ArticleBMC nursing2025
The digital intelligent precise nursing framework: theory development in health recommender system.
Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Development and usability testing of a health recommender system for symptom management in women with breast cancer who were receiving or had recently received chemotherapy.Asia-Pacific journal of oncology nursing · 2026Article
- Clinical nurses as digital guardians: unlocking the key determinants of digital resilience in the AI era.Frontiers in public health · 2026Article
- Nurses as guardians of time: the hidden clinical value of continuous care in geriatrics.Frontiers in public health · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundWith the rapid integration of artificial intelligence, the Internet of Things, and big data into healthcare, Health Recommender Systems (HRS) have emerged as powerful tools to support personalized care. However, their application in the nursing field lacks a theoretical foundation grounded in nursing science.
objectiveThis study aims to develop the Digital Intelligent Precise Nursing Framework, a theory-driven conceptual model for HRS adoption in nursing, to guide the design of intelligent recommendation systems that align with the holistic, person-centered principles of nursing.
methodsDrawing upon interdisciplinary literature and nursing paradigms, this study proposes a framework consisting of three interrelated components: multidimensional data, solution bank, and recommendation. Multidimensional data includes sensing modalities, information modalities, data types, and information sources. The solution bank is structured across two axes—target users and function types. Recommendation engines integrate data and solution strategies to generate user-centered inferential conclusions, supportive measures, and individualized action suggestions.
resultsThe framework enables intelligent nursing systems to synthesize heterogeneous data and deliver personalized, real-time, and context-aware interventions. It provides a foundation for moving nursing practice from evidence-based care to precision-guided decision-making.
conclusionThe Digital Intelligent Precise Nursing Framework offers a structured foundation for advancing intelligent HRSs in nursing by bridging nursing theory, health technology, and clinical reasoning. It supports the development of systems that are adaptive, interpretable, and responsive to users’ needs in diverse care settings.
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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.