The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to revolutionize healthcare delivery by leveraging natural language processing to provide concise, clinically relevant summaries of patients’ medical records. By reducing the burden on physicians the tool could addresses pressing issues like medical errors, patient safety, and provider burnout. Commercially, the proposed approach could provide the potential to streamline clinical workflows, improve revenue reimbursement, and reduce administrative burdens for healthcare providers. Its integration with national health information exchanges and leading electronic health record systems positions it as a pivotal tool for digital health companies and medical institutions, creating a scalable solution with broad market applicability. The proposed project addresses the critical challenge of reducing the time burden associated with processing unstructured electronic health records while ensuring the accuracy and comprehensiveness of patient care. Physicians often lack adequate tools to quickly synthesize patient histories, which can lead to missed follow-ups, medical errors, and inefficiencies. The project aims to develop and refine a machine-learning-enabled tool to generate clinically relevant, narrative summaries of medical records, enhancing decision-making and streamlining clinical workflows. The proposed research focuses on natural language processing techniques to