DATA SCIENCE APPLICATIONS AND PROCESSES

466  |  wileyonlinelibrary.com/journal/jnu J Nurs Sch. 2024;56:466–477.© 2023 Sigma Theta Tau International.

Received: 6 July 2023  | Revised: 14 October 2023  | Accepted: 8 December 2023

DOI: 10.1111/jnu.12954

P R O F E S S I O N A N D S O C I E T Y

Big data research in nursing: A bibliometric exploration of themes and publications

Bo Li1 | Kun Du1  | Guanchen Qu2 | Naifu Tang1

Bo Li and Kun Du share the co- first authorship.

1Department of Emergency Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China 2School of Artificial Intelligence, Shenyang University of Technology, Shenyang, China

Correspondence Kun Du, The First Affiliated Hospital of Zhengzhou University, No 1 East Jianshe Road, Erqi District, Zhengzhou City, He nan, Province, 450052, China. Email: [email protected]

Funding information the Key Research Project in Higher Education in Henan, China, Grant/Award Number: 22A320067; Medical science and technology public relations project jointly built by Henan Health Commission, Grant/Award Number: SBGJ202103076; Nursing research Special Fund of the First Affiliated Hospital of Zhengzhou University, Grant/Award Number: HLKY2023002

Abstract Aims: To comprehend the current research hotspots and emerging trends in big data research within the global nursing domain. Design: Bibliometric analysis. Methods: The quality articles for analysis indexed by the science core collection were obtained from the Web of Science database as of February 10, 2023.The descriptive, visual analysis and text mining were realized by CiteSpace and VOSviewer. Results: The research on big data in the nursing field has experienced steady growth over the past decade. A total of 45 core authors and 17 core journals around the world have contributed to this field. The author's keyword analysis has revealed five distinct clusters of research focus. These encompass machine/deep learning and ar- tificial intelligence, natural language processing, big data analytics and data science, IoT and cloud computing, and the development of prediction models through data mining. Furthermore, a comparative examination was conducted with data spanning from 1980 to 2016, and an extended analysis was performed covering the years from 1980 to 2019. This bibliometric mapping comparison allowed for the identification of prevailing research trends and the pinpointing of potential future research hotspots within the field. Conclusions: The fusion of data mining and nursing research has steadily advanced and become more refined over time. Technologically, it has expanded from initial natural language processing to encompass machine learning, deep learning, artifi- cial intelligence, and data mining approach that amalgamates multiple technologies. Professionally, it has progressed from addressing patient safety and pressure ulcers to encompassing chronic diseases, critical care, emergency response, community and nursing home settings, and specific diseases (Cardiovascular diseases, diabetes, stroke, etc.). The convergence of IoT, cloud computing, fog computing, and big data processing has opened new avenues for research in geriatric nursing management and community care. However, a global imbalance exists in utilizing big data in nursing research, emphasizing the need to enhance data science literacy among clinical staff worldwide to advance this field. Clinical Relevance: This study focused on the thematic trends and evolution of re- search on the big data in nursing research. Moreover, this study may contribute to the

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INTRODUC TION

Big data was described as one of the hottest trends in technology and was predicted to have a dramatic influence on the economy, science, and society (Delaney et al., 2017).Data science is the link- age of big data and nursing analysis (Delaney et al., 2017). It was defined as science using a well- known Venn diagram consisting of three overlapping circles: statistical knowledge, computer science, and substantive expertise, which was a linkage of big data and anal- ysis (Conway, 2013; Delaney et al., 2017). While there are different variations and frameworks, the data science process, also called data life cycle, from the perspective of data science research, involves 9 phases: experimental design, obtaining data, data exploration, databases and data structures including data cleaning/organizing, software engineering, feature selection, model estimation, sim- ulation and cross- validation, visualization, publication/archiving, overarching topics (Delaney et al., 2017; Rahul & Banyal, 2020; Stodden, 2020). In the integration of nursing and big data research, it is common for researchers to use computer science and statis- tical knowledge to conduct research according to the data science process.

Nursing big data research harnesses a continuous flow of computer technologies to unlock knowledge discovery and data mining. This dynamic field leverages a diverse array of tools and techniques, including descriptive analysis, visualization, cluster analysis, natural language processing (NLP), machine learning, and deep learning (Ludlow et al., 2021; Park et al., 2022; Topaz et al., 2021; Wan et al., 2021). Visualization is considered bene- ficial for healthcare leaders' decision- making as it increases the amount of information delivered and reduces the cognitive and intellectual burden associated with interpreting information; mul- tidimensional data dashboards serve as a commonly utilized vi- sualization tool, offering a comprehensive and accessible means of presenting and analyzing diverse sets of data in healthcare contexts (Kim et al., 2021; Park et al., 2022). In this field, nurs- ing scholars have launched a number of efforts, for example, Ludlow K and his team described a protocol of a dashboard design in residential and community- based aged care settings (Ludlow et al., 2021). Natural Language Processing (NLP) techniques play a crucial role in data mining (DM) by extracting valuable in- sights from textual data (Kao & Poteet, 2007). For instance, Woo et al. (2021) developed a NLP algorithm using 1,149,586 home care visit notes and 1,461,171 care coordination notes to auto- matically identify nursing notes and detect signs and symptoms

