scholarly journals Using data envelopment analysis to perform benchmarking in intensive care units

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260025
Author(s):  
Bianca B. P. Antunes ◽  
Leonardo S. L. Bastos ◽  
Silvio Hamacher ◽  
Fernando A. Bozza
Keyword(s):  
Intensive Care ◽  
Data Envelopment Analysis ◽  
Intensive Care Units ◽  
Matrix Method ◽  
Performance Metrics ◽  
Nursing Workload ◽  
Data Envelopment Analysis Model ◽  
Standardized Mortality Ratio ◽  
Data Envelopment ◽  
Using Data

Background Studies using Data Envelopment Analysis to benchmark Intensive Care Units (ICUs) are scarce. Previous studies have focused on comparing efficiency using only performance metrics, without accounting for resources. Hence, we aimed to perform a benchmarking analysis of ICUs using data envelopment analysis. Methods We performed a retrospective analysis on observational data of patients admitted to ICUs in Brazil (ORCHESTRA Study). The outputs in our data envelopment analysis model were the performance metrics: Standardized Mortality Ratio (SMR) and Standardized Resource Use (SRU); whereas the inputs consisted of three groups of variables that represented staffing patterns, structure, and strain, thus resulting in three models. We compared efficient and non-efficient units for each model. In addition, we compared our results to the efficiency matrix method and presented targets to each non-efficient unit. Results We performed benchmarking in 93 ICUs and 129,680 patients. The median age was 64 years old, and mortality was 12%. Median SMR was 1.00 [interquartile range (IQR): 0.79–1.21] and SRU was 1.15 [IQR: 0.95–1.56]. Efficient units presented lower median physicians per bed ratio (1.44 [IQR: 1.18–1.88] vs. 1.7 [IQR: 1.36–2.00]) and nursing workload (168 hours [IQR: 168–291] vs 396 hours [IQR: 336–672]) but higher nurses per bed ratio (2.02 [1.16–2.48] vs. 1.71 [1.43–2.36]) compared to non-efficient units. Units from for-profit hospitals and specialized ICUs presented the best efficiency scores. Our results were mostly in line with the efficiency matrix method: the efficiency units in our models were mostly in the “most efficient” quadrant. Conclusion Data envelopment analysis provides managers the information needed to identify not only the outcomes to be achieved but what are the levels of resources needed to provide efficient care. Different perspectives can be achieved depending on the chosen variables. Its use jointly with the efficiency matrix can provide deeper understanding of ICU performance and efficiency.

Study on Energy Saving and Emission Reduction Using Data Envelopment Analysis Model

2012 ◽  
Vol 524-527 ◽  
pp. 2437-2441
Author(s):  
Lei Chen ◽  
Hao Qiang Pang ◽  
Tian Yuan Liu ◽  
Guang Mu Zhu ◽  
Wen Quan Tao
Keyword(s):  
Energy Consumption ◽  
Data Envelopment Analysis ◽  
Energy Saving ◽  
Emission Reduction ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Analysis Model ◽  
Weight Method ◽  
Emission Reduction Target ◽  
Using Data

In the paper, on the basis of the comprehensive weight method, the total energy saving and emission reduction target is distributed to every region. Then the DEA model is adopted to evaluate the actual effect of the energy consumption and the emission reduction. Finally, the optimal scheme is put forward. Taking China as example to compare the efficiency of the program A, B and the program C, we get that program B is more suitable for China, and in more developed economical region, the task of energy saving and emission reduction is done better.

