Can large language models extract travel history from clinical text? A multi-annotator benchmark study

International travel can increase infection risk and spread antimicrobial resistance (AMR), yet clinical systems rarely capture travel history in a structured format. Extracting this information from free-text clinical documentation at scale could improve alerts for high-consequence infections and AMR surveillance. We developed an annotation framework covering key aspects of travel history, including destination, exposures, travel duration, and multiple temporal variables. The framework was used to annotate a corpus of 100 clinical case reports by five clinicians, yielding over 5,000 clinician

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