of urinary tract infection in home care settings (Woo et al., 2021). Topaz M and colleagues applied NLP techniques to accurately identify symptoms within 2.5 million narrative notes, and found that patients with a greater number of documented symptom cat- egories had a higher likelihood of emergency department visits or hospital admissions (Topaz et al., 2021). Deep learning is a subset of machine learning that specifically focuses on training artificial neural networks with multiple layers (deep neural networks) to automatically learn hierarchical representations of data (Janiesch et al., 2021). Furthermore, when conducting time series predic- tion studies, researchers often opt to use time bins and direct observations for Long Short- Term Memory (LSTM) machine learn- ing prediction instead of constructing traditional Auto- regressive Integrated Moving Average (ARIMA) models using feature vectors generated through time series analysis (Wan et al., 2021). For ex- ample, in studies on lung protective ventilation in intensive care units, researchers applied various state- of- the- art time series pre- diction methods to forecast the behavior of tidal volume metric per patient, 1 h in advance (Hagan et al., 2020).

Big data promotes the development of data science, and aca- demic researchers conducted literature reviews on prediction and knowledge discovery in nursing education, practice and other fields (Nicoll et al., 2021; O'Brien & O'Brien, 2021; Westra et al., 2017). Westra et al. (2017) reviewed articles from January 2009 to December 2015, and revealed three overarching research topics were knowledge discovery, prediction, and evaluation, but this ar- ticle excluded literature without the participation of nurses. The in- clusion of literature from non- nursing researchers is essential due to the interdisciplinary nature of data science and the presence of black box technology for nurses. Incorporating perspectives from non- professionals can offer unique insights and inspire new research directions in nursing. Nicoll et al. (2021) did bibliometric analysis on articles from 2014 to 2020, and demonstrated that education, practice, administration, and professional development are the main research subjects. However, the use of the research terms “nurs* and big data” in this study may have been too simplistic and narrow, resulting in a limited retrieval of research literature in the field of data science and care. It is important to consider using a more com- prehensive and diverse set of search terms and strategies to ensure a broader coverage of relevant literature and capture a more com- prehensive understanding of the intersection between nursing and big data. O'Brien and O'Brien (2021) conducted a literature search spanning from 2009 to 2019 on the topics of big data, data science, and machine learning, highlighting the relationship between these

understanding of researchers, journals, and countries around the world and generate the possible collaborations of them to promote the development of big data in nurs- ing science.

K E Y W O R D S bibliometrics, big data, nursing, visual analysis

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fields and their potential to enhance healthcare practice. However, no additional details were provided regarding the research institu- tions, investigators, or partnerships involved in the study.

Based on the information above, the following questions are raised regarding nursing and big data research:

a. Who are the most prolific and cited authors in nursing and big data research?

b. What are the major journals that publish nursing and big data research?

c. Which countries have shown a significant interest in this spe- cific area and where have cooperative efforts been established around the world?

d. What are the current areas of active research and emerging trends in this field?

e. How have the focal points of research evolved over the past few years in this field?

METHODS

Design

Bibliometrics is the analysis of published information (e.g., books, journal articles, datasets, blogs), akin to epidemiology in the medical context, employs statistical analysis to study published information and associated metadata, such as abstracts, keywords, and citations, revealing relationships among works, tracking topic trajectories, and assessing impact through rigorous statistical methods and cross- sectional studies (Ninkov et al., 2022).The utilization of a bibliomet- ric approach was well- suited to tackle the research question posed by this study.

VOSviewer and CiteSpace offer complementary advantages in bibliometric analysis. Cite Space excels in initial data processing, de- duplication, and time slicing, and VOS viewer utilizes probability theory and offers a diverse range of visual maps, including those for keywords and co- authors. It enables the creation of network visual- ization, overlay visualization, and density visualization while also sup- porting basic text mining (Chen, 2006; Van Eck & Waltman, 2010).

A performance and mapping bibliometric analysis was con- ducted from January to June 2023 to explore publications related to big data in nursing research. The analysis provided a comprehensive overview of important articles, journals, institutions, future trends, and collaborators in this field.

Search strategy

On February 10, 2023, a thorough search was performed on the Web of Science (WOS) core collection database for publications pertaining to big data in nursing research. The research terms were developed collaboratively by the members of the research team. The search period encompassed the years from 1980 to 2023.

The search formula utilized for this analysis is as follows: ((TS = (“Nursing” OR “Nurse*” OR “Care”)) AND (TS = (“big data”

OR “mega data” OR “large data” OR “large- scale data” OR “the great data” OR “data science” OR “data mining” OR “knowledge discover*” OR “knowledge mining” OR “deep learning” OR “in- depth learning” OR “depth learning” OR “deeper learning” OR “machine learning” OR “information retrieval” OR “information extraction” OR “data ware- hous*” OR “cloud comput*” OR “data bank” OR “data librar*”)))

To address potential selection bias, we proactively defined all cri- teria for screening literature before initiating the literature search. Subsequently, two authors independently assessed all search re- sults, utilizing a predefined data extraction form for determining inclusion. In cases of any discrepancies or disagreements regarding inclusion, these were diligently resolved through thorough discus- sions involving a third author.

