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HOME > Epidemiol Health > Volume 36; 2014 > Article
Data Profile
 A guide for the utilization of Health Insurance Review and Assessment Service National Patient Samples
Logyoung Kim, Jee-Ae Kim, Sanghyun Kim
Epidemiol Health 2014;36:e2014008.
DOI: https://doi.org/10.4178/epih/e2014008
Published online: July 30, 2014

Health Insurance Review and Assessment Service, Seoul, Korea

Correspondence: Jee-Ae Kim Health Insurance Review and Assessment Service, 267 Hyoryeong-ro, Seocho-gu, Seoul 137-927, Korea Tel: +82-2-2182-2600, Fax: +82-2-6710-5836, E-mail: kja0813@hiramail.net
• Received: April 14, 2014   • Accepted: July 29, 2014

©2014, Korean Society of Epidemiology

This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • The claims data of the Health Insurance Review and Assessment Service (HIRA) is an important source of information for healthcare service research. The claims data of HIRA is collected when healthcare service providers submit a claim to HIRA to be reimbursed for a service that they provided to patients. To improve the accessibility of healthcare service researchers to claims data of HIRA, HIRA has developed the Patient Samples which are extracted using a stratified randomized sampling method. The Patient Samples of HIRA consist of five tables: a table for general information (Table 20) containing socio-demographic information such as gender, age and medical aid, indicators for inpatient and outpatient services; a table for specific information on healthcare services provided (Table 30); a table for diagnostic information (Table 40); a table for outpatient prescriptions (Table 53) and a table for information on healthcare service providers (Table of providers). Researchers who are interested in using the Patient Sample data for research can apply via HIRA’s website (https://www.hira.or.kr).
  • Keywords: National Health Insurance, Claims data, Stratified sampling, Healthcare services, Cross-sectional data
South Korea has a universal health coverage system that the National Health Insurance covers approximately 98% of the overall Korean population. The claims data of Health Insurance Review and Assessment Service (HIRA) contains 46 million patients per year that account for 90% of the total population in Korea and include claims from almost 80,000 healthcare service providers across South Korea as of 2011. The claims data of HIRA includes patients’ diagnosis, treatment, procedures, surgical history, and prescription drugs which provide a valuable resource for healthcare service research. However, the complex structure and vast volume of claims data require considerable efforts on the part of the researcher to understand them. In addition, the applications to use the claims data of HIRA and their deliberation process take significant amount of time, and these processes restrict the researcher from accessing the data. Furthermore, the vast volume of the data can result in their inefficiency in conducting research. To resolve these limitations and improve accessibility for researchers to use the claims data, HIRA has developed the Patient Sample data that had passed validity tests performed by five different institutions.
The Patient Samples are stratified random samples of claims data of HIRA. The sizes for the samples had carefully been calculated and extracted to improve representativeness of the socio-demographic characteristics, diagnosis, healthcare services including prescription drugs for Korean patients on a year basis. HIRA has provided four samples with different groups in order to enhance reliability and representativeness by extracting a sample from a specific area. As claims data of HIRA consist of 10% of inpatients and 90% of outpatients, the National Patient Sample (NPS) may not have enough cases to investigate inpatient services for severe health conditions. To support the research in groups of which representativeness is not ensured in NPS data, currently available are samples that were separately sample: National Patient Sample (HIRA-NPS), National Inpatient Sample (HIRA-NIS), Adult Patient Sample (HIRA-APS), and Pediatric Patient Sample (HIRA-PPS) (Table 1).
The Patient Samples are annually updated each year because they are developed through the accumulation of claims data over a year-long cycle. However, the Patient Samples are cross-sectional and different patients are selected for the sample data each year to protect their privacy. As such, a specific individual or healthcare service provider is not subject to a longitudinal data extraction, even within the same type of Patient Samples. Therefore, it is not possible to conduct research that requires a long-term following of patients with the Patient Samples.
Case study from abroad
In the US, a federal research institution called the Agency for Healthcare Research and Quality (AHRQ) performs and supports research associated with healthcare services. AHRQ collects data from 37 state governments, community, and the healthcare industry organizations to form a healthcare database. One of AHRQ’s research programs, Healthcare Cost and Utilization Project (HCUP), constitutes the largest healthcare database in the US. Among the data provided by HCUP, the National Inpatient Sample (NIS) is the most comprehensive inpatient data, which includes all the healthcare communities within the American Hospital Association, except for physical therapy institutions. The NIS is based on data collected from 3,900 member healthcare institutions spanning 37 states. Among the member healthcare institutions, approximately 20% (800-1,100 institutions) are extracted for sampling to include all of the inpatient data (5-8 million cases) from the sample institutions (Table 2).
Data resource area and population coverage
The claims data of HIRA is collected when healthcare service providers in South Korea seek reimbursements for healthcare services that the National Health Insurance Corporation agrees to cover. The annual number of Korean patients that submitted health insurance claims is approximately 46 million. The claims data of HIRA is a national data compiled from healthcare providers across the country that corresponds to the number of claims submitted by patients. In addition, the claims from patients with medical aid program, government expenditures, and veteran patients are also included in the claims data.
Extraction method
Because the standards of differentiation for claims submitted to HIRA are clearly defined, the Patient Samples adopted stratified sampling, a probabilistic sample extraction method. Based on the two stratum of sex (2 strata) and age (16 strata), the sample was divided into a total of 32 strata before random extraction. Demographically stratification of claims data at the patient level secures representativeness of the claims data in accounting for the time series of data which differ based on types of the healthcare service settings (inpatient or outpatient), the cycle of claims data submissions from providers (daily or monthly), and types of diseases.
