Understanding User Perceptions of Human-centered AI-Enhanced Support Group Formation in Online Healthcare Communities
This addresses the problem of finding relevant peers in large online health communities for patients and caregivers, but it is incremental as it focuses on user perceptions rather than a new technical method.
This study assessed user perceptions of algorithmically personalized support group formation in online healthcare communities, finding high perceived value (mean 4.55/5) and 91.5% willingness to join, but conditional acceptance requiring trust and privacy.
Peer support is critical to managing chronic health conditions. Online health communities (OHCs) enable patients and caregivers to connect with similar others, yet their large scale makes it challenging to find the most relevant peers and content. This study assessed perceived value, preferred features, and acceptance conditions for algorithmically personalized support group formation within OHCs. A two-phase, mixed-methods survey (N=165) examined OHC participation patterns, personalization priorities, and acceptance of a simulated personalized support group. Perceived value of the simulated support group was high (mean 4.55/5; 62.8% rated 5/5) and 91.5% would join this group. The importance participants placed on peer matching strongly correlated with perceived value (\r{ho}=0.764, p<0.001). Qualitative findings revealed conditional acceptance: participants demand security, transparency, human oversight, and user control over data. Personalized support groups may be desired, but they will not be adopted unless trust, privacy, and algorithmic governance concerns are addressed.