Inclusion and exclusion criteria

The inclusion criteria for this study are as follows:

a. The published language is English. b. The researches topics are related to nursing, including but not

limited to emergency triage, nursing human resource manage- ment, falls, ulcers, symptom management, vital sign monitoring, disease observation, community nursing, home care, and post- discharge care, etc.

c. Studies related to big data, such as studies that employed web crawlers to collect data, studies that analyzed large volumes of multidimensional data, studies focusing on nursing documents or notes, and studies related to nursing databases, etc.

The exclusion criteria for this study are as follows:

a. Studies that only cover a small part of topics related to nursing. e. Studies that are only partially related to big data, such as research

on sensor development or system development without valida- tion using big data etc.

f. Studies that use randomly extracted data from databases as the data source. This criterion was based on the understanding that big data research aims to obtain a comprehensive understanding of the subjects studied, considering it as the fourth paradigm in science (Kitchin, 2014).

g. Studies with large samples that lack data extraction, cleaning, and other data science processes.

Data cleaning and analysis

In this study, a total of 14,444 literature sources were retrieved. After excluding letters, proceeding papers, meeting abstracts, re- prints, book reviews, and other non- relevant sources, 11,473 litera- ture sources remained. From these sources, the researchers read the

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abstracts and excluded non- relevant papers and duplicates, result- ing in a final set of 2388 literature sources. After further excluding non- English papers and non- related papers, we obtained a subset of 393 papers for analysis. We utilized VOS viewer 1.6.18 and Cite Space 6.2.R4 software for analyzing and visualizing the final dataset.

RESULTS

Performance analysis

Descriptive analysis of publications

In this study, we obtained 393 documents from 207 journals, au- thored by 1990 authors. These documents came from 792 organiza- tions in 51 countries. Additionally, we found that 13,759 references were cited from 5121 different journals.

The earliest research on large- scale databases in nursing science was published in 1994. However, there was a significant gap in re- search in this field thereafter. Therefore, in our study, we focused on the period from 2001 to the present to examine the develop- ments and advancements in this area. Starting from 2001, there has been a steady increase in the number of published papers each year. However, it is worth noting that the growth rate significantly accel- erated after 2013. For specific details, please refer to Figure 1.

Bibliometric analysis of authors

Analyzing the authors of the literature provides insights into rep- resentative scholars and core research forces in the field of study. Price highlighted that a significant portion of papers on the same topic are authored by a group of highly productive researchers, and

the number of authors within this group is approximately equal to the square root of the total number of all authors involved in the research (Price, 1963).

In the given formula (1), the variable n(x) represents the count of authors who have authored x number of papers. The symbol I, which is equal to nmax, refers to the number of papers contributed by the most productive authors in the field (in this case, nmax = 9). N rep- resents the total number of authors, while m denotes the minimum number of publications required for an author to be considered part of the core group.

According to Price's Law, the minimum number of publications required for an author to be considered part of the core group in a field can be determined using the formula:

In this case, with m approximately equal to 2.25, authors who have more than 2 publications are considered core authors within the field. There are a total of 45 core authors, contributing to 190 publications, which accounts for 48.34% of the total number of publications. This percentage approximately meets the 50% criterion proposed by Price. Hence, it can be inferred that further efforts are required to establish a more stable collaborative group of authors in the field of big data and nursing. Table 1 presents the highly productive authors who have authored more than 6 publications in this field.

Bibliometric analysis of journals

Based on Bradford's Law (Bradford, 1934), the distribution of lit- erature within a collection often exhibits significant asymmetry. By

(1) I

�

m+1

n(x) = √

N

(2)m = 0.749 × √

nmax.

F I G U R E 1  Distribution of publications from 2001 to 2023.

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arranging the number of papers published in journals in a descending order, it is possible to identify distinct regions: the core journal area, the relevant journal area, and the marginal journal area. The number of papers published in each of these areas follows a specific relation- ship, where the core journal area and the subsequent journal areas have a relationship of 1:a:a2. Therefore, it can be concluded that the

core journals in this field should publish more than 5 articles. The core nursing journals that contribute significantly to the literature in this field include Cin- Computers Informatics Nursing, Western Journal of Nursing Research, Nursing Research, Nursing Outlook, and Journal of Nursing Scholarship.

Bibliometric analysis of countries

To comprehend the countries making significant research contribu- tions in this field, the study analyzed the publication output of 51 countries. VOS viewer and SCI mago software were utilized to visu- alize and optimize the countries with three or more publications.

Figure 2 depicts the analysis results of national cooperation net- works in the field. In the visualization, the size of each dot corre- sponds to the number of papers published by the respective country, and the node lines represent the strength of collaborations between countries, where thicker lines indicate higher association strength. Notably, the figure highlights the significant imbalance in the distri- bution of research countries within the field of big data nursing, em- phasizing the varying levels of engagement and collaboration among different nations.

