Scoping review maps knowledge graph technology research landscape in nursing healthcare fields

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. 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. A total of 30 studies were included, with methodological studies as the main research design (n = 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. 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.