Analysis of the Trends in Emergency Patients: Using the National Health Insurance Claims Data

BackgroundKorea has single health insurance system and insurance claim information on almost all medical practices in Korean hospitals is collected and processed by the Health Insurance Review and Assessment Service (HIRA). Since information about prescription of almost all hospitals is available in National Health Insurance (NHI) claim data, recently established the Korea National Antimicrobial Use Analysis System (KONAS) has been using NHI claim data as data source. The purpose of this study is to validate the accuracy of NHI claim data.MethodsData on all antimicrobial agents prescribed in four tertiary-care hospitals in Korea between January 2019 and December 2019 were obtained using NHI claim data extracted by HIRA and data extracted by common data model based on electronic health record (EHR) in each hospital. Antibiotics and antifungal agents according to the Anatomical Therapeutic Chemical class J01 and J02 were included while antiviral, antitubercular, antiparasitic, and topical antimicrobial agents were excluded. Antimicrobial consumption was measured as days of therapy (DOT) and standardized to per 1,000 patient-days. The ratio of monthly antimicrobial consumption calculated using the NHI claim data compared to that calculated using the common data model was demonstrated (HIRA/EHR ratio).ResultsThe monthly HIRA/EHR ratio of broad-spectrum antibiotics predominantly used for hospital-onset infections was 1.08-1.12 and that of broad-spectrum antibiotics predominantly used for community-acquired infections was 1.11-1.21. The monthly HIRA/EHR ratio of other antimicrobial classes are as follows: antibacterial agents predominantly used for resistant gram-positive infections 1.15-1.31, narrow-spectrum beta-lactam agents 1.00-1.05, antifungal agents predominantly used for invasive candidiasis 1.00-1.27, and antibacterial agents predominantly used for extensive antibiotic-resistant gram-negative bacteria 0.70-1.09.ConclusionThe monthly antimicrobial consumption calculated using NHI claim data differs from that calculated using EHR data by up to 30%. It would be desirable to establish a system that can analyze and monitor antimicrobial consumption using EHR data in each hospital in Korea in the future.DisclosuresHyunki Woo, BS, Evidnet Inc.: Employee changhui Kim, BS, Evidnet Inc.: Employee.

Accuracy of diabetes diagnosis in health insurance claims data in Taiwan.

This study will determine opportunity of using the National Health Insurance (NHI) claims data for supplemental notifiable infectious disease surveillance system at national or regional levels.

By using the National Health Insurance (NHI) claim data of Taiwan, we sought to determine the predictors for nontraumatic lower extremity amputation (LEA) or peripheral revascularization procedures (PRP) in patients with peripheral artery disease (PAD). From the NHI claim data, we identified 12,206 patients with newly diagnosed PAD between 1998 and 2008, and followed them up to 2008. We explored the age, gender, and whether the patients had concomitant comorbid conditions, such as diabetes mellitus (DM), hypertension (HTN), atrial fibrillation (AF), stroke, hospitalization for coronary artery disease (CAD), myocardial infarction (MI), or heart failure (HF), and whether they were taking cilostazol at the time of recruitment. We searched for clinical parameters that might be important determinants for LEA or PRP in the study population. Of the 12,206 patients, 150 (1.2%) were found to undergo either LEA or PRP or both (LEA 81, PRP 53, both PRP and LEA 16). Old age, male gender, and history of hospitalization for CAD or MI and AF were found to be risk predictors for both procedures. Patients with DM were at lower risk for PRP (odds ratio 0.418, p = 0.001). Patients who were taking cilostazol had higher risk for LEA or PRP. HTN was not a risk predictor for LEA or PRP. From this nationwide study, we found that among PAD patients in Taiwan, age, male gender, AF, and hospitalization for CAD or MI are risk predictors for future LEA or PRP. DM is a negative predictor for PRP while both DM and HTN are not risk predictors for LEA.