TA B L E 1  Most important authors in the nursing big data research field.

Rank Author Documents Citations Average citation/ publication

1 Topaz 9 69 7.7

2 Clark 7 59 8.4

3 Carto 7 91 13.0

4 Westra 7 80 11.4

5 Lake 6 51 8.5

6 Moorman 6 51 8.5

7 Keim 6 63 10.5

8 Keenan 6 77 12.8

9 Wilkie 6 50 8.3

10 Yao 6 50 8.3

F I G U R E 2  Co- occurrence of countries in big data nursing research.

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Science mapping and content analysis

As author keywords often reflect the authors' intent to engage with readers within their research domain, this study will employ author keyword co- occurrence analysis to examine the evolution of research topics and identify the current research hot- spots (Železnik et al., 2017). Comparing bibliometric maps across dif- ferent time periods enables the anticipation of future research di- rections within the hospital library field and the identification of ongoing research hotspots (Kokol et al., 2018). In this paper, we conducted a co- occurrence analysis of author keywords spanning three distinct periods: 1980–2016, 1980–2019, and 1980–2023. Through this analysis, we have illuminated the evolutionary trajec- tory of research in this domain and offered insights into potential research hotspots.

Co- occurrence analysis on author keywords from 1980 to 2023

The content analysis of the author keyword cluster landscape is de- picted in Figure 3, yielding codes, subcategories, and themes, which are presented in Table 2. This analysis unveiled five distinct themes, namely: machine/deep learning and artificial intelligence, natural

language processing, big data analytics and data science, and IoT and cloud computing, prediction model development with data mining.

Co- occurrence analysis on author keywords from 1980 to 2019

The content analysis of the author keyword cluster landscape before 2019 is illustrated in Figure 4. This analysis has revealed four promi- nent themes, specifically: Natural language processing and machine learning, Big data and data science, Data mining, and IoT and cloud computing.

Co- occurrence analysis on author keywords from 1980 to 2016

The content analysis of the author keyword cluster landscape pre- ceding 2016 is showcased in Figure 5. This analysis has discerned three prominent themes, specifically: natural language processing and machine learning, big data, and data mining.

A detailed comparison list of the three figures can be found in Table 3. From 2016 to 2019, the research landscape witnessed emerging trends in various fields. During this period, “Critical care,”

F I G U R E 3  Co- occurrence analysis on author keywords from 1980 to 2023.

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“diabetes,” “machine learning,” “data analytics,” “data science,” “falls,” “predictive modeling,” “IoT,” and “cloud computing” emerged as novel research areas. In recent years, from 2020 to 2023, “Sepsis,” “chronic

diseases,” “emergency department,” “risk assessment,” “deep learn- ing,” and “artificial intelligence” have gained prominence as burgeon- ing research hotspots.

TA B L E 2  Research themes, codes, and subcategories from 1980 to 2023.

Theme Color More frequent codes Prevailing subcategories

Machine/deep learning and artificial intelligence

Red Machine learning (58); Predictive analysis (36); Readmission (17); Deep learning (15); Artificial intelligence (14); Emergency department (13); Diabetes (13); Chronic disease (7); Sepsis (7)

1. AI and deep learning for predictive models in diabetes, chronic diseases, and sepsis

2. Machine learning and predictive analysis for emergency department, and readmission

Natural language processing

Green Electronic health records (32); Natural language processing (30); Nursing informatics (17); Nursing (16); Falls (11); Nursing documentation (9); Quality care (7)

1. Utilizing nursing documentation, nursing informatics, and natural language processing to enhance falls prevention in nursing care.

2. Enhancing quality care through electronic health records and natural language processing

Big data analytics and data Science

Blue Big Data (46); Nurses (18); Data science (10); Data analytics (10); Critical care (9)

1. Applying data science and analytics to big data in nursing staff management

2. Utilizing data analytics for critical patient management in the context of big data

IoT and cloud computing

Purple Cloud computing (21); Internet of things (13); Healthcare (11)

Exploring cloud computing and IoT solutions to improve healthcare for chronic diseases like diabetes

Prediction model development with data mining

Yellow Data mining (27); Prediction model (14); Pressure ulcers (14); Risk assessment (9)

Developing data mining- based prediction model for pressure ulcers and risk assessment

F I G U R E 4  Co- occurrence analysis on author keywords from 1980 to 2019.

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F I G U R E 5  Co- occurrence analysis on author keywords from 1980 to 2016.

TA B L E 3  The evolution of research themes and hot spots.

Period of time

Themes and changes

Themes Research context Increased research topics

1980–2016 Natural language processing Patient safety (3); readmission (4); electronic health records (5); nursing documentations (4), etc.

Big data Nurses (3); nursing informatics (4), etc.

Data mining Pressure ulcer (4); quality of care (4), etc.

1980–2019 Natural language processing and machine learning

Electronic health record (18); quality of care (5); predictive analysis (19); readmission (10); diabetes (6); emergency department (7); critical care (5), etc.

