Allocation Optimization of Medical Resources in Emergency Departments

• Published in: CACS International Automatic Control Conference (Online) pp. 1 - 6
• Main Authors: Horng, Shih-Cheng, Lin, Shieh-Shing, Chen, Chih-Yueh
• Format: Conference Proceeding
• Language: English
• Published: IEEE 31.10.2024
• Subjects: Costs; Hospitals; Metaheuristics; MIMICs; Optimization; Optimization methods; Planning; Resource management; Safety; Training
• ISSN: 2473-7259
• DOI: 10.1109/CACS63404.2024.10773170

The emergency department (ED) is one of the busiest regions of a hospital and a high patient volume can impact even the most efficient processes. However, when the ED becomes overcrowded, the ability to deliver timely and effective care is compromised, leading to significant clinical repercussions for health systems. Hospital managers are struggling to balance staffing, safety and care in overcrowded EDs. As an important source of care for safety net populations and those without access to other care, EDs must become more collaborative, adaptable and agile to thrive under these difficult conditions. In this research, an algorithm cooperating driving training-based optimization with ordinal optimization is developed to decide the optimal allocation of emergency medical resources. Driving training-based optimization is a human-based metaheuristic approach that mimics the human activity of driving training. The proposed algorithm is presented to decide the optimal allocation of medical resources for minimizing average patient hospital stays and cost of medical waste simultaneously, and satisfy the specified constraints. The experimental results show that the proposed algorithm can obtain stable and best results on optimal allocation of medical resources problems.