Healthc Inform Res.  2023 Jan;29(1):75-83. 10.4258/hir.2023.29.1.75.

Keyword Network Analysis of Infusion Nursing from Posts on the Q&A Board in the Intravenous Nurses Café

• Park JY
• Lee J
• Hong B

• KMID: 2539400
• DOI: http://doi.org/10.4258/hir.2023.29.1.75

Abstract

Objectives
Portal sites have become places to share queries about performing nursing and obtain expert know-how. This study aimed to analyze topics of interest in the field of infusion nursing among nurses working in clinical settings.
Methods
In total, 169 user query data were collected from October 5, 2018 to December 25, 2021. This exploratory study analyzed the semantic structure of posts on the nurse question-and-answer board of an infusion nursing-related internet portal by extracting major keywords through text data analysis and conducting term frequency (TF) and term frequency-inverse document frequency (TF-IDF) analysis, N-gram analysis, and CONvergence of iteration CORrelation (CONCOR) analysis. Word cloud visualization was conducted utilizing the “wordcloud” package of Python to provide a visually engaging and concise summary of information about the extracted terms.
Results
“Infusion” was the most frequent keyword and the highest-importance word. “Infusion→line” had the strongest association, followed by “vein→catheter,” “line→change,” and “peripheral→vein.” Three topics were identified: the replacement of catheters, maintenance of the patency of the catheters, and securement of peripheral intravenous catheters, and the subtopics were blood sampling through central venous catheter, peripherally inserted central catheter management, evidence-based infusion nursing, and pediatric infusion nursing.
Conclusions
These findings indicate that nurses have various inquiries in infusion nursing. It is necessary to re-establish the duties and roles of infusion nurses, and to develop effective infusion nursing training programs.

Keyword

Intravenous, Vascular Access Devices, Nurses, Data Mining, Information Services

Figure

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  Figure 1 Analysis process. TF: term frequency, IDF: inverse document frequency, CONCOR: CONvergence of iteration CORrelation.

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  Figure 2 Word clouds using TF and TFIDF. (A) Q&A post TF (top30). (B) Q&A post TF-IDF (top30). TF: term frequency, IDF: inverse document frequency, iv: intravenous, ns: normal saline, cvc: central venous catheter, picc: peripherally inserted central catheter.

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  Figure 3 Network graph using N-gram analysis. 3way: 3-way stopcock, iv: intravenous, ns: normal saline, cvc: central venous catheter, picc: peripherally inserted central catheter.

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  Figure 4 Keyword network analysis using CONvergence of iteration CORrelation (CONCOR) analysis. iv: intravenous, ns: normal saline, cvc: central venous catheter, picc: peripherally inserted central catheter.

Reference

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