Critical care; diabetes; machine learning; data analytic; data science; falls; predictive model; IoT; cloud computing; data science; machine learning

Big data and data science Nurses (11); nursing informatics (9); data analytic (6), etc.

Data mining Pressure ulcer (8); falls (5); nursing home (5); prediction model (5), etc.

IoT and cloud computing Knowledge discovery (5); healthcare (5), etc.

1980–2023 Natural language processing Electronic health record (32); quality of care (7); falls (11), etc.

Sepsis; chronic disease; emergency department; risk assessment; deep learning; artificial intelligence

Big data analytics and data science Nurses (11); critical care (5), etc.

Prediction model development with data mining

Prediction model (5); pressure ulcers (8); risk assessment (9), etc.

IoT and cloud computing Healthcare (11); etc.

Machine/Deep learning and artificial intelligence

Predictive analysis (36); readmission (17); emergency department (13); diabetes (13); chronic disease (7); sepsis (7), etc.

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DISCUSSION

This study conducted a comprehensive review of the research lit- erature in the field of big data and nursing. It utilized bibliometric analysis to identify the core journals, high- yield scholars, and top- contributing countries that have published in this domain. The re- search themes were clustered based on author keywords, yielding five distinct categories. By comparing these categories across dif- ferent time periods, we were able to discern the evolving research trends and current research hotspots in this field. The research questions proposed in this paper have been effectively addressed, and the subsequent sections provide a detailed discussion of the aforementioned research outcomes.

The progression of the intersection between data mining and nursing

The integration of big data and nursing has evolved over time, marked by a continuous expansion of its scope and increasing re- finement. Initially, this integration was primarily seen in the de- velopment of nursing systems and the informatization of nursing practices. Nursing informatics researchers focused on the utilization of information systems such as the Omaha System and Clinical Care Classification system for accessing and analyzing patient records. A notable example is Feng and Chang's (2015) study, which involved the analysis of nursing documentations extracted from the Clinical Care Classification system to represent nursing record data across various nursing specialties. During this phase, the predominant data mining technique employed was natural language processing technology. For instance, Collins et al. (2013) utilized data mining methods to analyze 15 months' worth of electronic nursing docu- ments from a large urban academic medical center. Their research aimed to explore the relationship between nursing documents and patient mortality. The results of their study confirmed that certain characteristics within nursing documents could be effectively used to predict patient mortality. This finding underscores the potential of utilizing such data to identify and monitor patients at risk of dete- riorating health conditions.

As data science and data mining technologies continued to ad- vance, their integration with the nursing field deepened, encompass- ing a wide array of techniques such as natural language processing, machine learning, knowledge discovery, and predictive model devel- opment. In this stage, researchers gradually incorporated machine learning and natural language processing technologies into their work. For instance, Sterling et al. (2019) employed natural language processing technology and neural network model training to predict the prognosis of emergency patients using data from emergency tri- age records. Additionally, Monsen et al. (2017) leveraged big data and visualization technology to enhance the quality of care provided during home visits by public health nurses.

In recent years, there has been a significant shift toward the ex- tensive utilization of various computer science techniques, including

machine learning, deep learning, neural networks, and artificial in- telligence. Duan and Lin (2022) integrated big data and Internet of Things technology to assess the impact of intelligent medical data analysis technology on postoperative nursing efficiency, with the aim of transforming conventional postoperative care practices. This reflects the growing integration of sophisticated computational methods to gain deeper insights into patient outcomes and health- care practices in the nursing field.

As data mining methods have found integration within the nurs- ing domain, nursing research has undergone a deliberate evolution. This transformation has evolved from broad inquiries into chronic diseases to specialized investigations, notably focusing on areas such as diabetes and cardiovascular disease (Koleck et al., 2021). Furthermore, research emphasis has shifted from critical disease studies to comprehensive examinations of specific niches, with sepsis gaining prominence as an important subject of study. For instance, Wu et al. (2021) employed random forests, convolutional neural networks, and recurrent neural networks to predict early risk of adverse events among adult patients. In a related vein, Gao et al. (2021) extracted sentiment data from nursing notes, uncover- ing a significant correlation between this sentiment analysis and the 28- day mortality as well as the survival rate of patients with sepsis.

Patient safety has remained a long- standing research focal point, encompassing areas such as falls and pressure ulcers, with the current research trajectory predominantly centered on risk assessment and prediction, for example, Bliss et al. (2017) as- sessed racial and ethnic disparities in the healing of pressure ul- cers present at nursing home admission by examining three large data sets. Vera- Salmerón et al. (2022) conducted a risk assess- ment for pressure ulcers, considering patient activity, skin mois- ture, and mobility through the application of data mining methods like decision trees. In recent years, researchers have delved into patient safety management within specific departments, such as Han et al. (2022), who proposed algorithms- based big data advan- tages and technologies to improve the quality of nursing risk man- agement in emergency departments.

