JMIR Publications

ABSTRACT

Background:

Transparency is widely recognized as an important principle for the use of AI in healthcare. However, patient-facing transparency requires decisions about what information should be communicated, and little is known about how patients prioritize different types of information when asked to distinguish what matters most.

Objective:

To examine how patients prioritize transparency elements derived from a health system AI governance process, and to identify which types of information are most important for patient-facing communication.

Methods:

Three patient panels participated in structured discussions and a constrained choice prioritization exercise focused on 17 AI transparency elements derived from Duke Health’s AI governance process. Panels included a demographically sampled advisory panel (n=17), and members of community groups serving predominantly African American (n=12) and Hispanic/Latino (n=7) communities. Participants selected the four transparency elements they considered most important for patient-facing communication. Frequencies and percentages were compared across panels, and qualitative rapid analysis was used to contextualize and interpret prioritization patterns.

Results:

Across the three panels, the most frequently selected transparency elements were risks and limitations (21/34, 62%), how AI outputs are used in clinical care (18/34, 53%), the purpose of the AI tool (14/34, 41%), and intended care population (12/34, 35%). Elements describing the technical and organizational context, including algorithm type, software application, implementation location, and supporting department, were selected less frequently. Qualitative findings indicated that participants valued information that helped them understand how AI may influence care decisions and whether tools were relevant to patients with clinical or demographic characteristics similar to their own. Several priorities were broadly shared across panels, although notable differences emerged.

Conclusions:

When asked to prioritize AI transparency elements, participants favored information that helped them understand how AI could affect their care and whether tools were appropriate for patients like them. These findings suggest that patient-facing communication should emphasize these concerns while remaining responsive to community-specific priorities. More broadly, the study demonstrates how patient and community perspectives can help translate an existing AI governance process into priorities for patient-facing communication.