Healthcare Professionals' Perspectives on Agentic Artificial Intelligence in Clinical Work: A Qualitative Study

Background/Objectives: Artificial intelligence (AI) is increasingly integrated into healthcare, primarily supporting clinical decision-making. Agentic AI may extend these applications by autonomously planning, coordinating, and executing clinical and administrative workflows. However, empirical evidence on how healthcare professionals conceptualise agentic AI and anticipate its implications for work and occupational well-being remains limited. This study explores healthcare professionals' perceptions and expectations regarding agentic AI and its potential effects on healthcare work. Methods: We conducted a qualitative study using semi-structured interviews with 25 healthcare professionals from different clinical backgrounds in Germany. Interviews explored perceptions of agentic AI, focusing on workflow organisation, workload, professional autonomy, accountability, and occupational well-being. Data were analysed using reflexive thematic analysis following Braun and Clarke. Results: Five themes were identified: (1) from assisting decisions to organising work; (2) automation redistributes rather than removes work; (3) delegating action without delegating responsibility; (4) implications for working conditions and health- and work-related outcomes; and (5) meaningful human control requires organisational conditions. Participants distinguished between conventional AI-based decision support and more autonomous forms of AI in their accounts, using this contrast to describe potential changes in workflow organisation. Their accounts highlighted practical consequences of delegating workflow-related activities to AI systems, including changes in task structures, monitoring and verification demands, exception management, and the distribution of professional responsibility. Participants anticipated reduced routine administrative workload and improved care coordination, but also potential increases in cognitive and supervisory demands. Conclusions: Agentic AI may shift healthcare from AI-assisted decision-making towards AI-mediated workflow execution. Its effects on occupational well-being may therefore be bidirectional, combining workload relief with increased cognitive and supervisory demands. Appropriate governance, clearly defined accountability, meaningful human oversight, workforce training, and regulatory alignment will be essential for responsible implementation.