ArticleExpert systems with applications2022
Prioritizing and queueing the emergency departments' patients using a novel data-driven decision-making methodology, a real case study.
Article in Expert systems with applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Expanding research on clinical agent data to a virtual space of location, time, and context: exploration of data structures and limitations using a pragmatic digital twin.International journal of computer assisted radiology and surgery · 2026Article
- A Markov decision optimization of medical service resources for two-class patient queues in emergency departments via particle swarm optimization algorithm.Scientific reports · 2025Article
- Article
- Home Health Care Problem with Synchronization Visits and Considering Samples Transferring Time: A Case Study in Tehran, Iran.International journal of environmental research and public health · 2022Article
- A Pre-Generation of Emergency Reference Plan Model of Public Health Emergencies with Case-Based Reasoning.Risk management and healthcare policy · 2022Article
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Authors and funding
3 authors.
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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.
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