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

Abstract

ABSTRACT 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.

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