Characteristics of Digital Transformation Studies in Nursing: A Text-Mining Analysis of Abstracts

Characteristics of Nursing Digital Transformation Research Identified through Text Mining

Abstract

Background: Digital Transformation (DX) in healthcare is progressing rapidly to improve the quality and efficiency of care delivery in an aging society. This study aimed to clarify the current trends and thematic structure of DX-related studies in the nursing field.
Methods: Domestic literature published over the past five years related to “nursing” and “digital transformation” was extracted from the Ichushi-Web and CiNii databases, with 23 articles selected for analysis. Text mining was performed using KH Coder 3, and co-occurrence network analysis was applied to identify word associations with a Jaccard coefficient ≥ 0.50.
Results: A total of 4,977 words (1,109 different words) were extracted. The co-occurrence network analysis revealed two major clusters: “Educational DX” and “Clinical DX.”
The Educational DX cluster, terms such as “VR,” “learning materials,” “experiential learning,” and “practical training” were strongly associated, indicating a concentration of studies focused on experiential learning and instructional material development utilizing virtual reality technologies.
In contrast, the Clinical DX cluster was characterized by the strong linkage of terms such as “nursing,” “medical care,” “utilization,” “documentation,” and “collaboration,” reflecting a body of research addressing the standardization of electronic health records, information sharing, and workflow efficiency in clinical practice.
Conclusion: DX research in nursing is evolving along two major axes—education and clinical practice. Both contribute to improving care quality and efficiency, yet an integrated framework for evaluating outcomes and linking educational and clinical DX remains an important challenge for future studies.