Performance Analysis of Hospitals in Kerala Using Data Envelopment Analysis Model

2020 ◽  
Vol 22 (1) ◽  
pp. 25-40
Author(s):  
Saswat Barpanda ◽  
Neena Sreekumar
Keyword(s):  
Data Envelopment Analysis ◽  
Performance Analysis ◽  
Technical Efficiency ◽  
Hospital Performance ◽  
Vital Role ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Analysis Model ◽  
Input And Output ◽  
Using Data

Performance analysis in any industry plays a vital role in understanding the current scenario and thereby improving the overall efficiency. Using a sample of 20 hospitals randomly selected in Kerala, performance measures of quality were examined as they related to technical efficiency. Efficiency scores for the study hospitals were computed using data envelopment analysis (DEA). The study found that the technically efficient hospitals were performing well as far as quality measures were concerned. DEA can be used to benchmark both dimensions of hospital performance, that is, technical efficiency and quality. The variables selected for the study were divided under input and output measures. Using the DEA model, the factors considered were weighed and analysis was done. The input variables under study are bed number, trained medical staff and services offered. The output variables considered were outpatient rate, mortality rate and number of surgical operations in a month. Through the study, performance of each hospital is measured, and it aims to find out a relation between the input and output variables.

Data envelopment analysis for estimating efficiency of intensive care units: a case study in Iran

2018 ◽  
Vol 31 (4) ◽  
pp. 276-282 ◽  
Author(s):  
Mohammad Amin Bahrami ◽  
Sima Rafiei ◽  
Mahdieh Abedi ◽  
Roohollah Askari
Keyword(s):  
Intensive Care ◽  
Data Envelopment Analysis ◽  
Intensive Care Units ◽  
Service Providers ◽  
Returns To Scale ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Comparative Performance ◽  
Content Type ◽  
Low Efficiency

Purpose As hospitals are the most costly service providers in every healthcare systems, special attention should be given to their performance in terms of resource allocation and consumption. The purpose of this paper is to evaluate technical, allocative and economic efficiency in intensive care units (ICUs) of hospitals affiliated by Yazd University of Medical Sciences (YUMS) in 2015. Design/methodology/approach This was a descriptive, analytical study conducted in ICUs of seven training hospitals affiliated by YUMS using data envelopment analysis (DEA) in 2015. The number of physicians, nurses, active beds and equipment were regarded as input variables and bed occupancy rate, the number of discharged patients, economic information such as bed price and physicians’ fees were mentioned as output variables of the study. Available data from study variables were retrospectively gathered and analyzed through the Deap 2.1 software using the variable returns to scale methodology. Findings The study findings revealed the average scores of allocative, economic, technical, managerial and scale efficiency to be relatively 0.956, 0.866, 0.883, 0.89 and 0.913. Regarding to latter three types of efficiency, five hospitals had desirable performance. Practical implications Given that additional costs due to an extra number of manpower or unnecessary capital resources impose economic pressure on hospitals also the fact that reduction of surplus production plays a major role in reducing such expenditures in hospitals, it is suggested that departments with low efficiency reduce their input surpluses to achieve the optimal level of performance. Originality/value The authors applied a DEA approach to measure allocative, economic, technical, managerial and scale efficiency of under-study hospitals. This is a helpful linear programming method which acts as a powerful and understandable approach for comparative performance assessment in healthcare settings and a guidance for healthcare managers to improve their departments’ performance.

scholarly journals Efficiency of Red Crescent Society Units in Gilan Province and Their Ranking using Data Envelopment Analysis

2020 ◽  
Keyword(s):  
Data Envelopment Analysis ◽  
Organizational Performance ◽  
Efficiency Score ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Analysis Model ◽  
Super Efficiency ◽  
Using Data ◽  
Gilan Province

