• Journal of Preventive Medicine and Public Health
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• v.50 no.5
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• pp.294-302
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• 2017

Objectives: The objectives of this study were to investigate the agreement between medical history questionnaire data and claims data and to identify the factors that were associated with discrepancies between these data types. Methods: Data from self-reported questionnaires that assessed an individual's history of hypertension, diabetes mellitus, dyslipidemia, stroke, heart disease, and pulmonary tuberculosis were collected from a general health screening database for 2014. Data for these diseases were collected from a healthcare utilization claims database between 2009 and 2014. Overall agreement, sensitivity, specificity, and kappa values were calculated. Multiple logistic regression analysis was performed to identify factors associated with discrepancies and was adjusted for age, gender, insurance type, insurance contribution, residential area, and comorbidities. Results: Agreement was highest between questionnaire data and claims data based on primary codes up to 1 year before the completion of self-reported questionnaires and was lowest for claims data based on primary and secondary codes up to 5 years before the completion of self-reported questionnaires. When comparing data based on primary codes up to 1 year before the completion of selfreported questionnaires, the overall agreement, sensitivity, specificity, and kappa values ranged from 93.2 to 98.8%, 26.2 to 84.3%, 95.7 to 99.6%, and 0.09 to 0.78, respectively. Agreement was excellent for hypertension and diabetes, fair to good for stroke and heart disease, and poor for pulmonary tuberculosis and dyslipidemia. Women, younger individuals, and employed individuals were most likely to under-report disease. Conclusions: Detailed patient characteristics that had an impact on information bias were identified through the differing levels of agreement.

• Journal of Preventive Medicine and Public Health
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• v.52 no.4
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• pp.234-241
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• 2019

Objectives: To identify simultaneous behavioral changes in alcohol consumption, smoking, and weight using a fixed-effect model and to characterize their associations with disease status. Methods: This study included 7 000 529 individuals who participated in the national biennial health-screening program every 2 years from 2009 to 2016 and were aged 40 or more. We reconstructed the data into an individual-level panel dataset with 4 waves. We used a fixed-effect model for smoking, heavy alcohol drinking, and overweight. The independent variables were sex, age, lifestyle factors, insurance contribution, employment status, and disease status. Results: Becoming a high-risk drinker and losing weight were associated with initiation or resumption of smoking. Initiation or resumption of smoking and weight gain were associated with non-high-risk drinkers becoming high-risk drinkers. Smoking cessation and becoming a high-risk drinker were associated with normal-weight participants becoming overweight. Participants with newly acquired diabetes mellitus, ischemic heart disease, stroke, and cancer tended to stop smoking, discontinue high-risk drinking, and return to a normal weight. Conclusions: These results obtained using a large-scale population-based database documented interactions among lifestyle factors over time.

• Journal of Korean Medical Science
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• v.33 no.53
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• pp.343.1-343.8
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• 2018

Background: Linkage of public healthcare data is useful in stroke research because patients may visit different sectors of the health system before, during, and after stroke. Therefore, we aimed to establish high-quality big data on stroke in Korea by linking acute stroke registry and national health claim databases. Methods: Acute stroke patients (n = 65,311) with claim data suitable for linkage were included in the Clinical Research Center for Stroke (CRCS) registry during 2006-2014. We linked the CRCS registry with national health claim databases in the Health Insurance Review and Assessment Service (HIRA). Linkage was performed using 6 common variables: birth date, gender, provider identification, receiving year and number, and statement serial number in the benefit claim statement. For matched records, linkage accuracy was evaluated using differences between hospital visiting date in the CRCS registry and the commencement date for health insurance care in HIRA. Results: Of 65,311 CRCS cases, 64,634 were matched to HIRA cases (match rate, 99.0%). The proportion of true matches was 94.4% (n = 61,017) in the matched data. Among true matches (mean age 66.4 years; men 58.4%), the median National Institutes of Health Stroke Scale score was 3 (interquartile range 1-7). When comparing baseline characteristics between true matches and false matches, no substantial difference was observed for any variable. Conclusion: We could establish big data on stroke by linking CRCS registry and HIRA records, using claims data without personal identifiers. We plan to conduct national stroke research and improve stroke care using the linked big database.

