Healthcare is the maintenance or improvement of health via a series of preventive, diagnostic, and therapeutic actions for people’s physical and mental health. The increasingly complex decision-making problems in modern healthcare engineering systems have prompted researchers to tackle them by developing various advanced decision-making models. For this reason, multiple-criteria decision making (MCDM) is usually used by clinical decision-makers (DMs) to analyze healthcare issues from various perspectives and solve various decision-making problems, such as performance evaluation, alternative selection, and strategy improvement.

The advanced MCDM models based on expert experience or clinical data development are key issues for providing more robust medical and health decisions. However, the advanced MCDM models in the medical field are still insufficient; they still require many doctors, nurses, and academics to develop various clinical MCDM models to fill this research gap. The clinical MCDM models in the healthcare field can be roughly divided into three categories: (i) experience-based MCDM models (e.g., AHP, ANP, BWM, DEMATEL, etc.), which are based on the preference or knowledge of DMs for solving alternative performance or selection; (ii) the data-driven MCDM models (e.g., rough set, decision tree, random forest, etc.), which are based on the combination or integration of soft computing or artificial intelligence techniques with MCDM methods for solving alternative classification, attribute identification, or performance forecast.; and (iii) hybrid MCDM models which are applied by combining the former two approaches for solving more complex decision-making problems.

The interdisciplinary MCDM models in healthcare are one of the new research trends. These models will bring a more suitable clinical decision-making basis for researchers, clinicians, and clinical nurses in the real environment. This Special Issue aims to attract high-quality original or review papers which apply various MCDM methods in the healthcare field that address valuable topics related to healthcare.

Potential topics include but are not limited to the following:

• Artificial intelligence models or machine learning models in healthcare
• Uncertainty MCDM models (e.g., rough, fuzzy, or gray) in healthcare
• Hybrid MCDM model based on mixed health care data and domain-expert experience