Relationship between the legal nurse staffing standard and patient survival after perioperative cardiac arrest: A cross-sectional analysis of Korean administrative data

BackgroundPredictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of how risk adjustment models perform in predictive modeling in Taiwan or Asia has been rare. The aims of this study were to evaluate the performance of different risk adjustment models (the ACG risk adjustment system and prior expenditures) in predictive modeling, using Taiwan's National Health Insurance (NHI) claims data, and to compare characteristics of potentially high-expenditure subjects identified through different models.MethodsA random sample of NHI enrollees continuously enrolled in 2002 and 2003 (n = 164,562) was selected. Health status measures and total expenditures derived from 2002 NHI claims data were used to predict the possibility of becoming 2003 top users. Statistics-based indicators (C-statistics, sensitivity, & Predictive Positive Value) and characteristics of identified top groups by different models (expenditures and prevalence of manageable diseases) were presented.ResultsBoth diagnosis-based and prior expenditures models performed much better than the demographic model. Diagnosis-based models were better in identifying top users with manageable diseases; prior expenditures models were better in statistics-based indicators and identifying people with higher average expenditures. Prior expenditures status could correctly identify more actual top users than diagnosis-based or demographic models. The proportions of actual top users that could be identified by diagnosis-based models alone were much lower than that identified by prior expenditures status.ConclusionsPredicted top users identified by different models have different characteristics and there is little agreement between modes regarding which groups would be potentially top users; therefore, which model to use should depend on the purpose of predictive modeling. Prior expenditures are a more powerful tool than diagnosis-based risk adjusters in terms of correctly identifying more actual high expenditures users. There is still much room left for improvement of diagnosis-based models in predictive modeling.

BackgroundNumerous studies have shown that healthcare professionals are exposed to psychological distress. However, since most of these studies assessed psychological distress using self-reporting questionnaires, the magnitude of the problem is largely unknown. We evaluated the risks of mood disorders, anxiety disorders, sleep disorders, and any psychiatric disorders in workers in healthcare industry using Korea National Health Insurance (NHI) claims data from 2014, which are based on actual diagnoses instead of self-evaluation.MethodsWe used Korea 2014 NHI claims data and classified employees as workers in the healthcare industry, based on companies in the NHI database that were registered with hospitals, clinics, public healthcare, and other medical services. To estimate the standardized prevalence of the selected mental health disorders, we calculated the prevalence of diseases in each age group and sex using the age distribution of the Korea population. To compare the risk of selected mental disorders among workers in the healthcare industry with those in other industries, we considered age, sex, and income quartile characteristics and conducted propensity scored matching.ResultsIn the matching study, workers in healthcare industry had higher odds ratios for mood disorders (1.13, 95% CI: 1.11–1.15), anxiety disorders (1.15, 95% CI: 1.13–1.17), sleep disorders (2.21, 95% CI: 2.18–2.24), and any psychiatric disorders (1.44, 95% CI: 1.43–1.46) than the reference group did. Among workers in healthcare industry, females had higher prevalence of psychiatric disorders than males, but the odds ratios for psychiatric disorders, compared to the reference group, were higher in male workers in healthcare industry than in females.ConclusionsThe prevalence of mood disorders, anxiety disorders, sleep disorders, and all psychiatric disorders for workers in the healthcare industry was higher than that of other Korean workers. The strikingly high prevalence of sleep disorders could be related to the frequent night-shifts in these professions. The high prevalence of mental health problems among workers in healthcare industry is alarming and requires prompt action to protect the health of the “protectors.”

Background: The purpose of this study was to propose a method for developing a measure of hospital-wide all-cause risk-standard -ized readmissions using administrative claims data in Korea and to discuss further considerations in the refinement and implemen-tation of the readmission measure.Methods: By adapting the methodology of the United States Center for Medicare & Medicaid Services for creating a 30-day read-mission measure, we developed a 6-step approach for generating a comparable measure using Korean datasets. Using the 2010 Korean National Health Insurance (NHI) claims data as the development dataset, hierarchical regression models were fitted to calcu-late a hospital-wide all-cause risk-standardized readmission measure. Six regression models were fitted to calculate the readmission rates of six clinical condition groups, respectively and a single, weighted, overall readmission rate was calculated from the readmis-sion rates of these subgroups. Lastly, the case mix differences among hospitals were risk-adjusted using patient-level comorbidity variables. The model was validated using the 2009 NHI claims data as the validation dataset.Results: The unadjusted, hospital-wide all-cause readmission rate was 13.37%, and the adjusted risk-standardized rate was 10.90%, varying by hospital type. The highest risk-standardized readmission rate was in hospitals (11.43%), followed by general hospitals (9.40%) and tertiary hospitals (7.04%).Conclusion: The newly developed, hospital-wide all-cause readmission measure can be used in quality and performance evalua-tions of hospitals in Korea. Needed are further methodological refinements of the readmission measures and also strategies to im-plement the measure as a hospital performance indicator.Keywords: Patient readmission; Patient readmission; Quality indicators; Risk-adjustment; Hospital

