ArticleJournal of advanced nursing2026
An AI-Enabled Nursing Future With no Documentation Burden: A Vision for a New Reality.
Article in Journal of advanced nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Large Language Models in Chronic Disease Care: Mixed Methods Systematic Review and Thematic Synthesis.Journal of medical Internet research · 2026Pooled it
- Generative AI at the Bedside: An Integrative Review of Applications and Implications in Clinical Nursing Practice.Journal of clinical nursing · 2026Review
- Evolution of Nursing Education, Research and Practice: Insights to Foster Nurse Solidarity and Address Future Challenges.Journal of advanced nursing · 2026Article
- How Does Artificial Intelligence Align With Person-Centred Principles in Mental Health Nursing? A Scoping Review.International journal of mental health nursing · 2026Article
- Nanostructured electrode materials and flexible-substrate engineering for wearable multi-analyte biosensors in diabetes monitoring and personalized care: a comprehensive review.Journal of materials science. Materials in medicine · 2026Review
- Efficient Retrieval and Summarization of Nursing Policies and Procedures with Conversational AI on the Edge-Cloud Continuum.Journal of medical systems · 2026Article
- Knowledge and attitudes regarding AI-assisted documentation among clinical nurses in China: a cross-sectional study.BMC nursing · 2026Article
- Attitudes of psychiatric nurses towards the integration of artificial intelligence applications to clinical care: a qualitative study in China.BMC nursing · 2026Article
- Value Co-Creation Between Nurses and Generative Artificial Intelligence: A Grounded Theory Study.Journal of nursing management · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- AI Scribes in Health Care: Balancing Transformative Potential With Responsible Integration.JMIR medical informatics · 2025Article
- Capabilities of computerized decision support systems supporting the nursing process in hospital settings: a scoping review.BMC nursing · 2025Article
- Exploring perceptions of data risks in AI-enabled nursing research: A qualitative study.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsTo explore the potential of multimodal large language models in alleviating the documentation burden on nurses while enhancing the quality and efficiency of patient care.
designThis position paper is informed by expert discussions and a literature review.
methodsWe extensively reviewed nursing documentation practices and advanced technologies, such as multimodal large language models. We analysed key challenges, solutions and impacts to propose a futuristic multimodal large language model-driven model for nursing documentation.
resultsMultimodal large language models offer transformative capabilities by integrating multimodal audio, video and text data during patient encounters to dynamically update patient records in real time. This reduces manual data entry, enabling nurses to focus more on direct patient care. These systems also enhance care personalisation through predictive analytics and interoperability, which support seamless workflows and better patient outcomes. While predictive analytics could improve patient care by identifying trends and risk factors from nursing documentation, further research is required to validate its accuracy and clinical utility in real-world settings. Ethical, legal and practical challenges, including privacy concerns and biases in artificial intelligence models, require careful consideration for successful implementation.
conclusionTransitioning to multimodal large language model-driven documentation systems can significantly reduce administrative burdens, improve nurse satisfaction and enhance patient care. However, successful integration demands interdisciplinary collaboration, robust ethical frameworks and technological advancements. IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE: Implementing multimodal large language models could alleviate professional burnout, improve nurse-patient interactions, and provide dynamic, up-to-date patient records that facilitate informed decision making. These advancements align with the goals of patient-centred care by enabling more meaningful engagement between nurses and patients. IMPACT: The problem being addressed is the administrative burden of nursing documentation. We suggest that multimodal large language models minimise manual documentation, enhance patient care quality and significantly impact nurses and patients in diverse healthcare settings globally.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.