This study aimed to evaluate the effect of the TouchCare system, a digital health management system utilizing the internet of things (IoT), based on the usage levels of older adults. Methods: This is a secondary analysis of data from a quasi-experimental study examining the effects of an IoT-based digital healthcare system. Participants were equipped with the TouchCare application, a touch-tag, and context-aware artificial intelligence. Data on cognitive function, frailty, depressive symptoms, nutritional status, and fall efficacy were collected at baseline and after six months of using the system. The participants were divided into a high-engagement group (n = 22) and a low-engagement group (n = 24) based on how many days they used the application during the study. We used descriptive statistics, the paired t-test, the independent-samples t-test, and two-way mixed analysis of variance. Results: In total, 46 participants completed the evaluations (mean age, 76.6 years). Two-way mixed analysis of variance revealed no significant group-by-time interaction for cognitive function (p = .184), frailty (p = .338), depressive symptoms (p = .543), and nutritional status (p = .589). There was no significant difference in fall efficacy between the two groups (p = .091). The high-engagement group exhibited significant improvements in visuospatial and executive functions on the Montreal Cognitive Assessment (p = .029). Conclusion: The IoT-based mobile health management application demonstrated benefits in improving cognitive health among older adults. The findings suggest that active engagement with healthcare technology can positively affect health in this population, emphasizing the need for continuous support from nurses as health providers.

This study aimed to evaluate the effect of the TouchCare system, a digital health management system utilizing the internet of things (IoT), based on the usage levels of older adults. Methods: This is a secondary analysis of data from a quasi-experimental study examining the effects of an IoT-based digital healthcare system. Participants were equipped with the TouchCare application, a touch-tag, and context-aware artificial intelligence. Data on cognitive function, frailty, depressive symptoms, nutritional status, and fall efficacy were collected at baseline and after six months of using the system. The participants were divided into a high-engagement group (n = 22) and a low-engagement group (n = 24) based on how many days they used the application during the study. We used descriptive statistics, the paired t-test, the independent-samples t-test, and two-way mixed analysis of variance. Results: In total, 46 participants completed the evaluations (mean age, 76.6 years). Two-way mixed analysis of variance revealed no significant group-by-time interaction for cognitive function (p = .184), frailty (p = .338), depressive symptoms (p = .543), and nutritional status (p = .589). There was no significant difference in fall efficacy between the two groups (p = .091). The high-engagement group exhibited significant improvements in visuospatial and executive functions on the Montreal Cognitive Assessment (p = .029). Conclusion: The IoT-based mobile health management application demonstrated benefits in improving cognitive health among older adults. The findings suggest that active engagement with healthcare technology can positively affect health in this population, emphasizing the need for continuous support from nurses as health providers.

Purpose: Forming a patient safety culture is the first step in addressing patient safety issues. This study therefore examines the patient safety culture among healthcare professionals in South Korea. It also identifies the factors influencing patient safety culture and related activities. Methods: Among the survey studies conducted in South Korea, we reviewed those that measured patient safety culture using the Korean version of the Hospital Survey on Patient Safety Culture 1.0. We identified the level of patient safety culture, the relationship between patient safety culture and patient safety activities, and the factors affecting patient safety culture. Results: A total of 640 studies were identified through searches of seven domestic and international databases, with 58 included in the analysis. Studies targeting nurses accounted for 96.6%. The general characteristics of patient safety culture included length of service, age, working hours, department of work, hospital size, conflict occurrence, unreported incidents, and subjective safety management evaluations. A statistically significant correlation between patient safety culture and patient safety activities was confirmed in 84.5% of studies. Patient safety culture was the most frequently reported factor influencing patient safety activities. Conclusion Strategies to enhance patient safety culture can promote patient safety activities in South Korean healthcare institutions. Considering organizational and organizational members characteristics when devising these strategies can enhance their effectiveness.

Purpose: Forming a patient safety culture is the first step in addressing patient safety issues. This study therefore examines the patient safety culture among healthcare professionals in South Korea. It also identifies the factors influencing patient safety culture and related activities. Methods: Among the survey studies conducted in South Korea, we reviewed those that measured patient safety culture using the Korean version of the Hospital Survey on Patient Safety Culture 1.0. We identified the level of patient safety culture, the relationship between patient safety culture and patient safety activities, and the factors affecting patient safety culture. Results: A total of 640 studies were identified through searches of seven domestic and international databases, with 58 included in the analysis. Studies targeting nurses accounted for 96.6%. The general characteristics of patient safety culture included length of service, age, working hours, department of work, hospital size, conflict occurrence, unreported incidents, and subjective safety management evaluations. A statistically significant correlation between patient safety culture and patient safety activities was confirmed in 84.5% of studies. Patient safety culture was the most frequently reported factor influencing patient safety activities. Conclusion Strategies to enhance patient safety culture can promote patient safety activities in South Korean healthcare institutions. Considering organizational and organizational members characteristics when devising these strategies can enhance their effectiveness.

