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

Amr Mohamed AbdelAziz,Saleh Basalamah,Louai Alarabi,Abdeltawab Hendawi

doi:10.3390/a14020038

Amr Mohamed AbdelAziz, Saleh Basalamah + Show 2 more

Open Access

https://doi.org/10.3390/a14020038

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Abstract

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.

Highlights

The unprecedented spread of the Severe Acute Respiratory Syndrome Coronavirus 2(Covid-19) epidemic across the world has pushed the healthcare systems to their limits [1,2].The main problem comes from having too many patients that need medical assistance at the same time [3]
Having an effective admission system for Covid-19 patients can alleviate the burden over hospitals through admitting patients to the most suitable hospital according to their status in real time
Several experiments have been conducted over a real-life dataset to evaluate the effectiveness of the proposed method in admitting Covid-19 patients to suitable hospitals

Summary

Introduction

The main problem comes from having too many patients that need medical assistance at the same time [3]. Having an effective admission system for Covid-19 patients can alleviate the burden over hospitals through admitting patients to the most suitable hospital according to their status in real time. This can minimize the waiting time of patients and save patients with severe status. A MOP can be described as a set of m objectives as:. Each objective can be described mathematically as a set of equality or inequality equations as [23]: Hi ( x ) = 0;. Each parameter of an objective has a search space that can be described as: Xi l ≤ Xi ≤ Xiu ;

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Conclusion