IoT and associated processing tech: A new frontier in big data nursing integration

The integration of IoT, cloud computing, fog computing, and big data technology in medicine is reshaping healthcare delivery, enabling real- time monitoring, data- driven insights, and improved patient care. The Internet of Things and predictive analytics have emerged as powerful tools, offering immense potential for re- search in community and home care settings (Ahamed et al., 2022; Huang et al., 2021; Singh & Malhotra, 2021). Researchers utilize Internet of Things (IoT) technology to gather extensive data on patients with chronic conditions, enabling comprehensive analysis and classification. The primary goal is to leverage these data to enhance clinical decision- making and promote more effective and personalized care for individuals living with chronic diseases. For

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example, Kumar et al. (2018) used a systematic approach, utiliz- ing the UCI Repository dataset and medical sensors, to generate diabetes- related medical data for predicting severe cases of dia- betes. Lawler et al. (2021) developed a home and external envi- ronment patient monitoring system, validated for three disease categories—cardiovascular disease (CVD), hypertension (HPN), and chronic obstructive pulmonary disease (COPD)—leveraging emerging technologies like big data, cloud computing, and the Internet of Things (IoT).

Unbalanced international development

The state of global nursing big data research is highly imbalanced. Uneven cooperation among countries can lead to disparities in data representation, which in turn can result in biased analyses and an incomplete understanding of phenomena. The absence of collabora- tion often gives rise to variations in data collection methods, for- mats, and quality standards. These inconsistencies not only hinder effective comparisons but also limit collaboration opportunities and compromise the ability to derive accurate conclusions across differ- ent countries or regions. Underdeveloped regions face challenges in technological infrastructure, hindering comprehensive big data re- search. The scarcity of skilled nursing scientists limits effective data utilization and advanced research in nursing. Enhancing data science literacy among clinical front- line staff worldwide is crucial for foster- ing big data development in the discipline.

Recommendations for further research

Future research in the nursing discipline holds promise in several key areas. Firstly, there is an opportunity to delve deeper into nursing re- search related to big data, particularly in non- acute and critical care fields such as orthopedic nursing, pediatric nursing, and gynecologi- cal nursing. Currently, these domains are relatively underexplored compared to nursing human resource management, critical illness, and chronic disease research. Secondly, there is a need to focus on the practical application of big data within nursing. Many studies have introduced the concept and background of big data, but there is a gap in effectively harnessing big data for solving nursing chal- lenges. Future research should aim to bridge this gap by genuinely integrating big data into nursing research. Additionally, the field of nursing education has yet to fully explore the potential of big data. Future research endeavors can expand in this direction, exploring innovative ways to incorporate big data into nursing education and training, thereby better preparing nurses for the evolving health- care landscape. Lastly, the integration of the Internet of Things, cloud computing, fog computing, and big data within the context of nursing is still in its nascent stages. There is a wealth of uncharted territory in the related research of home care and community care, offering ample opportunities for exploration and advancement within the nursing field.

Implications for policy and practice

From the perspective of the national cooperation network examined in this study, it is evident that there exists a significant imbalance in cooperation between countries. Big data, being the fourth research paradigm, emphasizes a holistic approach to research. Consequently, overcoming the limitations of data silos becomes crucial for advancing nursing big data research. Additionally, during the literature review, a limited number of studies highlighted the importance of data security. Hence, it becomes the responsibility and obligation of countries or regions to establish data security policies, ensuring the protection of patient privacy and promoting ethical data use practices.

Furthermore, within the nursing field, this paper serves as a valu- able resource for researchers interested in delving into the realm of big data and nursing research. It offers a concise and comprehensive overview, providing a quick and accessible pathway to gain insights into this evolving field. By exploring the key findings and recommen- dations presented in this study, researchers can expedite their un- derstanding of the current landscape and identify potential avenues for future exploration in the intersection of big data and nursing.

Limitations

This study acknowledges several limitations due to various factors. Firstly, bibliometric analysis software imposes stringent criteria and standards on data selection. In order to maintain data quality and integrity, this study focused solely on journal papers indexed by SSCI and SCIE, thereby excluding other databases. Consequently, this ap- proach may have resulted in incomplete data analysis. We were com- pelled to exclude articles written in languages other than English, such as German and French, due to the analysts' limited proficiency in these languages. This exclusion, however, raises the possibility of introducing a selection bias. Additionally, quantitative analysis ne- cessitates the careful interpretation of data, requiring researchers to possess a deep and comprehensive understanding of the field. Without such expertise, the analysis may be prone to subjective in- terpretations. It is important to note that subjectivity can introduce bias and affect the overall findings and conclusions of the study.

CONCLUSION

In summary, the gradual integration of data mining technology into the nursing profession represents a transformative journey. Technologically, we have progressed from natural language process- ing to machine learning, deep learning, and artificial intelligence, embracing a wide range of data mining tools. Nursing profession- als must actively acquire and apply this knowledge effectively. From a healthcare perspective, the scope of application has expanded vastly, encompassing patient safety, chronic diseases, critical care, emergency response, and specific conditions like cardiovascular disease and sepsis. The convergence of IoT, cloud computing, and

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fog computing with big data has unlocked fresh research avenues in chronic disease management and community care. While these possibilities are exciting, numerous research initiatives remain in the proposal and platform- building stages. Nurses must take an ac- tive role in adopting these technologies to enhance patient care. Globally, a gap persists between big data and nursing research due to imbalanced international cooperation, technological challenges in underdeveloped regions, and a shortage of skilled nursing scientists. Elevating data science literacy among clinical personnel worldwide is crucial for advancing big data's role in nursing and ultimately improv- ing healthcare outcomes.