As the charge in the National Health Insurance claims data exhibit the maximum variance and best reflect the characteristics of the claims data, the charge was chosen as a sample variable. Under the assumption of acceptable sampling error range and normal distribution, the standard deviation and the sample size were calculated with the following equation.
Using the above equation, the patient sample sizes that best reflects the representativeness of the overall claims data was determined as shown in the Table 3. Table 3 presents the comparison result between the estimated population derived from adding weighted values to the HIRA sample data and the actual population. The estimated population and the actual population exhibited a 95% concordance demonstrating a high level of representativeness.
Explanation of variables
The Patient Samples consist of five tables: Table 20 (general specifications), Table 30 (health services), Table 40 (diagnosis information), Table 53 (outpatient prescription), and Table of Providers (Healthcare Service Provider Information).
All tables are linkable using a key ID. Table 20 (general specifications) includes the general characteristics of the patient such as socio-demographic characteristics (gender, age, and medical-aid program), major diagnosis, secondary diagnosis, payer’s amount, and patient’s out-of-pocket cost. Table 30 (health services) includes details in inpatient and outpatient healthcare services provided to patients such as procedures, treatment and prescription drugs for inpatients. Table 40 (diagnosis information) contains all of the diagnosis information that patients have had. Table 40 is used when a patient’s concomitant disease or a history of all conditions is deemed necessary. Table 53 (outpatient prescriptions) shows drug information that had been prescribed recorded for outpatients such as active ingredients, dosage and days of supply. Finally, Table of Providers includes information about the healthcare service provider that the patient had visited, including the type of healthcare service provider (primary care, secondary care, tertiary care), location, sizes, and ownership type (Table 4).
Strengths and weaknesses
The Patient Samples have several important strengths. The first one is representativeness of the total patient population in South Korea and has advantages in generalization for the population. Secondly, the Patient Samples have comprehensive yet specific information on healthcare services including prescription drugs provided under the fee-for-service system. Thirdly, as the Patient Samples passed the validity test [1], they are proved efficient in estimating population to save costs and time in conducting research. HIRA has made memorandum of understanding (MOU) with five academic societies in healthcare service (Korean Society for Preventive Medicine, Korean Association of Health Economics and Policy, Korean Society of Health Information and Health Statistics, Korean Academy of Health Policy and Management, Korean Society of Epidemiology) and performed tests to validate with the Patient Samples.
Among the major test results, “Korean prevalence of diabetes and evaluations of dipeptidyl peptidase-4 inhibitor use” demonstrated that the estimate on diabetes prevalence using sample data aligned with that of population-analysis. Moreover, the estimate on prescription of hypoglycemic agents aligned with the results of population-analysis. Additionally, outpatient prescription rates of each hypoglycemic agent were all within the 95% confidence interval. In the test of “The burden of social cost estimates of diseases associated with vision loss and blindness,” the Patient Samples and the population both exhibited higher health service use in female patients with major eye diseases (cataracts, glaucoma, macular degeneration, diabetic changes in the retina, and retinal vein occlusion) than in male patients. Furthermore, the Patient Samples and the population exhibited similar age trends with respect to health service use in cases of cataracts, glaucoma, and macular degeneration.
Despite strengths described above, a few limitations need to be noted when researcher is interested in utilizing the Patient Samples for research. First, the accuracy of diagnosis has been an issue due to the nature of claims data which is collected with a purpose of reimbursing healthcare services not of clinical purpose. Hence, it is possible that diagnosis information in claims data is susceptible to up-coding by providers seeking for higher reimbursement rate or diagnosis remains in the data even when they are ruled out after running lab tests. This implies patients with a certain diagnosis would not necessarily mean that they have the disease corresponding to the diagnosis. The inaccuracy of diagnosis information in claims data may not be a problem only for the claims data of HIRA but also most other claims data although the problem with diagnosis information in the claims data of HIRA can be more serious due to the fee-for-service system and reimbursement policies.
The study shows that diagnosis in the claims data of HIRA tends to be more accurate in cases of severe diseases rather than frequently occurring mild diseases [2]. In addition, they exhibit greater accuracy in inpatient setting than outpatient cases, and in hospitals rather than clinics [2]. To address the inaccuracy of diagnosis information, researchers use operational definition to identify patients with a certain disease rather than simply using diagnosis in the data [3-5]. Secondly, the Sample data may not have sufficient cases for rare disease and a certain age group with lower frequency. Third, the socioeconomic characteristics and risk-factors such as patient’s income, education, location, weight, height, mortality, and health behaviors such as smoking, drinking, exercise amount are deficient, resulting in limitations in conducting through research. HIRA has projects to link data from other institutions to enrich the patient socioeconomic and risk-factor variables. Finally, the Patient Samples are cross-sectional, making a longitudinal study following a same individual patient over years impossible. To protect the privacy of a patient, a patient ID and the healthcare service provider ID, alternative IDs are given alternative ID so that patients and providers are not identifiable. Sensitive information such as rare diseases and legally designated infectious diseases that can lead to the identification of a patient was also removed from the from the data.
Patient Samples can be obtained via website of HIRA by filling out the End User Agreement of the Patient Samples. The Patient Samples are provided in a DVD (text file) format and a fee for the samples is subject to be charged.
Go to HIRA website → government 3.0 information open → application for the use of medical information → sample data
http://www.hira.or.kr/dummy.do?pgmid=HIRAA070001000312&cmsurl=/cms/open/02/01/02/index.html.
Tel: 02-2182-2601
E-mail: kshyun84@hiramail.net
HIRA is currently working on the data dictionary in English.