Although much health services research has been conducted using national health insurance claims data in Korea, the validity of this method has not been ascertained. The objective of this study was to validate the use of claims data for health services research by comparing incidence rate of cancers found using insurance claims data against rates of the national cancer registry of Korea. An algorithm to estimate incidence rates using claims data was developed and applied. The claims data from 2005-2008 were acquired and the patients admitted to hospitals due to cancer in 2008 without admission to hospital from 2005- 2007 by the same diagnosis code were regarded as incident cases. The acquired results were compared with the values from the National Cancer Registry of Korea. The incidence rate of all cancers found using claims data was 363.1 per 100,000 people, which is very similar to the 361.9 per 100,000 rate of the national cancer registry. Also the age-, gender- and disease-specific rates between the two data sources were similar. Therefore, national health insurance claims data may be a worthwhile resource for health services research if appropriate algorithms are applied, especially considering the cost effectiveness of this method.

BackgroundThe National Health Insurance (NHI) programme was launched in 1995 in Taiwan and nearly 99% of 23 million citizens are enrolled in the NHI in 2010.ObjectiveTo examine the feasibility of...

This study systematically analyzed coronavirus disease 2019 (COVID-19) and vaccination details during pregnancy by using the national health insurance claims data. Population-based retrospective cohort data of 12,399,065 women aged 15-49 years were obtained from the Korea National Health Insurance Service claims database between 2019 and 2021. Univariate analysis was performed to compare the obstetric outcomes of pregnant women (ICD-10 O00-O94) and their newborns (ICD-10 P00-P96) with and without COVID-19. Univariate analysis was also performed to compare the age and obstetric outcomes of pregnant women receiving different types of vaccines. The percentage of pregnant women with COVID-19 during pregnancy was 0.11%. Some obstetric outcomes of pregnant women with COVID-19, including the rates of preterm birth or cesarean delivery, were significantly better than those of pregnant women without COVID-19. The rate of miscarriage was higher in pregnant women with COVID-19 than without COVID-19. However, the outcomes of newborns of women with and without COVID-19 were not significantly different. Regarding vaccination type, obstetric outcomes of pregnant women appeared to be worse with the viral vector vaccine than with the mRNA vaccine. To the best of our knowledge, this is the first study to systematically analyze COVID-19 and vaccination details during pregnancy using the national health insurance claims data in Korea. The obstetric outcomes in pregnant women with and without COVID-19 and their newborns were similar.

PurposeTaiwan has changed the coding system to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding since 2016. This study aimed to determine the optimal algorithms for identifying stroke risk factors in Taiwan’s National Health Insurance (NHI) claims data.Patients and MethodsWe retrospectively enrolled 4538 patients hospitalized for acute ischemic stroke (AIS), transient ischemic attack (TIA), or intracerebral hemorrhage (ICH) from two hospitals’ stroke registries, which were linked to NHI claims data. We developed several algorithms based on ICD-10-CM diagnosis codes and prescription claims data to identify hypertension, diabetes, hyperlipidemia, atrial fibrillation (AF), and ischemic heart disease (IHD) using registry data as the reference standard. The agreement of risk factor status between claims and registry data was quantified by calculating the kappa statistic.ResultsAccording to the registry data, the prevalence of hypertension, diabetes, hyperlipidemia, AF, and IHD among all patients was 77.5%, 41.5%, 47.9%, 12.1%, and 7.1%, respectively. In general, including diagnosis codes from prior inpatient or outpatient claims to those from the stroke hospitalization claims improved the agreement. Incorporating prescription data could improve the agreement for hypertension, diabetes, hyperlipidemia, and AF, but not for IHD. The kappa values of the optimal algorithms were 0.552 (95% confidence interval 0.524–0.580) for hypertension, 0.802 (0.784–0.820) for diabetes, 0.514 (0.490–0.539) for hyperlipidemia, 0.765 (0.734–0.795) for AF, and 0.518 (0.473–0.564) for IHD.ConclusionAlgorithms using diagnosis codes alone are sufficient to identify hypertension, AF, and IHD whereas algorithms combining both diagnosis codes and prescription data are more suitable for identifying diabetes and hyperlipidemia. The study results may provide a reference for future studies using Taiwan’s NHI claims data.

