INTRODUCTION

Following the National Medical Insurance Act legislation in 1963, the current single insurer, the National Health Insurance Service (NHIS), originated from medical insurance societies for companies with more than 500 employees and insurance societies for public officials and private school employees in 1977. Gradually, it expanded to cover the smaller companies and universal coverage of the self-employed by medical insurance societies in rural and urban areas in 1989. Finally, it was integrated into a single insurer, the National Health Insurance Corporation (NHIC), in 2000 to achieve equity among insurance funds in relation to the financial burden and other managerial issues. In the same year, the separation of prescribing and dispensing drugs was also implemented. The NHIC then changed its name to the NHIS in 2013 [1,2]. In 2008, the longterm care insurance (LTCI) system was introduced to alleviate the financial burden on unpaid family caregivers, helping elderly Koreans with difficulties performing activities of daily living or housework due to geriatric diseases [3].

In Korea, 97% of the population is obliged to enroll in the National Health Insurance (NHI) program. Patients pay approximately 5% to 30% of the total medical costs to clinics or hospitals, although some services are not covered by insurance, such as cosmetic surgery and some unproven therapies. Clinics and hospitals then submit claims to the Health Insurance Review & Assessment (HIRA) service for inpatient and outpatient care, including data on diagnoses as determined by the International Classification of Diseases, 10th revision (ICD-10), procedures, prescription records, demographic information, and direct medical costs to obtain reimbursement for the total medical costs (ranged from 70% to 95%). The remaining 3% of the population not insured by the NHI program are either covered by another medical aid (MA) program or are temporary or illegal residents [4].

In this study, we reviewed the structure, content, and means of using data procured from the NHI system and HIRA service for the benefit of Korean researchers and presented the latest publication trends for Korean healthcare data procured from the NHI and HIRA databases.

METHODS

NHI and HIRA databases

Quality of the NHI and HIRA databases

A single insurer, the NHIS, is a nonprofit organization responsible for operating a health insurance program, managing the enrollment of the insured and their dependents, collecting contributions and setting medical fee schedules. With the NHIS performing compulsory financial collections from the insured based on the law, the data collected from this operation are called “qualification and contribution data” [2].

The HIRA service is a value-based purchasing system for medical service quality improvement providing a review of incurred medical costs and reports from healthcare providers about medical services performed for HIRA. These reports are called “health insurance claims” and contain information on the diagnosis and status of outpatients and inpatients, name, dosage, prescription date and periods, and method of administration of drugs, and laboratory and imaging tests [2].

NHI and HIRA database contents

NHIS provides various support to accommodate the policy and academic research of health and medicine by using a sample research database, customized database, and health disease index database through the National Health Insurance Sharing Service (NHISS). The HIRA plays roles in reviewing submitted medical fees, claims from clinics and hospitals for reimbursement decisions, and quality of healthcare services to beneficiaries. The claims database of HIRA consists of four detailed categories, including general information on specification or payment specification (20T), consultation statements (30T), diagnosis statements determined by the ICD-10 (40T), and detailed statements about prescriptions (60T) (Table 1). Each data table is designated by the letters 20T, 30T, 40T, and 60T. Each consists of unique number-table delimiter, and T refers to “table.” Payment specification (20T) describes the personal identification, health and medical care institution, principal diagnosis, first additional diagnosis, days of medical care, date of medical care commencement, number of visits, and insurer and deduction payment, among others. The consultation statement (30T) provides detailed information on medical examination and treatment such as medical care, in-hospital administration of medicine, procedures, and surgery. The diagnosis statement, 40T, includes the principal diagnosis and up to nine additional diagnoses. The detailed statement of prescriptions (60T) for outpatient care provides prescription claims including the following: name of drug, date filled, days of supply, quantity dispensed, and price of each drug [4]. The data in the long-term insurance database include information on the presence of geriatric diseases and the cognitive function of the beneficiaries. Table 2 summarizes the data characteristics according to the NHIS program [2].

The sample research database refers to data standardized to a sharable form by extracting and deidentifying the sample to improve access and use by investigators because of the large size and personal information issues. It includes a sample cohort database, medical check-up database, elderly cohort database, working women cohort database, and infant medical check-up cohort database. Each database consists of a qualification database, treatment database, medical check-up database, and clinic database. The qualification database includes sex, age, income, region, and types of qualification (employee health insurance, self-employed health insurance, or MA program). The treatment database includes 20T, 30T, 40T, and 60T [5]. The medical check-up database contains major results from medical check-ups and behavior and habitual data from questionnaires including primary general medical check-up data and transition period check-up data [6]. Clinic database is composed of total 10 variables including status, facility, equipment, and personnel data of clinics by type, establishment, and location (city and state). The customized database includes health information data collected, managed, and maintained by the NHIS to be modified as requested for the purpose of policy and academic research. The health disease index database is an index of chronic disease management systematically calculated from the big data of the NHIS including both risk factors of chronic diseases and the processes of occurrence and complications [2].

