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

Author(s):  
Aleksandar Medarević ◽  
Dejana Vuković

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.

2020 ◽  
Vol 12 (3) ◽  
pp. 121
Author(s):  
Abdullah M. Alsabah ◽  
Hassan Haghparast-Bidgoli ◽  
Jolene Skordis

The recent drop in oil prices has challenged public sector financing in Kuwait. Technical and scale efficiency scores for fifteen public hospitals in Kuwait from 2010 to 2014 were estimated using a two-stage data envelopment analysis (DEA). Technical efficiency scores were regressed against institutional characteristics using Tobit regression to investigate the determinants of efficiency differences in hospitals. Semi-structured interviews were also carried out with fourteen public and private hospital managers to qualitatively explore their perceptions and experience about about factors affecting hospital efficiency. The mean technical efficiency score for all hospitals was 85.8%, an improvement of 2% since 2010. The mean pure technical efficiency score was 79.6%, improving from 75% in 2010 to 81.2% in 2014. The mean scale efficiency score was 91.8%, improving from 87.6% in 2010 to 94.2% in 2014. Only three hospitals were constantly technically and scale efficient. Tobit regression showed that hospital efficiency was significantly associated with the average length of patient stay. Hospitals with more than 400 beds were potentially more technically and scale efficient. The qualitative study revealed that external factors affecting efficiency commonly included implemention of legislative changes and decreasing bureaucracy, while internal factors included increasing bed capacity and improving qualifications and training of human resources. Most public hospitals in Kuwait were not technically and scale efficient, but improvements were observed. Potential factors that affected the efficiency of hospitals in Kuwait were identified. These findings are useful to decision-makers in Kuwait for developing strategies to improve public hospital efficiency.


2020 ◽  
Author(s):  
Rogers Ayiko ◽  
Paschal N. Mujasi ◽  
Joyce Abaliwano ◽  
Dickson Turyareeba ◽  
Rogers Enyaku ◽  
...  

Abstract Background: General hospitals provide a wide range of primary and secondary healthcare services. They accounted for 38% of government funding to health facilities, 8.8% of outpatient department visits and 28% of admissions in Uganda in the financial year 2016/17. We assessed the levels, trends and determinants of technical efficiency of general hospitals in Uganda from 2012/13 to 2016/17. Methods: We undertook input-oriented Data Envelopment Analysis to estimate technical efficiency of 78 general hospitals using data abstracted from the Annual Health Sector Performance Reports for 2012/13, 2014/15 and 2016/17. Trends in technical efficiency was analysed using Excel while determinants of technical efficiency were analysed using Tobit Regression Model in STATA 15.1. Results: The Average Constant Returns to Scale, Variable Returns to Scale and Scale Efficiency of general hospitals for 2016/17 were 49% (95% CI, 44% - 54%), 69% (95% CI, 65% - 74%) and 70% (95% CI, 65% - 75%) respectively. There was no statistically significant difference in the efficiency scores of public and private hospitals. Technical efficiency generally increased from 2012/13 to 2014/15, and dropped by 2016/17. Some hospitals were persistently efficient while others were inefficient over this period. Hospital size, geographical location, training status and average length of stay were statistically significant determinants of efficiency at 5% level of significance. Conclusion: The 69% average variable returns to scale technical efficiency indicates that the hospitals could generate the same volume of outputs using 31% (3,439) less staff and 31% (3,539) less beds. Benchmarking performance of the efficient hospitals would help to guide performance improvement in the inefficient ones. There is need to incorporate hospital size, geographical location, training status and average length of stay in the resource allocation formula and adopt annual hospital efficiency assessments.


2020 ◽  
Author(s):  
Rogers Ayiko ◽  
Paschal N. Mujasi ◽  
Joyce Abaliwano ◽  
Dickson Turyareeba ◽  
Rogers Enyaku ◽  
...  

