Journal of nursing management · 2026
NAVIGATE-AI Nursing Framework: A Scoping Review and Preliminary Expert Content Evaluation for Safe Bedside AI Use in Nursing.
СтатьяScoping ReviewОбзорНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Alqaisi Omar, Darwish Safia, Dibas Mohammed, Al-Ghabeesh Suhair
Аннотация
AIM: To synthesize evidence on artificial intelligence (AI) applications in nursing clinical practice and to develop and evaluate the content relevance of an evidence-informed, bedside-oriented mnemonic framework supporting nurses' safe and critical use of AI-supported recommendations.
BACKGROUND: AI is increasingly integrated into nursing clinical practice, yet bedside nurses require concise guidance to appraise and use AI-supported recommendations safely and ethically. Existing frameworks address organizational adoption or AI literacy education; a point-of-care cognitive aid for critical appraisal of AI outputs remains underdeveloped.
EVALUATION: A scoping review, reported in accordance with PRISMA-ScR, searched PubMed/MEDLINE, CINAHL, Scopus, ScienceDirect, and Web of Science for English-language publications from January 2015 to September 2026. Fourteen eligible sources were synthesized into five themes. An evidence-informed seven-component bedside cognitive guide was subsequently developed and evaluated by a purposively selected panel of 31 nursing and healthcare professionals using item- and scale-level content validity indices.
KEY ISSUE: Relevance I-CVI values ranged from 0.68 to 0.94, and the S-CVI/Ave was 0.78, below the prespecified reference threshold of 0.90. These findings provide preliminary evidence regarding the relevance, clarity, and perceived bedside applicability of the framework components, but do not establish scale-level content validity.
CONCLUSION: The framework is an evidence-informed cognitive aid, not a validated clinical decision rule. Further iterative expert consensus and prospective usability, feasibility, and implementation testing are required before broad application.
IMPLICATIONS FOR NURSING MANAGEMENT: Nurse managers and health-system leaders should prioritize structured AI-literacy education, clear governance, and escalation pathways for AI-supported recommendations, nurse involvement in AI procurement and implementation, and monitoring mechanisms for AI-related safety events to ensure safe AI integration into nursing practice.
BACKGROUND: AI is increasingly integrated into nursing clinical practice, yet bedside nurses require concise guidance to appraise and use AI-supported recommendations safely and ethically. Existing frameworks address organizational adoption or AI literacy education; a point-of-care cognitive aid for critical appraisal of AI outputs remains underdeveloped.
EVALUATION: A scoping review, reported in accordance with PRISMA-ScR, searched PubMed/MEDLINE, CINAHL, Scopus, ScienceDirect, and Web of Science for English-language publications from January 2015 to September 2026. Fourteen eligible sources were synthesized into five themes. An evidence-informed seven-component bedside cognitive guide was subsequently developed and evaluated by a purposively selected panel of 31 nursing and healthcare professionals using item- and scale-level content validity indices.
KEY ISSUE: Relevance I-CVI values ranged from 0.68 to 0.94, and the S-CVI/Ave was 0.78, below the prespecified reference threshold of 0.90. These findings provide preliminary evidence regarding the relevance, clarity, and perceived bedside applicability of the framework components, but do not establish scale-level content validity.
CONCLUSION: The framework is an evidence-informed cognitive aid, not a validated clinical decision rule. Further iterative expert consensus and prospective usability, feasibility, and implementation testing are required before broad application.
IMPLICATIONS FOR NURSING MANAGEMENT: Nurse managers and health-system leaders should prioritize structured AI-literacy education, clear governance, and escalation pathways for AI-supported recommendations, nurse involvement in AI procurement and implementation, and monitoring mechanisms for AI-related safety events to ensure safe AI integration into nursing practice.