Improving Patient Access and Comprehension of Clinical Notes: Leveraging Large Language Models to Enhance Readability and Understanding

Abstract

Patient access to clinical notes has demonstrated numerous benefits, including an increased sense of control over their condition, enhanced engagement, improved medication adherence, and greater clinician accountability. However, the presence of medical jargon, abbreviations, and complex medical concepts within clinical notes hinders patient comprehension, thus diminishing the positive effects of note accessibility. These notes, primarily intended for clinicians, often appear disorganized and contain an abundance of technical terms. Breast cancer patients, in particular, face information overload and experience taxing symptoms related to their treatment, exacerbating this issue. Although some clinicians are adapting their writing style to meet patients’ needs, time constraints limit the feasibility of comprehensive note-taking. We propose the development of a patient-facing tool, in the form of a web application, to make information contained in clinical notes more accessible by leveraging machine learning models to simplify, summarize, extract information from, and add context to clinical notes. Through a series of user studies, we demonstrate that our proposed augmentations to clinical notes significantly improve comprehension and enhance patients’ reading experience.