Why Interdisciplinary Teams Fail: A Systematic Analysis With Activity Theory in Clinical AI Collaboration

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. Yet, 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 systematically investigate persistent knowledge gaps in mismatched data coding themes and specialized languages, and also highlight how clinical data can act as boundary objects and human knowledge brokers to alleviate these challenges. Our work underscores the benefits of leveraging analytical frameworks like AT to systematically examine interdisciplinary teams' collaborative work and provide meaningful insights on best practices in future collaboration.