Integrating Artificial Intelligence and Contextual Factors in Emergency Care

Dear Colleagues,

Emergency medical systems, whether in urban or rural settings, face persistent challenges in ensuring timely and effective patient care. Health professionals often make critical decisions with limited information, under intense time pressure, and across highly variable environmental and resource conditions. Early Warning Tools (EWTs) have become fundamental in supporting early detection of clinical deterioration due to their simplicity and rapid applicability. Yet, conventional EWTs remain limited by static data inputs and the absence of continuous monitoring, particularly in resource-constrained or geographically diverse contexts.

The increasing availability of electronic health records (EHRs), along with real-time data from digital and environmental sources, offers new opportunities for more adaptive and inclusive early warning systems. By combining these data streams with artificial intelligence (AI) and big data analytics, it is possible to generate dynamic risk models that account for both clinical and contextual variables—such as urban density, infrastructure, or access to healthcare.

This broader, data-driven approach could enhance prognostic accuracy, tailor interventions to specific populations and settings, and support more equitable healthcare delivery. Ultimately, integrating AI with contextual awareness may redefine early risk assessment and decision-making in emergency medicine.

This Special Issue aims to collect innovative research that leverages AI to integrate these clinical and contextual data streams, ultimately improving the speed and accuracy of early warning systems in emergency care. We welcome submissions of original research and reviews on this topic.

Prof. Dr. Ancor Sanz-García
Prof. Dr. José Luis Martín-Conty
Guest Editors

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