Evaluating the Operational Efficiency and Quality of Tertiary Hospitals in Taiwan: The Application of the EBITDA Indicator to the DEA Method and TOBIT Regression

This study estimates the efficiency of 19 tertiary hospitals in Taiwan using a two-stage analysis of Data Envelopment Analysis (DEA) and TOBIT regression. It is a retrospective panel-data study and includes all the tertiary hospitals in Taiwan. The data were sourced from open information hospitals legally required to disclose to the National Health Insurance (NHI) Administration, Ministry of Health and Welfare. The variables, including five inputs (total hospital beds, total physicians, gross equipment, fixed assets net value, the rate of emergency transfer in-patient stay over 48 h) and six outputs (surplus or deficit of appropriation, length of stay, the total relative value units [RVUs] for outpatient services, total RVUs for inpatient services, self-pay income, modified EBITDA) were adopted into the Charnes, Cooper and Rhodes (CCR) and Banker, Charnes and Cooper (BCC) model. In the CCR model, the technical efficiency (TE) from 2015–2018 increases annually, and the average efficiency of all tertiary hospitals is 96.0%. In the BCC model, the highest pure technical efficiency (PTE) was in 2018 and the average efficiency of all medical centers is 99.1%. The average scale efficiency of all medical centers was 96.8% in the BBC model, meaning investment can be reduced by 3.2% and the current production level can be maintained with a fixed return to scale. Correlation coefficient analysis shows that all variables are correlated positively; the highest was the number of beds and the number of days in hospital (r = 0.988). The results show that TE in the CCR model was similar to PTE in the BCC model in four years. The difference analysis shows that more hospitals must improve regarding surplus or deficit of appropriation, modified EBITDA, and self-pay income. TOBIT regression reveals that the higher the bed-occupancy rate and turnover rate of fixed assets, the higher the TE; and the higher number of hospital beds per 100,000 people and turnover rate of fixed assets, the higher the PTE. DEA and TOBIT regression are used to analyze the other factors that affect medical center efficiency, and different categories of hospitals are chosen to assess whether different years or different types of medical centers affect operational performance. This study provides reference values for the improvable directions of relevant large hospitals’ inefficiency decision-making units through reference group analysis and slack variable analysis.

A Novel Hybrid Method Based on Three-Stage Extraction and DEA-CCR Models for Selecting the Optimal Conditions for Citronella Oil Extraction

In citronella oil extraction process by steam distillation, inefficient use of steam is the main cause of excessive energy consumption that affects energy cost and oil yield. This research is aimed to reduce the energy cost and increase the oil yield by studying the steam used in the process. The proposed method is the three-stage extraction model combined with the Data Envelopment Analysis developed by Charnes, Cooper and Rhodes (DEA-CCR model). Although the three-stage extraction model has been widely used, there is no research integrate this model with DEA-CCR model. It is well known that DEA-CCR model is an effective tool to evaluate efficiency of decision making units/alternatives. The advantages of this research were presented as the calculation of the optimum distillation conditions, including the steam flow rate and the distillation time, were achieved as discussed in this article. The study was comprised of 3 parts. Firstly, the three-stage extraction model for citronella oil was formulated. Secondly, the results of the proposed model were calculated under different conditions, classified by steam flow rates from 5,000 to 60,000 cm3/min for the distillation period of 15–180 min. Finally, the DEA-CCR model was utilized to evaluate and rank alternatives. The results expressed that the best condition for producing citronella oil was at the steam flow rate of 40,000 cm3/min and the distillation time of 60 min. The optimal energy cost and percentage of oil yield were equal to 0.440 kWh/mL and 0.7%, respectively. When comparing to the experimental results, the percentage error of optimal energy cost and oil yield were slightly different, with a value of 0.98% and 0.85%, respectively. Moreover, the energy consumption was also reduced by 34.6% compared to the traditional operating conditions.

Calculation of regional agricultural production efficiency and empirical analysis of its influencing factors-based on DEA-CCR model and Tobit model

The improvement of agricultural production efficiency and the transformation of production mode are the core of promoting agricultural modernization. Taking Chongqing as a sample case, this paper uses DEA-CCR Model, Malmquist Index and Tobit Model to calculate and analyze its agricultural production efficiency and its influencing factors, and accurately identifies the problems existing in its agricultural transformation and development. It has an important policy reference value for improving agricultural innovation and competitiveness, promoting the steady development of rural revitalization, and realizing agricultural modernization, which also provides some reference and enlightenment for countries or regions with similar characteristics of mountain agriculture development in the world to enhance regional agricultural production efficiency. Through empirical analysis and investigation, it is found that the overall agricultural production efficiency of Chongqing remains at the productivity level of 0.8 from 2009 to 2018, with an average annual growth rate of 12.8%, but there is a large gap in the level of regional development. Through Malmquist Index decomposition, it is found that agricultural technology progress has the greatest contribution to the improvement of production efficiency. Financial support for agriculture, urbanization level, regional economic development level and highway mileage have a significant positive impact on production efficiency, while the level of farmers’ disposable income has a negative impact on the increase of production efficiency, and the income gap between urban and rural residents fails to pass the significance test.