Research samples of NHI and HIRA

(1) NHI database

As mentioned in the method section, five types of databases are included in the sample research database. The sample cohort database is a patient sample of 2% randomly extracted from qualified NHI subscribers and MA beneficiaries. The medical check-up cohort database is composed of qualified individuals who were 40 to 79 years old in 2002 to 2003 and received a general medical check-up (approximately 510,000). The elderly cohort database is drawn from the elderly long-term nursing service insurance, targeting qualified individuals over the age of 60 as of 2002 (approximately 550,000). The sample cohort database, medical check-up cohort database, and elderly cohort database included data from 2002 to 2015, a span of 14 years. The working women cohort database consists of working women aged 15 to 64 as of 2007 (approximately 180,000) from 2007 to 2015 (9 years). The infant medical check-up cohort database is a 5% sample of each birth year from 2008 to 2012 for those who received their first or second infant medical check-up from 2008 to 2015 (8 years) [7].

(2) HIRA database

Four types of patient samples are provided by HIRA: 13% of the national inpatient sample (HIRA-NIS); 3% of the national patient sample (HIRA-NPS); 20% of the aged population sample (HIRA-APS); and 10% of the pediatric patient sample (HIRA-PPS) [8]. The patient samples from 2009 to 2015 are available, but a longitudinal study cannot be performed because of the inconsistency in data collection from individuals as each sample is cross-sectional.

Accessing NHI and HIRA databases and protection of personal information

To request NHI database data, Korean researchers can access raw big data by visiting seven research centers located nationwide or by remote access. Korean researchers are required to complete a study protocol with approval from an Institutional Review Board (IRB). The application for the data-request form is then reviewed within 25 days from the date of submission by the data provision review committee. After approval by HIRA committees, the HIRA extracts data from the data warehouse system, which is then only accessible to individual researchers for study [8]. Also through the healthcare big data open system (http://opendata.hira.or.kr), summary statistics related to healthcare services including prescriptions, medical conditions, and healthcare providers are publicly available.

According to the “Act on the Protection of Personal Information Maintained by Public Agencies,” the NHIS and HIRA provide data without individual identifiers by using an unidentifiable code representing each individual. There are data concerning the patient’s age, sex, diagnosis, and lists of prescribed drugs [2].

Journals sources and search strategies

As we described, the NHI and HIRA databases represent the entire Korean population because every Korean resident is universally eligible for the NHI program. Because the integration of information of NHI and MA beneficiaries in HIRA and NHI databases was introduced in 2006 and the government initiative promoting “Opening and Sharing Big Data for Value Creation” has been available since 2013, we have been able to use the NHI and HIRA databases for population-based and nationwide research on various diseases since 2013 [8]. To identify articles using either the NHI or HIRA databases, we queried the literature using PubMed with various combinations of terms from January 1, 2014 to December 31, 2019. We used terms for the literature search in the dataset of NHIS that were a combination of “Korea” [affiliation] and “NHIS” or “national health insurance” [all fields]. Combinations of the words “Korea” [affiliation] and “HIRA” or “Health Insurance Review and Assessment” [all fields] were used to search literature using the dataset released from the HIRA. Then from the literature extracted, the number of papers in each field published each year was calculated using the text words “diabetes,” “metabolism,” “endocrinology,” “osteoporosis,” “thyroid,” “bone,” “pituitary,” and “adrenal.” In addition, languages were limited to English. The study protocol received ethical approval from the IRB at the Yonsei University College of Medicine (No. 4-020-0778), which waived the need for informed consent because of the descriptive nature of this study and no personal information included.

RESULTS

Korean population served by the National Health Insurance system

Table 3 shows 100% of the Korean population is covered by the NHI program. In 2019, the NHI system covered 97.2% (n= 52,880,000) of the population including employee insured (70.4%, n=37,227,000) and self-employed insured (26.8%, n=14,164,000), and the MA system covered the remaining and 2.8% (n=1,485,000) of the population. Compared to 2013 and 2016, the proportion of health insurance coverage by NHI either employee or self-employed insured and MA beneficiaries in 2019 has not changed significantly.

