岩瀨 敬佑

Abstract Background Recent advancements in digital health technologies have led to the accumulation of large volumes of unstructured text data, particularly in electronic health records. Given that nursing documentation frequently includes detailed descriptions of daily living activities and psychosocial contexts, existing medical natural language processing (NLP) tools may not be directly applicable without adaptation. Moreover, the integration of standardized nursing terminologies into NLP systems remains limited. Although NLP has the potential to support risk detection, information management, and care planning, its practical applications in nursing have not yet been systematically mapped. Objective This scoping review aimed to map the practical applications of NLP in nursing practice, with a specific focus on real-world clinical use, particularly for assessment, documentation, and interventions, and to identify the study characteristics, application domains, outcomes, and challenges. Methods Following the Joanna Briggs Institute methodology and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, a systematic search was conducted across PubMed, CINAHL, the Cochrane Library, Scopus, Embase, and Ichushi-Web in October 2023 and June 2025. The population-concept-context framework (population: nurses; concept: NLP applications; context: clinical settings) was used to guide study selection. Two independent reviewers screened 2889 unique records and reviewed the full texts of 72 studies. Data were descriptively synthesized and thematically classified. Results Six studies met the inclusion criteria, indicating that empirically evaluated applications of NLP in real-world nursing practice remain limited. NLP applications were categorized into three domains: (1) text summarization and generation, (2) information extraction and classification, and (3) real-time detection and screening. NLP was applied across multiple stages of nursing practice, including handovers, assessment, care planning, continuity of care, and patient safety management. Notably, NLP was used as a form of augmented intelligence to complement, rather than replace, nurses’ clinical judgment and to support clinical decision-making. These findings also highlight a substantial gap between rapid technological advances and the limited implementation of NLP in real-world nursing practice. Conclusions This review highlights the substantial gap between rapid technological advances and the limited implementation of NLP in real-world nursing practice. NLP may help reduce manual review workload, support early risk identification, and facilitate individualized care planning in selected nursing contexts. However, direct empirical evidence regarding its effects on nursing workflows, nurses’ clinical decision-making, and patient-level outcomes remains limited. Future research should prioritize real-world implementation studies and evaluate nurses’ acceptance, adoption barriers, workflow integration, and the influence of NLP on clinical decision-making.