• Journal of Korean Medicine Rehabilitation
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• v.27 no.2
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• pp.67-75
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• 2017

Objectives The study of the clinical effects of traditional east asian medicine (TEAM) using Taiwan national health insurance claim dataset (NHIRD) is useful in Korean Medicine research. We reviewed the clinical studies of TEAM using NHIRD as a whole through this study. Methods We comprehensively searched PUBMED and NHIRD DB for clinical effects of TEAM study using NHIRD from inception to 17, January 2017. As a result, 40 studies investigating the contribution of TEAM intervention to health benefit have been confirmed. We analyzed publication time, target disease, sample size, outcome measurement and main result of 40 searched studies. Results The number of TEAM studies using NHIRD grdually increasing. The topics of the team study using NHIRD covered a wide range of subjects including cardiovascular disease, tumor, gynecological disease, diabetes and kidney disease. The studies have shown large samples and reported significant effects on severe diseases. Conclusions The results of this study suggest that the study of Korean Medicine using Big data will be useful for decision making related to health care in Korea. However, considering the limited domestic Korean health insurance data, it will be necessary to activate the big data research of Korean Medicine through the establishment of a separate cohort in Korea.

• Health Policy and Management
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• v.32 no.2
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• pp.154-163
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• 2022

Background: The costliness index (CI) is an index that is used in various ways to improve the quality of medical care and the management of appropriate treatment in medical institutions. However, the current calculation method for CI has a limitation in reflecting the actual medical cost of the patient unit because the outpatient and inpatient costs are evaluated separately. It is desirable to calculate the CI by integrating the medical cost into the episode unit. Methods: We developed an episode-based CI method using the episode classification system of the Centers for Medicare and Medicaid Services to the National Inpatient Sample data in Korea, which can integrate the admission and ambulatory care cost to episode unit. Additionally, we compared our new method with the previous method. Results: In some episodes, the correlation between previous and episode-based CI was low, and the proportion of outpatient treatment costs in total cost and readmission rates are high. As a result of regression analysis, it is possible that the level of total medical costs of the patient unit in low volume medical institute and rural area has been underestimated. Conclusion: High proportion of outpatient treatment cost in total medical cost means that some medical institutions may have provided medical services in the ambulatory care that are ancillary to inpatient treatment. In addition, a high readmission rate indicates insufficient treatment service for inpatients, which means that previous CI may not accurately reflect actual patient-based treatment costs. Therefore, an integrated patient-unit classification system which can be used as a more effective CI indicator is needed.

• The Journal of the Korean dental association
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• v.57 no.11
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• pp.644-653
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• 2019

Since 2013, adults aged over 20 can receive national health insurance scaling once a year in South Korea. In this study, we analyzed the usage status of national health insurance care service for periodontal disease in 2010-2018 by using Healthcare big data of the Health Insurance Review and Assessment Service. The increase rate of the dental care users was very high at 7.8 and 11.2% in 2013 and 2014, respectively. These are higher than the increase rate of all medical institution users, which is between -1.7 and 3.7%. In 2017, the rate of dental use was 44.4%, which has increased more than 10% compared to 2012. Percent receiver of national health insurance scaling was 19.5% in 2017. The 20s had the highest rate of 23.2%. The rate decreased with age. Based on these results, it can be evaluated that the expansion of national health insurance coverage for scaling improves accessibility to dental care. A more long-term assessment of the effect of periodic dental examination and scaling on reducing the prevalence of periodontal disease is needed. National health insurance coverage should be extended to oral hygiene education and supportive periodontal therapy in order to prevent periodontal disease.

• Journal of Multimedia Information System
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• v.5 no.3
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• pp.171-178
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• 2018

According to the National Health Insurance Corporation in 2008, there were 17,764,428 physical therapy patients, exceeding 31 percent for the population covered by health insurance. This means that three out of 10 Koreans received physical therapy. And now, 10 years later, due to the aging population and the increase in the sports population, the number of patients with physical therapy is expected to be much more than a decade ago. Among them, many physical therapy patients were orthopedic and neurologic disorder. However, in the medical field applied to physical therapy, it is widely applied across all medical fields, including orthopedics, neurosurgery, pediatrics, gynecology, thoracic surgery and dentistry. It is believed that various cases of patients receiving physical therapy will be secured. as mentioned earlier, there will be a large number of patients with physical therapy treatments, making big data analytics easier. based on this, physical therapy applications are thought to be helpful in the analogy of disease and the development of effective physical therapy and will ultimately promote the development of physical therapy.