Recent publications associated with NHI and HIRA data

According to trend analysis of NHI and HIRA database research, domestic and international research-project papers are increasing (Fig. 1A). Research on various topics related to health care, public health policies, and diseases including cancer, infection, heart diseases, cerebrovascular diseases, hypertension, diabetes mellitus, and endocrine diseases has been published. The total number of publications from 2014 to 2019 using the NHI and HIRA databases was 2,541 and 655, respectively. Published studies using the NHI and HIRA databases dramatically increased from 178 in 2014 to 743 in 2019 and from 60 to 165, respectively. Since 2014, publications related to endocrinology using NHI and HIRA database have also increased (Fig. 1B). A total of 5,465 studies were included according to the search method of our study using the terms of “diabetes,” “metabolism,” “endocrinology,” “osteoporosis,” “thyroid,” “bone,” “pituitary,” and “adrenal.” Of the endocrinology-related journals, 83.1% of the studies adopted the NHI database. Diabetes-related big data studies accounted for 36.7%. Metabolism-, osteoporosis-, thyroid-, bone-, pituitary-, and adrenal-related research comprised 21.0%, 9.7%, 8.5%, 4.3%, 3.5%, and 0.1%, respectively.

DISCUSSION

Big data studies on Korean public healthcare using the NHI and HIRA databases are becoming increasingly popular. This phenomenon could be attributed to the strengths of the compulsory nature of the NHI system providing universal coverage for the Korean population and consequently the serial population data that are readily available. In this study, we reviewed the structure, content, and means of using data procured from the Korean NHI system by Korean researchers, especially endocrinologists, and we present the latest publication trends on Korean healthcare.

Fig. 2 illustrates the operational structure of the NHI system. Three key regulators of the healthcare system include the Ministry of Health and Welfare (MOHW), NHIS, and HIRA. The MOHW supervises the NHI program through the formulation and implementation of policies. The NHIS is a nonprofit organization and the single insurer that manages the NHI program. It is responsible for managing enrolled and insured individuals and their dependents (spouse, direct lineal ascendants or descendants, and unmarried brothers or sisters) and collecting contributions and setting medical fee schedules. The HIRA has a quality control role, evaluates healthcare performance, and reviews medical billing and claims. The HIRA also determines whether health care services are medically necessary and ensures that the services are delivered to beneficiaries at an appropriate level and cost [5]. The NHI, MA, and LTCI are the three main health care programs for universal coverage in Korea. The NHI program classified covers the whole population as either employee or self-employed as a social insurance benefits scheme with the following features: short-term insurance and compulsory contributions made according to ability to pay. The contribution to NHI is calculated based on an employee’s wage and is paid by the company’s employer. In the case of the self-employed, the cost is calculated based on the household income, property, income, vehicle(s) owned, age, and gender. The MA program is managed by the Korean government. It is a public assistance scheme that secures the minimum livelihood of low-income households and assists with self-help by providing medical services. The LTCI program is based on the principle of social solidarity. The program provides benefits for at least 6 months. Although NHI covers the whole population as a compulsory scheme, not all items of healthcare are covered by the program. The aim is to cover the prevention and treatment of sickness and injury that result from daily life and childbirth. The program also covers health promotion and rehabilitation. The NHI provides the same benefits package regardless of the contributions made that are determined by the individual’s ability to pay.

NHI and HIRA databases were used in various research areas including healthcare and public health policies, medical adherence, prescribing patterns, adverse events, cost-effectiveness, burden of disease, healthcare service utilization, disease incidence and prevalence, and outcomes. As availability and accessibility of NHI and HIRA data have grown in recent years, the data provision review committee has granted use to over 3,000 studies from 2014 to 2019 [9]. In this study, 83.1% of endocrinology-related big data studies used the NHI database. Despite the similarity of the two databases, their content is different, where the NHI database’s main sections include healthcare use, sociodemographic variables, health screening, and mortality, whereas the HIRA research database’s main sections include general demographic characteristics, healthcare use, diagnoses, and outpatient prescriptions [10]. Additionally, NHI sample databases include longitudinal cohorts, enabling researchers to perform long-term follow-up studies, whereas the HIRA sample databases include separate cohorts for each year, only suitable for conducting a cross-sectional study or short-term follow-up (less than 1 year) studies. This is because patients in the HIRA sample databases are stratified and re-sampled annually, and patient information cannot be linked across years within the HIRA sample database [11].