CLINICAL RESOURCES

World Bank Open Data free and open access to global development data https:// data. world bank. org/ . University of Minnesota. School of Nursing. Center for Nursing Informatics https:// nursi ng. umn. edu/ cente rs/ cente r- nursi ng- infor matics/ cente r- nursi ng- infor matic s- overview. PULSUS. Data Science and Machine Learning Conferences https:// www. cmeso ciety. com/ data- scien ce- and- machi ne- learn ing- meeti ngs- works hops. Chinese University MOOC https:// www. icour se163. org/ chann el/ 3002. htm.

ACKNOWLEDG MENTS The authors would like to acknowledge the financial support pro- vided by the Key Research Project in Higher Education in Henan, China (22A320067). Their funding played a crucial role in conduct- ing this research. We are deeply thankful to Yuezhi Dong and Deying Shen for their invaluable encouragement and fruitful discussions, which greatly enriched our understanding of the subject matter.

CONFLIC T OF INTERE S T S TATEMENT We affirm that all authors have stated that they had no interests that might be perceived as posing a conflict or bias.

DATA AVAIL ABILIT Y S TATEMENT The data that support the findings of this study are available from the corresponding author upon reasonable request.

ORCID Kun Du https://orcid.org/0000-0002-3430-6804

R E FE R E N C E S Ahamed, J., Manan Koli, A., Ahmad, K., Jamal, A., & Gupta, B. B. (2022).

CDPS- IoT: Cardiovascular disease prediction system based on iot using machine learning. International Journal of Interactive Multimedia and Artificial Intelligence, 7, 78–86.

Bliss, D. Z., Gurvich, O., Savik, K., Eberly, L. E., Harms, S., Mueller, C., & Wiltzen, K. (2017). Racial and ethnic disparities in the healing of pressure ulcers present at nursing home admission. Archives of Gerontology and Geriatrics, 72, 187–194.

Bradford, S. C. (1934). Sources of information on specific subjects. Engineering, 137, 85–86.

Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377.

Collins, S. A., Cato, K., Albers, D., Scott, K., Stetson, P. D., Bakken, S., & Vawdrey, D. K. (2013). Relationship between nursing documenta- tion and patients' mortality. American Journal of Critical Care, 22(4), 306–313.

Conway. (2013). The data science Venn diagram. https:// drewc onway. com/ zia/ 2013/3/ 26/ the- data- scien ce- venn- diagram

Delaney, C. W., Weaver, C. A., Warren, J. J., Clancy, T. R., & Simpson, R. L. (2017). Big data- enabled nursing: Education, research and practice. Springer.

Duan, N., & Lin, G. (2022). Effect of intelligent medical data Technology in Postoperative Nursing Care. BioMed Research International, 2022, 9681769.

Feng, R. C., & Chang, P. (2015). Usability of the clinical care classification system for representing nursing practice according to specialty. Computers, Informatics, Nursing, 33(10), 448–455.

Gao, Q., Wang, D., Sun, P., Luan, X., & Wang, W. (2021). Sentiment anal- ysis based on the nursing notes on in- hospital 28- day mortality of sepsis patients utilizing the MIMIC- III database. Computational and Mathematical Methods in Medicine, 2021, 3440778.

Hagan, R., Gillan, C. J., Spence, I., McAuley, D., & Shyamsundar, M. (2020). Comparing regression and neural network techniques for personalized predictive analytics to promote lung protective ven- tilation in intensive care units. Computers in Biology and Medicine, 126, 104030.

Han, W., Wang, S., & Gao, J. (2022). Application of data mining technology- based nursing risk Management in Emergency Department Care. Mathematical Problems in Engineering, 2022, 1–12.

Huang, A., Cao, J., & Zhang, H. (2021). Construction of patient ser- vice system based on QFD in internet of things. The Journal of Supercomputing, 77(3), 2155–2171.

Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685–695.

Kao, A., & Poteet, S. R. (2007). Natural language processing and text min- ing. Springer Science & Business Media.

Kim, S. H., Kim, H. J., & Shin, G. (2021). Self- management Mobile virtual reality program for women with gestational diabetes. International Journal of Environmental Research and Public Health, 18(4), 1539.

Kitchin, R. (2014). Big data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 2053951714528481.

Kokol, P., Završnik, J., & Vošner, H. B. (2018). Bibliographic- based identi- fication of hot future research topics: An opportunity for hospital librarianship. Journal of Hospital Librarianship, 18(4), 315–322.

Koleck, T. A., Topaz, M., Tatonetti, N. P., George, M., Miaskowski, C., Smaldone, A., & Bakken, S. (2021). Characterizing shared and dis- tinct symptom clusters in common chronic conditions through natural language processing of nursing notes. Research in Nursing & Health, 44(6), 906–919.

