A chance-constrained network DEA approach for evaluating medical service and quality efficiency: a case study of TaiwanA chance-constrained network DEA approach for evaluating medical service and quality efficiency: a case study of TaiwanS.-W. Hung et al.
被引:0
作者:
Shiu-Wan Hung
论文数: 0引用数: 0
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机构:
National Central University,Department of Business AdministrationNational Central University,Department of Business Administration
Shiu-Wan Hung
[1
]
Kai-Chu Yang
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h-index: 0
机构:
National Central University,Department of Business AdministrationNational Central University,Department of Business Administration
Kai-Chu Yang
[1
]
Wen-Min Lu
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h-index: 0
机构:
Chinese Culture University,Department of International Business AdministrationNational Central University,Department of Business Administration
Wen-Min Lu
[2
]
Minh-Hieu Le
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h-index: 0
机构:
Ho Chi Minh University of Banking,Faculty of Business AdministrationNational Central University,Department of Business Administration
Minh-Hieu Le
[3
]
机构:
[1] National Central University,Department of Business Administration
[2] Chinese Culture University,Department of International Business Administration
[3] Ho Chi Minh University of Banking,Faculty of Business Administration
Data envelopment analysis;
Range directional measure;
Directional distance function;
Enhanced Russell measure;
Medical services;
Medical quality;
D O I:
10.1007/s10729-025-09700-2
中图分类号:
学科分类号:
摘要:
Healthcare efficiency is a critical concern for medical institutions, particularly in balancing service delivery and quality outcomes. This study aims to estimate the medical service efficiency (MSE) and medical quality efficiency (MQE) of 21 county-level and city-level medical institutions in Taiwan over the period from 2015 to 2019. We introduce a novel chance-constrained network Data Envelopment Analysis (DEA) model that integrates the advantages of the range directional measure (RDM), directional distance function (DDF), and enhanced Russell efficiency measure (ERM) to evaluate these efficiencies. Our findings reveal that non-metropolitan areas outperform metropolitan areas in MSE, while metropolitan areas excel in MQE. Furthermore, a truncated regression model is employed to identify the factors influencing MSE and MQE. The results indicate that the number of labor force and county or city attributes significantly negatively impact MSE, whereas these factors positively influence MQE. This study provides targeted optimization suggestions for medical institutions aiming to improve their operational and quality efficiencies.