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Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
Published on: January 15, 2017
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Prioritizing and queueing the emergency departments' patients using a novel data-driven decision-making methodology,
Mohammad Alipour-Vaezi1, Amir Aghsami1,2, Fariborz Jolai1
1School of Industrial and Systems Engineering, College of Engineering, University of Tehran, P.O. Box 11155-4563, Tehran, Iran.
Expert Systems with Applications
|February 7, 2022
Summary
This study proposes a patient prioritization method for healthcare queues during epidemics like COVID-19. It aims to minimize infection spread by classifying patients based on health risk and optimizing hospital resources.
Area of Science:
- Healthcare management
- Epidemiology
- Operations research
Background:
- Epidemic disruptions, such as the COVID-19 pandemic, cause significant increases in emergency department patient volumes.
- Hospitals are unavoidable aggregation points, posing infection risks for patients, especially those with pre-existing conditions.
Purpose of the Study:
- To develop a patient classification and prioritization method to minimize infection rates within healthcare queuing systems.
- To optimize the allocation of healthcare resources, specifically the number of treatment systems (servers).
Main Methods:
- Utilizing data mining models and expert opinions for patient classification and risk assessment.
- Employing a mixed-integer programming model to determine the optimal number of servers.
- Applying the grasshopper optimization algorithm to refine resource allocation.
Main Results:
- A novel method for classifying and prioritizing patients based on health risk during epidemics.
- A framework for optimizing the number of healthcare treatment systems to manage patient flow and reduce infection transmission.
- Demonstration of a data-driven approach to enhance healthcare system resilience during public health crises.
Conclusions:
- Prioritizing patients by health risk in healthcare queues can effectively reduce infection rates.
- Optimizing server allocation using mathematical and algorithmic models improves healthcare system efficiency during pandemics.
- The proposed method offers a scalable solution for managing patient flow and mitigating disease spread in hospital settings.
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