SBIR Phase II : A tool to automate a narrative patient summary of the medical chart for outpatient physicians

About This Grant

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 analyze broad and unstructured medical data. By integrating state-of-the-art models and a federated learning structure to address data-sharing constraints, the project aims to ensure adaptability across various healthcare environments. Anticipated technical results include high-fidelity summaries, robust integration with electronic health record systems, and real-time capabilities for physicians to access and query patient records. The research scope includes fine-tuning large language models, implementing speech-to-text integrations, and developing retrieval-augmented generation systems for personalized physician queries. Methods involve annotating diverse datasets, employing advanced evaluation metrics, and rigorous testing with medical professionals. This project is expected to produce a scalable, clinically validated tool to potentially enhance physician efficiency, reduce medical errors, and ultimately improve patient outcomes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.