Understanding User Perceptions of Human-centered AI-Enhanced Support Group Formation in Online Healthcare Communities

TL;DR

This study explores user perceptions and acceptance factors for algorithmically personalized support groups in online healthcare communities, emphasizing trust, privacy, and human oversight as key to adoption.

Contribution

It provides empirical insights into user preferences, perceived value, and conditions for acceptance of personalized support groups in online health settings.

Findings

High perceived value of personalized support groups (mean 4.55/5)

Strong correlation between peer matching importance and perceived value (r=0.764)

Acceptance depends on security, transparency, and human oversight

Abstract

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.