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

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.

Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review

“…In turn, Moss et al [80] simulated clinical presentations and patient flows through the Australian health care system, including expansion of available acute care capacity and alternative clinical assessment pathways. Other interesting related studies are illustrated in Nepomuceno et al [81], Mehrotra et al [77], AbdelAziz et al [40], Aggarwal et al [41], and Araz et al [45].…”

“…Single Tang et al [93] Discrete event simulation Nepomuceno et al [81] Data envelopment analysis (DEA) Mehrotra et al [77] Stochastic optimization AbdelAziz et al [40] Multi-objective pareto optimization Peng et al [85]; Moss et al [80] Simulation Aggarwal et al [41] Additive utility assumption Araz et al [45] System dynamics Hybrid Garbey et al [62] Markov chains, stochastic optimization Albahri et al [42] Entropy, TOPSIS De Nardo et al [58] Potentially all pairwise ranking of all possible alternatives (PAPRIKA), multi-criteria decision making (MCDM) Parker et al [84] Linear programming, mixed-integer programming Zeinalnezhad et al [98] Colored petri nets, discrete event simulation Zhang & Cheng. [100] Logistic regression, Markov chains Abadi et al [39] Hybrid salp swarm algorithm and genetic algorithm (HSSAGA) Haddad et al [67] Simulation, optimization…”

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

“…In addition to the availability of medical staff, it is important to consider healthcare facility capacities and readiness to accept new patients. To optimize the admission process, the authors in [198] formulated a multi-objective problem using a Pareto-Optimization based algorithm, where the model chooses the most suitable hospital for the patient (based on hospital readiness level and the patient's condition) with the least admission time. Studies [34,35,199] considered the problem of selecting the optimal location for temporary hospitals and medical vehicles.…”

An Overview of Healthcare Data Analytics With Applications to the COVID-19 Pandemic

“…Pareto Optimization (PO) is a common approach to solve optimization problems with multiple objectives. [135] applied PO to tackle problems posed by COVID-19, which can infect many people quickly resulting in huge and sudden requests of medical care at various levels. Coping with how, when and where to admit COVID-19 patients efficiently is a complex multiobjective optimization problem.…”