Objectives: Assessing healthcare quality at the national level is essential for evaluating health system performance and identifying areas requiring improvement. This study examined long-term trends in healthcare quality in Korea from 2008 to 2023 using internationally comparable indicators. Methods: We conducted a trend analysis of healthcare quality in Korea using the Organization for Economic Cooperation and Development (OECD) Health Care Quality and Outcomes framework and quality indicators. Indicators across multiple domains, including acute care, primary care, prescribing in primary care, mental healthcare, and patient experiences, were analyzed and compared with OECD averages. Results: Healthcare quality in Korea improved across several domains. Thirty-day mortality for acute myocardial infarction decreased from 14.2% in 2008 to 10.2% in 2023, while mortality for ischemic stroke declined from 8.7% to 5.5%. Avoidable hospitalizations decreased substantially, with hospitalizations for chronic obstructive pulmonary disease declining by 59.7%. The proportion of patients with diabetes receiving cholesterol-lowering treatment increased from 44.1% to 82.5%. However, the proportion of broad-spectrum antibiotic prescriptions remained substantially higher than the OECD average (40.3 vs. 15.5%). In mental healthcare, excess mortality ratios increased from 4.3 in 2010 to 4.9 in 2023 for schizophrenia and from 3.5 to 4.3 for bipolar disorder, while post-discharge suicide rates showed little improvement. Patient experience indicators related to patient-physician communication improved and approached OECD averages. Conclusions Healthcare quality in Korea improved substantially between 2008 and 2023, particularly in acute care outcomes and chronic disease management. However, persistent challenges remain in areas such as antibiotic use, polypharmacy, and mental healthcare. These findings provide internationally comparable evidence to inform future health policy and healthcare quality improvement efforts.

Objectives: This study aimed to investigate the impact of coronavirus disease 2019 (COVID-19) on admission rates and in-hospital mortality among patients with ischemic and hemorrhagic stroke. Methods: We constructed a dataset detailing the monthly hospitalizations and mortality rates of inpatients with stroke from January 2017 to December 2021. Employing an interrupted time series analysis, we explored the impact of COVID-19 on hospitalizations and 30-day in-hospital mortality among stroke patients. Results: The number of ischemic stroke admissions decreased by 18.5%, from 5335 to 4348, immediately following the COVID-19 outbreak (p<0.001). The in-hospital mortality rate for ischemic stroke increased slightly from 3.3% to 3.4% immediately after the outbreak, although it showed a decreasing trend over time. The number of hemorrhagic stroke admissions fell by 7.5%, from 2014 to 1864, immediately following the COVID-19 outbreak. The 30-day in-hospital mortality rate for hemorrhagic stroke initially decreased from 12.9% to 12.7%, but subsequently showed an increasing trend. Conclusions We confirmed that COVID-19 impacted both the admission and death rates of stroke patients. The admission rate for both ischemic and hemorrhagic strokes decreased, while in-hospital mortality increased. Specifically, in-hospital mortality from ischemic stroke rose initially after the outbreak before stabilizing. Additionally, our findings indicate variable effects based on sex, age, and socioeconomic status, suggesting that certain groups may be more susceptible. This underscores the need to identify and support vulnerable populations to mitigate adverse health outcomes.

Objectives: This study aimed to investigate the impact of coronavirus disease 2019 (COVID-19) on admission rates and in-hospital mortality among patients with ischemic and hemorrhagic stroke. Methods: We constructed a dataset detailing the monthly hospitalizations and mortality rates of inpatients with stroke from January 2017 to December 2021. Employing an interrupted time series analysis, we explored the impact of COVID-19 on hospitalizations and 30-day in-hospital mortality among stroke patients. Results: The number of ischemic stroke admissions decreased by 18.5%, from 5335 to 4348, immediately following the COVID-19 outbreak (p<0.001). The in-hospital mortality rate for ischemic stroke increased slightly from 3.3% to 3.4% immediately after the outbreak, although it showed a decreasing trend over time. The number of hemorrhagic stroke admissions fell by 7.5%, from 2014 to 1864, immediately following the COVID-19 outbreak. The 30-day in-hospital mortality rate for hemorrhagic stroke initially decreased from 12.9% to 12.7%, but subsequently showed an increasing trend. Conclusions We confirmed that COVID-19 impacted both the admission and death rates of stroke patients. The admission rate for both ischemic and hemorrhagic strokes decreased, while in-hospital mortality increased. Specifically, in-hospital mortality from ischemic stroke rose initially after the outbreak before stabilizing. Additionally, our findings indicate variable effects based on sex, age, and socioeconomic status, suggesting that certain groups may be more susceptible. This underscores the need to identify and support vulnerable populations to mitigate adverse health outcomes.