INTRODUCTION: Efficiency is one of the basic measures of organizational performance, and in general, refers to the degree or quality of the achievement of the desired goals. This study aimed to investigate the efficiency of Red Crescent population branches in Gilan province and rank them based on the data envelopment analysis model. METHODS: This research was applied in terms of purpose and documentary in terms of implementation. For the purposes of the study, 16 branches of the Red Crescent Society in Gilan province were analyzed with GAMS software (version 24) in 2018. In this research, the input-oriented Banker, Charnes, and Cooper model was used as one of the basic models of data envelopment analysis to measure the relative efficiency of 16 branches of the Red Crescent Society in Gilan province. Afterward, the Anderson-Peterson super-efficiency model was used to rank the efficient units. FINDINGS: Based on the findings, six branches, namely Astara, Rasht, Bandar Anzali, Tallish, Lahijan, and Sowme’eh Sara, had an efficiency score of one. In other words, they were recognized as efficient branches and could provide solutions and models as reference branches for inefficient branches to help them reach the efficiency limit. Afterward, the Anderson-Peterson super-efficiency model was used to rank the efficient units. According to this model, the efficiency scores of the studied units were more than one and the Rasht branch had the highest efficiency. CONCLUSION: The results provide researchers as well as branch managers and staff with suggestions that can help them make better decisions. An important point to keep in mind is that in order to increase the efficiency of a branch, it is necessary to reduce input costs and increase outputs based on reference branches. Due to the fact that this research uses an input-oriented approach, its purpose is to increase the indicators that enhance the efficiency of branches.

scholarly journals REDUCTION OF NON-PERFORMING LOANS IN THE BANKING INDUSTRY: AN APPLICATION OF DATA ENVELOPMENT ANALYSIS

2017 ◽  
Vol 18 (5) ◽  
pp. 833-851 ◽  
Author(s):  
Chih-Ching YANG
Keyword(s):  
Data Envelopment Analysis ◽  
Finite Number ◽  
Relative Efficiency ◽  
Banking Industry ◽  
Negative Impact ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Analysis Model ◽  
The World ◽  
Using Data

The increase in non-performing loans around the world has had quite a negative impact on many nations’ banking systems. To address these problems, many creative regulatory solutions and well-designed risk techniques have been utilized in the hope of reducing non-performing loans to an acceptable level. The purpose of this study is to apply a newly developed data envelopment analysis model to suggest the most efficient plan (called Plan 4) to reduce non-performing loans that can maximize the efficiency of the entire banking industry’s control over the bad debts. For comparison purpose, three other reduction plans are also represented. The four plans are presented using data from Taiwan’s banking industry. The empirical results show that among the plans presented, Plan 4 shows the most effective allocation of the industry-wide reduction target. The plan focuses on a finite number of banks, helping identify the key units to improve industry-wide efficiency. The findings implicitly suggest that the regulator should devise more incentive measures to encourage target banks to perform the non-performing loan reduction task. Our results also suggest that for the regulator, forcing banks to cut their non-performing loans by the same ratio will not help improve the relative efficiency of the industry.

Data Envelopment Analysis (DEA) in the Psychological Sciences

2019 ◽  
Author(s):  
Jeffrey A. Shero ◽  
Sara Ann Hart
Keyword(s):  
Data Envelopment Analysis ◽  
Aggressive Behavior ◽  
Personality Traits ◽  
Educational Administration ◽  
Latent Variable ◽  
Organizational Management ◽  
Data Envelopment ◽  
Targeted Interventions ◽  
Component Skills ◽  
Using Data

Using methods like linear regression or latent variable models, researchers are often interested in maximizing explained variance and identifying the importance of specific variables within their models. These models are useful for understanding general ideas and trends, but often give limited insight into the individuals within said models. Data envelopment analysis (DEA), is a method with roots in organizational management that make such insights possible. Unlike models mentioned above, DEA does not explain variance. Instead, it explains how efficiently an individual utilizes their inputs to produce outputs, and identifies which input is not being utilized optimally. This paper provides readers with a brief history and past usages of DEA from organizational management, public health, and educational administration fields, while also describing the underlying math and processes behind said model. This paper then extends the usage of this method into the psychology field using two separate studies. First, using data from the Project KIDS dataset, DEA is demonstrated using a simple view of reading framework identifying individual efficiency levels in using reading-based skills to achieve reading comprehension, determining which skills are being underutilized, and classifying and comparing new subsets of readers. Three new subsets of readers were identified using this method, with direct implications leading to more targeted interventions. Second, DEA was used to measure individuals’ efficiency in regulating aggressive behavior given specific personality traits or related skills. This study found that despite comparable levels of component skills and personality traits, significant differences were found in efficiency to regulate aggressive behavior on the basis of gender and feelings of provocation.

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