Concerning the strengths and limitations of the NHI and HIRA databases, the NHI database covers detailed information regarding medical examinations, therapeutics including prescription and long-term follow-up of the insured. This allows the researchers to perform longitudinal studies whereas the HIRA database cannot follow individuals. As we described earlier, HIRA data covers healthcare service records from neonates to the elderly in a full range of healthcare settings. This representative and comprehensive dataset broadens Korean research to fields that might not be easily measured using randomized controlled trials by providing demographic information. Using longitudinal data collected from NHI and MA beneficiaries, researchers can conduct cohort studies and investigate long-term outcomes. Furthermore, healthcare agencies provide information to the HIRA, which ensures the reliability of the data, and the vast sample size secures statistical power for complicated analyses [8]. However, as in the HIRA database, the discrepancy between real-world diagnosis of the insured and data collected from the NHI database, the validity of the research might be compromised. Therefore, the researchers should properly extract samples for the studies by using appropriate definitions and carefully designed inclusion criteria to avoid misleading results. Despite the strengths of the HIRA database, it lacks information such as the severity of diseases and personal health history such as smoking or alcohol consumption. This personal information might interfere with the main outcomes of the research [10]. Also, uncovered healthcare services such as cosmetic surgeries are not included, and discrepancies between diagnosis information and actual health conditions may impose bias to compromise a study’s validity [10]. Nonetheless, the benefits overcome the limitations of the NHI and HIRA databases, and these limitations could be further addressed by strategies for the optimal use of the NIH and HIRA databases.

In conclusion, the NHI and HIRA databases represent the entire Korean population and can be used as a population database. The NHIS and HIRA databases have been important resources for endocrinology research and have provided guidelines for Korean endocrinologists to conduct world-leading population-based epidemiology and disease research.

NOTES

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

AUTHOR CONTRIBUTIONS

Conception or design: B.W.L.

Acquisition, analysis, or interpretation of data: H.K.K., S.O.S.

Drafting the work or revising: J.H.N., I.K.J., B.W.L.

Final approval of the manuscript: B.W.L.

Acknowledgements

The authors thank Medical Illustration & Design, part of the Medical Research Support Services of Yonsei University College of Medicine, for all artistic support related to this work.

Fig. 1.

(A) Year-specific number of publications using the National Health Insurance (NHI) and Health Insurance Review & Assessment (HIRA) databases (2014 to 2019). (B) Endocrinology-related search terms in the NHI and HIRA databases (2014 to 2019).

Fig. 2.

How the National Health Insurance system works. NHIS, National Health Insurance Service; HIRA, Health Insurance Review & Assessment.

REFERENCES

• 1. Jeong HS. Korea’s National Health Insurance: lessons from the past three decades. Health Aff (Millwood) 2011;30:136-44.ArticlePubMed
• 2. Song SO, Jung CH, Song YD, Park CY, Kwon HS, Cha BS, Park JY, Lee KU, Ko KS, Lee BW. Background and data configuration process of a nationwide population-based study using the Korean national health insurance system. Diabetes Metab J 2014;38:395-403.ArticlePubMedPMC
• 3. Kwon S. Future of long-term care financing for the elderly in Korea. J Aging Soc Policy 2008;20:119-36.ArticlePubMed
• 4. Jung CH, Seo GH, Suh S, Bae JC, Kim MK, Hwang YC, Kim JH, Lee BW. The population-based risk of need for coronary revascularization according to the presence of type 2 diabetes mellitus and history of coronary heart disease in the Korean population. PLoS One 2015;10:e0128627.ArticlePubMedPMC
• 5. Park YT, Yoon JS, Speedie SM, Yoon H, Lee J. Health insurance claim review using information technologies. Healthc Inform Res 2012;18:215-24.ArticlePubMedPMC
• 6. Seong SC, Kim YY, Park SK, Khang YH, Kim HC, Park JH, Kang HJ, Do CH, Song JS, Lee EJ, Ha S, Shin SA, Jeong SL. Cohort profile: the National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) in Korea. BMJ Open 2017;7:e016640.ArticlePubMedPMC
• 7. National Health Insurance Sharing Service. Terminology. Available from: https://nhiss.nhis.or.kr/bd/ab/bdaba012eng.do (cited 2020 Sep 12).
• 8. Kim JA, Yoon S, Kim LY, Kim DS. Towards actualizing the value potential of Korea health insurance review and assessment (HIRA) data as a resource for health research: strengths, limitations, applications, and strategies for optimal use of HIRA data. J Korean Med Sci 2017;32:718-28.ArticlePubMedPMCPDF
• 9. Chung H, Kim SY, Kim HS. Clinical research from a health insurance database: practice and perspective. Korean J Med 2019;94:463-70.ArticlePDF
• 10. Kim L, Kim JA, Kim S. A guide for the utilization of health insurance review and assessment service national patient samples. Epidemiol Health 2014;36:e2014008.ArticlePubMedPMCPDF
• 11. Kim S, Kim MS, You SH, Jung SY. Conducting and reporting a clinical research using Korean healthcare claims database. Korean J Fam Med 2020;41:146-52.ArticlePubMedPMCPDF

