Imagine a world where AI could predict if a heart failure patient might end up back in the hospital within 30 days. That's the intriguing possibility explored by researchers who used Large Language Models (LLMs) to analyze social determinants of health (SDOH). Think of SDOH as the conditions in which people live, grow, and age – factors like access to education, the safety of their neighborhoods, and their economic stability. This research delves into whether these social factors, combined with traditional clinical data, can enhance our predictive capabilities. The team found that focusing on 'Neighborhood and Built Environment' SDOH data, alongside patient clinical information, gave the most accurate predictions. But how can researchers efficiently sift through the mountains of public SDOH data? That’s where LLMs come in. These AI powerhouses excel at automating the task of organizing and classifying data, effectively identifying which bits of information fall under specific SDOH domains. Even without specialized training, some LLMs performed surprisingly well at classifying these variables. This study hints at the potential of LLMs to streamline the integration of SDOH data, possibly paving the way for more personalized, proactive healthcare. Integrating publicly available social data could enhance hospital readmission predictions, ultimately improving patient outcomes and the efficiency of our healthcare system. The challenge now lies in optimizing data labeling techniques and incorporating even richer SDOH information from social media posts, satellite imagery, and other unconventional sources. This research sparks excitement about the future of AI in healthcare – a future where social and clinical data converge to provide a more holistic view of patient health.