Prioritizing the elective surgery patient admission in a Chinese public tertiary hospital using the hesitant fuzzy linguistic ORESTE method

In public health systems around the world, there are not enough medical resources to provide elective (e.g., scheduled or non-emergency) services for all patients immediately. One feasible solution is to prioritize patients by taking into account a variety of factors, such as disease severity, waiting time, and disease types. This is a typical Multiple Criteria Decision Making (MCDM) problem. To solve this problem, in this paper, we first conduct an investigation on the admission process, and obtain 16 indicators affecting patients' admission, which form a criteria system. Since there is much vague and uncertain information which can be depicted by the hesitant fuzzy linguistic term set effectively for these indicators, we then apply a powerful MCDM method, named the hesitant fuzzy linguistic ORESTE, to prioritize the elective surgery patient admission in a Chinese public tertiary hospital, the West China Hospital. Robust results are obtained by performing a sensitivity analysis with six scenarios. We also compare the results with those derived by other HFL-MCDM methods. It is illustrated that the hesitant fuzzy linguistic ORESTE can help hospitals flexibly manage the patient admissions. (C) 2019 Elsevier B.V. All rights reserved.