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"Social network analysis"

Original Articles
Voice of Customer Analysis of Nursing Care in a Tertiary Hospital: Text Network Analysis and Topic Modeling
Hyunjung Ko, Nara Han, Seulki Jeong, Jeong A Jeong, Hye Ryoung Yun, Eun Sil Kim, Young Jun Jang, Eun Ju Choi, Chun Hoe Lim, Min Hee Jung, Jung Hee Kim, Dong Hyu Cho, Seok Hee Jeong
J Korean Acad Nurs Adm 2024;30(5):529-542.   Published online December 31, 2024
DOI: https://doi.org/10.11111/jkana.2024.30.5.529
Purpose
This study aimed to explore customer perspectives of nursing services in tertiary hospitals.
Methods
The data comprised mobile Voice Of Customer (VOC) data related to “nursing” or “nurses” generated from June 25, 2019, to December 31, 2022, in a tertiary hospital. A total of 44,727 VOC data points were collected, of which 4,040 were selected for the final analysis. Text network analysis and topic modeling were conducted using NetMiner 4.5.1.
Results
Topic modeling identified five topics for positive aspects and four topics for areas requiring improvement. The positive aspects were: 1) sincere nursing care; 2) rapid response from professional medical staff; 3) teamwork for delivering customer-centric services; 4) provision and coordination of system-based healthcare services; and 5) customer-focused responsiveness. The areas requiring improvement were: 1) demand for skilled nursing care tailored to customer expectations; 2) demand for enhanced communication and reduced mechanical responses; 3) demand for appropriate handling of diverse situations; and 4) demand for overall improvements to the healthcare system, including reservation systems.
Conclusion
These results may be used to enhance customer and patient experiences in tertiary hospitals and are necessary for utilization from a hospital management perspective.
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Purpose
This study was explored the current nursing issues in the COVID-19 era by analyzing newspaper articles. Methods: The search term “nurse” was used to collect newspaper articles from 2020. Pre-processing and semantic network analysis and topic group clustering were conducted using the NetMiner program to analyze the collected articles. Results: A total of 256 articles were identified, and four topics were extracted. The core keywords were “medical care”, “hospital”, “COVID-19” and “medical workforce”. The extracted topics were “work environment related to response to infectious diseases”, “utilization of nursing resources”, “excessive nursing work”, and “shortage of nursing workforce”. Conclusion: Our findings provide useful information for understanding social perspectives of critical nursing issues in the COVID-19 era. Most of the extracted topics are the actual problems that nurses have faced for a long time. Now, nurses must proactively address current nursing issues to respond to national disasters and provide quality nursing care.

Citations

Citations to this article as recorded by  
  • Analysis of Headline News about Nurses Before and After the COVID-19 Pandemic
    Su-Mi Baek, Myonghwa Park
    Journal of Korean Academy of Nursing Administration.2022; 28(4): 319.     CrossRef
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