DUs in the Philippines. Novelty/Originality of this study: An up-to-date empirical analysis of the situation to substantiate the claim of the reform’s efficiency in the Philippines is lacking in the literature. This paper is thus providing a comprehensive theoretical debate by examining some indications of EPIRA's efficiency in the Distribution Sector in the Philippines.

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Measuring the Efficiency of a Hospital based on the Econometric Stochastic Frontier Analysis (SFA) Method

Cited by 16 publications

References 6 publications

Hospital efficiency measurement in the west of Iran: data envelopment analysis and econometric approach

Hospital efficiency measurement in the west of Iran: data envelopment analysis and econometric approach

A performance analysis of Brazilian public health: TOPSIS and neural networks application

Examining Epira and Its Effect on Technical and Operational Efficiencies of the Top Five Private Distribution Utilities in the Philippines: Sfa and Dea Approaches

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