In this qualitative study of 10,233 unique statin-related discussions, an AI pipeline was developed to analyze these discussions, which were automatically categorized into 100 topics and 6 thematic groups. Sentiment analysis of these discussions showed that most of them had a neutral or negative sentiment. Findings from this study suggest that AI-enabled analysis of social media data may generate insights into public perceptions and help guide strategies for addressing barriers to statin use and adherence.

Heart failure (HF) mortality exceeds 20% in the first year after diagnosis.1 Although medical treatment decreases mortality and increases quality of life, particularly among those with reduced ejection fraction (EF), these improvements are enhanced with timely diagnosis and therapy initiation. Early recognition of heart failure (HF) can reduce morbidity, yet HF is often diagnosed only after symptoms require urgent treatment. In this paper, we sought to describe predictors of HF diagnosis in the acute care vs outpatient setting within the Veterans Health Administration (VHA).