This scoping review followed the Joanna Briggs Institute methodology [17] and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines [18] (Checklist 1). This design was selected to map the existing evidence base and identify gaps in the evolving domain of NLP in nursing practice.

The literature search was conducted using PubMed, CINAHL, the Cochrane Library, Scopus, Embase, and Ichushi-Web (Japan). The initial search was conducted on October 5, 2023. In the updated search, performed on June 13, 2025, the phrase “data mining” was added to enhance the search’s sensitivity and comprehensiveness. The comprehensive search strategies for each database are tabulated in Multimedia Appendix 1. Equivalent search strategies were adapted for each database, as appropriate. The search strategy was designed to prioritize foundational NLP and data mining approaches with established evidence of clinical integration, reflecting the implementation-oriented scope of this review. Accordingly, the review focuses on empirically evaluated applications within nursing workflows, rather than capturing the broad spectrum of emerging technologies such as generative AI or LLMs.

Using the Joanna Briggs Institute population–concept-context framework, the inclusion criteria were as follows: nurses (population), NLP applications in nursing practice (concept), and clinical settings (context). Eligible studies were peer-reviewed original articles in English or Japanese that described NLP applications in nursing practice (eg, assessment, documentation, or interventions). The eligibility criteria were specifically defined to identify studies describing the use of NLP within real-world nursing practice and clinical workflows. Studies were excluded if they (1) focused solely on technical model performance or diagnostic accuracy without clinical application or nursing involvement, (2) focused on physician-centered or other nonnursing applications without explicit involvement of nurses, or (3) were gray literature (eg, conference abstracts or proceedings) lacking sufficient methodological detail. Among studies addressing similar topics (eg, automated identification from clinical or radiology reports), only those that described applications within nursing-related clinical workflows (eg, assessment, documentation, or interventions) were included.

Two independent reviewers screened studies in 2 stages: screening of titles and abstracts, followed by full-text review. Disagreements were resolved through consultation with a third author. 19-2119-24

Data were extracted using a standardized form (see Multimedia Appendix 2) that included author, year, country, design, population, nursing practice domain, NLP techniques, performance metrics, human intervention points, and outcomes. The extracted data were descriptively synthesized. NLP techniques were categorized according to their methodological approach (eg, rule-based and machine learning), and applications were thematically classified according to clinical context (assessment, documentation, and interventions). Common themes among outcomes and challenges were aggregated.

This study is a scoping review based on previously published literature and did not involve human participants. Therefore, ethics approval was not required in accordance with the authors’ institutional policies.

From the 1535 unique records in the initial search, 24 studies underwent full-text review, and 3 studies were included [19-21]. The updated search yielded an additional 1354 unique records and 48 full-text reviews, adding 3 more studies. Overall, 2889 unique records were screened, 72 full-text studies were reviewed, and 6 studies were included [19-24]. Hand searching the 11 references from relevant reviews identified no further studies.

As illustrated in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram, most of the records excluded during the title and abstract screening and the full-text review stages focused on the technical development or performance evaluation of NLP models and did not describe nursing practice or clinical workflow applications. The screening and selection process was conducted systematically and transparently based on predefined inclusion and exclusion criteria (Figure 1).

Table 1 summarizes the characteristics of the included studies. The examined studies were published between 2012 and 2025 [19-24]. Four studies were conducted in the United States [19,21,23,24], 1 in the United Kingdom [22], and 1 in Taiwan [20]. The study designs included a pilot implementation study [20], retrospective and prospective implementation evaluation studies [19,21-23], and an observational implementation study [24].

The primary data sources used across these studies consisted of unstructured text documented in EHRs, including clinical notes, nursing notes, incident reports, and handover records. The NLP techniques involved rule-based approaches [22-24], machine learning–based classification and prediction methods [19,20,23], and natural language generation [22]. Notably, all included studies implemented NLP as part of existing clinical systems involving nurses, rather than as a stand-alone technical intervention. For a more comprehensive description of study characteristics, including detailed setting information and specific data extraction methods, see Multimedia Appendix 3.

The applications of NLP identified in the included studies were categorized according to the nursing process stages (Tables 1 and 2).

In this scoping review, only 6 studies [19-24] met the inclusion criteria, highlighting a substantial implementation gap between the rapid advancement of NLP technologies and their integration into real-world nursing practice. The limited number of eligible studies is an important finding in itself. Rather than focusing solely on whether NLP can technically process nursing documentation, this review highlights the broader question of why implementation-oriented NLP research in nursing remains so limited. The findings of this review suggest that this implementation gap reflects interrelated technical, organizational, and professional and cultural barriers to implementation.

Unlike previous reviews, which have predominantly focused on the technical aspects of NLP or broader implementation and ethical issues related to AI in health care [12,14,29], this review examined how NLP has been integrated into nursing workflows and the extent to which nurses are involved in these systems in real-world clinical settings. Although recent interest has shifted toward LLM-based applications, empirical evidence regarding the implementation of these emerging technologies remains limited. Consistent with previous comprehensive reviews [28], there is limited empirical evidence regarding the effects of NLP on clinical outcomes, nurses’ decision-making, and integration into routine nursing practice. This pattern is also evident in more recent research. For example, a recent review [15] reported that only 12.5% of 415 studies using advanced LLMs focused on clinical workflow support, whereas most applications addressed clinical decision support or patient information support. Similarly, although ambient AI technologies have recently received substantial attention, peer-reviewed empirical evidence regarding their implementation in nursing practice remains limited. Notably, although some studies have reported usability or perceived helpfulness [22], nurse acceptance and implementation barriers have rarely been examined explicitly, highlighting another important gap in the current evidence base. These findings suggest that the successful implementation of NLP in nursing depends not only on technological performance but also on organizational support, professional engagement, and well-defined implementation processes.

