Background: Transthoracic echocardiography (TTE) requires time-intensive integration of quantitative measure‑ ments and qualitative visual assessment. Fully automated artificial intelligence (AI)-based analysis may reduce total analysis time while preserving accuracy, but systematic real-world validation remains limited. Methods: This prospective, single-center pilot study enrolled 40 TTE examinations. Identical deidentified DICOM datasets were independently provided to a trained cardiac sonographer and a fully automated AI system comprising quantitative and qualitative visual interpretation modules. All outputs were compared with a cardiologist-adjudicated reference standard. Primary endpoints were total analysis time and noninferiority of AI-derived left ventricular ejection fraction (LVEF) versus the reference standard, with a prespecified margin of 3 percentage points (one-sided α = 0.025). Results: Median analysis time was 94 s (interquartile range [IQR], 82–106 s) for the AI workflow versus 490 s (IQR, 438–626 s) for the human workflow (P < 0.001). AI-derived LVEF met the noninferiority criterion (mean difference, 0.00 percentage points; upper one-sided 95% confidence bound, 1.41 percentage points; P < 0.001), with an intraclass correlation coefficient (ICC) of 0.902 (95% confidence interval, 0.822–0.947). ICCs for secondary quantitative indi‑ ces ranged from 0.625 to 0.989. For aortic regurgitation severity grading, AI’s overall accuracy was 75.0% (quadratic weighted κ = 0.762), compared with 82.5% for human interpretation (κ = 0.812, McNemar P = 0.579). Conclusions Fully automated AI-assisted TTE analysis substantially reduced total analysis time while maintaining noninferior LVEF accuracy and acceptable performance across secondary quantitative and qualitative indices. These findings support the use of AI as a practical workflow accelerator in routine echocardiography.

Purpose: This paper aimed to investigate the levels and predictors of moral distress, compassion fatigue, and compassion satisfaction among nurses caring for patients with cancer and to identify predictors for the variables. Methods: A cross-sectional, descriptive correlational study was conducted on 245 nurses from hospitals in South Korea. Data was collected through online surveys from May to June 2025. Variables were measured using the Korean version of the Moral Distress Scale-Revised and the Professional Quality of Life Scale-5. Data were analyzed using a t-test, ANOVA, Pearson’s correlation, and multiple regression analysis. Results: Nurses reported moderate-to-high levels of moral distress, compassion fatigue, and compassion satisfaction, with religious affiliation predicting lower moral distress. Nurses with 3~5 years of experience caring for cancer patients exhibited lower moral distress than those with less than 3 years of experience. Employment in tertiary hospitals and the availability of support programs were predictors of lower moral distress, while caring for cancer patients throughout one’s career predicted higher moral distress. Advanced practice nurses, nurses providing advanced clinical support, and nurses who had completed self-care education were predictors of greater compassion fatigue. In contrast, religious affiliation, having more than five years of nursing experience, and possession of additional oncology nursing certifications significantly explained the variance in compassion satisfaction among nurses. Conclusion Moral distress, compassion fatigue, and compassion satisfaction varied by nurses’ personal and professional characteristics. Multilevel interventions, including structured self-care education and institutional support systems, are needed to alleviate emotional burden and promote professional well-being among oncology nurses.

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 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.

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.

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.