Figure & Data

References

Citations

Citations to this article as recorded by  

• Medical service utilization patterns among adults with insomnia: A retrospective cohort study
  Min Kyung Hyun, Jang Won Lee
  European Journal of Integrative Medicine.2024; 67: 102325.     CrossRef
• COVID-19-related cardiovascular disease risk due to weight gain: a nationwide cohort study
  Su Kyoung Lee, Yohwan Lim, Seogsong Jeong, Hyun Wook Han
  European Journal of Medical Research.2024;[Epub]     CrossRef
• Kidney Health Plan 2033 in Korea: bridging the gap between the present and the future
  Do Hyoung Kim, Young Youl Hyun, Jin Joo Cha, Sua Lee, Hyun Kyung Lee, Jong Wook Choi, Su-Hyun Kim, Sang Youb Han, Cheol Whee Park, Eun Young Lee, Dae Ryong Cha, Sung Gyun Kim, Chun Soo Lim, Sun-Hee Park
  Kidney Research and Clinical Practice.2024; 43(1): 8.     CrossRef
• Bipolar disorder and the risk of cardiometabolic diseases, heart failure, and all-cause mortality: a population-based matched cohort study in South Korea
  You-Bin Lee, Hyewon Kim, Jungkuk Lee, Dongwoo Kang, Gyuri Kim, Sang-Man Jin, Jae Hyeon Kim, Hong Jin Jeon, Kyu Yeon Hur
  Scientific Reports.2024;[Epub]     CrossRef
• Evidence for the biopsychosocial model of suicide: a review of whole person modeling studies using machine learning
  Earvin S. Tio, Melissa C. Misztal, Daniel Felsky
  Frontiers in Psychiatry.2024;[Epub]     CrossRef
• Nationwide Study of the Epidemiology, Diagnosis, and Treatment of Hypersensitivity Pneumonitis in Korea
  Hae In Jung, Dal Ri Nam, Seung-Hun You, Jae-Woo Jung, Kang-Mo Gu, Sun-Young Jung
  Journal of Korean Medical Science.2024;[Epub]     CrossRef
• Quantitative risk factor analysis of prior disease condition and socioeconomic status with the multiple myeloma development: nationwide cohort study
  Suein Choi, Eunjin Kim, Jinhee Jung, Sung-Soo Park, Chang-Ki Min, Seunghoon Han
  Scientific Reports.2024;[Epub]     CrossRef
• Association of physical activity with fractures in kidney transplant recipients: A Korean nationwide cohort study
  Sungmi Kim, Jin‐Hyung Jung, Kyungho Lee, Junseok Jeon, Dong Wook Shin, Hye Ryoun Jang, Jung Eun Lee, Kyungdo Han, Wooseong Huh
  Clinical Transplantation.2024;[Epub]     CrossRef
• Does endometriosis increase the risks of endometrial hyperplasia and endometrial cancer?
  Hoon Kim, Hyun Jung Kim, Hyeong Sik Ahn
  Gynecologic Oncology.2023; 169: 147.     CrossRef
• Cardiovascular Outcomes according to Comorbidities and Low-Density Lipoprotein Cholesterol in Korean People with Type 2 Diabetes Mellitus
  Min Kyong Moon, Junghyun Noh, Eun-Jung Rhee, Sang Hyun Park, Hyeon Chang Kim, Byung Jin Kim, Hae Jin Kim, Seonghoon Choi, Jin Oh Na, Young Youl Hyun, Bum Joon Kim, Kyung-Do Han, In-Kyung Jeong
  Diabetes & Metabolism Journal.2023; 47(1): 45.     CrossRef
• Association between Low-Density Lipoprotein Cholesterol Level and Cardiovascular Outcomes in Korean Adults: A Nationwide Cohort Study
  Junghyun Noh, Min Kyong Moon, Eun-Jung Rhee, Sang Hyun Park, Hyeon Chang Kim, Byung Jin Kim, Hae Jin Kim, Seonghoon Choi, Jin Oh Na, Young Youl Hyun, Bum Joon Kim, Kyung-Do Han, In-Kyung Jeong
  Diabetes & Metabolism Journal.2023; 47(1): 59.     CrossRef
• Predicting the Risk of Insulin-Requiring Gestational Diabetes before Pregnancy: A Model Generated from a Nationwide Population-Based Cohort Study in Korea
  Seung-Hwan Lee, Jin Yu, Kyungdo Han, Seung Woo Lee, Sang Youn You, Hun-Sung Kim, Jae-Hyoung Cho, Kun-Ho Yoon, Mee Kyoung Kim
  Endocrinology and Metabolism.2023; 38(1): 129.     CrossRef
• Genital tract infection and pelvic surgery contribute to the development of endometriosis
  Ae Ra Han, Suehyun Lee, Jaehun Cha, Jong-Yeup Kim, Dong-Kyu Kim, Jae Won Han, Chul Jung Kim, Sung Ki Lee
  Journal of Reproductive Immunology.2023; 156: 103831.     CrossRef