The authors have no conflicts of interest to declare for this study.

Supplementary material is available at http://www.e-epih.org/.
epih-36-e2014008-supplemetal data.pdf
Table 1.
Sample sizes and computation of each sample data
Sample type Computation standard
HIRA - National Inpatient Sample (2009-2011) 700,000 inpatients per year (13%), approximately 400,000 outpatients per year (1%)
HIRA - National Patient Sample (2010-2011) 1.4 million patients overall per year (3%)
HIRA - Adult Patient Sample (2010-2011) Approximately 1 million patients over the age of 65 per year (20%)
HIRA - Pediatric Patient Sample (2010-2011) Approximately 1.1 million patients under the age of 20 per year (10%)

The size of each sample data is either 1.5 million patients or 20% of the total number in the corresponding area of the original data

HIRA, Health Insurance Review and Assessment Service.

Table 2.
Comparison of the nations’ sample datasets
Country-based comparison Korea (HIRA) US (AHRQ) Taiwan (NHIRD)
Sampling unit Patient sampling Hospital sampling Patient sampling
Unit of data provided Patient based Institution case-based (discharge data) Patient data
Stratification variable Demographic characteristic (sex, age group) Hospital characteristics Geographic location Simple random sampling
Data providee All researchers All researchers National research institutions and researchers (general public uses the educational data set)
Sampling unit Inpatients, all patients, pediatric and teen patients, elderly patients under 1.5 million people per category Approximately 7 million institutional cases (inpatient data) Health-plan registrees, 1 million people

HIRA, Health Insurance Review and Assessment Service; AHRQ, Agency for Healthcare Research and Quality; NHIRD, National Health Insurance Research Database.

Table 3.
Comparison between Patient Samples and the actual population (unit: person)
Sample type Sample size (%) Estimated population Actual population
HIRA - National Patient Sample 1,375,842 (3) 45,861,321 47,026,505
HIRA - National Inpatient Sample 765,564 (13) 5,888,921 6,026,063
HIRA - Adult Patient Sample 1,073,183 (19) 5,365,917 5,650,511
HIRA - Pediatric Patient Sample 1,026,648 (10) 10,266,474 10,681,503

The percentage of the total patients in claims data.

Based on data from 2011.

