It explores the use of electronic health records and large language models to facilitate advanced predictive analytics in personalized health care: the ability to tap EHR data at fine detail, further advance predictive models for better patient outcomes through LLM-based models of decision support. The health pattern analysis against risk factors brings forth an integrated model with predictable accuracy much beyond the traditional approach. Such an approach caters to the challenge of heterogeneity and scalabilities in data and contributes to precision medicine. Our results indicate that LLM-enhanced EHR systems can dramatically boost the diagnostic precision, thus personalizing the treatment; eventually, this goes a long way in changing healthcare delivery.