ArticleCureus2024
Embracing Artificial Intelligence: Revolutionizing Nursing Documentation for a Better Future.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed.
- Review
- Attitudes of psychiatric nurses towards the integration of artificial intelligence applications to clinical care: a qualitative study in China.BMC nursing · 2026Article
- Translational Potential and Explainability of Artificial Intelligence-Based Clinical Decision Support for Adults in Intensive Care: A Scoping Review.Journal of multidisciplinary healthcare · 2026Review
- Precision management in chronic disease: An AI empowered perspective on medicine-engineering crossover.iScience · 2025Review
- Artificial Intelligence in Nursing: Technological Benefits to Nurse's Mental Health and Patient Care Quality.Healthcare (Basel, Switzerland) · 2024Review
- Progress Achieved, Landmarks, and Future Concerns in Biomedical and Health Informatics.Healthcare (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nursing documentation stands as a critical aspect of healthcare delivery, ensuring comprehensive patient records and facilitating communication among healthcare providers. However, traditional documentation methods are often time-consuming and prone to errors, diverting nurses' attention from direct patient care. This editorial explores the transformative potential of artificial intelligence (AI) in revolutionizing nursing documentation processes. By leveraging AI-driven technologies, such as natural language processing and machine learning, healthcare organizations can automate data entry, extract key clinical information, and generate personalized care plans, thereby streamlining workflows and improving documentation accuracy. This editorial also examines various AI-powered software applications and platforms that facilitate nursing documentation, highlighting their benefits in terms of efficiency, accuracy, and clinical decision support. Furthermore, it discusses considerations such as privacy, security, and the need for nurse training to effectively integrate AI into nursing practice. By embracing AI in nursing documentation, healthcare organizations can empower nurses to devote more time to patient care while enhancing the quality and safety of healthcare delivery.
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.