Nurses' Lived Experiences of Generative Artificial Intelligence‐Enabled Shift Handover Innovation: A Descriptive Phenomenological Study

AIM: To explore nurses' lived experiences of a generative artificial intelligence-enabled shift handover innovation. DESIGN: A descriptive phenomenological study guided by Husserl's philosophical framework and operationalized through Colaizzi's seven-step analytical method. METHODS: Purposive sampling was used to recruit 18 registered nurses at an Integrated General Hospital in Singapore. Semi-structured individual interviews (n = 12) served as the primary data source, followed by two confirmatory focus group discussions (n = 6 per group) incorporating six previously interviewed participants alongside six additional participants to validate and refine emerging themes. Data were collected between January and June 2025 and analysed using Colaizzi's seven-step phenomenological method. RESULTS: Five interconnected themes emerged: (1) the burden of fragmented documentation; (2) navigating technological change with cautious optimism; (3) anchoring innovation in familiar clinical frameworks; (4) anticipating barriers to seamless integration; and (5) envisioning enhanced patient safety and professional practice. CONCLUSION: Participants experienced a tension between documentation demands and direct patient care. Their conditional acceptance of AI assistance, contingent upon accuracy, clinical oversight, and workflow integration, reflects a sophisticated professional stance rather than resistance. The findings illuminate the essence of navigating the intersection of traditional practice and technological innovation. IMPACT: This study offers insights into nurses' lived experiences of AI-enabled handover innovation. The findings can inform user-centred implementation strategies that align technological innovation with nursing values and workflow realities. REPORTING METHOD: This study adhered to the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines. PATIENT OR PUBLIC CONTRIBUTION: Nursing staff contributed to the refinement of interview guides through pilot testing and provided feedback on preliminary findings through member checking procedures.