Decision support model for the patient admission scheduling problem based on picture fuzzy aggregation information and TOPSIS methodology

Shahzaib Ashraf; Noor Rehman; Saleem Abdullah; Bushra Batool; Mingwei Lin; Muhammad Aslam; Shahzaib Ashraf; Noor Rehman; Saleem Abdullah; Bushra Batool; Mingwei Lin; Muhammad Aslam

doi:10.3934/mbe.2022146

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 3: 3147-3176. doi: 10.3934/mbe.2022146

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Decision support model for the patient admission scheduling problem based on picture fuzzy aggregation information and TOPSIS methodology

1.
Department of Mathematics and Statistics, Bacha Khan University, Charsadda 24420, Khyber Pakhtunkhwa, Pakistan
2.
Department of Mathematics, Abdul Wali Khan University, Mardan 23200, Khyber Pakhtunkhwa, Pakistan
3.
Department of Mathematics, University of Sargodha, Sargodha, Pakistan
4.
College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China
5.
Department of Mathematics, College of Sciences, King Khalid University, Abha 61413, Saudi Arabia

Academic Editor: Higinio Ramos

Received: 03 August 2021 Revised: 28 December 2021 Accepted: 10 January 2022 Published: 20 January 2022

Health care systems around the world do not have sufficient medical services to immediately offer elective (e.g., scheduled or non-emergency) services to all patients. The goal of patient admission scheduling (PAS) as a complicated decision making issue is to allocate a group of patients to a limited number of resources such as rooms, time slots, and beds based on a set of preset restrictions such as illness severity, waiting time, and disease categories. This is a crucial issue with multi-criteria group decision making (MCGDM). In order to address this issue, we first conduct an assessment of the admission process and gather four (4) aspects that influence patient admission and design a set of criteria. Even while many of these indicators may be accurately captured by the picture fuzzy set, we use an advanced MCGDM approach that incorporates generalized aggregation to analyze patients' hospitalization. Finally, numerical real-world applications of PAS are offered to illustrate the validity of the suggested technique. The advantages of the proposed approaches are also examined by comparing them to various existing decision methods. The proposed technique has been proved to assist hospitals in managing patient admissions in a flexible manner.
Citation: Shahzaib Ashraf, Noor Rehman, Saleem Abdullah, Bushra Batool, Mingwei Lin, Muhammad Aslam. Decision support model for the patient admission scheduling problem based on picture fuzzy aggregation information and TOPSIS methodology[J]. Mathematical Biosciences and Engineering, 2022, 19(3): 3147-3176. doi: 10.3934/mbe.2022146

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Abstract

Health care systems around the world do not have sufficient medical services to immediately offer elective (e.g., scheduled or non-emergency) services to all patients. The goal of patient admission scheduling (PAS) as a complicated decision making issue is to allocate a group of patients to a limited number of resources such as rooms, time slots, and beds based on a set of preset restrictions such as illness severity, waiting time, and disease categories. This is a crucial issue with multi-criteria group decision making (MCGDM). In order to address this issue, we first conduct an assessment of the admission process and gather four (4) aspects that influence patient admission and design a set of criteria. Even while many of these indicators may be accurately captured by the picture fuzzy set, we use an advanced MCGDM approach that incorporates generalized aggregation to analyze patients' hospitalization. Finally, numerical real-world applications of PAS are offered to illustrate the validity of the suggested technique. The advantages of the proposed approaches are also examined by comparing them to various existing decision methods. The proposed technique has been proved to assist hospitals in managing patient admissions in a flexible manner.

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