Abstract Background: General hospitals provide a wide range of primary and secondary healthcare services. They accounted for 38% of government funding to health facilities, 8.8% of outpatient department visits and 28% of admissions in Uganda in the financial year 2016/17. We assessed the levels, trends and determinants of technical efficiency of general hospitals in Uganda from 2012/13 to 2016/17. Methods: We undertook input-oriented Data Envelopment Analysis to estimate technical efficiency of 78 general hospitals using data abstracted from the Annual Health Sector Performance Reports for 2012/13, 2014/15 and 2016/17. Trends in technical efficiency was analysed using Excel while determinants of technical efficiency were analysed using Tobit Regression Model in STATA 15.1. Results: The Average Constant Returns to Scale, Variable Returns to Scale and Scale Efficiency of general hospitals for 2016/17 were 49% (95% CI, 44% - 54%), 69% (95% CI, 65% - 74%) and 70% (95% CI, 65% - 75%) respectively. There was no statistically significant difference in the efficiency scores of public and private hospitals. Technical efficiency generally increased from 2012/13 to 2014/15, and dropped by 2016/17. Some hospitals were persistently efficient while others were inefficient over this period. Hospital size, geographical location, training status and average length of stay were statistically significant determinants of efficiency at 5% level of significance. Conclusion: The 69% average variable returns to scale technical efficiency indicates that the hospitals could generate the same volume of outputs using 31% (3,439) less staff and 31% (3,539) less beds. Benchmarking performance of the efficient hospitals would help to guide performance improvement in the inefficient ones. There is need to incorporate hospital size, geographical location, training status and average length of stay in the resource allocation formula and adopt annual hospital efficiency assessments.


2018 ◽  
Vol 10 (6) ◽  
pp. 141
Author(s):  
Francis Kimani Mwihia ◽  
James Machoki M’ Imunya ◽  
Germano Mwabu ◽  
Urbanus M. Kioko ◽  
Benson B. A. Estambale

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.


2020 ◽  
Author(s):  
Rogers Ayiko ◽  
Paschal N. Mujasi ◽  
Joyce Abaliwano ◽  
Dickson Turyareeba ◽  
Rogers Enyaku ◽  
...  

Abstract Background: General hospitals provide a wide range of primary and secondary healthcare services. They accounted for 38% of government funding to health facilities, 8.8% of outpatient department visits and 28% of admissions in Uganda in the financial year (FY) 2016/17. We assessed the levels, trends and determinants of technical efficiency of general hospitals in Uganda from FY 2012/13 to FY 2016/17. Methods: We undertook input-oriented Data Envelopment Analysis to estimate technical efficiency of 78 general hospitals using data abstracted from the Annual Health Sector Performance Reports for FYs 2012/13, 2014/15 and 2016/17. Trends in technical efficiency was analysed using Excel while determinants of technical efficiency were analysed using Tobit Regression Model in STATA 15.1. Results: Average Constant Returns to Scale, Variable Returns to Scale and Scale Efficiency of general hospitals for FY 2016/17 were 49% (95% CI, 44% - 54%), 69% (95% CI, 65% - 74%) and 70% (95% CI, 65% - 75%) respectively. There was no statistically significant difference in the efficiency scores of public and private hospitals. Technical efficiency generally increased from FY 2012/13 to 2014/15, and dropped by FY 2016/17. Some hospitals were persistently efficient while others were inefficient over this period. Hospital size, geographical location, training status and average length of stay were statistically significant determinants of efficiency at 5% level of significance.Conclusion: The 69% average variable returns to scale technical efficiency indicates that the hospitals could generate the same volume of outputs using 31% (3,439) less staff and 31% (3,539) less beds. Benchmarking performance of the efficient hospitals would help to guide performance improvement in the inefficient ones. There is need to incorporate hospital size, geographical location, training status and average length of stay in the resource allocation formula and adopt annual hospital efficiency assessments.


2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Rogers Ayiko ◽  
Paschal N. Mujasi ◽  
Joyce Abaliwano ◽  
Dickson Turyareeba ◽  
Rogers Enyaku ◽  
...  

