An empirical account of how professional judgment is negotiated through AI-generated representations is contributed and a redesign probe is introduced as a way to engage with AI integration in practice.
Abstract
Designing Human–AI collaboration in healthcare requires understanding how generative AI (GenAI) reshapes how care is represented and made accountable through documentation. Instead of treating GenAI as a tool for automation, this paper examines how its introduction reconfigures care documentation practices by reshaping judgment and responsibility in nursing work. We report on a speculative co-design workshop with eight hospital nurses, where GenAI was used as a redesign probe to explore how documentation tasks might be distributed across four roles: human, system, shared, and invisible agent. Through AI-mediated scenario reconfigurations, nurses articulated which forms of judgment and relational work could be supported by systems and which remained inseparable from human practice. AI-generated scenarios were often operationally plausible yet professionally misaligned, surfacing tensions around responsibility, visibility, and trust. The paper contributes an empirical account of how professional judgment is negotiated through AI-generated representations and introduces a redesign probe as a way to engage with AI integration in practice.
As the technical ease of deploying Generative AI (GenAI) has led to its rapid adoption in healthcare training workflows, a critical gap has emerged in understanding how it reconfigures key aspects of work practice; e.g., instructional authority, responsibility, and interpretation in CPR training. In this speculative st...
Tina Westergaard Milbak, M. Cohn, J. Halse et al.· Proceedings of the 14th Nord...· 1 citation
Ambient artificial intelligence scribes (ambient scribes) are increasingly used across healthcare systems to support clinical documentation. Implementation strategies and associated research have been mainly concerned with the ability to accurately and efficiently produce clinical notes. Some concerns have been raised,...
L. Seuren, Robin Williams, K. Cresswell· BMJ Digital Health & AI· 2 citations
Legitimacy is a key concern when introducing data about work in healthcare, especially as data-driven systems move closer to real-world application and begin shaping how care work is represented, interpreted, and shared across stakeholders. This paper reports healthcare workers’ (HCWs’) perspectives on data sharing in...
Trine Rask Nielsen, Naja L. Holten Møller· ACM Transactions on Computin...· 0 citations
Clinical reasoning research has predominantly conceptualised reasoning as individual cognition, yet contemporary clinical practice distributes reasoning across teams, tools, and time. This study examines collaborative clinical reasoning (CCR) as a social practice constituted through observable interactional mechanisms....
Ching-Yi Lee, Hung-Yi Lai, Ching-Hsin Lee et al.· Advances in health sciences...· 0 citations
Artificial intelligence (AI) is reshaping healthcare work, professional
decision-making, and human–AI collaboration. However, limited research
explains how leadership can support AI integration while preserving
professional agency. This study developed an integrated model linking
Responsible AI Leadership, Human–AI Co-...
H. Maleki, Mahdi Kermanian· European Journal of Prosthod...· 0 citations
As AI becomes increasingly adept at communicating with humans in natural language and complementing human skills in various collaborative and teamwork settings, how the CSCW community understands the evolving role of communication in human-AI teams (HATs) urgently needs to keep pace with these technological advancement...
Wen Duan, Rui Zhang, Nan Weng et al.· Proceedings of the ACM on Hu...· 0 citations
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