This research presents a new framework for clinical summarization that combines the TinyLlama model with MIMIC-III and FHIR data using the Model Context Protocol (MCP). Unlike cloud-based models like Med-PaLM, our approach uses local processing to cut costs and protect patient data with AES-256 encryption and strict access controls, meeting HIPAA and GDPR standards. It retrieves FHIR-compliant data from public servers (e.g., \texttt{hapi.fhir.org}) for interoperability across hospital systems. Tested on discharge summaries, it achieves ROUGE-L F1 scores of 0.96 for MIMIC-III and 0.84 for FHIR, beating baselines like BioBERT (0.61, p < 0.001) due to efficient preprocessing and MCP’s accurat... More >