A Multi-Objective Optimization Method for Hospital Admission Problem—A Case Study on Covid-19 Patients

• Published in: Algorithms Vol. 14; no. 2; p. 38
• Main Authors: AbdelAziz, Amr Mohamed, Alarabi, Louai, Basalamah, Saleh, Hendawi, Abdeltawab
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2021
• Subjects: Algorithms; Coronaviruses; COVID-19; Criteria; Health care; Health services; hospital use; Hospitals; Internships; Medical research; Mopping; multi-objective optimization; Multiple objective analysis; Objectives; Optimization; Pareto optimization; Patients; Physicians; Real time; Severe acute respiratory syndrome; Severe acute respiratory syndrome coronavirus 2; smart health
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a14020038

The wide spread of Covid-19 has led to infecting a huge number of patients, simultaneously. This resulted in a massive number of requests for medical care, at the same time. During the first wave of Covid-19, many people were not able to get admitted to appropriate hospitals because of the immense number of patients. Admitting patients to suitable hospitals can decrease the in-bed time of patients, which can lead to saving many lives. Also, optimizing the admission process can minimize the waiting time for medical care, which can save the lives of severe cases. The admission process needs to consider two main criteria: the admission time and the readiness of the hospital that will accept the patients. These two objectives convert the admission problem into a Multi-Objective Problem (MOP). Pareto Optimization (PO) is a common multi-objective optimization method that has been applied to different MOPs and showed its ability to solve them. In this paper, a PO-based algorithm is proposed to deal with admitting Covid-19 patients to hospitals. The method uses PO to vary among hospitals to choose the most suitable hospital for the patient with the least admission time. The method also considers patients with severe cases by admitting them to hospitals with the least admission time regardless of their readiness. The method has been tested over a real-life dataset that consisted of 254 patients obtained from King Faisal specialist hospital in Saudi Arabia. The method was compared with the lexicographic multi-objective optimization method regarding admission time and accuracy. The proposed method showed its superiority over the lexicographic method regarding the two criteria, which makes it a good candidate for real-life admission systems.