JMIR Publications

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. Because nursing documentation frequently includes detailed descriptions of daily living activities and psychosocial contexts, existing medical natural language processing (NLP) tools are not directly applicable. Moreover, the integration of standardized nursing terminologies into NLP systems remains limited. Although NLP can potentially support patient safety, risk detection, and care planning, its practical uses in nursing have not been systematically mapped.

Objective:

This scoping review aimed to map practical applications of NLP in nursing—particularly in assessment, documentation, and interventions—and to identify study characteristics, application domains, outcomes, and challenges.

Methods:

Following the Joanna Briggs Institute methodology and PRISMA-ScR guidelines, a systematic search was conducted across PubMed, CINAHL, the Cochrane Library, Scopus, Embase, and Ichushi-Web in October 2023 and June 2025. The PCC framework (Population: nurses, Concept: NLP applications, Context: clinical settings) was used for study selection. Two independent reviewers screened 2,889 unique records and performed a full-text review of 72 articles. Data were descriptively synthesized and thematically classified.

Results:

Six studies met the rigorous inclusion criteria, reflecting the nascent stage of the field. NLP applications were categorized into (1) text summarization and generation, (2) information extraction and classification, and (3) real-time detection and screening. NLP was applied across multiple nursing practice stages, including handovers, assessment, care planning, continuity of care, and patient safety management. Notably, NLP did not function as a direct substitute for nurses’ decision-making; rather, it served as an augmented intelligence that complemented nurses’ clinical judgment and supported appropriate nursing interventions.

Conclusions:

Within nursing, NLP may reduce the burden of documentation, support early risk detection, and facilitate individualized care planning. By complementing nurses’ clinical reasoning, NLP may contribute to improved patient safety and quality of care. Future research should focus on real-world clinical implementation and empirically evaluate NLP’s impact on nursing workflows and decision-making processes.