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.

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.

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.

The survival rate of children admitted in the neonatal intensive care unit (NICU) after birth is on the increase; hence, proper evaluation and care of their neurodevelopment has become an important issue. Neurodevelopmental assessments of individual domains regarding motor, language, cognition, and sensory perception are crucial in planning prompt interventions for neonates requiring immediate support and rehabilitation treatment. These assessments are essential for identifying areas of weakness and designing targeted interventions to improve future functional outcomes and the quality of lives for both the infants and their families. However, initial stratification of risk to select those who are in danger of neurodevelopmental disorders is also important in terms of cost-effectiveness. Efficient and robust functional evaluations to recognize early signs of developmental disorders will help NICU graduates receive interventions and enhance functional capabilities if needed. Several age-dependent, domain-specific neurodevelopmental assessment tools are available; therefore, this review summarizes the characteristics of these tools and aims to develop multidimensional, standardized, and regular follow-up plans for NICU graduates in Korea.