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.

Background: The objective of this study was to determine whether manually performed calibration and quality control (QC) processes could be replaced with an automated laboratory system when installed analyzers fail to provide automated calibration and QC functions. Methods: Alanine aminotransferase (ALT), total cholesterol (TC), creatinine (Cr), direct bilirubin (DB), and lipase (Lip) items were used as analytes. We prepared pooled serum samples at 10 levels for each test item and divided them into two groups; five for the analytical measurement range (AMR) group and five for the medical decision point (MDP) group. Calibration and QC processes were performed for five consecutive days, and ALT, TC, Cr, DB, and Lip levels were measured in the two groups using automated and manual methods. Precision and the mean difference between the calibration and QC methods were evaluated using the reported values of the test items in each group. Results: Repeatability and within-laboratory coefficients of variation (CVs) between the automated system and the conventional manual system in the AMR group were similar. However, the mean reported values for test items were significantly different between the two systems. In the MDP group, repeatability and within-laboratory CVs were better with the automation system. All calibration and QC processes were successfully implemented with the Aptio total laboratory automation system. Conclusion The Aptio total laboratory automation system could be applied to routine practice to improve precision and efficiency.