The application of big data and the development of nursing science: A discussion paper

A guide to understanding big data for the nurse scientist: A discursive paper

Big data refers to extremely large data generated at high volume, velocity, variety, and veracity. The nurse scientist is uniquely positioned to leverage big data to suggest novel hypotheses on patient care and the healthcare system. The purpose of this paper is to provide an introductory guide to understanding the use and capability of big data for nurse scientists. Herein, we discuss the practical, ethical, social, and educational implications of using big data in nursing research. Some practical challenges with the use of big data include data accessibility, data quality, missing data, variable data standards, fragmentation of health data, and software considerations. Opposing ethical positions arise with the use of big data, and arguments for and against the use of big data are underpinned by concerns about confidentiality, anonymity, and autonomy. The use of big data has health equity dimensions and addressing equity in data is an ethical imperative. There is a need to incorporate competencies needed to leverage big data for nursing research into advanced nursing educational curricula. Nursing science has a great opportunity to evolve and embrace the potential of big data. Nurse scientists should not be spectators but collaborators and drivers of policy change to better leverage and harness the potential of big data.

Bibliometric Analysis of Development Trends and Research Hotspots in the Study of Data Mining in Nursing Based on CiteSpace

Backgrounds

With the advent of the big data era, hospital information systems and mobile care systems, among others, generate massive amounts of medical data. Data mining, as a powerful information processing technology, can discover non-obvious information by processing large-scale data and analyzing them in multiple dimensions. How to find the effective information hidden in the database and apply it to nursing clinical practice has received more and more attention from nursing researchers.

Aim

To look over the articles on data mining in nursing, compiled research status, identified hotspots, highlighted research trends, and offer recommendations for how data mining technology might be used in the nursing area going forward.

Methods

Data mining in nursing publications published between 2002 and 2023 were taken from the Web of Science Core Collection. CiteSpace was utilized for reviewing the number of articles, countries/regions, institutions, journals, authors, and keywords.

Results

According to the findings, the pace of data mining in nursing progress is not encouraging. Nursing data mining research is dominated by the United States and China. However, no consistent core group of writers or organizations has emerged in the field of nursing data mining. Studies on data mining in nursing have been increasingly gradually conducted in the 21st century, but the overall number is not large. Institution of Columbia University, journal of Cin-computers Informatics Nursing, author Diana J Wilkie, Muhammad Kamran Lodhi, Yingwei Yao are most influential in nursing data mining research. Nursing data mining researchers are currently focusing on electronic health records, text mining, machine learning, and natural language processing. Future research themes in data mining in nursing most include nursing informatics and clinical care quality enhancement.

Conclusion

Research data shows that data mining gives more perspectives for the growth of the nursing discipline and encourages the discipline's development, but it also introduces a slew of new issues that need researchers to address.

A text mining and network analysis of topics and trends in major nursing research journals

AIM

This study is set to determine the main topics of the nursing field and to show the changing perspectives over time by analysing the abstracts of several major nursing research journals using text mining methodology.

DESIGN

Text mining and network analysis.

METHODS

Text analysis combines automatic and manual operations to identify patterns in unstructured data. Detailed searches covering 1998-2021 were conducted in PubMed archives to collect articles from six nursing journals: Journal of Advanced Nursing, International Journal of Nursing Studies, Western Journal of Nursing Research, Nursing Research, Journal of Nursing Scholarship and Research in Nursing and Health. This study uses a four-phase text mining and network approach, gathering text data and cleaning, preprocessing, text analysis and advanced analyses. Analyses and data visualization were performed using Endnote, JMP, Microsoft Excel, Tableau and VOSviewer versions. From six journals, 17,581 references in PubMed were combined into one EndNote file. Due to missing abstract information, 2496 references were excluded from the study. The remaining references (n = 15,085) were used for the text mining analyses.

RESULTS

Eighteen subjects were determined into two main groups; research method topics and nursing research topics. The most striking topics are qualitative research, concept analysis, advanced practice in the downtrend, and literature search, statistical analysis, randomized control trials, quantitative research, nurse practice environment, risk assessment and nursing science. According to the network analysis results, nursing satisfaction and burnout and nursing practice environment are highly correlated and represent 10% of the total corpus. This study contributes in various ways to the field of nursing research enhanced by text mining. The study findings shed light on researchers becoming more aware of the latest research status, sub-fields and trends over the years, identifying gaps and planning future research agendas. No patient or public contribution.

