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

Alipour-Vaezi M., Aghsami A., Jolai F.

Expert Systems with Applications, vol.195, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 195
  • Publication Date: 2022
  • Doi Number: 10.1016/j.eswa.2022.116568
  • Journal Name: Expert Systems with Applications
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, Public Affairs Index, Civil Engineering Abstracts
  • Keywords: Classification, COVID-19, Data Mining, Grasshopper Optimization Algorithm, Queueing Systems
  • Ankara Yıldırım Beyazıt University Affiliated: No

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.