Impact of unemployment by sex and marriage rate on fertility decline: Estimates for Turkey and Greece using CCR model

The article analyses the effect of unemployment by sex and marriage rate on fertility changes in Greece and Turkey. The empirical part of the study is based on annual time series data retrieved from the World Bank and National Statistical Offices of Turkey and Greece for 1991–2019. Canonical Cointegrating Regression model is applied for the two countries separately, allowing to quantify the effects of the determinants (crude marriage rate and unemployment rate by sex) on the variation of fertility rate. CCR models show these determinants to be the most significant factors of fertility dynamics in both countries. The results from Engle-Granger and the Phillips-Ouliaris tau (t-statistics) tests confirm the cointegration, i.e., long-term relationship between the variables only for Turkey’s CCR model. However, it was found that in Greece, female unemployment impacts fertility rate negatively and male unemployment has a positive effect on fertility rate; for Turkey modelling shows the opposite relationship. The results of the study suggest that economic uncertainties might be one of the factors contributing to fertility decline in these countries, long-term or in the coming years.

Diagnosis of Ovarian Neoplasms Using Nomogram in Combination With Ultrasound Image-Based Radiomics Signature and Clinical Factors

Objectives: To establish and validate a nomogram integrating radiomics signatures from ultrasound and clinical factors to discriminate between benign, borderline, and malignant serous ovarian tumors.Materials and methods: In this study, a total of 279 pathology-confirmed serous ovarian tumors collected from 265 patients between March 2013 and December 2016 were used. The training cohort was generated by randomly selecting 70% of each of the three types (benign, borderline, and malignant) of tumors, while the remaining 30% was included in the validation cohort. From the transabdominal ultrasound scanning of ovarian tumors, the radiomics features were extracted, and a score was calculated. The ability of radiomics to differentiate between the grades of ovarian tumors was tested by comparing benign vs borderline and malignant (task 1) and borderline vs malignant (task 2). These results were compared with the diagnostic performance and subjective assessment by junior and senior sonographers. Finally, a clinical-feature alone model and a combined clinical-radiomics (CCR) model were built using predictive nomograms for the two tasks. Receiver operating characteristic (ROC) analysis, calibration curve, and decision curve analysis (DCA) were performed to evaluate the model performance.Results: The US-based radiomics models performed satisfactorily in both the tasks, showing especially higher accuracy in the second task by successfully discriminating borderline and malignant ovarian serous tumors compared to the evaluations by senior sonographers (AUC = 0.789 for seniors and 0.877 for radiomics models in task one; AUC = 0.612 for senior and 0.839 for radiomics model in task 2). We showed that the CCR model, comprising CA125 level, lesion location, ascites, and radiomics signatures, performed the best (AUC = 0.937, 95%CI 0.905–0.969 in task 1, AUC = 0.924, 95%CI 0.876–0.971 in task 2) in the training as well as in the validation cohorts (AUC = 0.914, 95%CI 0.851–0.976 in task 1, AUC = 0.890, 95%CI 0.794–0.987 in task 2). The calibration curve and DCA analysis of the CCR model more accurately predicted the classification of the tumors than the clinical features alone.Conclusion: This study integrates novel radiomics signatures from ultrasound and clinical factors to create a nomogram to provide preoperative diagnostic information for differentiating between benign, borderline, and malignant ovarian serous tumors, thereby reducing unnecessary and risky biopsies and surgeries.

Material Selection in Green Design: A Method Combining DEA and TOPSIS

In order to rationalize material selection in green design, this study presents an attempt to combine the methods of generalized Data Envelopment Analysis (DEA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). By establishing a green material index system, the G-CCR model of generalized DEA was first used to select effective materials from the candidate samples, and TOPSIS was then used to sort the effective suppliers. The combined DEA/TOPSIS model helps to rank the materials by quality, and then integrate both the merits ofG-CCR model and the key characteristics of TOPSIS. The results of this study showed that the combined DEA/TOPSIS model can screen and exclude materials with poor performance when selecting wood for the furniture industry. Therefore, the combined model that is presented in this study provides a more rational and evidentiary basis for material selection in green design.

Process Efficiency Analysis of Selected Automotive Companies in Europe

The automotive industry has lately been undergoing major changes having a considerable impact on the whole vehicle sector. This does not only refer to what is technologically new on vehicles themselves, but also to the new modern management methods, frequently associated with the Industry 4.0 concept. As well as other companies, car factories are pushing their costs downwards to increase their production efficiency. This paper analyses the economic situation of 5 car manufacturers, two of which having their factories in the Czech Republic and three in Germany. The task was to ascertain efficiency of individual companies in order to propose possible improvements. To do this, the Data Envelopment Analysis (DEA) for two models (CCR - model based on Charnes, Cooper & Rhodes and BCC - model based on Banker, Charnes & Cooper) was used. The BCC model was found to be more applicable to the established efficiencies, than the CCR one.

Theoretical developments in balance model and models with virtual weight restrictions

The balance model (BM) is a unit invariant model in which takes into account the imposed strategies for DMUs through adding some balance constraints to the basic DEA models in general and the CCR model in particular. Also, balance factor is calculated along with efficiency, effectiveness and similar concepts. The mentioned balance constraints belong to the virtual weight restrictions (VWRs) category. However, to date, the consequences of incorporating weight restrictions (e.g. absolute weight restrictions, assurance regions type I (ARI), assurance regions type II (ARII)) within the classical DEA models have been explored by scholars, but are not considered for virtual weight restrictions. This paper analyses properties of the balance model and subsequently the models with VWRs by an illustrative example. The results show that models with such restrictions correctly maximize the relative efficiency (RE) of the assessed DMU despite the fact that none of the DMUs might be fully efficient (i.e. with efficiency score of 1). In addition, feasibility conditions are discussed. Finally, the proposed method will be applied to assess the branches of a specialized bank of Iran as a real application.