Table 4.
Main variables in the Patient Samples
Table Variables
Table 20 (general specification) Billing statement identification code (key ID), patient ID, provider’s ID, stratification variables, strata, age, gender, sample weight, DRG billing number, claims types, date of admission, insurance type, hospital arrival path way, major diagnosis, secondary diagnosis, injury from public service, days of care, initial date of care, final date of care, days in hospitalization, payer’s amount, patient’s out of pocket cost, total amount, surgical status
Table 30 (healthcare services) Billing statement identification code (Key ID), service category, classification type, unit price, total price, daily dosages, days of supply, quantity of supply, service codes, drug codes
Table 40 (diagnosis information) Billing statement identification code (key ID), indicator for major diagnosis, department, diagnosis
Table 53 (outpatient prescriptions) Billing statement identification code (key ID), classification type, unit price, total price, daily dosages, days of supply, quantity of supply, service codes, drug codes
Table of Providers Provider ID, type of providers, presence of special equipment (CT, MRI, PET), location, number of beds, number of staff per 50 beds - physicians, dentists, acupuncturists, and nurses

IDs are given an alternative ID to protect private information.

  • 1. Kim L, Sakong J, Kim Y, Kim S, Kim S, Tchoe B, et al. Developing the inpatient sample for the National Health Insurance claims data. Health Policy Manag 2013;23:152-161 (Korean).Article
  • 2. Park BJ, Sung J, Park K, Seo SW, Kim SH. Studying on diagnosis accuracy for health insurance claims data in Korea. Seoul: Seoul National University; 2003. p 17-29 (Korean).
  • 3. Kim J, Kim K, Kim Y, Yoo KH, Lee CK, Yoon HK, et al. The association between inhaled long-acting bronchodilators and less in-hospital care in newly-diagnosed COPD patients. Respir Med 2014;108:153-161.ArticlePubMed
  • 4. Kim J, Lee JH, Kim Y, Kim K, Oh YM, Yoo KH, et al. Association between chronic obstructive pulmonary disease and gastroesophageal reflux disease: a national cross-sectional cohort study. BMC Pulm Med 2013;13:51.ArticlePubMedPMC
  • 5. Lee CH, Kim K, Hyun MK, Jang EJ, Lee NR, Yim JJ. Use of inhaled corticosteroids and the risk of tuberculosis. Thorax 2013;68:1105-1113.ArticlePubMed

