Background: Patients are increasingly given access to their electronic medical records (EMRs) to help them keep track of their care, but many may have a difficult time understanding what is in them. Programs such as NoteAid assist in translating medical records and may increase the number of patients who actively use their EMRs, a development which may improve the management of chronic diseases. Purpose: To work on a translation system developed by the University of Massachusetts Informatics group to make outpatient records more understandable for adult patients with chronic disease by using and testing a machine-learning database (NoteAid). Patients’ self-management of chronic disease may improve, as they increase their understanding of medical terminology. Methods: A test version of NoteAid was used with volunteer adult patients during face-to-face sessions in an outpatient office at a health system in Southeastern Pennsylvania. These sessions were used to test NoteAid’s effectiveness as a tool to improve patients’ understanding of their EMRs. Patients read their own office note from a recent visit without the use of NoteAid, and then interpreted the same note using it. Results: 13 participants participated over a two-month period with 85% reporting they would use the system from a patient portal and 100% answering strongly agree or agree when asked if the NoteAid system helped them comprehend their clinical EMR notes. Conclusions: Machine-learning databases like NoteAid have the potential to improve the management of chronic diseases. By integrating these systems into an informative and user-friendly portal, patients are afforded the opportunity to improve understanding of their EMRs. Keywords: medical terms, patient understanding, health literacy, chronic disease, and electronic health record usability