In the Middle, Not on Top: AI-Mediated Communication for Patient-Provider Care Relationships
This addresses the challenge of integrating AI into healthcare communication to enhance relationship-centered care, though it appears incremental in its application of existing mediation concepts.
The paper tackles the problem of positioning AI in clinical settings to support trust and connection in patient-provider relationships by proposing a 'middle, not top' approach where AI mediates communication without replacing human judgment, and finds through studies of the CLEAR system that this configuration redistributes interpretive work and reduces relational friction.
Relationship-centered care relies on trust and meaningful connection. As AI enters clinical settings, we must ask not just what it can do, but how it should be positioned to support these values. We examine a "middle, not top" approach where AI mediates communication without usurping human judgment. Through studies of CLEAR, an asynchronous messaging system, we show how this configuration addresses real-world constraints like time pressure and uneven health literacy. We find that mediator affordances (e.g., availability, neutrality) redistribute interpretive work and reduce relational friction. Ultimately, we frame AI mediation as relational infrastructure, highlighting critical design tensions around framing power and privacy.