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      Clinics in Orthopedic Surgery.2023; 15(3): 395.     CrossRef
    • Risk of overactive bladder after hysterectomy for uterine fibroids
      Jin-Sung Yuk, Jun Ho Lee
      International Urogynecology Journal.2023; 34(8): 1823.     CrossRef
    • Risk of acute myocardial infarction, stroke, and venous thromboembolism among patients with anti-neutrophil cytoplasmic antibody-associated vasculitis in South Korea: A nationwide population-based study
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      Joint Bone Spine.2023; 90(2): 105498.     CrossRef
    • Prescription changes in patients with gastrointestinal disorders after withdrawal of ranitidine: a nationwide population-based cohort study
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    • Clinical characteristics of patients with recurrent Kawasaki disease: a nationwide cohort study of 19 456 patients with minimum 3-year follow up
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      Archives of Disease in Childhood.2023; 108(4): 307.     CrossRef
    • National trend of the treatment for chronic venous diseases in Korea between 2010 and 2020
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      Annals of Surgical Treatment and Research.2023; 104(1): 27.     CrossRef
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      Min-Hyung Kim, Sojung Park, Yu Rang Park, Wonjun Ji, Seul-Gi Kim, Minji Choo, Seung-Sik Hwang, Jae Cheol Lee, Hyeong Ryul Kim, Chang-Min Choi
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    • Menopausal hormone therapy and the risk of cataracts in postmenopausal women in South Korea
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    • Real-World Comparison of Transcatheter Versus Surgical Aortic Valve Replacement in the Era of Current-Generation Devices
      Young Kyoung Sa, Byung-Hee Hwang, Woo-Baek Chung, Kwan Yong Lee, Jungkuk Lee, Dongwoo Kang, Young-Guk Ko, Cheol Woong Yu, Juhan Kim, Seung-Hyuk Choi, Jang-Whan Bae, In-Ho Chae, Yun-Seok Choi, Chul Soo Park, Ki Dong Yoo, Doo Soo Jeon, Hyo-Soo Kim, Wook-Sun
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    • Seasonal Trends in the Prevalence and Incidence of Viral Encephalitis in Korea (2015–2019)
      Seon Jin Lee, Jeong Min Kim, Ha Rim Keum, Sang Won Kim, Hee Sun Baek, Jun Chul Byun, Yu Kyung Kim, Saeyoon Kim, Jae Min Lee
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    • Real world co‐prescribing contraindicated drugs with fluconazole and itraconazole
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      Pharmacoepidemiology and Drug Safety.2023; 32(7): 752.     CrossRef
    • Incidence of Pregnancy-Associated Venous Thromboembolism: Second Nationwide Study
      Hun-Gyu Hwang, Ju Hyun Lee, Soo-Mee Bang
      Thrombosis and Haemostasis.2023; 123(09): 904.     CrossRef
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      Ha Rim Keum, Seon Jin Lee, Jeong Min Kim, Sang Won Kim, Hee Sun Baek, Jun Chul Byun, Yu Kyung Kim, Saeyoon Kim, Jae Min Lee
      Children.2023; 10(3): 529.     CrossRef
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      Seung-Woo Yang, Sang-Hee Yoon, Myounghwan Kim, Yong-Soo Seo, Jin-Sung Yuk
      Journal of Clinical Medicine.2023; 12(5): 1738.     CrossRef
    • Utilization of triple antithrombotic therapy in patients with atrial fibrillation undergoing percutaneous coronary intervention
      Hye-Jeong Choi, Yonghyuk Lee, Susin Park, Nam Kyung Je
      European Journal of Clinical Pharmacology.2023; 79(4): 541.     CrossRef
    • Menopausal hormone therapy and the risk of type 2 diabetes mellitus: Health Insurance Database in South Korea–based retrospective cohort study
      Jin-Sung Yuk, Jung Min Kim
      Menopause.2023; 30(5): 497.     CrossRef
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      Cancers.2023; 15(6): 1708.     CrossRef
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      Journal of Computer Assisted Tomography.2023;[Epub]     CrossRef
    • The differential risk of severe hyponatraemia based on the use patterns of hyponatraemia-inducing medications in older adults
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      Age and Ageing.2023;[Epub]     CrossRef
    • Endometrial cancer risk with menopausal hormone therapy: Health Insurance Database in South Korea‐based cohort study
      Jin‐Sung Yuk
      International Journal of Gynecology & Obstetrics.2023; 162(2): 502.     CrossRef
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      Menopause.2023;[Epub]     CrossRef
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      Sunwon Kim, Jong-Seok Lee, Jungkuk Lee, Yong-Hyun Kim, Jin-Seok Kim, Sang-Yup Lim, Seong Hwan Kim, Jeong-Cheon Ahn, Woo-Hyuk Song
