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TRUVACE RECORD VERSION
record: TRV-2026-1752
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-17T22:02:48.496282Z
status: published
lens: p_space
sector: health
headline: Code With Care: A Strategic Blueprint for Nurse-Led AI in Healthcare
dek: 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,…
gain_title: (none)
problem_title: Structural barriers limit nurse participation in AI innovation, and legal and ethical concerns reveal a gap between nurses' accountability and their authority over AI-enabled care.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Structural barriers limit nurse participation in AI innovation, and legal and ethical concerns reveal a gap between nurses' accountability and their authority over AI-enabled care.
problem_evidence: structural barriers limited nurse participation in innovation | a gap between accountability and authority
quick_read: An international think tank hosted by the Brocher Foundation in November 2024 brought together experts in nursing, informatics, ethics, philosophy, medicine, computer science and law. Their workshop outputs were analysed qualitatively and yielded six themes on how nursing expertise could be built into AI-based health technologies.

The authors argue that nursing involvement in healthcare AI is a governance matter, not just user feedback, covering organisational structures, education, legal accountability and professional roles. Because the recommendations come from expert discussion and still need empirical validation, they point to priorities rather than proven outcomes.
limitation: The recommendations come from expert elicitation and the authors state they require empirical validation before their value can be established.
tag: Evidence-backed problem
key_points: The study used a structured three-day international think tank and reflexive thematic analysis of workshop outputs. | Nurses were framed as co-developers who can shape AI problem definition, design and implementation. | AI education was viewed as building critical interpretive capacity, not only technical literacy. | Nurses were seen as frontline actors for identifying AI bias and inequity. | Competency frameworks were seen as a way to clarify role-specific expectations in AI-enabled care.
rundown: Participants represented several disciplines, including nursing, informatics, ethics, philosophy, medicine, computer science and law. Activities included structured presentations, facilitated subgroup discussions, plenary synthesis and collaborative drafting of recommendations, and the study followed SRQR reporting guidelines.

The six findings describe nursing expertise as sociotechnical governance. They cover co-development roles, the educational aim of interpretive capacity, structural barriers to participation, an accountability-authority gap, bias and inequity detection, and competency frameworks.

The authors point to priority actions including nurse-led innovation, competence development, cross-sector collaboration, and educational and policy frameworks that recognise nursing expertise in AI development.
sources:
- peer_reviewed | Journal of Advanced Nursing | https://doi.org/10.1111/jan.70755 | 2026-10-07
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