AI in the ED Cologne

This retrospective, non-interventional study evaluates the prognostic performance of open-weight Large Language Models (LLMs) in the setting of a German academic emergency department. Using a full census of all consecutive emergency department cases at University Hospital Cologne between 01 January 2023 and 31 December 2025 (approximately 100,000 cases), the study assesses whether LLMs can make reliable prognostic predictions (e.g., hospital admission, imaging, diagnosis, placement) based on the initial history, vital signs, and triage category. In addition, it quantifies how strongly automated anonymization and perturbation procedures affect the models' diagnostic accuracy. This is an Investigator-Initiated Trial (IIT) with no intervention on patients.

Inclusion Criteria: * All consecutive treatment cases at the Central Emergency Department of University Hospital Cologne during the period 01 January 2023 - 31 December 2025 (full census, consecutive inclusion). Exclusion Criteria: * Documented objection to the scientific use of the data pursuant to Art. 21 General data protection Regulation (GDPR). * Cases lacking the minimum data required for analysis (triage/history and documented outcome).