Objectives: This study aimed to investigate the impact of coronavirus disease 2019 (COVID-19) on admission rates and in-hospital mortality among patients with ischemic and hemorrhagic stroke. Methods: We constructed a dataset detailing the monthly hospitalizations and mortality rates of inpatients with stroke from January 2017 to December 2021. Employing an interrupted time series analysis, we explored the impact of COVID-19 on hospitalizations and 30-day in-hospital mortality among stroke patients. Results: The number of ischemic stroke admissions decreased by 18.5%, from 5335 to 4348, immediately following the COVID-19 outbreak (p<0.001). The in-hospital mortality rate for ischemic stroke increased slightly from 3.3% to 3.4% immediately after the outbreak, although it showed a decreasing trend over time. The number of hemorrhagic stroke admissions fell by 7.5%, from 2014 to 1864, immediately following the COVID-19 outbreak. The 30-day in-hospital mortality rate for hemorrhagic stroke initially decreased from 12.9% to 12.7%, but subsequently showed an increasing trend. Conclusions We confirmed that COVID-19 impacted both the admission and death rates of stroke patients. The admission rate for both ischemic and hemorrhagic strokes decreased, while in-hospital mortality increased. Specifically, in-hospital mortality from ischemic stroke rose initially after the outbreak before stabilizing. Additionally, our findings indicate variable effects based on sex, age, and socioeconomic status, suggesting that certain groups may be more susceptible. This underscores the need to identify and support vulnerable populations to mitigate adverse health outcomes.

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.

Purpose: The purpose of this study is to determine the level of health equity in relation to cancer incidence. Materials and Methods: We used the National Health Insurance claims data of the National Health Insurance Service between 2005 and 2022 and annual health insurance and medical aid beneficiaries between 2011 and 2021 to investigate the disparities of cancer incidence. We calculated age-sex standardized cancer incidence rates by cancer and year according to the type of insurance and the trend over time using the annual percentage change. We also compared the hospital type of the first diagnosis by cancer type and year and cancer incidence rates by cancer type and region in 2021 according to the type of insurance. Results: The total cancer incidence increased from 255,971 in 2011 to 325,772 cases in 2021. The absolute difference of total cancer incidence rate between the NHI beneficiaries and the medical aid (MA) recipients increased from 510.1 cases per 100,000 population to 536.9 cases per 100,000 population. The odds ratio of total cancer incidence for the MA recipients increased from 1.79 (95% confidence interval [CI], 1.77 to 1.82) to 1.90 (95% CI, 1.88 to 1.93). Disparities in access to hospitals and regional cancer incidence were profound. Conclusion This study examined health inequities in relation to cancer incidence over the last decade. Cancer incidence was higher in the MA recipients, and the gap was widening. We also found that regional differences in cancer incidence still exist and are getting worse. Investigating these disparities between the NHI beneficiaries and the MA recipients is crucial for implementing of public health policies to reduce health inequities.

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.

OBJECTIVES: In Korea, the National Health Insurance Service (NHIS) covers essential healthcare expenses, including cataract surgery. To address concerns that private health insurance (PHI) might have inflated the need for such procedures, we investigated the extent of the PHI-attributable increase in cataract surgery and its impact on NHIS-reimbursed expenses. METHODS: This retrospective, observational study uses nationwide claims data for cataract surgery from 2016 to 2020. We examined trends in utilization and cost, and we estimated the excess numbers of (1) cataract operations attributable to PHI and (2) types of intraocular lenses used for cataract surgery in 2020. RESULTS: Between 2016 and 2020, a 36.8% increase occurred in the number of cataract operations, with increases of 63.5% and 731.8% in the total healthcare costs reimbursed by NHIS and PHI, respectively. Over a 5-year period, the surgical rate per 100,000 people doubled for patients aged <65 years (from 328 in 2016 to 664 in 2020). Among the 619,771 cases in 2020 of cataract surgery reimbursed by the Korean diagnosis-related group system, more non-NHIS-covered intraocular lenses were used for patients aged <65 years than ≥65 years (68.1 vs. 14.2%). In 2020 alone, an estimated 129,311 excess operations occurred, accounting for an excess cost of US$115 million. CONCLUSIONS A dramatic increase in the number and cost of cataract operations has occurred over the last 5 years. The PHI-related increase in operations resulted in increased costs to NHIS. Measures to curtail the non-indicated use of cataract surgery should be implemented regarding PHI.

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.