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.

The Sound of Silence: Unspoken Meaning in the Discourse of Pregnant and Breastfeeding Women on Environmental Risks and Food Safety in Spain.

(1) Background: In recent years, a growing number of qualitative health research studies have performed discourse analysis of data from participants' narratives. However, little attention has been paid to the gaps and silences within these narratives. The aim of the present study is to interpret the silences detected in the discourse of pregnant and breastfeeding women concerning environmental risks and food safety.