J Korean Acad Nurs Adm.  2024 Dec;30(5):529-542. 10.11111/jkana.2024.30.5.529.

Voice of Customer Analysis of Nursing Care in a Tertiary Hospital: Text Network Analysis and Topic Modeling

Affiliations
  • 1Nurse, Division of Nursing, Jeonbuk National University Hospital
  • 2Professor, Department of Obstetrics and Gynecology, Jeonbuk National University Hospital-Jeonbuk National University Medical School · Research Institute of Clinical Medicine of Jeonbuk National University ․ Biomedical Research Institute of Jeonbuk National University Hospital
  • 3Professor, College of Nursing· Research Institute of Nursing Science, Jeonbuk National University

Abstract

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.

Keyword

Nurses; Nursing services; Consumer satisfaction; Data mining; Social network analysis
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