The Transformative Potential of Large Language Models in Mining Electronic Health Records Data: Content Analysis.

Background: In this study, we evaluate the accuracy, efficiency, and cost-effectiveness of large language models in extracting and structuring information from free-text clinical reports, particularly in identifying and classifying patient comorbidities within oncology electronic health records. We specifically compare the performance of gpt-3.5-turbo-1106 and gpt-4-1106-preview models against that of specialized human evaluators.

Patterns of care and outcome for patients with glioblastoma diagnosed during 2008-2010 in Spain.

Background: To assess management patterns and outcome in patients with glioblastoma multiforme (GBM) treated during 2008-2010 in Spain.

Methods: Retrospective analysis of clinical, therapeutic, and survival data collected through filled questionnaires from patients with histologically confirmed GBM diagnosed in 19 Spanish hospitals.

Results: We identified 834 patients (23% aged >70 years).