The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the development of a machine learning-enabled medical record summarization tool designed to provide a narrative summary that can aid physicians in patient care. On average, physicians spend just 3 minutes reviewing a patient?s medical record, and during this time they must interpret unstructured Electronic Health Records (EHR) that can make it difficult for physicians to identify information essential to patient care and diagnosis. By targeting the rich clinical data embedded in unstructured clinical notes, the proposed tool could provide clinically relevant information and a contextual understanding of a patient?s medical history. If successful, the proposed solution will reduce the data burden placed on doctors, mitigate the risk of missing valuable information that could affect patient diagnosis or lead to costly medical errors, and maximize downstream effects on patient outcomes. This Small Business Innovation Research (SBIR) Phase I project aims to leverage advances in natural language processing (NLP) to assist doctors by automating the process of electronic health record review. The underlying innovation is an extractive-abstractive pipeline that determines what content in the medical record is the most salient and should be summarized through a transformer (a machine learning model). This project aims to advance this summarization tool to more challenging use cases, primarily summarizing the outpatient record, a task made challenging by the large scope of the data, clinical redundancies, different data structures, and sources inherent to outpatient data, all of which need to be accounted for in model training and validation. Objectives include to 1) developing an outpatient summarization model and demonstrating the ability to produce summaries that semantically match reference text with a high level of fluency, 2) validating the utility of artificial intelligence (AI)-generated outpatient summaries to provide significant value to physicians, 3) evaluating the ability of AI-generated summaries that provide information relevant to future patient visit through ablation study, and 4) incorporating checks for bias in the existing model. 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.