• Big Data Research in the Field of Endocrine Diseases Using the Korean National Health Information Database
  Sun Wook Cho, Jung Hee Kim, Han Seok Choi, Hwa Young Ahn, Mee Kyoung Kim, Eun Jung Rhee
  Endocrinology and Metabolism.2023; 38(1): 10.     CrossRef
• Comorbidity Differences by Trajectory Groups as a Reference for Identifying Patients at Risk for Late Mortality in Childhood Cancer Survivors: Longitudinal National Cohort Study
  Hyery Kim, Hae Reong Kim, Sung Han Kang, Kyung-Nam Koh, Ho Joon Im, Yu Rang Park
  JMIR Public Health and Surveillance.2023; 9: e41203.     CrossRef
• Investigation of the Relationship Between Psychiatry Visit and Suicide After Deliberate Self-harm: Longitudinal National Cohort Study
  Hye Hyeon Kim, Chanyoung Ko, Ji Ae Park, In Han Song, Yu Rang Park
  JMIR Public Health and Surveillance.2023; 9: e41261.     CrossRef
• Long-term exposure to particulate matter and risk of Alzheimer’s disease and vascular dementia in Korea: a national population-based Cohort Study
  Jung-Im Shim, Garam Byun, Jong-Tae T. Lee
  Environmental Health.2023;[Epub]     CrossRef
• Association of Physical Activity With SARS-CoV-2 Infection and Severe Clinical Outcomes Among Patients in South Korea
  YoHwan Lim, Myeong Hoon Lee, Seogsong Jeong, Hyun Wook Han
  JAMA Network Open.2023; 6(4): e239840.     CrossRef
• Age at Mortality in Patients with Type 2 Diabetes Who Underwent Kidney Transplantation: An Analysis of Data from the Korean National Health Insurance and Statistical Information Service, 2006 to 2018
  Sun Ok Song, Eugene Han, Kang Ju Son, Bong-Soo Cha, Byung-Wan Lee
  Journal of Clinical Medicine.2023; 12(9): 3160.     CrossRef
• Nationwide patterns of hydroxychloroquine dosing and monitoring of retinal toxicity in patients with systemic lupus erythematosus
  Jae-Eun Lee, Dal Ri Nam, Yoon-Kyoung Sung, Yu Jeong Kim, Sun-Young Jung
  Scientific Reports.2023;[Epub]     CrossRef
• Associations of updated cardiovascular health metrics, including sleep health, with incident diabetes and cardiovascular events in older adults with prediabetes: A nationwide population-based cohort study
  Kyoung Hwa Ha, Dae Jung Kim, Seung Jin Han
  Diabetes Research and Clinical Practice.2023; 203: 110820.     CrossRef
• Association of Socioeconomic Status With Long-Term Outcome in Survivors After Out-of-Hospital Cardiac Arrest: Nationwide Population-Based Longitudinal Study
  Kyung Hun Yoo, Yongil Cho, Jaehoon Oh, Juncheol Lee, Byuk Sung Ko, Hyunggoo Kang, Tae Ho Lim, Sang Hwan Lee
  JMIR Public Health and Surveillance.2023; 9: e47156.     CrossRef
• Antimicrobial and Antibiofilm Effects of Lactiplantibacillus plantarum THK-j112 Isolated from Kimchi against Streptococcus mutans
  Du-Na Yu, Eun-Ji Yi, Je-Yong Jung, Tae-Hoo Yi, Moochang Kook
  Current Topic in Lactic Acid Bacteria and Probiotics.2023; 9(1): 9.     CrossRef
• The Risk of Type 2 Diabetes Mellitus according to Changes in Obesity Status in Late Middle-Aged Adults: A Nationwide Cohort Study of Korea
  Joon Ho Moon, Yeonhoon Jang, Tae Jung Oh, Se Young Jung
  Diabetes & Metabolism Journal.2023; 47(4): 514.     CrossRef
• Proton pump inhibitor use and the risk of metachronous gastric cancer after H. pylori eradication in patients who underwent endoscopic resection for gastric neoplasms: A population‐based cohort study
  Eun Jeong Gong, Hye‐Kyung Jung, Bora Lee, Jitaek Hong, Jong Wook Kim, Cheol Min Shin, Young Hoon Youn, Kwang Jae Lee
  Alimentary Pharmacology & Therapeutics.2023; 58(7): 668.     CrossRef
• Risk Factors for Lower Extremity Amputation in Patients with End-Stage Kidney Disease: A Nationwide Cohort Study
  Min Jun Seo, Dong Geon Lee, Se Yun Ko, Ga Yeong Song, Geon Yeong Lee, Sung Hwa Kim, Dae Ryong Kang, Jiye Kim, Jun Young Lee
