CHCI Planning Grant Awarded to Maaz Gardezi, Elham Morshedzadeh, Hoda Eldardiry, Wallace Santos Lages, and Andre Albert Muelenaer for their human-centered AI Project

A CHCI Planning grant was awarded to Maaz Gardezi (Sociology), Elham Morshedzadeh (Industrial Design), Hoda Eldardiry (Computer Science), Wallace Santos Lages (Visual Arts), and Andre Albert Muelenaer (Biomedical Engineering and Mechanics) for their project ”Trustworthy by design: Using human-centered AI for improving healthcare training effectiveness.” CHCI planning grants support new or existing teams to perform activities, such as team building and project scoping, that are necessary to enable the submission of a competitive large proposal in the future. This year, the grant was awarded to a human-centered AI project in alignment with the 2022 CHCI workshop theme, “Human-Centered AI for Research, Innovation, and Creativity”.

Telemedicine cart (TC) enables healthcare providers to connect with remote patients in real-time. Training is essential for healthcare providers to effectively use TC for improving care delivery. To embrace any training, healthcare workers need to believe they can trust telehealth training technology. The proposed project builds the foundational knowledge for readying 21st century healthcare workforce to comfortably participate in telemedicine. The project uses a design justice approach to explore ways in which the design of these technologies are equitable across different axes of social differences, such as race, class, and gender.

Researchers will use the planning grant to systematically understand the necessary social and technical conditions for designing an intelligent telehealth training system that healthcare workers can trust and use. In the project, they define two distinct ‘users’ of the cart: the healthcare provider (physician/nurse practitioner), who receive information remotely, but do not interact directly with the TC. The other user, nurse/therapist, operates the cart and all of the peripherals utilized in the remote examination, such as the otoscope, dermatoscope, and stethoscope. 

The research question motivating this project is: Under what conditions do users consider an intelligent training system to be trustworthy? The project has two objectives: (1) identify the theoretical social and technical factors that influence users’ trust in intelligent training systems; and (2) use existing data from user studies, thematic topic modeling, and machine learning algorithms to test and validate the theoretical relationships identified in objective 1. Results from the two objectives will support the development of a research grant proposal to be submitted to external funding agencies.