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Evaluation of smart long-term care information strategy portfolio decision model: the national healthcare environment in Taiwan.
Yang, Chih-Hao; Chen, Yen-Chi; Hsu, Wei; Chen, Yu-Hui.
Afiliação
  • Yang CH; Department of Accounting, Ming Chuan University, Shilin, Taipei, 111005 Taiwan, ROC.
  • Chen YC; Department of Accounting Information, National Taipei University of Business, Zhongzheng, Taipei, 100025 Taiwan, ROC.
  • Hsu W; Department of Health Care Management, National Taipei University of Nursing and Health Sciences, Beitou, Taipei, 112303 Taiwan, ROC.
  • Chen YH; Department of Financial Management, National Defense University, Beitou, Taipei, 112305 Taiwan, ROC.
Ann Oper Res ; : 1-32, 2023 Apr 29.
Article em En | MEDLINE | ID: mdl-37361069
A globally aging population results in the long-term care of people with chronic illnesses, affecting the living quality of the elderly. Integrating smart technology and long-term care services will enhance and maximize healthcare quality, while planning a smart long-term care information strategy could satisfy the variety of care demands regarding hospitals, home-care institutions, and communities. The evaluation of a smart long-term care information strategy is necessary to develop smart long-term care technology. This study applies a hybrid Multi-Criteria Decision-Making (MCDM) method, which uses the Decision-Making Trial and Evaluation Laboratory (DEMATEL) integrated with the Analytic Network Process (ANP) for ranking and priority of a smart long-term care information strategy. In addition, this study considers the various resource constraints (budget, network platform cost, training time, labor cost-saving ratio, and information transmission efficiency) into the Zero-one Goal Programming (ZOGP) model to capture the optimal smart long-term care information strategy portfolios. The results of this study indicate that a hybrid MCDM decision model can provide decision-makers with the optimal service platform selection for a smart long-term care information strategy that can maximize information service benefits and allocate constrained resources most efficiently.
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Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 1_ASSA2030 Base de dados: MEDLINE Tipo de estudo: Evaluation_studies / Health_economic_evaluation / Prognostic_studies Idioma: En Revista: Ann Oper Res Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 1_ASSA2030 Base de dados: MEDLINE Tipo de estudo: Evaluation_studies / Health_economic_evaluation / Prognostic_studies Idioma: En Revista: Ann Oper Res Ano de publicação: 2023 Tipo de documento: Article