Visual Art Design of Digital Works Guided by Big Data

With the rapid development of digital technology, the development speed of digital media is also relatively fast. Digital media technology has a great impact on people’s lifestyles and aesthetic concepts, and it also has a greater impact on visual art, creative thinking communication methods, and expression methods. In this study, the quality enhancement of digital images has been intensively studied based on the guidance of big data of eye-movement gaze points. A large amount of visual data are collected from public social resources, and the optimization research of image sensory quality is carried out in-depth using the acquired big data. Next, the region of interest (ROI) is obtained by combining the data with a two-dimensional Gaussian distribution model-fitting method, and the obtained data clustered and improved based on the K-means clustering algorithm to obtain ROI fixation points. Finally, discontinuities in the choice of sharpness in graphics and video playback are pointed out, and the final fixation data analysis is utilized. Results show that targeted optimization is very effective in improving the quality of digital images and saving space, enabling users to enjoy higher-quality visual digital images. The proposed method can be used to improve the dynamic resolution of images and videos.

Healthcare professionals' perceptions on medication communication challenges and solutions - text mining and manual content analysis - cross-sectional study

BACKGROUND

Communication challenges contribute to medication incidents in hospitals, but it is unclear how communication can be improved. The aims of this study were threefold: firstly, to describe the most common communication challenges related to medication incidents as perceived by healthcare professionals across specialized hospitals for adult patients; secondly, to consider suggestions from healthcare professionals with regard to improving medication communication; and thirdly, to explore how text mining compares to manual analysis when analyzing the free-text content of survey data.

METHODS

This was a cross-sectional, descriptive study. A digital survey was sent to professionals in two university hospital districts in Finland from November 1, 2019, to January 31, 2020. In total, 223 professionals answered the open-ended questions; respondents were primarily registered nurses (77.7 %), physicians (8.6 %), and pharmacists (7.3 %). Text mining and manual inductive content analysis were employed for qualitative data analysis.

RESULTS

The communication challenges were: (1) inconsistent documentation of prescribed and administered medication; (2) failure to document orally given prescriptions; (3) nurses' unawareness of prescriptions (given outside of ward rounds) due to a lack of oral communication from the prescribers; (4) breaks in communication during care transitions to non-communicable software; (5) incomplete home medication reconciliation at admission and discharge; (6) medication lists not being updated during the inpatient period due to a lack of clarity regarding the responsible professional; and (7) work/environmental factors during medication dispensation and the receipt of verbal prescriptions. Suggestions for communication enhancements included: (1) structured digital prescriptions; (2) guidelines and training on how to use documentation systems; (3) timely documentation of verbal prescriptions and digital documentation of administered medication; (4) communicable software within and between organizations; (5) standardized responsibilities for updating inpatients' medication lists; (6) nomination of a responsible person for home medication reconciliation at admission and discharge; and (7) distraction-free work environment for medication communication. Text mining and manual analysis extracted similar primary results.

CONCLUSIONS

Non-communicable software, non-standardized medication communication processes, lack of training on standardized documentation, and unclear responsibilities compromise medication safety in hospitals. Clarification is needed regarding interdisciplinary medication communication processes, techniques, and responsibilities. Text mining shows promise for free-text analysis.

Completion of electronic nursing documentation of inpatient admission assessment: Insights from Australian metropolitan hospitals

INTRODUCTION

Electronic nursing documentation is an essential aspect of inpatient care and multidisciplinary communication. Analysing data in electronic medical record (eMR) systems can assist in understanding clinical workflows, improving care quality, and promoting efficiency in the healthcare system. This study aims to assess timeliness of completion of an electronic nursing admission assessment form and identify patient and facility factors associated with form completion in three metropolitan hospitals.

MATERIALS AND METHODS

Records of 37,512 adult inpatient admissions (November 2018-November 2019) were extracted from the hospitals' eMR system. A dichotomous variable descriptive of completion of the nursing assessment form (Yes/No) was created. Timeliness of form completion was calculated as the interval between date and time of admission and form completion. Univariate and multivariate multilevel logistic regression were used to identify factors associated with form completion.

