Project Summary/Abstract The new OpenNotes rule in the United States enables all patients easy access to their medical records. The policy affords many benefits to patients such as increased accessibility, improved transparency, and better communication with medical providers. Additionally, OpenNotes may improve health disparities by holding providers more accountable for their documentation practices and medical decisions. However, concerns have emerged about the negative impacts of this policy on the quality of clinical documentation. Notes may become less detailed and patients may not understand the medical terminology, problems that may be more prevalent in populations that experience health disparities. While the nationwide rollout of OpenNotes may have enormous implications for the quality of documentation and patient perceptions of care, little empirical evidence is available to guide policy decisions on how to adjust the policy or mitigate harm. We will characterize the effects of OpenNotes on clinical documentation through an empirical computational analysis comparing the change in the clinical language before and after the policy implementation. In support of our analysis, we will develop clinical Natural Language Processing (cNLP) based tools to analyze medical language focused on the most likely types of documentation changes. We are especially focused on how these changes manifest for populations that experience health disparities. Our focus will be on mental health care due to the particularly sensitive nature of these notes. Furthermore, health disparities are particularly pronounced in mental health care, and stigmatizing language in behavioral medicine is of high concern. Our study will characterize changes in medical record language before and after implementation of OpenNotes policies. We will utilize cNLP tools that can identify changes and patterns in language use in EHR notes comparing language before and after OpenNotes went into effect. We will also evaluate the effect of OpenNotes policies on language in patient records for populations that experience health disparities. We will include an analysis of the presence of stigmatizing language about the patient and examine how policy changes either entrenched or improved existing healthcare disparities. Our analysis will draw on clinical notes from patient care settings within the Johns Hopkins Medical System and two specialties: mental health, selected because of the potential high impact of OpenNotes, and primary care, a control setting for which we expect minimal impact.