Journal of advanced nursing · 2026
Code With Care: A Strategic Blueprint for Nurse-Led AI in Healthcare.
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Peltonen Laura-Maria, Topaz Maxim, Michalowski Martin, Ronquillo Charlene, Mitchell James, Babic Aleksandar, Alhuwail Dari, Junger Alain, McCarthy Molly K, Hardiker Nicholas R, Meier Christoph A, Stiglic Gregor, Lindsköld Lars, Nyrup Rune, Weber Patrick, Zhang Zhihong, Pruinelli Lisiane
Аннотация
AIM: Artificial intelligence (AI) is reshaping healthcare delivery. This work aims to explore how nursing expertise can be fully integrated into the development of AI-based health technologies and to assess the implications for clinical practice, education, leadership, industry, policy and research.
METHODS: This qualitative expert elicitation study drew on data generated during a structured three-day international think tank hosted by the Brocher Foundation in November 2024. Interdisciplinary experts in nursing, informatics, ethics, philosophy, medicine, computer science and law participated in structured presentations, facilitated subgroup discussions, plenary synthesis activities and collaborative development of recommendations. Reflexive thematic analysis was used to analyse workshop outputs and identify cross-cutting patterns, tensions and implications.
RESULTS: Six interrelated thematic findings were generated. Participants framed nursing expertise as a form of sociotechnical governance rather than simply end-user input: (1) nurses were positioned as co-developers who can shape AI problem definition, design and implementation; (2) AI education was viewed as building critical interpretive capacity, not only technical literacy; (3) structural barriers limited nurse participation in innovation; (4) legal and ethical concerns reflected a gap between accountability and authority; (5) nurses were positioned as frontline actors for identifying AI bias and inequity; and (6) competency frameworks were viewed as tools for clarifying role-specific expectations in AI-enabled care.
DISCUSSION: The findings suggest that embedding nursing expertise in healthcare AI is a matter of sociotechnical governance, requiring attention to organisational structures, education, legal accountability, professional roles and mechanisms for identifying safety, equity and workflow concerns.
IMPLICATIONS FOR THE PROFESSION: This study provides an expert-informed framework for strengthening nursing participation in healthcare AI development. While the recommendations require empirical validation, they indicate priority areas for action, including nurse-led innovation, competence development, cross-sector collaboration, and educational and policy frameworks that recognise nursing expertise in AI development.
REPORTING METHOD: The study adhered to the Standards for Reporting Qualitative Research (SRQR) guidelines.
METHODS: This qualitative expert elicitation study drew on data generated during a structured three-day international think tank hosted by the Brocher Foundation in November 2024. Interdisciplinary experts in nursing, informatics, ethics, philosophy, medicine, computer science and law participated in structured presentations, facilitated subgroup discussions, plenary synthesis activities and collaborative development of recommendations. Reflexive thematic analysis was used to analyse workshop outputs and identify cross-cutting patterns, tensions and implications.
RESULTS: Six interrelated thematic findings were generated. Participants framed nursing expertise as a form of sociotechnical governance rather than simply end-user input: (1) nurses were positioned as co-developers who can shape AI problem definition, design and implementation; (2) AI education was viewed as building critical interpretive capacity, not only technical literacy; (3) structural barriers limited nurse participation in innovation; (4) legal and ethical concerns reflected a gap between accountability and authority; (5) nurses were positioned as frontline actors for identifying AI bias and inequity; and (6) competency frameworks were viewed as tools for clarifying role-specific expectations in AI-enabled care.
DISCUSSION: The findings suggest that embedding nursing expertise in healthcare AI is a matter of sociotechnical governance, requiring attention to organisational structures, education, legal accountability, professional roles and mechanisms for identifying safety, equity and workflow concerns.
IMPLICATIONS FOR THE PROFESSION: This study provides an expert-informed framework for strengthening nursing participation in healthcare AI development. While the recommendations require empirical validation, they indicate priority areas for action, including nurse-led innovation, competence development, cross-sector collaboration, and educational and policy frameworks that recognise nursing expertise in AI development.
REPORTING METHOD: The study adhered to the Standards for Reporting Qualitative Research (SRQR) guidelines.