How AI-supported automated triage shapes nurses’ work engagement: a self-determination theory perspective

Abstract Artificial intelligence (AI)-supported automated triage systems that incorporate auto-anamnesis, algorithmic triage, and text-based communication with healthcare professionals are increasingly deployed to optimize workflows and expand access to care. However, their influence on the work engagement and psychological wellbeing of healthcare professionals remains underexplored. Using self-determination theory as an analytical lens, we examined how the design and functionality shape nurses’ experiences of autonomy, competence, and relatedness, which are three core psychological needs essential for sustaining intrinsic motivation and engagement. Data were collected through semi-structured interviews with 29 nurses and complemented by contextual inquiries. Our findings reveal that while the system streamlines routine tasks and information organization, it simultaneously constrains nurses’ ability to exercise professional judgement, manage their workflow, and sustain meaningful social interactions. Rigid triage logic, fragmented system integration, and the loss of subtle communicative cues in text-based consultations challenge autonomy and competence. Moreover, altered work practices affect collegiality and emotional connection with patients. This study contributes to the human–computer interaction discourse by demonstrating how AI-supported healthcare automation interacts with professionals’ motivational dynamics and by highlighting design directions to better align AI-supported automated triage systems with users’ psychological needs and working conditions.