Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

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.

Purpose: Acute myocardial infarction (AMI) and stroke are leading global causes of death and can be used to assess acute care quality. We examined the 30-day mortality trends after emergency department admission for AMI and stroke in Korea from 2008 to 2019, focusing on regional and income disparities. Materials and Methods: The AMI and stroke patients admitted to hospitals in Korea were collected from the claims data. We analyzed age and sex-standardized 30-day mortality for AMI, as well as hemorrhagic and ischemic strokes. Disparities in mortality were analyzed using absolute differences and relative ratios between the Organization for Economic Cooperation Development (OECD) and Korea, and among income levels and regions in Korea. A 12-year joinpoint regression was used to determine the annual percent change and the average annual percent change. Results: The trends in the 30-day AMI mortality of Korea were not significantly changed from 2008 to 2019; the gap remained at 1.2 between the OECD and Korea. Korea maintained lower mortality rates for hemorrhagic and ischemic stroke than the mean of OECD. In Korea, the 30-day hemorrhagic stroke mortality showed a constant decreasing trend for the higher-income group living in urban areas; it led to a widened gap based on income levels in urban areas. The 30-day mortality for ischemic stroke tended to decrease in the higher-income group and urban areas. Conclusion National-level intervention is needed to manage regional and income-based disparities in AMI and stroke 30-day mortality. It is important to understand the variance in mortality rate by different geographical regions and income levels to establish an appropriate public health strategy.

Currently, antimicrobial resistance (AMR) is a major threat to global public health. The antimicrobial stewardship program (ASP) has been proposed as an important approach to overcome this crisis. ASP supports the optimal use of antimicrobials, including appropriate dosing decisions, administration duration, and administration routes. In Korea, efforts are being made to overcome AMR using ASPs as a national policy. The current study aimed to develop core elements of ASP that could be introduced in domestic medical facilities. A Delphi survey was conducted twice to select the core elements through expert consensus.The core elements for implementing the ASP included (1) leadership commitment, (2) operating system, (3) action, (4) tracking, (5) reporting, and (6) education. To ensure these core elements are present at medical facilities, multiple departments must collaborate as teams for ASP operations. Establishing a reimbursement system and a workforce for ASPs are prerequisites for implementing ASPs. To ensure that ASP core elements are actively implemented in medical facilities, it is necessary to provide financial support for ASPs in medical facilities, nurture the healthcare workforce in performing ASPs, apply the core elements to healthcare accreditation, and provide incentives to medical facilities by quality evaluation criteria.

Purpose: During the COVID-19 pandemic, Korea has temporarily expanded coverage of teleconsultation to ensure access to essential health services. As a preliminary study, we investigated service utilization patterns and the characteristics of doctors and patients involved in these temporary teleconsultation services. Materials and Methods: Using national health insurance claims data from February 23, 2020 to June 30, 2020 from the Health Insurance Review and Assessment Service, 228269875 cases were identified. Among them, 567390 cases that received teleconsultation services were included in our study. We performed descriptive analyses according to the types of healthcare institutions. Results: In total, 6193 healthcare institutions provided teleconsultation. Of these, 5466 (88.3%) were clinics. Physicians providing teleconsultations were most likely to be doctors of internal medicine (34.0%) or pediatricians (7.0%) and based in the Seoul Metropolitan area (30.4%). In terms of patients undergoing teleconsultation, the most common major disease categories treated were circulatory system diseases (I00–I99). In a detailed analysis, hypertensive diseases (I10–I15) were the most common diagnoses, with a total of 88726 cases (15.6%), followed by diabetes mellitus at 60298 cases (10.6%). The proportion of Medical Aid recipients receiving teleconsultations was higher (9.5%) than other socioeconomic groups. Among all participants, 356622 cases (84.6%) were from a return visit, and 108838 cases (19.2%) received teleconsultation services without being prescribed drugs. Conclusion Temporarily allowed teleconsultation services were provided mostly to the following patients: 1) those scheduled for revisitation, 2) those with chronic diseases, and 3) those living in pandemic hotspots.

Purpose: During the COVID-19 pandemic, Korea has temporarily expanded coverage of teleconsultation to ensure access to essential health services. As a preliminary study, we investigated service utilization patterns and the characteristics of doctors and patients involved in these temporary teleconsultation services. Materials and Methods: Using national health insurance claims data from February 23, 2020 to June 30, 2020 from the Health Insurance Review and Assessment Service, 228269875 cases were identified. Among them, 567390 cases that received teleconsultation services were included in our study. We performed descriptive analyses according to the types of healthcare institutions. Results: In total, 6193 healthcare institutions provided teleconsultation. Of these, 5466 (88.3%) were clinics. Physicians providing teleconsultations were most likely to be doctors of internal medicine (34.0%) or pediatricians (7.0%) and based in the Seoul Metropolitan area (30.4%). In terms of patients undergoing teleconsultation, the most common major disease categories treated were circulatory system diseases (I00–I99). In a detailed analysis, hypertensive diseases (I10–I15) were the most common diagnoses, with a total of 88726 cases (15.6%), followed by diabetes mellitus at 60298 cases (10.6%). The proportion of Medical Aid recipients receiving teleconsultations was higher (9.5%) than other socioeconomic groups. Among all participants, 356622 cases (84.6%) were from a return visit, and 108838 cases (19.2%) received teleconsultation services without being prescribed drugs. Conclusion Temporarily allowed teleconsultation services were provided mostly to the following patients: 1) those scheduled for revisitation, 2) those with chronic diseases, and 3) those living in pandemic hotspots.