Artificial Intelligence For Smart Case Prioritization: Enhancing Notification Center Efficiency In Healthcare Emergencies

Abstract

This review explores the integration of artificial intelligence (AI) in healthcare notification centers to improve triage processes and case prioritization. Traditional manual methods of classifying cases as critical, moderate, or minor are often time-consuming and subject to human error, which can delay emergency responses and compromise patient outcomes. With the growing availability of AI-driven tools, notification centers are increasingly able to process large volumes of data in real-time, providing rapid and accurate case categorization. This article synthesizes recent studies on AI algorithms—such as machine learning, deep learning, and natural language processing (NLP)—applied in emergency healthcare systems. It highlights evidence on improved accuracy, reduced triage time, and enhanced coordination between healthcare providers. Key challenges, including ethical concerns, algorithmic bias, data privacy, and implementation costs, are also examined. Furthermore, the review identifies strategies for integrating AI into existing healthcare infrastructures and discusses its potential impact on improving patient safety, healthcare efficiency, and resource allocation. The findings suggest that AI-powered notification centers could significantly transform emergency healthcare delivery by ensuring timely interventions and reducing the burden on frontline workers.