Abstract Background General hospitals provide a wide range of primary and secondary healthcare services. They accounted for 38% of government funding to health facilities, 8.8% of outpatient department visits and 28% of admissions in Uganda in the financial year 2016/17. We assessed the levels, trends and determinants of technical efficiency of general hospitals in Uganda from 2012/13 to 2016/17. Methods We undertook input-oriented data envelopment analysis to estimate technical efficiency of 78 general hospitals using data abstracted from the Annual Health Sector Performance Reports for 2012/13, 2014/15 and 2016/17. Trends in technical efficiency was analysed using Excel while determinants of technical efficiency were analysed using Tobit Regression Model in STATA 15.1. Results The average constant returns to scale, variable returns to scale and scale efficiency of general hospitals for 2016/17 were 49% (95% CI, 44–54%), 69% (95% CI, 65–74%) and 70% (95% CI, 65–75%) respectively. There was no statistically significant difference in the efficiency scores of public and private hospitals. Technical efficiency generally increased from 2012/13 to 2014/15, and dropped by 2016/17. Some hospitals were persistently efficient while others were inefficient over this period. Hospital size, geographical location, training status and average length of stay were statistically significant determinants of efficiency at 5% level of significance. Conclusion The 69% average variable returns to scale technical efficiency indicates that the hospitals could generate the same volume of outputs using 31% (3439) less staff and 31% (3539) less beds. Benchmarking performance of the efficient hospitals would help to guide performance improvement in the inefficient ones. There is need to incorporate hospital size, geographical location, training status and average length of stay in the resource allocation formula and adopt annual hospital efficiency assessments.


2018 ◽  
Vol 2 (1) ◽  
pp. 22-41
Author(s):  
Imroatul Amaliyah

This research aims to calculate and analyze the level of technical efficiency and total factor productivity change of manufacture industry, and to examine the factors that influence the value of technical efficiency of manufacture industry in East Java. The method used for this research is Data Envelopment Analysis (DEA) and Malmquist Index with Bootstrapping approach, and Tobit regression. This research used micro data from Indonesian Large and Medium-Scale Industry Survey within the year of 2007 to 2013. The results of this research are: (1) the estimated result of DEA with bootstrapping approach using output-oriented variable return to scale (VRS) assumption shows that the level of technical efficiency of manufacture industry in East Java has been not good enough and overall, it still has the potential to increase its output to reach an efficient condition; (2) the estimated result of Tobitregression demonstrates that the level of technical efficiency of the company is influenced by the company’s size, HHI, capital labor ratio, export and types of company ownership; (3) the estimated result of Malmquist Index with Bootstrapping approach shows that theaverage of total factor productivity change (TFPCH) of manufacture industry from 2007 to 2013 hasexhibited a positive change. The main factor that affects TFPCH, in order, are technological change, efficiency change, and efficiency scale change.


2020 ◽  
Author(s):  
Peng Li ◽  
Cunhui Wang ◽  
Nian-nian Li ◽  
Heng Wang

Abstract BackgroundCounty-level public hospitals play an important role in China's medical tertiary health care network. Since a new round of medical reforms occurred in 2009, county-level public hospitals have conducted continuous exploration and reforms. To analyze the efficiency and productivity of 36 county-level public hospitals based three provinces in China.Methods.We randomly selected 12 county-level hospitals from each 3 provinces based on economic levels and regional differences in China, finally, a total of 36 county-level hospitals were chosen, and a self-made questionnaire was used to investigate hospital operations for collecting data from 2011 to 2015. 2011–2015 is the twelfth five-year period of China's national economic and social development. Four input indicators and three output indicators were selected. Data envelopment analysis and the Malmquist index methods were used to measure the efficiency and productivity by the key indicators for each region.Results.On average, four input indicators in three regions have continued to grow from 2011 to 2015. The output in the three regions is directly proportional to the upward trend in inputs. On average, the three output indicators of hospitals in the eastern region are higher than the central and western regions. The technical efficiency of county-level public hospitals in the central, eastern, and western regions of China were on an upward trend, and the number of the technical efficiency, the pure technical efficiency, and the scale efficiency values reaching 1 in the three regions was more than half, respectively. The average of total factor productivity change for 2011–2015 in the central, eastern, and western regions was 1.016, 0.997, and 0.930, respectively.Conclusions.The efficiency of the central Chinese region was mainly affected by pure technical efficiency; however, scale efficiency changed to affect the efficiency of the eastern and western hospitals in 2015. The increase in production efficiency in the central region was driven by the technical efficiency. In the future hospital management, management innovation needs to be strengthened. The decline in productivity in the eastern region was due to the decline in the technical efficiency. At present, the eastern region pays attention to management innovation, but it is necessary to be alert to the adverse consequences of the expansion of hospital scale. The decline in productivity in the western region was due to the decline in the technological efficiency and technical efficiency. The hospitals should strengthen hospital management and blind scale expansion. Financial subsidies in the western region have a significant role in promoting the development of hospitals. The internal management innovation of hospitals in the eastern region has a positive effect on the technical efficiency of hospitals. The medical reform measures in the central region have positively promoted the efficiency of hospitals.