Previous reviews have noted that health care NLP has primarily emphasized algorithm development, data extraction, and classification tasks, with relatively limited attention to supporting nurses’ judgment, decision-making, and care processes [12,14]. Specifically, one review [12] reported that many studies primarily used NLP for classification, whereas an integrative review [14] highlighted the need for improved evaluation, standardization, and broader application of NLP in nursing. Building on these observations, this review shifted the focus from technical accuracy alone to empirical evidence of how NLP is used in nursing practice and how it may support clinical decision-making. The included studies suggest that NLP may reduce manual review workload, improve patient identification, and facilitate clinical decision-making in selected nursing contexts [19,21,24]. Collectively, these findings indicate that NLP may support clinical workflows by functioning as an analytical tool and a decision support resource. However, direct empirical evidence regarding patient outcomes remains limited.

These findings suggest that NLP should not be conceptualized as a technology that fully automates nurses’ clinical judgment. Rather, it may be better understood as a form of augmented intelligence that complements and supports cognitive processes in nursing practice. In particular, NLP may support clinical reasoning by processing large volumes of unstructured data during the early stages of assessment and information processing [30]. This perspective aligns with the principle that clinical decision support systems should be integrated into clinical workflows while preserving professional judgment [31]. The reported usefulness of automatically generated summaries [22] is consistent with this perspective and with the broader concept of the convergence of human and artificial intelligence [32].

Previous studies have identified several challenges associated with clinical decision support systems, including omission and commission errors [33] and concerns regarding data security and patient privacy [20]. As NLP becomes increasingly incorporated into clinical decision support and nursing workflows, related risks, including automation bias, warrant careful consideration. However, empirical evidence on how these challenges affect the implementation of NLP in nursing practice remains limited.

Accordingly, future implementation efforts should focus on the safe and responsible integration of NLP into nursing practice. Because NLP applications process sensitive personal health information, robust privacy protection and data security are essential [20,34,35]. Previous studies have also emphasized the importance of explainability and responsible AI implementation [36]. Building on these considerations, future research should explore strategies to improve AI literacy among nurses and thereby facilitate the responsible implementation of NLP in clinical practice.

Taken together, these findings suggest that NLP should be considered not merely as a technical tool but as part of clinical systems that involve nurse engagement. This perspective is conceptually aligned with the Non-Adoption, Abandonment, Scale-Up, Spread, and Sustainability framework [26], particularly regarding NLP adoption, integration into routine clinical workflows, and long-term sustainability in nursing practice. The technical, organizational, and professional barriers identified in this review may represent key challenges to the adoption, routine integration, and long-term sustainability of NLP in nursing practice. This review, therefore, provides an implementation-oriented perspective that may guide future research and support the integration of NLP into nursing practice.

This scoping review has several limitations. First, the literature search was restricted to studies published in English and Japanese, potentially introducing language bias. Second, the number of databases searched was limited, and gray literature, including doctoral dissertations, conference proceedings, and unpublished reports, was excluded. Consequently, some relevant studies may have been missed. Furthermore, as this review specifically focused on NLP applications in nursing practice, it excluded studies that did not explicitly involve nurses in clinical workflows (eg, physician-centered applications). Future reviews should incorporate broader database coverage and gray literature to provide a more exhaustive overview of the field. Third, the number of included studies was small, reflecting the limited implementation of NLP in nursing practice and the early stage of implementation-oriented research in this field. Accordingly, this review provides a map of current research trends rather than definitive conclusions, and further empirical studies are warranted as the field evolves. Fourth, the search strategy was predominantly designed to capture foundational NLP and data mining techniques. Therefore, it may not have fully captured the most recent studies focusing on generative AI and ambient AI technologies, which have proliferated rapidly in recent years. Given the recent surge in studies using these technologies for nursing documentation, summarization, and care planning, this review’s findings may not fully reflect this emerging body of work. Future studies should compare traditional NLP approaches with newer generative AI technologies to explore their distinct impacts on nursing practice. Fifth, a geographic bias exists, as the included studies were conducted in a limited number of regions, specifically the United Kingdom, the United States, and Taiwan. Consequently, the findings may have limited generalizability to diverse nursing environments with different health care systems and cultural contexts. Finally, in accordance with the methodological nature of a scoping review, this study did not include a formal appraisal of the methodological quality or the risk of bias of the included studies. Therefore, caution should be exercised when interpreting the findings. Future research should incorporate systematic quality assessment to strengthen the evidence base and validate the conclusions drawn.

This scoping review suggests that NLP may support nursing practice by extracting and generating clinically relevant information from unstructured nursing-related text. However, empirical evidence regarding its implementation in real-world nursing practice remains limited. Rather than replacing professional judgment, NLP may function as a form of augmented intelligence that supports nurses’ clinical decision-making. In selected nursing contexts, NLP may help reduce manual review workload, facilitate early risk identification, and support information processing. Nevertheless, direct empirical evidence regarding its effects on nursing workflows, nurses’ clinical decision-making, and patient-level outcomes is limited. Future research should examine nurses’ acceptance, adoption barriers, and implementation processes in real-world clinical settings.

Although previous studies have often focused on system development and technical performance, future research should prioritize implementation-oriented evaluations and assess how NLP influences nurses’ clinical decision-making in practice. To support the responsible integration of AI technologies into nursing practice, nurses may require sufficient AI literacy to critically appraise and interpret AI-generated outputs. Developing this knowledge and these skills may help nurses use AI technologies while maintaining professional judgment in an increasingly digital health care environment.

The principal contribution of this review is to identify the substantial gap between rapid technological advances and the limited implementation of NLP in real-world nursing practice, rather than to establish the effectiveness of NLP.