A fuzzy rule-based efficient hospital bed management approach for coronavirus disease-19 infected patients

Area of Science:

• Healthcare Management
• Infectious Disease Control
• Computational Intelligence

Background:

• The COVID-19 pandemic presents a global health crisis with rapid exponential spread.
• Severe shortages of hospital and emergency beds exacerbate the challenges of managing COVID-19 patient care.
• Effective resource allocation strategies are critical for maximizing patient survival during pandemics.

Purpose of the Study:

• To propose a fuzzy rule-based approach for optimizing hospital bed management for COVID-19 patients.
• To minimize the demand for hospital and emergency beds in critical scarcity scenarios.
• To enhance the survival probability of COVID-19 patients by prioritizing severe cases.

Main Methods:

• Development of a priority-based fuzzy rule-based system for bed allocation.
• Prioritization of severe COVID-19 cases over mild cases.
• Comparative analysis with the First-Come First-Serve (FCFS) method.
• Simulation using MATLAB R2015b.

Main Results:

• The fuzzy rule-based approach demonstrated an optimized allocation of limited hospital beds.
• Prioritization of severe cases led to improved survival probability compared to FCFS.
• The system effectively addresses bed management challenges in worst-case pandemic scenarios.

Conclusions:

• The proposed fuzzy logic system offers an effective strategy for managing hospital beds during COVID-19 surges.
• Prioritization based on severity is crucial for improving patient outcomes and resource utilization.
• This approach provides a valuable tool for healthcare systems facing resource constraints during infectious disease outbreaks.