  Journal of Clinical Medicine.2023; 12(17): 5641.     CrossRef
• Poorer outcomes following COVID-19 infection for patients with depression: A cohort analysis in South Korea
  Su Kyoung Lee, Yohwan Lim, Seogsong Jeong, Hyun Wook Han
  Annals of the Academy of Medicine, Singapore.2023; 52(8): 411.     CrossRef
• The role of income and frequency of dental visits in the relationship between dental sealant use and resin fillings after extended coverage: a retrospective cohort study
  Dong-Hun Han, Hee-Yeon Kang, Jae-In Ryu
  BMC Oral Health.2023;[Epub]     CrossRef
• Effect of changes in the hearing aid subsidy on the prevalence of hearing loss in South Korea
  Chul Young Yoon, Junhun Lee, Tae Hoon Kong, Young Joon Seo
  Frontiers in Neurology.2023;[Epub]     CrossRef
• Visual Impairment Risk After Alcohol Abstinence in Patients With Newly Diagnosed Open-Angle Glaucoma
  Yoon Jeong, Su Hwan Kim, Goneui Kang, Hyung-Jin Yoon, Young Kook Kim, Ahnul Ha
  JAMA Network Open.2023; 6(10): e2338526.     CrossRef
• Impact of statin treatment on cardiovascular risk in patients with type 1 diabetes: a population-based cohort study
  Joonsang Yoo, Jimin Jeon, Minyoul Baek, Sun Ok Song, Jinkwon Kim
  Journal of Translational Medicine.2023;[Epub]     CrossRef
• Socioeconomic Disparities in the Association Between All-Cause Mortality and Health Check-Up Participation Among Healthy Middle-Aged Workers: A Nationwide Study
  Byungyoon Yun, Juyeon Oh, Jaesung Choi, Laura S. Rozek, Heejoo Park, Juho Sim, Yangwook Kim, Jongmin Lee, Jin-Ha Yoon
  Journal of Korean Medical Science.2023;[Epub]     CrossRef
• Increased Estimated GFR Is Negatively Associated With the Risk of SARS-CoV-2 Infection and Severe COVID-19 Within Normal to Mildly Decreased Levels: Nested Case-Control Study
  Yohwan Lim, Myeong Hoon Lee, Su Kyoung Lee, Seogsong Jeong, Hyun Wook Han
  Journal of Korean Medical Science.2023;[Epub]     CrossRef
• Risk of Pregnancy Complications and Low Birth Weight Offsprings in Korean Women With Rheumatic Diseases: A Nationwide Population-Based Study
  Jin-Su Park, Min Kyung Chung, Hyunsun Lim, Jisoo Lee, Chan Hee Lee
  Journal of Korean Medical Science.2022;[Epub]     CrossRef
• Associations of variability in body weight and glucose levels with the risk of hip fracture in people with diabetes
  Jeongmin Lee, Kyungdo Han, Sang Hyun Park, Mee Kyoung Kim, Dong-Jun Lim, Kun-Ho Yoon, Moo-Il Kang, Seung-Hwan Lee
  Metabolism.2022; 129: 155135.     CrossRef
• Travel Time for Dental Care Services Based on Patient Preference in South Korea
  Han-A Cho, Bo-Ra Kim, Hosung Shin
  International Journal of Environmental Research and Public Health.2022; 19(4): 2051.     CrossRef
• The Basic Concept of Claim Data-based Research
  Su Bee Park, Jae Myung Cha
  The Korean Journal of Helicobacter and Upper Gastrointestinal Research.2022; 22(1): 70.     CrossRef
• Trends of severe hypoglycemia in patients with type 2 diabetes in Korea: A longitudinal nationwide cohort study
  Jae‐Seung Yun, Kyungdo Han, Seung‐Hyun Ko
  Journal of Diabetes Investigation.2022; 13(8): 1438.     CrossRef
• National General Health Screening Program in Korea: history, current status, and future direction
  Dong Wook Shin, Juhee Cho, Jae Hyun Park, BeLong Cho
  Precision and Future Medicine.2022; 6(1): 9.     CrossRef
• Cumulative Exposure to High γ-Glutamyl Transferase Level and Risk of Diabetes: A Nationwide Population-Based Study
  Ji-Yeon Park, Kyungdo Han, Hun-Sung Kim, Jae-Hyoung Cho, Kun-Ho Yoon, Mee Kyoung Kim, Seung-Hwan Lee
  Endocrinology and Metabolism.2022; 37(2): 272.     CrossRef
• Understanding and Utilizing Claim Data from the Korean National Health Insurance Service (NHIS) and Health Insurance Review & Assessment (HIRA) Database for Research
  Dae-Sung Kyoung, Hun-Sung Kim
