Application scope of knowledge graphs in nursing: a scoping review

Objective: The application of knowledge graph technology in the healthcare field is increasingly in-depth, yet there is a lack of literature that systematically sorts out the overall research landscape of its application in nursing from a macro perspective. This study aimed to systematically depict the research panorama of knowledge graph applications in nursing, stratify existing studies by stages through the construction of an exploratory evidence maturity framework, reveal structural gaps and translation barriers, and provide insights for subsequent in-depth research and practical applications. Methods: This study adopted the Arksey scoping review reporting framework and followed the PRISMA-ScR checklist for reporting. We systematically searched databases including Wanfang, CNKI, VIP, SinoMed, PubMed, Embase, Web of Science, CINAHL, and the Cochrane Library, and summarized and analyzed the included articles. The research results were comprehensively collated and divided into five phases: the Construction Phase, System Performance Evaluation Phase, Usability Evaluation Phase, Preliminary Application Phase, and Application Phase. Results: = 15), covering themes such as nursing education, disease management, health education, clinical nursing decision support, risk prediction, and psychological support. Analysis based on the evidence maturity framework showed that there were 12 studies (40%) in the knowledge graph Construction Phase, six (20%) in the System Performance Evaluation Phase, eight (26.7%) in the Usability Evaluation Phase, one (3.3%) in the Preliminary Application Phase, and three (10%) in the Application Phase. The research focus exhibited obvious domain bias: studies on nursing education scenarios dominated, while those on clinical nursing scenarios were relatively scarce. Conclusion: Knowledge graph research in nursing is still in the exploratory stage, dominated by evidence on technical construction, with no objective validation of its clinical application effects. Future research should adopt an evidence-driven approach, focusing on clinical application, optimizing study design and advancing data standardization, thereby enabling knowledge graphs to deliver evidence-based support in nursing practice. Scoping review registration: https://osf.io/, identifier 10.17605/OSF.IO/F7SB5.

Authors

Institutions

Publication Details

Journal
Frontiers in Public Health
Published
2026-04-15
DOI
https://doi.org/10.3389/fpubh.2026.1763063
Primary Topic
Advanced Graph Neural Networks
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Application scope of knowledge graphs in nursing: a scoping review

Weidong Zhao, Yue Wang, Hanghang Jin, X T Pan et al.
Frontiers in Public Health
Advanced Graph Neural Networks
article

Application scope of knowledge graphs in nursing: a scoping review

Weidong Zhao, Yue Wang, Hanghang Jin, X T Pan, Rongyue Guo, Xin Wang, Kechen Zhu
article en

Abstract

Objective: The application of knowledge graph technology in the healthcare field is increasingly in-depth, yet there is a lack of literature that systematically sorts out the overall research landscape of its application in nursing from a macro perspective. This study aimed to systematically depict the research panorama of knowledge graph applications in nursing, stratify existing studies by stages through the construction of an exploratory evidence maturity framework, reveal structural gaps and translation barriers, and provide insights for subsequent in-depth research and practical applications. Methods: This study adopted the Arksey scoping review reporting framework and followed the PRISMA-ScR checklist for reporting. We systematically searched databases including Wanfang, CNKI, VIP, SinoMed, PubMed, Embase, Web of Science, CINAHL, and the Cochrane Library, and summarized and analyzed the included articles. The research results were comprehensively collated and divided into five phases: the Construction Phase, System Performance Evaluation Phase, Usability Evaluation Phase, Preliminary Application Phase, and Application Phase. Results: = 15), covering themes such as nursing education, disease management, health education, clinical nursing decision support, risk prediction, and psychological support. Analysis based on the evidence maturity framework showed that there were 12 studies (40%) in the knowledge graph Construction Phase, six (20%) in the System Performance Evaluation Phase, eight (26.7%) in the Usability Evaluation Phase, one (3.3%) in the Preliminary Application Phase, and three (10%) in the Application Phase. The research focus exhibited obvious domain bias: studies on nursing education scenarios dominated, while those on clinical nursing scenarios were relatively scarce. Conclusion: Knowledge graph research in nursing is still in the exploratory stage, dominated by evidence on technical construction, with no objective validation of its clinical application effects. Future research should adopt an evidence-driven approach, focusing on clinical application, optimizing study design and advancing data standardization, thereby enabling knowledge graphs to deliver evidence-based support in nursing practice. Scoping review registration: https://osf.io/, identifier 10.17605/OSF.IO/F7SB5.

Frontiers in Public HealthVol. 14
Zhengzhou University (CN)
Zhengzhou University, Division of Graduate Education
Openalex Percentile: Top 22%
Advanced Graph Neural Networks
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.