In the Middle, Not on Top: AI-Mediated Communication for Patient-Provider Care Relationships

TL;DR

This paper explores how AI can mediate patient-provider communication to support trust and connection without replacing human judgment, using the CLEAR messaging system as a case study.

Contribution

It introduces a 'middle, not top' AI mediation approach that enhances relational aspects of care while addressing practical constraints and design tensions.

Findings

AI mediation redistributes interpretive work and reduces relational friction.

Mediator affordances like availability and neutrality improve communication.

Framing AI as relational infrastructure highlights key design tensions.

Abstract

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.