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

Ravi Shankar; Amaevia Lim; Qian Xu

doi:10.1111/jan.70629

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

J Adv Nurs. 2026 Apr 24. doi: 10.1111/jan.70629. Online ahead of print.

Authors

Ravi Shankar¹, Amaevia Lim², Qian Xu³

Affiliations

¹ Clinical Research & Innovation Office, Tan Tock Seng Hospital, National Healthcare Group, Singapore, Singapore.
² Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
³ School of Civil, Aerospace and Design Engineering, University of Bristol, Bristol, UK.

PMID: 42027174
DOI: 10.1111/jan.70629

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.

Keywords: descriptive phenomenology; focus group; generative artificial intelligence; innovation; lived experience; nursing; patient safety; qualitative research; shift handover.