This study aimed to evaluate the effect of the TouchCare system, a digital health management system utilizing the internet of things (IoT), based on the usage levels of older adults. Methods: This is a secondary analysis of data from a quasi-experimental study examining the effects of an IoT-based digital healthcare system. Participants were equipped with the TouchCare application, a touch-tag, and context-aware artificial intelligence. Data on cognitive function, frailty, depressive symptoms, nutritional status, and fall efficacy were collected at baseline and after six months of using the system. The participants were divided into a high-engagement group (n = 22) and a low-engagement group (n = 24) based on how many days they used the application during the study. We used descriptive statistics, the paired t-test, the independent-samples t-test, and two-way mixed analysis of variance. Results: In total, 46 participants completed the evaluations (mean age, 76.6 years). Two-way mixed analysis of variance revealed no significant group-by-time interaction for cognitive function (p = .184), frailty (p = .338), depressive symptoms (p = .543), and nutritional status (p = .589). There was no significant difference in fall efficacy between the two groups (p = .091). The high-engagement group exhibited significant improvements in visuospatial and executive functions on the Montreal Cognitive Assessment (p = .029). Conclusion: The IoT-based mobile health management application demonstrated benefits in improving cognitive health among older adults. The findings suggest that active engagement with healthcare technology can positively affect health in this population, emphasizing the need for continuous support from nurses as health providers.

Purpose: Forming a patient safety culture is the first step in addressing patient safety issues. This study therefore examines the patient safety culture among healthcare professionals in South Korea. It also identifies the factors influencing patient safety culture and related activities. Methods: Among the survey studies conducted in South Korea, we reviewed those that measured patient safety culture using the Korean version of the Hospital Survey on Patient Safety Culture 1.0. We identified the level of patient safety culture, the relationship between patient safety culture and patient safety activities, and the factors affecting patient safety culture. Results: A total of 640 studies were identified through searches of seven domestic and international databases, with 58 included in the analysis. Studies targeting nurses accounted for 96.6%. The general characteristics of patient safety culture included length of service, age, working hours, department of work, hospital size, conflict occurrence, unreported incidents, and subjective safety management evaluations. A statistically significant correlation between patient safety culture and patient safety activities was confirmed in 84.5% of studies. Patient safety culture was the most frequently reported factor influencing patient safety activities. Conclusion Strategies to enhance patient safety culture can promote patient safety activities in South Korean healthcare institutions. Considering organizational and organizational members characteristics when devising these strategies can enhance their effectiveness.

This study aimed to evaluate the effect of the TouchCare system, a digital health management system utilizing the internet of things (IoT), based on the usage levels of older adults. Methods: This is a secondary analysis of data from a quasi-experimental study examining the effects of an IoT-based digital healthcare system. Participants were equipped with the TouchCare application, a touch-tag, and context-aware artificial intelligence. Data on cognitive function, frailty, depressive symptoms, nutritional status, and fall efficacy were collected at baseline and after six months of using the system. The participants were divided into a high-engagement group (n = 22) and a low-engagement group (n = 24) based on how many days they used the application during the study. We used descriptive statistics, the paired t-test, the independent-samples t-test, and two-way mixed analysis of variance. Results: In total, 46 participants completed the evaluations (mean age, 76.6 years). Two-way mixed analysis of variance revealed no significant group-by-time interaction for cognitive function (p = .184), frailty (p = .338), depressive symptoms (p = .543), and nutritional status (p = .589). There was no significant difference in fall efficacy between the two groups (p = .091). The high-engagement group exhibited significant improvements in visuospatial and executive functions on the Montreal Cognitive Assessment (p = .029). Conclusion: The IoT-based mobile health management application demonstrated benefits in improving cognitive health among older adults. The findings suggest that active engagement with healthcare technology can positively affect health in this population, emphasizing the need for continuous support from nurses as health providers.

Digital health is rapidly growing worldwide and its area is expanding from wellness to treatment due to digital therapeutics (DTx). This study compared DTx in the Korean context with other countries to better understand its political and practical implications. DTx is generally the same internationally, often categorized as software as a medical device. It provides evidence-based therapeutic interventions for medical disabilities and diseases.Abroad, DTx support entailed state subsidies and fundraising and national health insurance coverage. In the case of national health insurance coverage, most cases were applied to mental diseases. Moreover, in Japan, DTx related to hypertension will possibly be under discussion for national health insurance coverage in 2022. In overseas countries, coverage was decided only when the clinical effects were equivalent to those provided by existing technology, and in the UK, real usage data for DTx and associated evaluations were reflected by national health coverage determination. Prices were either determined through closed negotiations with health insurance operating agencies and manufacturers or established based on existing technology. Concerning the current situation, DTx dealing with various diseases including hypertension are expected to be developed near in the future, and the demand for use and compensation will likely increase. Therefore, it is urgent to define and prepare for DTx, relevant support systems, and health insurance coverage listings. Several support systems must be considered, including government subsidies, science/technology funds, and health insurance.

Deep learning-based applications have great potential to enhance the quality of medical services. The power of deep learning depends on open databases and innovation. Radiologists can act as important mediators between deep learning and medicine by simultaneously playing pioneering and gatekeeping roles. The application of deep learning technology in medicine is sometimes restricted by ethical or legal issues, including patient privacy and confidentiality, data ownership, and limitations in patient agreement. In this paper, we present an open platform, MI2RLNet, for sharing source code and various pre-trained weights for models to use in downstream tasks, including education, application, and transfer learning, to encourage deep learning research in radiology. In addition, we describe how to use this open platform in the GitHub environment. Our source code and models may contribute to further deep learning research in radiology, which may facilitate applications in medicine and healthcare, especially in medical imaging, in the near future. All code is available at https://github.com/mi2rl/MI2RLNet.