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      Gi Chan Park, Jung Wha Chung, Eun Sun Jang, Jin-Wook Kim
      Digestive and Liver Disease.2023; 55(10): 1368.     CrossRef
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      Jing Chen, Ju-Young Shin, Hyungwoo Kim, Ju Hwan Kim, Ahhyung Choi, Hee Jin Cheong, Yeon-Mok Oh, Adrienne Guignard, Sumitra Shantakumar
      COPD: Journal of Chronic Obstructive Pulmonary Disease.2023; 20(1): 126.     CrossRef
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      The Korean Journal of Internal Medicine.2023; 38(3): 338.     CrossRef
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      Jihye Hyun, Moon Soo Ha, Seung Young Oh, Jong Hyun Tae, Byung Hoon Chi, In Ho Chang, Tae-Hyoung Kim, Soon Chul Myung, Tuan Thanh Nguyen, Jung Hoon Kim, Jin Wook Kim, Yong Seong Lee, Jooyoung Lee, Se Young Choi
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    • Dementia Incidence Rate Before and After Implementing the National Responsibility Policy for Dementia Care in Patients With Vascular Risk Factors in Korea
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    • Outcomes of prostate cancer patients after robot-assisted radical prostatectomy compared with open radical prostatectomy in Korea
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    • Healthcare usage and cost for plantar fasciitis: a retrospective observational analysis of the 2010–2018 health insurance review and assessment service national patient sample data
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    • Association of Early Hysterectomy With Risk of Cardiovascular Disease in Korean Women
      Jin-Sung Yuk, Byung Gyu Kim, Byoung Kwon Lee, Jongkwon Seo, Gwang Sil Kim, Kyongjin Min, Hye Young Lee, Young Sup Byun, Byung Ok Kim, Seung-Woo Yang, Myoung-Hwan Kim, Sang-Hee Yoon, Yong-Soo Seo
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    • Incidence and prevalence of Spinal and bulbar muscular atrophy in South Korea: a nationwide population-based study
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      Journal of Neurology.2023; 270(10): 5017.     CrossRef
    • Current treatments for endometriosis in South Korea: an analysis of nationwide data from 2010 to 2019
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    • Skin cancer risk of menopausal hormone therapy in a Korean cohort
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    • Impact of statin treatment on cardiovascular events in patients with retinal vein occlusion: a nested case-control study in Korea
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    • National Health Insurance Data Analysis for the Second-Wave Development of Korean Medicine Clinical Practice Guidelines in South Korea
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      Journal of Pharmacopuncture.2023; 26(2): 198.     CrossRef
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      Healthcare.2023; 11(16): 2353.     CrossRef
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      Yen Min Wang, Yu-Cheol Lim, Deok-Sang Hwang, Yoon Jae Lee, In-Hyuk Ha, Ye-Seul Lee
      Medicina.2023; 59(9): 1505.     CrossRef
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      Yonsei Medical Journal.2023; 64(9): 549.     CrossRef
    • Menopausal hormone therapy and risk of dementia: health insurance database in South Korea-based retrospective cohort study
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      Frontiers in Aging Neuroscience.2023;[Epub]     CrossRef
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      Hyun Lee, Sang Hyuk Kim, Sun-Kyung Lee, Hayoung Choi, Sung Jun Chung, Dong Won Park, Tai Sun Park, Ji-Yong Moon, Tae-Hyung Kim, Sang-Heon Kim, Jang Won Sohn, Ho Joo Yoon
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    • Menopausal hormone therapy and risk of seropositive rheumatoid arthritis: A nationwide cohort study in Korea
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      Doori Kim, Eun-San Kim, Yoon Jae Lee, Jee Young Lee, Kyong Sun Park, So Young Jung, Changsop Yang, Chang-Hyun Han, In-Hyuk Ha
      Journal of Pain Research.2023; Volume 16: 3197.     CrossRef
    • The elderly population are more vulnerable for the management of colorectal cancer during the COVID-19 pandemic: a nationwide, population-based study
      Hong Sun Kang, Seung Hoon Jeon, Su Bee Park, Jin Young Youn, Min Seob Kwak, Jae Myung Cha
      Intestinal Research.2023; 21(4): 500.     CrossRef
    • Therapeutic Duplication as a Medication Error Risk in Fixed-Dose Combination Drugs for Dyslipidemia: A Nationwide Study
      Wonbin Choi, Hyunji Koo, Kyeong Hye Jeong, Eunyoung Kim, Seung-Hun You, Min-Taek Lee, Sun-Young Jung
