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.

Purpose
To describe clinical nurses’ experience and grievance in an online community using a co-occurrence network and topic modeling. Methods: We analyzed posts from Nurscape, which is the largest online community for nurses in Korea. After extracting posts using web scrapping, text preprocessing was done to detect nouns. In a visualization phase, co-occurrence network analysis and latent dirichlet allocation-based topic modeling were applied. Results: A total of 13,200 posts were analyzed. The co-occurrence network’s core keywords were newly graduate nurse, general ward, career, turnover, and grievance. The topic modeling showed four topics: (1) ‘Clinical practice-related difficulties’ described clinical hardships, such as the heavy workload of nurses; (2) ‘Concerns about resignation’ incorporated keywords asking for advice on resignation; (3) ‘Searching for information on employment/reemployment’ focused on the working conditions or working climate of a specific hospital; and (4) ‘Organizational action call’ captured the voices urging organized actions to improve nurses’ work environment. Conclusion: Clinical nurses share experiences through the online community and seek advice or information and urge organizational action. Professional nursing organizations should identify and deal with problems that nurses are currently facing. The results of this study can contribute to establishing the policy direction of nursing organizations.

Citations

• National Petition Analysis Related to Nursing: Text Network Analysis and Topic Modeling
  HyunJung Ko, Seok Hee Jeong, Eun Jee Lee, Hee Sun Kim
  Journal of Korean Academy of Nursing.2023; 53(6): 635.     CrossRef
• Perspectives of Frontline Nurses Working in South Korea during the COVID-19 Pandemic: A Combined Method of Text Network Analysis and Summative Content Analysis
  SangA Lee, Tae Wha Lee, Seung Eun Lee
  Journal of Korean Academy of Nursing.2023; 53(6): 584.     CrossRef
• An Exploratory Study on Current Nursing Issues in the COVID-19 era through Newspaper Articles: The Application of Text Network Analysis
  Young Joo Lee
  Journal of Korean Academy of Nursing Administration.2022; 28(3): 307.     CrossRef
• Analysis of Online Community Data on Industrial Engineering
  Juhee Han, Jinyi Yu, Younghoon Lee
  Journal of the Korean Institute of Industrial Engineers.2022; 48(3): 280.     CrossRef