Data-driven approaches to improving emergency response times and patient outcomes

Abstract

Background: Data-driven disaster management represents a transformative shift from traditional methods, crucial amid increasing natural and man-made disasters. The escalation in climate-related threats and high-risk population densities has underscored the inadequacy of conventional disaster management strategies. This research explores the potential of big data analytics to revolutionize disaster preparedness, response coordination, and recovery efforts. Aim: This study aims to investigate the application of big data analytics in enhancing disaster management strategies, focusing on how extensive datasets can improve risk mitigation, response efficiency, and recovery processes. Methods: The research employs a comprehensive review of data-driven disaster management techniques, including Geographic Information Systems (GIS), Artificial Intelligence (AI), and the Internet of Things (IoT). It analyzes how these technologies utilize big data to predict, prepare for, and manage disasters. Additionally, the study examines the role of data-driven decision support systems and process mining in refining disaster management approaches. Results: Findings reveal that big data analytics significantly enhances predictive capabilities, response efficiency, and recovery operations. GIS technologies offer detailed spatial insights, AI improves predictive modeling, and IoT provides real-time situational awareness. The integration of these technologies supports more effective disaster preparedness and response strategies, although challenges in data quality and ethical concerns persist.