• Dementia and Neurocognitive Disorders
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• v.22 no.4
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• pp.121-129
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• 2023

Background and Purpose: As it becomes an aging society, interest in senile diseases is increasing. Alzheimer's dementia (AD) and osteoporosis are representative senile diseases. Various studies have reported that AD and osteoporosis share many risk factors that affect each other's incidence. This aimed to determine if active medication treatment of AD could affect the development of osteoporosis. Methods: The Health Insurance Review and Assessment Service provided data consisting of diagnosis, demographics, prescription drug, procedures, medical materials, and healthcare resources. In this study, data of all AD patients in South Korea who were registered under the national health insurance system were obtained. The cohort underwent conversion to an Observational Medical Outcomes Partnership-Common Data Model version 5 format. Results: This study included 11,355 individuals in the good persistent group and an equal number of 11,355 individuals in the poor persistent group from the National Health Claims database for AD drug treatment. In primary analysis, the risk of osteoporosis was significantly higher in the poor persistence group than in the good persistence group (hazard ratio, 1.20 [95% confidence interval, 1.09-1.32]; p<0.001). Conclusions: We found that the good persistence group treated with anti-dementia drugs for AD was associated with a significant lower risk of osteoporosis in this nationwide study. Further studies are needed to clarify the pathophysiological link in patients with two chronic diseases.

• Health Policy and Management
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• v.30 no.1
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• pp.15-25
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• 2020

While healthcare data sets include extensive information about patients, many researchers have limitations in analyzing them due to their intrinsic characteristics such as heterogeneity, longitudinal irregularity, and noise. In particular, since the majority of medical history information is recorded in text codes, the use of such information has been limited due to the high dimensionality of explanatory variables. To address this problem, recent studies applied word embedding techniques, originally developed for natural language processing, and derived positive results in terms of dimensional reduction and accuracy of the prediction model. This paper reviews the deep learning-based natural language processing techniques (word embedding) and summarizes research cases that have used those techniques in the health care field. Then we finally propose a research framework for applying deep learning-based natural language process in the analysis of domestic health insurance data.

• Archives of Plastic Surgery
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• v.45 no.5
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• pp.441-448
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• 2018

Background Previously, surveys have been used to investigate breast reconstruction statistics. Since 2015, breast reconstruction surgery after mastectomy has been covered by the National Health Insurance Service in Korea, and data from breast reconstruction patients are now available from the Health Insurance Review and Assessment Service (HIRA). We investigated statistics in breast reconstruction in Korea through statistics provided by the HIRA Big Data Hub. Methods We investigated the number of cases in mastectomy and breast reconstruction methods from April 1, 2015 to December 31, 2016. Data were furnished by the HIRA Big Data Hub and accessed remotely online. Results were tabulated using SAS Enterprise version 6.1. Results The 31,155 mastectomy cases included 7,088 breast reconstruction cases. Implant-based methods were used in 4,702 cases, and autologous methods in 2,386. The implant-based reconstructions included 1,896 direct-to-implant and 2,806 tissue-expander (2-stage) breast reconstructions. The 2-stage tissue-expander reconstructions included 1,624 expander insertions (first stage) and 1,182 expander-to-permanent-implant exchanges (second stage). Of the autologous breast reconstructions, 705 involved latissimus dorsi muscle flaps, 498 involved pedicled transverse rectus abdominis myocutaneous (TRAM) flaps, and 1,183 involved free-tissue transfer TRAM flaps, including deep inferior epigastric perforator free-tissue transfer flaps. There were 1,707 nipple-areolar complex reconstructions, including 1,565 nipple reconstructions and 142 areola reconstructions. The 1-year mean number of breast reconstructions was 4,050. Conclusions This was the first attempt to evaluate the total number of breast reconstruction procedures using accurate, comprehensive data, and our findings may prove valuable as a foundation for future statistical studies of breast reconstruction procedures in Korea.