AI and Machine Learning for Optimizing Healthcare Resource Allocation in Crisis Situations
Abstract
Efficient resource allocation during healthcare crises, such as pandemics or natural disasters, is crucial to ensuring optimal care for patients. This paper explores the use of AI and machine learning models to optimize the distribution of healthcare resources such as hospital beds, ventilators, and medical staff during emergencies. By analyzing historical data, real-time patient influx, and geographic information, AI can predict resource shortages and suggest effective strategies for resource management. The paper also addresses challenges in data integration, real-time decision-making, and the need for accurate forecasting models during crisis situations.
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