TEA-IS: A hybrid DEA-TOPSIS approach for assessing performance and synergy in Chinese health care

Antunes, Jorge; Hadi-Vencheh, A.; Jamshidi, Ali; Tan, Yong

doi:10.1016/j.dss.2022.113916

Cited by 11 publications

(6 citation statements)

References 49 publications

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“…The literature has also examined the socioeconomic factors and their impact on different industries (Wanke et al, 2023b;Antunes et al, 2023aAntunes et al, , 2023bZhao et al, 2022). More specifically, in the banking context, Wanke et al (2023b) investigate the impact of a series of contextual variables, including inflation, human development index, GDP growth, infant mortality, life expectancy, and energy use on bank efficiency through a generalized linear autoregressive moving average model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Compared to other methods, the proposed model is more flexible, easy to estimate and interpret. In the healthcare context, Antunes et al (2023a) examine the impact of a number of socioeconomic factors, including GDP, illiteracy, disposable income, sewer, gas pollution, and mortality rate, on healthcare performance in China through machine learning techniques. Similar techniques were also applied by Antunes et al (2023b) to investigate a number of socioeconomic factors, including research and development expenses, FDI, paved road, GDP per capita, population density, innovation index, and second industry share, on road transportation sustainability performance in China.…”

Section: Literature Reviewmentioning

confidence: 99%

“…We also noticed that some previous studies used stochastic frontier analysis or input distance function. The parametric analysis is affected by the curse of dimensionality, which may yield performance scores biased towards one (Antunes et al, 2023a). The building of MCDM models would be able to help alleviate the issue.…”

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confidence: 99%

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Foreign Direct Investment Performance Drivers at the Country Level: A Robust Compromise Multi-Criteria Decision-Making Approach

Wanke,

Tan,

Antunes

et al. 2024

Technological and Economic Development of Economy

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This paper focuses on the performance drivers of Foreign Direct Investment (FDI) at the country level, exploring the socio-demographic specifics of donor and receiver countries. To this end, a novel Robust Compromise (RoCo) Multi-Criteria Decision-Making (MCDM) model is proposed using non-linear programming solved by genetic algorithms. The model builds upon established traditional models for alternative ranking and criteria weighting. Subsequently, a stochastic robust regression is performed, building upon previously computed bootstrapped Tobit, Simplex, and Beta regressions to handle performance scores ranging between 0 and 1. The goal is to test FDI performance against a set of contextual variables. The findings suggest that the performance of FDI is relatively low, and relevant improvements should be made. Our second stage analysis reports that higher GDP per capita and good social welfare, including lower infant mortality and higher life expectancy, contribute to the improvement in FDI performance. Furthermore, it is found that a large percentage of women in the total population, wealth concentration in the destination country, as well as the degree of urbanization, are helpful to improve FDI performance. Finally, we find that FDI performance is mainly concentrated on industries that are high-tech and high value-added.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Foreign Direct Investment Performance Drivers at the Country Level: A Robust Compromise Multi-Criteria Decision-Making Approach

Wanke,

Tan,

Antunes

et al. 2024

Technological and Economic Development of Economy

Self Cite

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“…DEA assisted by MCDM has been proposed in analyzing EDs [ 91 , 92 ]. Abdel-Basset [ 93 ] applied DEA integrated with MCDM to assess the efficiency of EDs in 20 hospitals.…”

Section: Techniques and Viewsmentioning

confidence: 99%

The Application of Data Envelopment Analysis to Emergency Departments and Management of Emergency Conditions: A Narrative Review

