ASL Consent in the Digital Informed Consent Process.

There is an estimated 500,000 people in the U.S. who are deaf and who use ASL and live in the U.S. Compared to the general population, deaf people are at greater risk of having chronic health problems and experience significant health disparities and inequities (Sanfacon, Leffers, Miller, Stabbe, DeWindt, Wagner, & Kushalnagar, 2020; Kushalnagar, Reesman, Holcomb, & Ryan, 2019; Kushalnagar & Miller, 2019). Much of the disparities are explained by the barriers in the environment, such as the unavailability of materials in ASL and lack of healthcare professionals who know how to provide deaf patient-centered care. Intersecting social determinants of health (e.g., intrinsic - low education; and extrinsic - barrier to healthcare services) create a mutually constituted vulnerability for health disparities when a person is deaf (Kushalnagar & Miller, 2019; Lesch, Brucher, Chapple, R., & Chapple, K., 2019; Smith & Chin, 2012). Moreover, the longstanding history of inequitable access to language and education, and a lack of printed information and materials, leave people who are deaf and use ASL unaware of opportunities to participate in cutting-edge research/clinical trials. An unintended consequence, therefore, is that PIs neglect to include people who are deaf and use ASL in their subject sample pools, and this marginalized population continues to be at disparity for health outcomes and also clinical research participation. One barrier is the unavailability of informed consent materials that are accessible in ASL. The current research study conducted by our team at the Center for Deaf Health Equity at Gallaudet University attempts to address the language barrier to the consent process through a careful reconsideration of its traditional English format and the development of an American Sign Language (ASL) informed consent app. This team successfully leveraged existing machine learning methods to develop a way to