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

2026-04-01

AI Summary

This study addresses concerns that artificial intelligence (AI) may erode trust in clinical settings by proposing a “mediating rather than dominant” AI role. The authors embed AI as relational infrastructure within CLEAR, an asynchronous communication platform, to mitigate challenges arising from time pressures and disparities in health literacy through mediated dialogue. Emphasizing neutrality and usability, this design redistributes interpretive labor and reduces relational friction without supplanting clinical judgment. Empirical findings demonstrate that the approach enhances communication efficiency while surfacing critical design tensions around power dynamics and privacy protection. The work thus offers a novel paradigm for deploying trustworthy AI in healthcare that supports—rather than substitutes—human-centered clinical relationships.

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.

Problem

• AI-mediated communication
• relationship-centered care
• trust
• clinical AI
• human-AI collaboration

Innovation

• AI-mediated communication
• relationship-centered care
• relational infrastructure
• asynchronous messaging
• design tensions