Mirmozaffari,

Kamal

2023

Healthcare

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The healthcare industry is one application for data envelopment analysis (DEA) that can have significant benefits for standardizing health service delivery. This narrative review focuses on the application of DEA in emergency departments (EDs) and the management of emergency conditions such as acute ischemic stroke and acute myocardial infarction (AMI). This includes benchmarking the proportion of patients that receive treatment for these emergency conditions. The most frequent primary areas of study motivating work in DEA, EDs and management of emergency conditions including acute management of stroke are sorted into five distinct clusters in this study: (1) using basic DEA models for efficiency analysis in EDs, i.e., applying variable return to scale (VRS), or constant return to scale (CRS) to ED operations; (2) combining advanced and basic DEA approaches in EDs, i.e., applying super-efficiency with basic DEA or advanced DEA approaches such as additive model (ADD) and slack-based measurement (SBM) to clarify the dynamic aspects of ED efficiency throughout the duration of a first-aid program for AMI or heart attack; (3) applying DEA time series models in EDs like the early use of thrombolysis and percutaneous coronary intervention (PCI) in AMI treatment, and endovascular thrombectomy (EVT) in acute ischemic stroke treatment, i.e., using window analysis and Malmquist productivity index (MPI) to benchmark the performance of EDs over time; (4) integrating other approaches with DEA in EDs, i.e., combining simulations, machine learning (ML), multi-criteria decision analysis (MCDM) by DEA to reduce patient waiting times, and futile transfers; and (5) applying various DEA models for the management of acute ischemic stroke, i.e., using DEA to increase the number of eligible acute ischemic stroke patients receiving EVT and other medical ischemic stroke treatment in the form of thrombolysis (alteplase and now Tenecteplase). We thoroughly assess the methodological basis of the papers, offering detailed explanations regarding the applied models, selected inputs and outputs, and all relevant methodologies. In conclusion, we explore several ways to enhance DEA’s status, transforming it from a mere technical application into a strong methodology that can be utilized by healthcare managers and decision-makers.

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“…DEA is a non-parametric linear programming-based technique that holds extensive application in assessing the efficiency of DMUs across diverse domains encompassing businesses [3], financial institutions [4], educational establishments [5,6], healthcare facilities [7,8], and software projects [9]. Moreover, by correlating efficiency to other relevant indicators, the relevance of DEA extends to fields such as risk management and bankruptcy prediction.…”

Section: Introductionmentioning

confidence: 99%

Performance Measurement: Machine Learning as a Complement to DEA for Continuous Efficiency Estimation

Khoubrane,

Ramli,

Mohd Khairi

2024

Mal. J. Fund. Appl. Sci.

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Data Envelopment Analysis (DEA) is a well-established non-parametric technique for performance measurement to access the efficiency of Decision-Making Units (DMUs). However, its inability to predict the efficiency values of new DMUs without re-conducting the analysis on the entire dataset has led to the integration of Machine Learning (ML) in previous studies to address this limitation. Yet, such integration often lacks a thorough evaluation of ML's adaptability in replacing current DEA process. This paper presents the results of an empirical study that employed eight ML models, two DEA variants, and a dataset of S&P500 companies. The findings demonstrated ML’s remarkable precision in predicting efficiency values derived from a single DEA run and comparable performance in predicting the efficiency of new DMUs, thus eliminating the need for repeated DEA. This discovery highlights ML’s robustness as a complementary tool for DEA in continuous efficiency estimation, rendering the practice of re-conducting DEA unnecessary. Notably, boosting models within the Ensemble Learning category consistently outperformed other models, highlighting their effectiveness in the context of DEA efficiency prediction. Particularly, CatBoost demonstrated its superiority as the top-performing model, followed by LightGBM in the second position in most cases. When extended to five enlarged datasets, it shows that the model exhibits superior R² values in the CRS scenario.

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TEA-IS: A hybrid DEA-TOPSIS approach for assessing performance and synergy in Chinese health care

Cited by 11 publications

References 49 publications

Foreign Direct Investment Performance Drivers at the Country Level: A Robust Compromise Multi-Criteria Decision-Making Approach

Foreign Direct Investment Performance Drivers at the Country Level: A Robust Compromise Multi-Criteria Decision-Making Approach

The Application of Data Envelopment Analysis to Emergency Departments and Management of Emergency Conditions: A Narrative Review

Performance Measurement: Machine Learning as a Complement to DEA for Continuous Efficiency Estimation

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