Background: Patient education is linked to better health outcomes and is a core component of Family Medicine, where providers see a variety of patient health problems (Simonsmeier, 2022). Developing and maintaining an evidence-based and inclusive patient education library is a resource-intensive. Content libraries at academic medical centers often are not inclusive of Family Medicine. Moreover, users cannot tailor content to individual patient needs, and accessing content is cumbersome. Objective: We aimed to close this education gap by developing an AI-assisted tool where clinicians can easily generate trustworthy education content for diverse patient needs. Methods: Our tool combines a web-scraper that pulls data from mayoclinic.org, feeds it into a standalone user interface (UI) enabled by a large language model (LLM), which allows users to generate printable education based on inputs, such as disease name, content headers, text size, and patient reading level. We validated the LLM’s accuracy and completeness using volunteer medical students. We plan to evaluate the tool’s usability, time savings, and user satisfaction with a pilot study comparing the traditional workflow to our tool. Results & Future Work: Two times during the development process, output forms were evaluated by multiple different clinicians to confirm medical accuracy and readability. Post-pilot, we will investigate translating the tool into clinical practice. Mayo Choice Award Family medicine providers handle an incredibly large volume of diseases and diagnoses, so having easy-to-access, adjustable educational material is incredibly important as it decreases clerical burden for clinicians and increases patient health literacy (Hart, 2015). Currently, even if providers are able to locate the educational forms without interrupting their workflow to visit the public website, they cannot adjust educational material reading level or text sizes to tailor to individual patient needs without extra steps, which inhibits patients from fully understanding their diagnosis and relevant follow-up, including vital self-care instructions that lead to better patient outcomes (Simonsmeier, 2022). Overall, this tool provides the educational materials for over 400 diagnoses commonly seen in family medicine all in one place, while also allowing providers to tailor the reading level and text size to each patient, which will lead to overall better health outcomes. Works Cited Hart, S., 2015. Patient education accessibility. Medical Writing 24, 190– 194. Simonsmeier BA, Flaig M, Simacek T, Schneider M. What sixty years of research says about the effectiveness of patient education on health: a second order meta-analysis. Health Psychol Rev. 2022 Sep;16(3):450-474. doi: 10.1080/17437199.2021.1967184. Epub 2021 Aug 24. PMID: 34384337.
