The Role of Emerging Technologies in Health Emergency Planning and Preparedness
Authors: Seitzinger Patrick , Kalra Jay 
Published: October 2023

Predicting and planning healthcare utilization in disaster situations is a complex and evolving challenge that requires new tools to augment existing systems of practice. Disasters and humanitarian emergencies are an inevitable threat to the health of populations across all continents and demographics. Morbidity and mortality from natural and infectious disasters are increasing at an alarming rate, leaving health systems unprepared and overwhelmed. Artificial Intelligence (AI) has emerged as a tool with tremendous capabilities in processing vast amounts of data to provide the best available evidence to inform disaster response strategies. This study described the role, implementation, and implications of AI-augmented disaster prediction and planning. Disaster prediction was previously beyond our grasp but with the advent of AI modelling and data-driven prediction technologies are becoming a growing reality. AI technologies have demonstrated the capability to incorporate, analyse, and summarize multifaceted data sources including climate factors, environmental factors, social media feeds, movements patterns, health data, and historical records. This technology can extract meaningful patterns to provide the likelihood and severity of catastrophic events. Examples include models that analyse existing earthquake data to identify patterns of seismic activity to forecast earthquake events with growing accuracy. Similarly, the Global Public Health Intelligence Network scans headlines across the globe to detect emerging health threats. Disaster planning involves optimizing resources and supports to vulnerable populations to prepare for the possibility of acute events capable of causing mass injuries and loss of life. There are insufficient resources available to prepare all health regions and populations for the possibility of disasters. By simulating different scenarios and outcomes, AI algorithms identify high risk geographic regions and populations to inform strategic resource allocation and deployment strategies. By analysing population densities, infrastructure stability, healthcare capacity, transportation networks, shelters, AI-augmented systems can optimize evacuation roots to appropriate healthcare resources while providing specific health needs assessments of affected populations. In these ways, the implementation of AI technologies can strengthen existing disaster preparedness strategies in an iterative and evidence driven way. While advancements in certain silos are being made on a consistent basis, a collaborative system has yet to be established. The accuracy of insights provided by AI systems is contingent on availability and validity of data. AI-systems are a relatively novel rapidly evolving field with inherent weakness such as assumptions, biases, lack of explainability and data privacy concerns that pose unforeseen challenges to regulatory and legislative frameworks. While evidence driven decisions may provide the highest probability of successful disaster planning and response strategies, they can not account for the ethical and social factors that are essential to effective implementation. Effective disaster planning and preparedness is a complex and iterative process that requires collaboration among medical, policy leaders, governmental programs, non-governmental organizations, communities, and other stakeholders. Only by integrating diverse perspectives and utilizing the best tools available, can we promote resiliency and health protection for vulnerable populations across the globe.