The utilization of health insurance claims data has expanded significantly, enabling researchers to conduct epidemiological studies on a large scale. This review examines key statistical methods for addressing baseline differences and conducting cohort analyses using Korean National Health Insurance claims data. Propensity score matching and inverse probability of treatment weighting are widely used to mitigate selection bias and enhance causal inference in observational studies. These methods help improve study validity by balancing covariates between treatment and control groups. Additionally, survival analysis techniques, such as the Cox proportional hazards model, are essential for assessing time-to-event outcomes and estimating hazard ratios while accounting for censoring. However, the application of these statistical methods is accompanied by challenges, including unmeasured confounding, instability in weight estimation, and violations of model assumptions. To address these limitations, emerging approaches, such as Doubly robust estimation, machine learning-based causal inference, and the marginal structural model, have gained prominence. These techniques offer greater flexibility and robustness in real-world data analysis. Future research should focus on refining methodologies for integrating high-dimensional health datasets and leveraging artificial intelligence to enhance predictive modeling and causal inference. Furthermore, the expansion of international collaborations and the adoption of standardized data models will facilitate large-scale multi-center studies. Ethical considerations, including data privacy and algorithmic transparency, should also be prioritized to ensure responsible data use. Maximizing the utility of health insurance claims data requires interdisciplinary collaboration, methodological advancements, and the implementation of rigorous statistical techniques to support evidence-based healthcare policy and improve public health outcomes.

Objectives: This study aimed to compare the cost-utility of traditional Korean medicine (TKM) alone versus combined TKM and Western medicine (TKM+WM) as treatment strategies for the prevention of depression in women with menopausal disorders. We utilized nationwide real-world claims data provided by the Health Insurance Review and Assessment Service (HIRA) of South Korea.Methods: A retrospective cohort study was conducted using customized national health insurance claims data from the Health Insurance Review and Assessment Service (HIRA) between 2015 and 2023. Women diagnosed with menopausal disorders (KCD code N95) at Korean medicine institutions were identified and followed for three years from the index date. Based on treatment patterns during the episode period, participants were classified into a traditional Korean medicine (TKM) only group and a TKM plus Western medicine (TKM+WM) group. The incidence of depression was defined using ICD-10 codes F32–F33. Quality-adjusted life years (QALYs) were estimated using utility weights of 0.85 (without depression) and 0.65 (with depression), and incremental cost-effectiveness ratios (ICERs) were calculated. Sensitivity analyses were performed to assess the impact of changes in utility values and discount rates.Results: The TKM+WM group had higher medical costs (160,817 KRW vs. 73,862 KRW) and achieved higher QALYs (2.37 vs. 2.18) compared to the TKM-only group. The resulting incremental cost-effectiveness ratio (ICER) was estimated at 445,657 KRW per QALY gained, which is substantially below the widely accepted willingness-to-pay (WTP) threshold in South Korea (30 million KRW/QALY). Sensitivity analyses confirmed the robustness of these findings across varying assumption.Conclusions: Based on real-world claims data analysis, combined Korean and Western medicine treatment showed favorable cost-effectiveness compared to Korean medicine alone for managing menopausal women at risk of depression in the Korean healthcare context. However, these observational findings should be interpreted with caution regarding causal inference. Further prospective studies are needed to confirm these associations and inform evidence-based policy decisions regarding integrative care and reimbursement strategies.

To determine the association between frontal lobe function and risk of hip fracture in patients with Alzheimer disease (AD).Retrospective cohort study using multicenter hospital-based dementia registry and national health insurance claim data was done. Participants who had available data of neuropsychological test, national health insurance claim, and other covariates were included. A total of 1660 patients with AD were included based on Stroop Test results. A total of 1563 patients with AD were included based on the Controlled Oral Word Association Test (COWAT) results. Hip fracture was measured by validated identification criteria using national health insurance claim data. Frontal lobe function was measured by Stroop Test and COWAT at baseline.After adjusting for potential covariates, including cognitive function in other domains (language, verbal and nonverbal memory, and attention), the Cox proportional hazard regression analysis revealed that risk of a hip fracture was decreased with a hazard ratio (HR) of 0.98 per one point of increase in the Stroop Test (adjusted HR = 0.98, 95% confidence interval [CI]: 0.97–1.00) and 0.93 per one point increase in COWAT (adjusted HR = 0.93, 95% CI: 0.88–0.99).The risk of hip fracture in AD patients was associated with baseline frontal lobe function. The result of this research presents evidence of association between frontal lobe function and risk of hip fracture in patients with AD.