Kumar, P. M., Lokesh, S., Varatharajan, R., Chandra Babu, G., & Parthasarathy, P. (2018). Cloud and IoT based disease prediction and diagnosis system for healthcare using fuzzy neural classifier. Future Generation Computer Systems, 86, 527–534.

Lawler, J., Cook, R., Dosanj, R., Trevatt, P., & Leary, A. (2021). Examining the impact of diabetes inpatient specialist nursing in acute trusts in London. Journal of Advanced Nursing, 77(10), 4081–4088.

Ludlow, K., Westbrook, J., Jorgensen, M., Lind, K. E., Baysari, M. T., Gray, L. C., & Haddock, R. (2021). Co- designing a dashboard of predic- tive analytics and decision support to drive care quality and client outcomes in aged care: A mixed- method study protocol. BMJ Open, 11(8), e048657.

Monsen, K. A., Peterson, J. J., Mathiason, M. A., Kim, E., Votava, B., & Pieczkiewicz, D. S. (2017). Discovering public health nurse- specific

    | 477BIBLIOMETRIC ANALYSIS OF BIG DATA IN NURSING RESEARCH

family home visiting intervention patterns using visualization tech- niques. Western Journal of Nursing Research, 39(1), 127–146.

Nicoll, L. H., Wrigley, J., & Wyatt, T. H. (2021). Big data in nursing: A bibliometric analysis. Online Journal of Issues in Nursing, 26(3), 1–9.

Ninkov, A., Frank, J. R., & Maggio, L. A. (2022). Bibliometrics: Methods for studying academic publishing. Perspectives on Medical Education, 11(3), 173–176.

O'Brien, R. L., & O'Brien, M. W. (2021). CE: Nursing orientation to data science and machine learning. The American Journal of Nursing, 121(4), 32–39.

Park, S., Bekemeier, B., Flaxman, A., & Schultz, M. (2022). Impact of data visualization on decision- making and its implications for pub- lic health practice: A systematic literature review. Informatics for Health & Social Care, 47(2), 175–193.

Price, D. J. d. S. (1963). Little science, big science. Columbia University Press.

Rahul, K., & Banyal, R. K. (2020). Data life cycle Management in big Data Analytics. Procedia Computer Science, 173, 364–371.

Singh, K., & Malhotra, J. (2021). Cloud based ensemble machine learn- ing approach for smart detection of epileptic seizures using higher order spectral analysis. Physical and Engineering Sciences in Medicine, 44(1), 313–324.

Sterling, N. W., Patzer, R. E., Di, M., & Schrager, J. D. (2019). Prediction of emergency department patient disposition based on natural lan- guage processing of triage notes. International Journal of Medical Informatics, 129, 184–188.

Stodden, V. (2020). The data science life cycle: A disciplined approach to advancing data science as a science. Communications of the ACM, 63(7), 58–66.

Topaz, M., Koleck, T. A., Onorato, N., Smaldone, A., & Bakken, S. (2021). Nursing documentation of symptoms is associated with higher risk of emergency department visits and hospitalizations in homecare patients. Nursing Outlook, 69(3), 435–446.

Van Eck, N., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.

Vera- Salmerón, E., Domínguez- Nogueira, C., Romero- Béjar, J. L., Sáez, J. A., & Mota- Romero, E. (2022). Decision- tree- based approach for pressure ulcer risk assessment in immobilized patients. International Journal of Environmental Research and Public Health, 19(18), 11161.

Wan, Z., Wang, X.- Z., Fu, N.- T., Li, Y., Xiao, W.- D., & Zheng, D.- H. (2021). Primary closure versus T- tube drainage following laparoscopic common bile duct exploration in patients with previous biliary sur- gery. The American Surgeon, 87(1), 50–55.

Westra, B. L., Sylvia, M., Weinfurter, E. F., Pruinelli, L., Park, J. I., Dodd, D., & Delaney, C. W. (2017). Big data science: A literature review of nursing research exemplars. Nursing Outlook, 65(5), 549–561.

Woo, K., Adams, V., Wilson, P., Fu, L. H., Cato, K., Rossetti, S. C., & Topaz, M. (2021). Identifying urinary tract infection- related information in home care nursing notes. Journal of the American Medical Directors Association, 22(5), 1015–1021.e1012.

Wu, C. L., Wu, M. J., Chen, L. C., Lo, Y. C., Huang, C. C., Yu, H. H., & Sheu, R. K. (2021). AEP- DLA: Adverse event prediction in hospi- talized adult patients using deep learning algorithms. IEEE Access, 9, 55673–55689.

Železnik, D., Blažun Vošner, H., & Kokol, P. (2017). A bibliometric analysis of the journal of advanced nursing, 1976- 2015. Journal of Advanced Nursing, 73(10), 2407–2419.

How to cite this article: Li, B., Du, K., Qu, G. & Tang, N. (2024). Big data research in nursing: A bibliometric exploration of themes and publications. Journal of Nursing Scholarship, 56, 466–477. https://doi.org/10.1111/jnu.12954

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