Abstract
Several efforts have been made by hospital administrators to provide high-quality care for Covid-19-infected patients admitted to the Emergency Department (ED). Estimating the intervention priority level of Covid-19 patients is pivotal to underpin this process. However, diagnoses vary from one doctor to another considering various factors including experience, intuition, and education level. This problem is even more sharpened especially in absence of patient background and medical records. In addition, there is a large volume of infected persons requiring emergency care and a wide range of health and socio-personal particularities that need to be considered when deciding if these patients will be only treated at the ED, hospitalized, or returned home. Therefore, this study proposes a new integrated Multi-Criteria Decision-Making (MCDM) approach to identify the intervention priority level of Covid-19 infected patients. First, the Trapezoidal Neutrosophic Fuzzy Analytic Hierarchy Process (TNF-AHP) method was implemented to calculate the criteria and sub-criteria weights considering uncertainty and knowledge level. Second, the Intuitionistic Fuzzy Compromise Combined Solution (IF-CoCoSo) was applied to estimate the intervention priority level of Covid-19 patients and define actions to improve their health condition. The proposed approach was validated in a Mexican public hospital. As a result, an MCDM model comprising 5 criteria and 27 sub-criteria was designed to support this decision. Specifically, "Socio-personal characteristics" (overall weight = 0.195), "Hypogeusia" (overall weight = 0.0812), and "Chronic lung disease" (overall weight = 0.089) were found to be the most important decision elements in defining the intervention priority level of Covid-19 patients.
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Perez-Aguilar, A., Ortiz-Barrios, M., Pancardo, P., Orrante-Weber-Burque, F. (2023). A Hybrid Fuzzy MCDM Approach to Identify the Intervention Priority Level of Covid-19 Patients in the Emergency Department: A Case Study. In: Duffy, V.G. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. HCII 2023. Lecture Notes in Computer Science, vol 14029. Springer, Cham. https://doi.org/10.1007/978-3-031-35748-0_21
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