2020 ◽  
Author(s):  
Rogers Ayiko ◽  
Paschal N. Mujasi ◽  
Joyce Abaliwano ◽  
Dickson Turyareeba ◽  
Rogers Enyaku ◽  
...  

Abstract Background General hospitals provide a wide range of primary and secondary healthcare services. They accounted for 38% of government funding to hospitals, 8.8% of OPD visits and 28% of admissions in Uganda in FY 2016/17. We assessed the levels, trends and determinants of technical efficiency of general hospitals in Uganda from FY 2012/13 to FY 2016/17.Methods We undertook input-oriented Data Envelopment Analysis to estimate technical efficiency of 78 general hospitals using data abstracted from the Annual Health Sector Performance Reports for FY 2012/13, 2014/15 and 2016/17. Trends in technical efficiency was analysed using Excel while determinants of technical efficiency were analysed using Tobit Regression Model in STATA 15.1.Results Average Constant Returns to Scale, Variable Returns to Scale and Scale Efficiency of general hospitals for FY 2016/17 were 49% (95% CI, 44% − 54%), 69% (95% CI, 65% − 74%) and 70% (95% CI, 65% − 75%) respectively. There was no statistically significant difference in the efficiency scores of public and private hospitals. Technical efficiency generally increased from FY2012/13 to 2014/15, and dropped by FY 2016/17. Some hospitals were persistently efficient while others were inefficient over this period. Hospital size, geographical location, training status and average length of stay were statistically significant determinants of efficiency at 5% level of significance.Conclusion The 69% average variable returns to scale technical efficiency indicates that the hospitals could generate the same volume of outputs using 31% (3,439) less staff and 31% (3,539) less beds. Benchmarking performance of the efficient hospitals would help to guide performance improvement in the inefficient ones. There is need to incorporate hospital size, geographical location, training status and average length of stay in the resource allocation formula and adopt annual hospital efficiency assessments.


2016 ◽  
Vol 9 (3) ◽  
pp. 116 ◽  
Author(s):  
Ashraf Mahate ◽  
Samer Hamidi ◽  
Fevzi Akinci

<p><strong>OBJECTIVE:</strong> The main purpose of this study is to estimate the technical efficiency of the United Arab Emirates (UAE) hospitals and examine the effect of hospital size on estimated technical efficiency scores.</p><p><strong>METHODS: </strong>Using 2012 data from Ministry of Health, Dubai Health Authority, and Health Authority in Abu Dhabi,<strong> </strong>we employed a nonparametric method, data envelopment analysis (DEA), to estimate the technical efficiency of 96 private and governmental hospitals in the UAE. Efficiency scores are calculated using both Banker, Charnes, and Cooper (BCC) and Charnes, Cooper, and Rhodes (CCR) models. </p><p><strong>RESULTS: </strong>The average technical efficiency of the UAE hospitals is estimated at 59% based on the BBC model and at 48% based on the CCR model. The optimal size of a hospital in the UAE is between 100 to 300 beds. We also found evidence of economies of scope between the provision of outpatient and inpatient care in the UAE hospitals.</p><p><strong>CONCLUSION: </strong>Our findings indicate that only one third of the UAE hospitals are technically efficient. There is evidence to suggest that there are considerable efficiency gains yet to be made by many UAE hospitals. Additional empirical research is needed to inform future health policies aimed at improving both the technical and allocative efficiency of hospital services in the UAE. </p>


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