Technical efficiency and its affecting factors in Tehran’s public hospitals: DEA approach and Tobit regression

Asiabar, Ali Sarabi; Sharifi, Tahere; Rezapour, Aziz; Firouzabadi, Seyed Mohammad Ali Khatami; Haghighat-Fard, Payam; Mohammadpour, Saeed

doi:10.47176/mjiri.34.176

Cited by 6 publications

(10 citation statements)

References 7 publications

(9 reference statements)

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“…Kohl et al included 18 studies of hospital operations in their systematic review of the literature in which the Tobit regression model was applied in the second phase using transformed DEA scores as dependent variables [ 15 ]. A search of the Medline database on the 9th of November, revealed an additional 16 such papers that have been published since that systematic review was conducted [ 10 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. Among the published papers, studies focused on European hospital systems were well-represented with two studies from Turkey and one study each from Ukraine, Greece, Poland, and the Netherlands [ 29 , 41 , 42 , 43 , 44 ].…”

Section: Methodsmentioning

confidence: 99%

Efficiency and Productivity of Public Hospitals in Serbia Using DEA-Malmquist Model and Tobit Regression Model, 2015–2019

Medarević

Vukovic

2021

IJERPH

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Improving productivity within health systems using limited resources is a matter of great concern. The objectives of the paper were to evaluate the productivity, efficiency, and impact of environmental factors on efficiency in Serbian hospitals from 2015–2019. Data envelopment analysis, Malmquist index and Tobit regression were applied to hospital data from this period, and public hospitals in Serbia exhibited a great variation regarding their capacity and performance. Between five and eight hospitals ran efficiently from 2015 to 2019, and the productivity of public hospitals increased whereas technical efficiency decreased in the same period. Tobit regression indicated that the proportion of elderly patients and small hospital size (below 200 beds) had a negative correlation with technical efficiency, while large hospital size (between 400 and 600 beds), the ratio of outpatient episodes to inpatient days, bed turnover rate and the bed occupation rate had a positive correlation with technical efficiency. Serbian public hospitals have considerable space for technical efficiency improvement and public action must be taken to improve resource utilization.

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Section: Methodsmentioning

confidence: 99%

Efficiency and Productivity of Public Hospitals in Serbia Using DEA-Malmquist Model and Tobit Regression Model, 2015–2019

Medarević

Vukovic

2021

IJERPH

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“…In other words, either output expansion or input decrease is the primary evaluation goal. More than half of the studies (55.06%, 49 of 89) [46–48,50–54,58,60,64–69,71–73,75–77,80,91–94,96,98,99,103–107,109–111,113–116,118,121,124,127,128,130,135] applied input-orientated DEA models. The researchers indicated that the choice of input orientation was made to be in line with the fact that the majority of healthcare units aim to minimize inputs given a goal level of output.…”

Section: Resultsmentioning

confidence: 99%

“…Among the studies, 80 (89.89%) conducted extended analysis in their research [46–56,58–64,66–69,71–77,79–94,96–98,101–116,118–125,128–135]. The type of data applied were almost equally distributed among the studies 51 (57.30%) [47,49,52,58–60,67,70–73,75–77,79,80,83–85,88,91,92,94,96,97,100,101,103,105–109,111,112,114–116,120–122,124,125,127,129–135] used panel data and 38 (42.70%) [46,48,50,51,53–57,61–66,68,69,74,78,81,82,86,87,89,90,93,95,98,99,102,104,110,113,117–119,123,128] used cross sectional data in their research. Forty extended analyses were identified within the included studies.…”

Section: Resultsmentioning

confidence: 99%

“…Each study may apply one or multiple extended analysis (some mentioned as “stages”). Out of 80 articles that applied extended analysis, 46 studies integrated two or more extended analysis in their DEA measurement [46,47,51–54,58,60,63,64,67,68,71,73–75,77,79,80,83–86,88,91–94,96,101,102,107–109,114–116,119–121,128,130,131,133–135]. The maximum number of extended analyses observed within the studies were five [67,92,94].…”

Section: Resultsmentioning

confidence: 99%

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Approach in Inputs & Outputs Selection of Data Envelopment Analysis (DEA) Efficiency Measurement in Hospital: A Systematic Review

