To analyze the association between the intensity of care and the various discharge destinations of patients treated in the emergency department (ED) of a tertiary referral center.

To analyze the length of emergency department (ED) stay prior to hospital admission, its causes, and repercussions on patient health.

To design a standardized methodology for documenting pharmaceutical interventions (PIs) in hospital emergency departments (EDs), to identify the clinical situations in which they are most frequently performed, and evaluate their economic impact.

. To evaluate the impact of respiratory syncytial virus (RSV) prophylaxis with nirsevimab during its 2nd year of administration and analyze the differences between the different strategies implemented across Spanish autonomous communities (ACs) during the current epidemic season.

Sepsis remains one of the leading causes of mortality in emergency departments (EDs). Despite advances in definitions and management protocols, early identification continues to be a critical challenge due to the nonspecific presentation of the disease. Early management is based on 3 fundamental pillars: source control, antibiotics, and hemodynamic resuscitation, all of which require early intervention. Tools such as the SOFA score, biomarkers (C-reactive protein, procalcitonin, lactate), and protocols like the Sepsis Code have improved detection and management. However, the clinical heterogeneity of sepsis and limitations of current models hinder their universal implementation. Artificial intelligence (AI) is emerging as a key tool to improve early detection of sepsis through the analysis of large volumes of clinical data. Open data, following FAIR principles (Findable, Accessible, Interoperable, Reusable), facilitate the development of robust and personalized algorithms, minimizing bias and enhancing scientific collaboration. Spain generates vast amounts of clinical data in its EDs but lacks a unified database. The creation of an open system with data use agreements would enable the development of predictive models specific to its population. The use of A.I. in combination with specific databases promises to improve treatment personalization, reduce mortality, and optimize resources in sepsis care, changing the current paradigm of clinical management.

To estimate the probability of adverse events based on the length of emergency department (ED) stay prior to hospitalization in patients aged > 65 treated in Spanish EDs.