      Korean Journal of Clinical Pharmacy.2023; 33(3): 168.     CrossRef
    • The increased risk of colorectal cancer in the women who underwent hysterectomy from the South Korean National Health Insurance Database
      Jin -Sung Yuk, Seung-Woo Yang, Sang-Hee Yoon, Myoung Hwan Kim, Yong-Soo Seo, Yujin Lee, Jungbin Kim, Keunho Yang, Geumhee Gwak, Hyunjin Cho
      BMC Women's Health.2023;[Epub]     CrossRef
    • Association between breast diseases and symptomatic uterine fibroids by using South Korean National Health Insurance database
      Jin-Sung Yuk, Seung-Woo Yang, Sang-Hee Yoon, Myoung Hwan Kim, Yong-Soo Seo, Yujin Lee, Yilseok Joo, Jungbin Kim, Sam-Youl Yoon, Hyunjin Cho, Keunho Yang, Geumhee Gwak
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    • Relationship between myomectomy and risk of osteoporosis or fracture: A population-based cohort study
      Jin-Sung Yuk, Sang-Hee Yoon, Astawus Alemayehu Feleke
      PLOS ONE.2023; 18(11): e0294405.     CrossRef
    • Machine learning-based prediction model for emergency department visits using prescription information in community-dwelling non-cancer older adults
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    • Association Between Menopausal Hormone Therapy and Risk for Parkinson’s Disease
      Jin-Sung Yuk, Seong Ho Jeong
      Journal of Parkinson's Disease.2023; 13(8): 1357.     CrossRef
    • Association Between Migraine and Gastrointestinal Disease in Pediatric Patients: A Propensity Score Weighting Approach
      Kiyon Rhew
      Neuropsychiatric Disease and Treatment.2023; Volume 19: 2607.     CrossRef
    • Comparison of diagnosis-based risk adjustment methods for episode-based costs to apply in efficiency measurement
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    • Impact of COVID-19 Pandemic on Performance of Gastrointestinal Endoscopy
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    • An economic evaluation of pharmacopuncture versus usual care for chronic neck pain: a pragmatic randomized controlled trial
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    • Development and validation of a machine learning-based fall-related injury risk prediction model using nationwide claims database in Korean community-dwelling older population
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      BMC Geriatrics.2023;[Epub]     CrossRef
    • The Risk of Stress Urinary Incontinence After Hysterectomy for Uterine Fibroids
      Jin-Sung Yuk, In-Chang Cho, Jun Ho Lee
      International Neurourology Journal.2023; 27(4): 252.     CrossRef
    • Potentially Inappropriate Gastrointestinal Medication for Patients with the Common Cold
      Minjeong Kim, Nam Kyung Je
      Research in Clinical Pharmacy.2023; 1(2): 100.     CrossRef
    • Understanding long-listed pharmaceutical products without competition in South Korea: policy implications in managing generic entrants and pharmaceutical expenditures
      Kyung-Bok Son
      Expert Review of Pharmacoeconomics & Outcomes Research.2022; 22(5): 815.     CrossRef
    • Pregnancy-associated risk factors and incidence of systemic sclerosis in primiparous women: A nationwide population-based cohort study
      Kyung-Ann Lee, JongSun Kim, Wonho Choi, Hyun-Sook Kim, Gi Hyeon Seo
      Modern Rheumatology.2022; 32(1): 149.     CrossRef
    • Androgen Deprivation Therapy in Patients with Prostate Cancer is Associated with the Risk of Subsequent Alzheimer's Disease but Not with Vascular Dementia
      Jong Won Kim, Do Kyung Kim, Hye Sun Lee, Ju-Young Park, Hyun Kyu Ahn, Jee Soo Ha, Dongu Lee, Kang Su Cho
      The World Journal of Men's Health.2022; 40(3): 481.     CrossRef
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      Kyun-Ho Shin, Seung-Beom Han, Ji-Eun Song
      The Journal of Arthroplasty.2022; 37(1): 49.     CrossRef
    • Trends in hospital visits and healthcare costs of gout and seropositive rheumatoid arthritis in Korea from 2010 to 2017 using National Healthcare Claims
      Kyeong Min Son, Ju-Ryoung Kim, Hang A Park, Hyun Ah Kim
      The Korean Journal of Internal Medicine.2022; 37(3): 681.     CrossRef
    • Incidence of bowel injury during gynecologic surgery for benign indications: A nationwide cross‐sectional study of cases from 2009 to 2018
      Jin‐Sung Yuk, Hyunjin Cho, Myoung H. Kim, Geumhee Gwak, Yong‐Soo Seo, Keunho Yang, Seung W. Yang, Byung‐Noe Bae, Sang‐Hee Yoon, Yujin Lee
      International Journal of Gynecology & Obstetrics.2022; 158(2): 338.     CrossRef
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      Joong Kyong Ahn, Jiwon Hwang, Gi Hyeon Seo
      Revue du Rhumatisme.2022; 89(2): 153.     CrossRef
    • Underutilisation of prophylactic G-CSF in breast cancer patients receiving adjuvant docetaxel/cyclophosphamide chemotherapy
