ArticleJournal of clinical nursing2026
Nurses' Insights on the Braden Scale and Their Vision for Artificial Intelligence Innovations: A Mixed Methods Study.
Article in Journal of clinical nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Reimagining nursing practice in the era of AI: a qualitative systematic review and meta-synthesis of nurses' lived experiences.Journal of health, population, and nutrition · 2026Pooled it
- Nurses' Insights on the Braden Scale and Their Vision for Artificial Intelligence Innovations: A Mixed Methods Study.Journal of clinical nursing · 2026Article
- AI in Health care: A Catalyst for Enhancement, Not Replacement.Journal of clinical nursing · 2026Article
- Early Detection of Deep Tissue Pressure Injury in Intensive Care Using Hemodynamics-Based Machine Learning: A Retrospective Cohort Study.International wound journal · 2026Observational
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsThis study aimed to explore nurses' experiences with the Braden Scale, assess their readiness for artificial intelligence (AI) technologies, and understand the innovations they envision for clinical practice.
designExplanatory sequential mixed design.
methodsThe study included 118 nurses in the quantitative data and 42 in focus groups. Quantitative data were collected using the MAIRS-MS. Qualitative data were analysed using phenomenological approaches and MAXQDA.
resultsThe average age was 33.38 ± 7.42 years and 88.1% were women. The average length of professional experience is 11.66 ± 8.22 years. The average time to administer the Braden Scale was 5.02 ± 4.36 min. While 55.1% of the participants found the Braden Scale inadequate, 55.9% stated that a more comprehensive risk assessment scale was needed and the MAIRS-MS score was 78.48 ± 16.66. The sub-themes were identified: Simple and quick applicability, early risk identification, validity and reliability issues, neglecting other risk factors, making it more comprehensive and specific, developing of a new risk assessment scale, technological improvements, patient data treasure chest, creating avatars and converting speech-to-text.
conclusionsThis study highlights critical gaps in the Braden Scale's effectiveness. Nurses identified significant shortcomings, including non-specificity and the neglect of key risk factors, which undermine its utility in clinical settings. They emphasised that stronger risk predictions and personalised care plans can be achieved by AI technology. IMPLICATIONS FOR PROFESSIONAL CARE: This study emphasises the need to revise the Braden Scale or develop a new one due to its limitations in risk assessment, providing crucial information to improve patient care and offering new perspectives on AI integration in PI risk assessment for nursing practice. IMPACT: This study highlights nurses' experiences and suggestions for improving the Braden Scale in clinical practice, emphasising their expectations for AI technology and its potential to revolutionise patient care. REPORTING
methodThe study report was prepared following the Good Reporting of A Mixed Methods Study (GRAMMS) checklist. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.
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