Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

We retrospectively examined current trends in ordering for erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) testing. All claims corresponding to ESR and CRP testing for hospital visits in 2022 were obtained from a platform operated by the Health Insurance and Review Agency. The annual (2018–2022) utilization and cost of ESR and CRP, total inpatient days, and patient encounters with outpatients were retrieved. The number of ESR and CRP tests gradually increased over 5 years, except a slight decrease in 2020. The proportion of claims with co-ordering of ESR and CRP tests was 46.64%. More than 60% co-ordering claims were observed in orthopedic surgery, neurosurgery, and plastic surgery departments. The proportion of co-orders was relatively high in inpatient setting and primary hospitals. This study indicated frequent co-ordering patterns of ESR and CRP tests, highlighting an urgent need for diagnostic stewardship programs on ESR and CRP testing in Korea.

We retrospectively examined current trends in ordering for erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) testing. All claims corresponding to ESR and CRP testing for hospital visits in 2022 were obtained from a platform operated by the Health Insurance and Review Agency. The annual (2018–2022) utilization and cost of ESR and CRP, total inpatient days, and patient encounters with outpatients were retrieved. The number of ESR and CRP tests gradually increased over 5 years, except a slight decrease in 2020. The proportion of claims with co-ordering of ESR and CRP tests was 46.64%. More than 60% co-ordering claims were observed in orthopedic surgery, neurosurgery, and plastic surgery departments. The proportion of co-orders was relatively high in inpatient setting and primary hospitals. This study indicated frequent co-ordering patterns of ESR and CRP tests, highlighting an urgent need for diagnostic stewardship programs on ESR and CRP testing in Korea.

We retrospectively examined current trends in ordering for erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) testing. All claims corresponding to ESR and CRP testing for hospital visits in 2022 were obtained from a platform operated by the Health Insurance and Review Agency. The annual (2018–2022) utilization and cost of ESR and CRP, total inpatient days, and patient encounters with outpatients were retrieved. The number of ESR and CRP tests gradually increased over 5 years, except a slight decrease in 2020. The proportion of claims with co-ordering of ESR and CRP tests was 46.64%. More than 60% co-ordering claims were observed in orthopedic surgery, neurosurgery, and plastic surgery departments. The proportion of co-orders was relatively high in inpatient setting and primary hospitals. This study indicated frequent co-ordering patterns of ESR and CRP tests, highlighting an urgent need for diagnostic stewardship programs on ESR and CRP testing in Korea.

We retrospectively examined current trends in ordering for erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) testing. All claims corresponding to ESR and CRP testing for hospital visits in 2022 were obtained from a platform operated by the Health Insurance and Review Agency. The annual (2018–2022) utilization and cost of ESR and CRP, total inpatient days, and patient encounters with outpatients were retrieved. The number of ESR and CRP tests gradually increased over 5 years, except a slight decrease in 2020. The proportion of claims with co-ordering of ESR and CRP tests was 46.64%. More than 60% co-ordering claims were observed in orthopedic surgery, neurosurgery, and plastic surgery departments. The proportion of co-orders was relatively high in inpatient setting and primary hospitals. This study indicated frequent co-ordering patterns of ESR and CRP tests, highlighting an urgent need for diagnostic stewardship programs on ESR and CRP testing in Korea.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background: Healthcare professionals often experience moral distress while providing endof-life care. This study explored how physicians and nurses experienced moral distress when they cared for critically and terminally ill patients in tertiary hospitals in South Korea. Methods: This study used semi-structured in-depth interviews. A total of 22 people in two tertiary hospitals were interviewed, nine (40.9%) of which were physicians and 13 (59.1%) were nurses. The recorded interview files and memos were analyzed using grounded theory. Results: Most physicians and nurses encountered similar feelings of anger, helplessness, and burden owing to a lack of appropriate resources for end-of-life care. However, the factors and contexts of their moral distress differed. Nurses mainly addressed poorly organized end-of-life care, intensive labor conditions without support for nurses, and providing care without participation in decision-making. Meanwhile, physicians addressed the prevailing misperceptions on end-of-life care, communication failure between physicians owing to hierarchy and fragmented disciplines, the burden of responsibility in making difficult decisions, and the burden of resource allocation. Conclusion Differences in moral distress between physicians and nurses leave them isolated and can affect communication regarding healthcare. Mutual understanding between job disciplines will enhance their communication and help resolve conflicts in end-of-life care.

Purpose: Cancer patients need ongoing care from healthcare providers to maintain continuity of treatment. Much research has been conducted on digital health services for providing continuous management of discharged cancer patients. This review aimed to identify nurse-led digital health interventions for discharged cancer patients. Methods: This scoping review was conducted using JBI methodology. The population was post-discharge adult cancer patients, the concept was nurse-led digital health intervention, and the context was open. Databases including PubMed, Embase, CINAHL, Cochrane Library, KoreaMed, and RISS were searched.Data were summarized about the general characteristics of the article, participants, interventions, and outcomes. Results: Fifty-seven studies were included, with ten studies that focused on the elderly. One third of the participants included in this review had colorectal cancer (32.7%). Telephone was the most frequently used format, while the others were applications, the internet, and telemonitoring. The nurses’ main roles consisted of counseling, symptom monitoring, and education. Conclusion The development of nurseled digital health intervention for the elderly will be necessary, and studies using more diverse technologies will need to be conducted. Digital health interventions for post-discharge colorectal cancer patients could be applied in practice. Nurses should provide emotional support while providing digital health interventions.