Background: In South Korea, nontuberculous mycobacteria (NTM) is not a notifiable disease, while the absence of a national surveillance system hampers accurate assessment of its incidence. Therefore, this study utilized National Health Insurance Service (NHIS) claims data to investigate nationwide trends in NTM occurrence over the past decade. Methods: We used NHIS claims (2010−2022) to assemble a cohort with International Classification of Diseases, 10th Revision A31 (A31.0, A31.1, A31.8, A31.9). For incidence, cases diagnosed in 2010−2011 were excluded. Incidence was estimated under three definitions: ≥2 outpatient visits or ≥1 inpatient admission with A31 during the study period; same as A, but with ≤180 days between visits; meeting B plus ≥1 antibiotic prescription within 180 days (treatment initiation). Age-standardized prevalence and incidence were calculated using the 2010 Korean population. Results: A total of 178,287 newly diagnosed NTM cases were identified from 2012 to 2022 (mean age 51.4 years; 66.8 % female). The age-standardized prevalence increased from 15.5 to 69.8 per 100,000 in 2010 to 2022. Incidence peaked in 2017 (38.9/100,000), then declined to 26.9 in 2022. Age-specific incidence of NTM infection showed distinct sex-related patterns. Among men, incidence was consistently concentrated in older adults, particularly those ≥80 years, throughout 2012−2022. In contrast, women experienced a marked epidemiologic shift beginning in 2017, with incidence in their 20s and 30s surpassing older age groups. Medical Aid beneficiaries consistently showed higher incidence rates. By region, Daejeon and Chungnam showed the greatest increase in incidence rates in 2022, compared to 2012. Conclusion NTM infection is increasing in Korea, with distinct epidemiologic patterns by sex, age, and socioeconomic status. The rising burden, especially among young women and the socioeconomically disadvantaged, warrants targeted public health strategies.

Purpose: This study aimed to develop a digital multimedia-based educational intervention for hematopoietic stem cell (HSC) donors and to evaluate its effects on anxiety, knowledge, attitudes toward hematopoietic stem cell donation, and educational satisfaction. Methods: This quasi-experimental study employed a nonequivalent control group non-synchronized pretest-posttest design. Participants were 60 HSC donors admitted to a general hospital in Seoul, Republic of Korea, between April and December 2024. The participants were assigned to an experimental group (n=30) and a control group (n=30). The experimental group received a digital multimediabased educational intervention in addition to the usual education using a standardized handout, whereas the control group received the usual education only. The intervention was provided in three sessions: on the day of admission (20 minutes), on the day of stem cell collection (10 minutes), and on the day of discharge (10 minutes). Data were collected at admission and discharge using structured questionnaires. Anxiety was analyzed using generalized estimating equations (GEE), knowledge and attitudes using mixed ANOVA, and educational satisfaction using an independent t-test. Results: The experimental group showed significantly lower anxiety (p=.048) and higher knowledge levels (p=.005) than the control group. No significant differences were found between the groups in attitudes toward donation or educational satisfaction. Conclusion The digital multimedia-based educational intervention was effective in reducing anxiety and improving knowledge among HSC donors. This intervention may enhance donors’ understanding of the donation process and support a more positive donation experience. Further multicenter studies with larger samples and longitudinal follow-up measurements are needed to verify the long-term effects and generalizability of the intervention.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Purpose: This study aimed to investigate the influence of perceptions of patient safety culture, job stress, and nursing work environment on patient safety nursing activities among emergency room nurses. Methods: This correlational study was conducted from June 5 to July 31, 2024, and targeted 114 emergency room nurses in Busan. A structured self-report questionnaire was used to collect data. Descriptive statistics, independent sample t-tests, one-way ANOVA, Pearson's correlation coefficients, and multiple regression analyses were employed for data analysis. Results: Patient safety nursing activities significantly differed by age (F=6.17, p=.001) and total clinical experience (F=8.89, p<.001) among the participants' general characteristics. Positive correlations were identified with perceptions of patient safety culture (r=.70, p<.001) and nursing work environment (r=.27, p=.003). Multiple regression analysis indicated that perception of patient safety culture (β=.72, p<.001) and total clinical experience (β=-.32, p=.011) were significant predictors, accounting for 50.5% (F=20.24, p<.001) of the variance. Conclusion The findings indicated that perceptions of patient safety culture and total clinical experience are critical factors to be considered when designing interventions to enhance patient safety nursing activities among emergency room nurses.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Purpose: This study aimed to investigate the influence of perceptions of patient safety culture, job stress, and nursing work environment on patient safety nursing activities among emergency room nurses. Methods: This correlational study was conducted from June 5 to July 31, 2024, and targeted 114 emergency room nurses in Busan. A structured self-report questionnaire was used to collect data. Descriptive statistics, independent sample t-tests, one-way ANOVA, Pearson's correlation coefficients, and multiple regression analyses were employed for data analysis. Results: Patient safety nursing activities significantly differed by age (F=6.17, p=.001) and total clinical experience (F=8.89, p<.001) among the participants' general characteristics. Positive correlations were identified with perceptions of patient safety culture (r=.70, p<.001) and nursing work environment (r=.27, p=.003). Multiple regression analysis indicated that perception of patient safety culture (β=.72, p<.001) and total clinical experience (β=-.32, p=.011) were significant predictors, accounting for 50.5% (F=20.24, p<.001) of the variance. Conclusion The findings indicated that perceptions of patient safety culture and total clinical experience are critical factors to be considered when designing interventions to enhance patient safety nursing activities among emergency room nurses.

Purpose: This study aimed to investigate the influence of perceptions of patient safety culture, job stress, and nursing work environment on patient safety nursing activities among emergency room nurses. Methods: This correlational study was conducted from June 5 to July 31, 2024, and targeted 114 emergency room nurses in Busan. A structured self-report questionnaire was used to collect data. Descriptive statistics, independent sample t-tests, one-way ANOVA, Pearson's correlation coefficients, and multiple regression analyses were employed for data analysis. Results: Patient safety nursing activities significantly differed by age (F=6.17, p=.001) and total clinical experience (F=8.89, p<.001) among the participants' general characteristics. Positive correlations were identified with perceptions of patient safety culture (r=.70, p<.001) and nursing work environment (r=.27, p=.003). Multiple regression analysis indicated that perception of patient safety culture (β=.72, p<.001) and total clinical experience (β=-.32, p=.011) were significant predictors, accounting for 50.5% (F=20.24, p<.001) of the variance. Conclusion The findings indicated that perceptions of patient safety culture and total clinical experience are critical factors to be considered when designing interventions to enhance patient safety nursing activities among emergency room nurses.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.