Bridging Knowledge Gaps in Clinical AI: An Activity Theory Perspective on Interdisciplinary Data Work for Telehealth

TL;DR

This study analyzes interdisciplinary clinical AI collaborations using Activity Theory, highlighting how shared data and broker roles facilitate coordination and address knowledge gaps in telehealth.

Contribution

It offers a novel application of Activity Theory to understand collaboration barriers and strategies in clinical AI development.

Findings

Shared clinical data act as boundary objects facilitating collaboration.

Interdisciplinary collaborators serve as knowledge brokers to bridge gaps.

Insights into best practices for early-stage clinical AI teamwork.

Abstract

Advanced AI technologies are increasingly integrated into clinical domains to advance patient care. The design and development of clinical AI technologies necessitate seamless collaboration between clinical and technical experts. However, such interdisciplinary teams are often unsuccessful, with a lack of systematic analysis of collaboration barriers and coping strategies. This work examines two clinical AI collaborations in the context of speech-language pathology via semi-structured interviews with six clinical and seven technical experts. Using Activity Theory (AT) as our analytical lens, we examine persistent knowledge gaps and collaboration tensions across clinical and technical workflows, and show how clinical data can function as boundary objects while interdisciplinary collaborators may act as knowledge brokers to help address these challenges. Our findings contribute to CSCW research on interdisciplinary teams' data work by showing how shared clinical data, boundary objects, and broker roles shape coordination in early-stage clinical AI collaboration, and by providing insights into best practices for future collaboration.