Purpose The purpose of this study was to understand the mediating effect of workplace incivility on the relationship between nursing organizational culture and turnover intention among nurses. Design A descriptive survey was used to collect data. The participants were 170 nurses with more than six months of clinical experience at university hospitals or hospitals with over 500 beds in South Korea. Methods Data were collected using self‐report questionnaires. Collected data were analyzed using descriptive statistics, t‐test, ANOVA, Scheffé test, and Pearson's correlation. Baron and Kenny's three‐step hierarchical regression analysis and the Sobel test were used to determine the mediating effect of workplace incivility on the relationship between nursing organizational culture and nurses’ turnover intention. Results This study found a full mediating effect of workplace incivility on the association between relationship‐oriented culture and turnover intention (Z = −3.02, p = 0.003) and a partial mediating effect of workplace incivility on the association between hierarchy‐oriented culture and turnover intention (Z = 2.36, p = 0.018). Conclusion This study empirically confirmed that nursing organizational culture and workplace incivility directly or indirectly influenced turnover intention, which highlights the seriousness of workplace incivility. Clinical relevance This study suggests that there is a need to establish a concrete strategy to avoid a hierarchy‐oriented culture and create a relationship‐oriented culture. It is important to develop a variety of intervention programs to reduce workplace incivility in order to prevent nurses’ turnover.

Background This study focuses on the potential of health big data in the South Korean context. Despite huge data reserves and pan-government efforts to increase data use, the utilization is limited to public interest research centered in public institutions that have data. To increase the use of health big data, it is necessary to identify and develop measures to meet the various demands for such data from individuals, private companies, and research institutes. Objective The aim of this study was to identify the perceptions of and demands for health big data analysis and use among workers in health care–related occupations and to clarify the obstacles to the use of health big data. Methods From May 8 to May 18, 2022, we conducted a web-based survey among 390 health care–related workers in South Korea. We used Fisher exact test and analysis of variance to estimate the differences among occupations. We expressed the analysis results by item in frequency and percentage and expressed the difficulties in analyzing health big data by mean and standard deviation. Results The respondents who revealed the need to use health big data in health care work–related fields accounted for 86.4% (337/390); 65.6% (256/390) of the respondents had never used health big data. The lack of awareness about the source of the desired data was the most cited reason for nonuse by 39.6% (153/386) of the respondents. The most cited obstacle to using health big data by the respondents was the difficulty in data integration and expression unit matching, followed by missing value processing and noise removal. Thus, the respondents experienced the greatest difficulty in the data preprocessing stage during the health big data analysis process, regardless of occupation. Approximately 91.8% (358/390) of the participants responded that they were willing to use the system if a system supporting big data analysis was developed. As suggestions for the specific necessary support system, the reporting and provision of appropriate data and expert advice on questions arising during the overall process of big data analysis were mentioned. Conclusions Our findings indicate respondents’ high awareness of and demand for health big data. Our findings also reveal the low utilization of health big data and the need to support health care workers in their analysis and use of such data. Hence, we recommend the development of a customized support system that meets the specific requirements of big data analysis by users such as individuals, nongovernmental agencies, and academia. Our study is significant because it identified important but overlooked failure factors. Thus, it is necessary to prepare practical measures to increase the utilization of health big data in the future.

BACKGROUND This study focuses on the potential of health big data in the Korean context. Despite the availability of health big data and pan-government efforts to increase data use, the use of health big data is restricted to public institutions that hold big data and conduct public interest. In order to increase the use of health big data, it is necessary to identify and develop measures to meet various demands for health big data from individuals, private companies, and research institutes. Despite the need to enhance the usefulness of health big data and develop a system for supporting health big data analysis and use, there has been limited research on the subject. OBJECTIVE To identify the perceptions of and demands for health big data analysis and use among workers in healthcare-related occupations and clarify the obstacles to the use of health big data. METHODS From May 8 to May 18, 2022, we conducted an online survey among 390 healthcare-related workers in Korea. We used the chi-square test to estimate the differences between occupations. We expressed the results analysis by item in frequency and percentage and expressed the difficulties in analyzing health big data in mean and standard deviation. RESULTS The respondents revealing the need to use health big data, and low utilization of health big data in work-related fields accounted for 86.4% and 38.2%, respectively. The respondents who never used health big data accounted for 65.6%. The lack of awareness about the source of the desired data was the most cited reason for non-use by 39.6% of the respondents. Among the respondent users, the most cited obstacle to using health big data was the “difficulty in data integration and expression unit matching,” followed by “missing value processing and noise removal” and “data removal and type conversion that did not fit the purpose.” The respondents who were willing to use a system supporting big data analysis and use accounted for 91.8%. Healthcare service development was most cited as the application area for health big data by 21% of the respondents. CONCLUSIONS These findings indicate respondents’ high awareness of and demand for health big data. However, they also reveal the low utilization of health big data and the need to support health care workers in their analysis and use of health big data. Hence, this study recommends the development of a customized support system that meets specific requirements of big data analysis by users such as individuals, non-governmental agencies, and academia.