RESULTS

An admission assessment form was completed for 78.4% (n = 29,421) of inpatient admissions. Of those, 78% (n = 22,953) were completed within the first 24 h of admission, 13.3% (n = 3,910) between 24 and 72 h from admission, and 8.7% (n = 2,558) beyond 72 h from admission. Patient length of hospital stay, admission time, and admitting unit's nursing hours per patient day were associated with form completion. Patient gender, age, and admitting unit type were not associated with form completion.

DISCUSSION

Form completion rate was high, though more emphasis needs to be placed on the importance of timely completion to allow for adequate patient care planning. Staff education, qualitative understanding of delayed form completion, and streamlined guidelines on nursing admission and eMR use are recommended.

Tourism Experience and Construction of Personalized Smart Tourism Program Under Tourist Psychology

The present work aims to boost tourism development in China, grasp the psychology of tourists at any time, and provide personalized tourist services. The research object is the tourism industry in Macau. In particular, tourists' experiences are comprehensively analyzed in terms of dining, living, traveling, sightseeing, shopping, and entertaining as per their psychological changes using approaches including big data analysis, literature analysis, and field investigation. In this case, a model of tourism experience formation path is summarized, and a smart travel solution is proposed based on psychological experience. In the end, specific and feasible suggestions are put forward for the Macau tourism industry. Results demonstrate that the psychology-based smart travel solution exerts a significant impact on tourists' tourism experience. Specifically, the weight of secular tourism experience is 0.523, the weight of aesthetic tourism experience is 0.356, and the weight of stimulating tourism experience is 0.121. Tourists prefer travel destinations with excellent urban security and scenic authenticity. They give the two indexes comprehensive scores of 75.14 points and 73.12 points, respectively. The proposed smart travel solution can grasp the psychology of tourists and enhance their tourism experiences. It has strong practical and guiding significances, which can promote constructing smart travel services in Macau and enhancing tourism experiences.

Application of Big data scientific research analysis platform in clinical medical research

With the rapid development of evidence-based medicine, translational medicine, and pharmacoeconomics in China, as well as the country’s strong commitment to clinical research, the demand for physicians’ research continues to increase. In recent years, real-world studies are attracting more and more attention in the field of health care, as a method of post-marketing re-evaluation of drugs, RWS can better reflect the effects of drugs in real clinical settings. In the past, it was difficult to ensure data quality and efficiency of research implementation because of the large sample size required and the large amount of medical data involved. However, due to the large sample size required and the large amount of medical data involved, it is not only time-consuming and labor-intensive, but also prone to human error, making it difficult to ensure data quality and efficiency of research implementation. This paper analyzes and summarizes the existing application systems of big data analytics platforms, and concludes that big data research analytics platforms using natural language processing, machine learning and other artificial intelligence technologies can help RWS to quickly complete the collection, integration, processing, statistics and analysis of large amounts of medical data, and deeply mine the intrinsic value of the data, real-world research in new drug development, drug discovery, drug discovery, drug discovery, and drug discovery. It has a broad application prospect for multi-level and multi-angle needs such as economics, medical insurance cost control, indications/contraindications evaluation, and clinical guidance.

Big Data Applications in Medical Field: A Literature Review

Digital imaging and medical reporting have acquired an essential role in healthcare, but the main challenge is the storage of a high volume of patient data. Although newer technologies are already introduced in the medical sciences to save records size, Big Data provides advancements by storing a large amount of data to improve the efficiency and quality of patient treatment with better care. It provides intelligent automation capabilities to reduce errors than manual inputs. Large numbers of research papers on big data in the medical field are studied and analyzed for their impacts, benefits, and applications. Big data has great potential to support the digitalization of all medical and clinical records and then save the entire data regarding the medical history of an individual or a group. This paper discusses big data usage for various industries and sectors. Finally, 12 significant applications for the medical field by the implementation of big data are identified and studied with a brief description. This technology can be gainfully used to extract useful information from the available data by analyzing and managing them through a combination of hardware and software. With technological advancement, big data provides health-related information for millions of patient-related to life issues such as lab tests reporting, clinical narratives, demographics, prescription, medical diagnosis, and related documentation. Thus, Big Data is essential in developing a better yet efficient analysis and storage healthcare services. The demand for big data applications is increasing due to its capability of handling and analyzing massive data. Not only in the future but even now, Big Data is proving itself as an axiom of storing, developing, analyzing, and providing overall health information to the physicians.