Evaluation of smart long-term care information strategy portfolio decision model: the national healthcare environment in Taiwan

Chih-Hao Yang (), Yen-Chi Chen, Wei Hsu and Yu-Hui Chen
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Chih-Hao Yang: Ming Chuan University
Yen-Chi Chen: National Taipei University of Business
Wei Hsu: National Taipei University of Nursing and Health Sciences
Yu-Hui Chen: National Defense University

Annals of Operations Research, 2023, vol. 326, issue 1, No 17, 505-536

Abstract: Abstract 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.

Keywords: Smart long-term care information strategy portfolio; Multi-criteria decision making; Analytic network process; Zero–one goal programming (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10479-023-05358-7

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