Zubir,

Noor,

Rizal

et al. 2023

Preprint

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Data Envelopment Analysis (DEA) has been employed as a performance evaluation tool in the evaluation of efficiency and productivity in numerous fields. This includes hospitals in particular as well as the broad healthcare industry. This review examines 89 papers that discuss the use of DEA in hospitals, paying particular attention to approaches for choosing inputs and outputs as well as the most recent developments in DEA studies. English articles with empirical data from year 2014-2022 (Web of Science, Scopus, PubMed, ScienceDirect, Springer Link, and Google Scholar) were extracted based on PRISMA methodology. DEA Model parameters were specified based on previous studies and approaches were identified narratively. The approaches can be grouped into four: (1) Literature review, (2) Data availability, (3) Systematic method and (4) Expert judgement. The approaches were applied as one strategy either by itself or in combination with others. This review's emphasis on approaches used in hospital may constrain its conclusions. There might be another strategy or method used to select the input and output for a DEA study in a different area or strategies based on different viewpoints. The trend for DEA application were quite similar to previous studies. There is no evidence that one model fits all DEA model parameters better than another. Based on the reviewed literature, we offer some recommendations and methodological principles for DEA studies.

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“…In the data envelopment analysis method, the quality of indicators selected has a serious impact on the research results. In the current research, most of the indicators are selected using qualitative methods ( 35 , 36 ), such as the Delphi method ( 37 ). Some scholars use quantitative methods to select indicators, such as principal component analysis (PCA) ( 38 ) and efficiency contribution measurement (ECM) ( 39 ).…”

Section: Introductionmentioning

confidence: 99%

Measuring the efficiency of public hospitals: A multistage data envelopment analysis in Fujian Province, China

Sun

Ye²,

Zhang³

et al. 2023

Front. Public Health

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ObjectiveThe present study aimed to evaluate the operational efficiency of public hospitals in Fujian Province and the factors responsible for the inefficiency of these hospitals and provide relevant suggestions for health policymakers in allocating service resources.MethodIn the first stage of the research, the variables affecting the efficiency of hospitals were extracted by qualitative and quantitative methods, including literature optimization, gray related analysis and gray clustering evaluation. In the second stage, the data envelopment analysis (DEA) method was used to evaluate the operational efficiency of 49 hospitals of different levels and types selected by sampling in 2020. Finally, a Tobit regression model with introduced institutional factors and background factors was established to study the main influencing factors of hospital inefficiency.ResultsIn the first stage, 10 input variables and 10 output variables necessary from the mangers' point of view were identified to test efficiency. In the second stage, the average comprehensive TE, PTE, and SE of 49 sample hospitals was 0.802, 0.888, and 0.902, respectively. 22.45% of these hospitals met the effective criteria, i.e., the overall effective rate was 22.45%. The low SE value of the hospital was the main reason hindering the improvement of the comprehensive efficiency value. The overall effective rate of secondary public hospitals (30.77%) was higher than that of tertiary public hospitals (19.44%), and the overall effective rate of public specialized hospitals (30%) was higher than that of general public hospitals (18.92%). Based on the third stage results, the bed occupancy rate (BOR) and the proportion of beds (POB) were major factors affecting the operation efficiency of grade III hospitals (p < 0.01). However, the operating efficiency of grade II hospitals was significantly affected by POB and regional per capita GDP(GDPPC) (p < 0.05). Moreover, the impact of BOR and GDPPC was positive, and POB was negatively correlated with hospital operation efficiency.ConclusionsThe study results indicated that the overall operation efficiency of public hospitals in Fujian Province is low. This study revealed that intervention should be strengthened from a policy and management perspective to improve the operation efficiency of public hospitals.

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Technical efficiency and its affecting factors in Tehran’s public hospitals: DEA approach and Tobit regression

Cited by 6 publications

References 7 publications

Efficiency and Productivity of Public Hospitals in Serbia Using DEA-Malmquist Model and Tobit Regression Model, 2015–2019

Efficiency and Productivity of Public Hospitals in Serbia Using DEA-Malmquist Model and Tobit Regression Model, 2015–2019

Approach in Inputs & Outputs Selection of Data Envelopment Analysis (DEA) Efficiency Measurement in Hospital: A Systematic Review

Measuring the efficiency of public hospitals: A multistage data envelopment analysis in Fujian Province, China

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