Exploring Interdisciplinary Team Collaboration in Clinical NLP Projects Through the Lens of Activity Theory

2024-09-30

arXiv.org

AI Summary

Interdisciplinary collaboration between clinicians and AI researchers—particularly speech-language pathologists (SLPs) and natural language processing (NLP) scientists—is frequently undermined by blurred disciplinary boundaries, fragmented terminologies, and divergent interpretations of clinical data; existing literature lacks systematic analysis of underlying mechanisms and actionable mitigation strategies. Method: This study pioneers the application of activity theory to examine SLP–NLP collaboration, employing semi-structured interviews and thematic analysis across multiple clinical NLP projects. Contribution/Results: We identify three core barriers: (1) professional discourse conflict, (2) data interpretation tension, and (3) absence of effective knowledge mediation. We empirically validate clinical data’s dual role as a “boundary object”—facilitating yet also complicating cross-domain coordination. Building on this, we propose the novel paradigm of “AI as knowledge mediator” and design an AI-driven knowledge-brokering framework, offering a transferable, theory-grounded collaboration model and practical guidelines for clinical NLP initiatives.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Application Domains: Humanities & Computational Social Science

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Natural Language Processing (NLP) techniques have been increasingly integrated into clinical projects to advance clinical decision-making and improve patient outcomes. Such projects benefit from interdisciplinary team collaborations. This paper explores challenges and opportunities using two clinical NLP projects as case studies, where speech-language pathologists (SLPs) and NLP researchers jointly developed technology-based systems to improve clinical workflow. Through semi-structured interviews with five SLPs and four NLP researchers, we collected collaboration practices and challenges. Using Activity Theory as an analytical framework, we examined collaborative activities, challenges, and strategies to bridge interdisciplinary gaps. Our findings revealed significant knowledge boundaries and terminological barriers between SLPs and NLP researchers when both groups relied on clinical data as boundary objects to facilitate collaboration, although this approach has limitations. We highlight the potential opportunities of AI technologies as knowledge brokers to overcome interdisciplinary collaboration challenges.

Problem

• Analyzing barriers in clinical-technical team collaboration
• Investigating knowledge gaps in data coding and language
• Exploring strategies to improve interdisciplinary AI teamwork

Innovation

• Activity Theory analyzes interdisciplinary team barriers
• Clinical data serves as boundary objects
• Human knowledge brokers bridge gaps