Prioritizing and queueing the emergency departments’ patients using a novel data-driven decision-making methodology, a real case study

Abstract

One of the principal problems in epidemic disruptions like the COVID-19 pandemic is that the number of patients needing hospitals’ emergency departments’ services significantly grows. Since COVID-19 is an infectious disease, any aggregation has to be prevented accordingly. However, few aggregations cannot be prevented, including hospitals. To the best of our knowledge, COVID-19 is a life-threatening disease, especially for people in poor health conditions. Therefore, it sounds reasonable to optimize the health care queuing systems to minimize the infection rate by prioritizing patients based on their health condition so patients with a higher risk of infection will leave the queue sooner. In this paper, relying on data mining models and expert’s opinions, we propose a method for patient classification and prioritizing. The optimal number of servers (treatment systems) will be determined by benefiting from a mixed-integer model and the grasshopper optimization algorithm.

Home Health Care Problem with Synchronization Visits and Considering Samples Transferring Time: A Case Study in Tehran, Iran

“…Further, exploring priority queueing systems for determining the optimal nurses [ 93 , 94 ] integrated with HHP would be an intriguing future research area due to the growing demand for nurses with various conditions.…”

Data-Driven Queueing Science Approach to Manage Secondary Queue Formation of Passenger Flow in Indian Railways: A Case of Overbooking on Vaishali Train

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Designing a post-disaster humanitarian supply chain using machine learning and multi-criteria decision-making techniques

“…The learning process begins with observations or data, such as examples, direct experiences, or instructions, to arrive at a pattern in the data and make better decisions. The main goal is to allow the computer to learn automatically without human intervention and be able to adjust its actions accordingly (Alipour-Vaezi et al, 2022a). Using these tools, we can predict the social damage, accidents and physical problems/illnesses of people who have had an accident, and ultimately lead to improved chain performance.…”

“…The learning process begins with observations or data, such as examples, direct experiences, or instructions, to arrive at a pattern in the data and make better decisions. The main goal is to allow the computer to learn automatically without human intervention and be able to adjust its actions accordingly (Alipour-Vaezi et al. , 2022a).…”