  Journal of Lipid and Atherosclerosis.2022; 11(2): 103.     CrossRef
• Clinical and Cost-Saving Effects of the Drug Utilization Review Modernization Project in Inpatient and Outpatient Settings in Korea
  Dongwon Yoon, Inmyung Song, Ha-Lim Jeon, Sungho Bea, Ahhyung Choi, Hyesung Lee, Ju-Young Shin
  Journal of Patient Safety.2022; 18(6): 605.     CrossRef
• Adherence to healthy lifestyle behaviors as a preventable risk factor for severe hypoglycemia in people with type 2 diabetes: A longitudinal nationwide cohort study
  Jae‐Seung Yun, Kyungdo Han, Yong‐Moon Park, Eugene Han, Yong‐ho Lee, Seung‐Hyun Ko
  Journal of Diabetes Investigation.2022; 13(9): 1533.     CrossRef
• Analysis of sedation and general anesthesia in patients with special needs in dentistry using the Korean healthcare big data
  Jieun Kim, Hyuk Kim, Kwang-Suk Seo, Hyun Jeong Kim
  Journal of Dental Anesthesia and Pain Medicine.2022; 22(3): 205.     CrossRef
• Improvement in Age at Mortality and Changes in Causes of Death in the Population with Diabetes: An Analysis of Data from the Korean National Health Insurance and Statistical Information Service, 2006 to 2018
  Eugene Han, Sun Ok Song, Hye Soon Kim, Kang Ju Son, Sun Ha Jee, Bong-Soo Cha, Byung-Wan Lee
  Endocrinology and Metabolism.2022; 37(3): 466.     CrossRef
• Visual Acuity Outcomes in Diseases Associated with Reduced Visual Acuity: An Analysis of the National Health Insurance Service Database in Korea
  Sang-Yeob Kim, Byeong-Yeon Moon, Hyun-Gug Cho, Dong-Sik Yu
  International Journal of Environmental Research and Public Health.2022; 19(14): 8689.     CrossRef
• Reproductive Life Span and Severe Hypoglycemia Risk in Postmenopausal Women with Type 2 Diabetes Mellitus
  Soyeon Kang, Yong-Moon Park, Dong Jin Kwon, Youn-Jee Chung, Jeong Namkung, Kyungdo Han, Seung-Hyun Ko
  Diabetes & Metabolism Journal.2022; 46(4): 578.     CrossRef
• Lipid cutoffs for increased cardiovascular disease risk in non-diabetic young people
  Mee Kyoung Kim, Kyungdo Han, Hun-Sung Kim, Kun-Ho Yoon, Seung-Hwan Lee
  European Journal of Preventive Cardiology.2022; 29(14): 1866.     CrossRef
• Current Trends of Big Data Research Using the Korean National Health Information Database
  Mee Kyoung Kim, Kyungdo Han, Seung-Hwan Lee
  Diabetes & Metabolism Journal.2022; 46(4): 552.     CrossRef
• Increasing trends in mortality and costs of infectious diseases in Korea: trends in mortality and costs of infectious diseases
  Dahye Baik, Byung-Woo Kim, Moran Ki
  Epidemiology and Health.2022; 44: e2022010.     CrossRef
• How to Use Health Insurance Data Effectively for Healthcare Research
  Ilsu Park
  Journal of Health Informatics and Statistics.2022; 47(Suppl 2): S31.     CrossRef
• Remnant cholesterol and the risk of cardiovascular disease in type 2 diabetes: a nationwide longitudinal cohort study
  Ji Hye Huh, Kyung-do Han, Yun Kyung Cho, Eun Roh, Jun Goo Kang, Seong Jin Lee, Sung-Hee Ihm
  Cardiovascular Diabetology.2022;[Epub]     CrossRef
• Metformin and Cervical Cancer Risk in Patients with Newly Diagnosed Type 2 Diabetes: A Population-Based Study in Korea
  Hyun Min Kim, Min Jin Kang, Sun Ok Song
  Endocrinology and Metabolism.2022; 37(6): 929.     CrossRef
• Effect of Sex on Clinical Outcomes in Patients with Coronavirus Disease: A Population-Based Study
  Kyu Hyang Cho, Sang Won Kim, Jong Won Park, Jun Young Do, Seok Hui Kang
  Journal of Clinical Medicine.2020; 10(1): 38.     CrossRef
• Increased Age of Death and Change in Causes of Death Among Persons With Diabetes Mellitus From the Korean National Health Insurance and Statistical Information Service, 2006 to 2018
  Eugene Han, Sun Ok Song, Hye Soon Kim, Kang Ju Son, Sun Ha Jee, Bong-Soo Cha, Byung-Wan Lee
  SSRN Electronic Journal .2020;[Epub]     CrossRef