      Minhee Ku, Nam Kyung Je
      Journal of Chemotherapy.2022; 34(8): 534.     CrossRef
    • Incidence, prevalence and risk of stroke in patients with Takayasu arteritis: a nationwide population-based study in South Korea
      Sung Soo Ahn, Minkyung Han, Yong-Beom Park, Inkyung Jung, Sang-Won Lee
      Stroke and Vascular Neurology.2022; 7(2): 149.     CrossRef
    • Metabolic Disease Incidence After Allogeneic Stem Cell Transplantation: A Nationwide Korean Case-Control Study
      Jeonghoon Ha, See Hyun Park, Sung-Soo Park, Seunghoon Han
      The Journal of Clinical Endocrinology & Metabolism.2022; 107(4): 943.     CrossRef
    • Development of eClaim system for private indemnity health insurance in South Korea: Compatibility and interoperability
      Sungchul Bae, Byoung-Kee Yi
      Health Informatics Journal.2022; 28(1): 146045822110710.     CrossRef
    • Importance of a usual source of care in choosing low-priced generic drugs: a cross-sectional study
      Kyung-Bok Son, Eui-Kyung Lee
      Family Practice.2022; 39(5): 791.     CrossRef
    • Risk factors of chronic opioid use after surgical procedures in noncancer patients
      Kyu-Nam Heo, Young-Mi Ah, Ju-Yeun Lee
      European Journal of Anaesthesiology.2022; 39(2): 161.     CrossRef
    • Association Between Dementia Development and COVID-19 among Individuals Who Tested Negative for COVID-19 in South Korea: A Nationwide Cohort Study
      Namwoo Kim, Sungtaek Son, Saemina Kim, Jieun Lee, Yong Min Ahn, Dong Keon Yon, Bong-Jin Hahm
      American Journal of Alzheimer's Disease & Other Dementias®.2022; 37: 153331752110723.     CrossRef
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      Youlim Kim, Tai Joon An, Yong Bum Park, Kyungjoo Kim, Do Yeon Cho, Chin Kook Rhee, Kwang-Ha Yoo
      Tuberculosis and Respiratory Diseases.2022; 85(1): 74.     CrossRef
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      Bo-Guen Kim, Minwoong Kang, Jihyun Lim, Jin Lee, Danbee Kang, Minjung Kim, Jinhee Kim, Hyejeong Park, Kyung Hoon Min, Juhee Cho, Kyeongman Jeon
      BMC Pulmonary Medicine.2022;[Epub]     CrossRef
    • Underutilization of anticoagulants in patients with nonvalvular atrial fibrillation in the era of non-vitamin K antagonist oral anticoagulants
      Susin Park, Nam Kyung Je
      International Journal of Arrhythmia.2022;[Epub]     CrossRef
    • Epidemiological profile of myasthenia gravis in South Korea using the national health insurance database
      Jin‐Sung Park, Kyung Yoon Eah, Jin‐Mo Park
      Acta Neurologica Scandinavica.2022; 145(5): 633.     CrossRef
    • Prevalence and Comorbidities of Attention Deficit Hyperactivity Disorder Among Adults and Children/Adolescents in Korea
      Jeong-Cheol Seo, Duk-In Jon, Se-Hoon Shim, Hyung-Mo Sung, Young Sup Woo, Jeongwan Hong, Sung‐Yong Park, Jeong Seok Seo, Won-Myong Bahk
      Clinical Psychopharmacology and Neuroscience.2022; 20(1): 126.     CrossRef
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      Hun-Sung Kim
      Endocrinology and Metabolism.2022; 37(1): 62.     CrossRef
    • Incidence Rates of Myomectomy-Related Mortality and Venous Thromboembolism in South Korea: A Population-Based Study
      Jin-Sung Yuk, Myounghwan Kim
      Frontiers in Medicine.2022;[Epub]     CrossRef
    • Postoperative Thromboembolism According to the Type of Surgery: A Nationwide Study in the Republic of Korea
      Ka-Won Kang, Ji Yoon Lee, Byung-Hyun Lee, Min Ji Jeon, Eun Sang Yu, Dae Sik Kim, Se Ryeon Lee, Chul Won Choi, Yong Park, Hwa Jung Sung, Byung Soo Kim
      Journal of Clinical Medicine.2022; 11(6): 1477.     CrossRef
    • Cost-effectiveness of antiviral therapy in untreated compensated cirrhosis patient with serum HBV–DNA level < 2000 IU/mL
      Hankil Lee, Sungin Jang, Sang Hoon Ahn, Beom Kyung Kim
      Hepatology International.2022; 16(2): 294.     CrossRef
    • Healthcare Utilization for Lateral Epicondylitis: A 9-Year Analysis of the 2010–2018 Health Insurance Review and Assessment Service National Patient Sample Data
      Yujin Cho, Jiyoon Yeo, Ye-Seul Lee, Eun Jung Kim, Dongwoo Nam, Yeon-Cheol Park, In-Hyuk Ha, Yoon Jae Lee
      Healthcare.2022; 10(4): 636.     CrossRef
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      Sung Yub Jung, Eun‐San Kim, Koh‐Woon Kim, Jae‐Heung Cho, Yoon Jae Lee, In‐Hyuk Ha
      Journal of Oral Rehabilitation.2022; 49(7): 691.     CrossRef
    • Uterine fibroids increase the risk of thyroid goiter and thyroid nodules
      Jin-Sung Yuk, Jung Min Kim
      Scientific Reports.2022;[Epub]     CrossRef
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      Hun-Gyu Hwang, Ju Hyun Lee, Sang-A Kim, Yang-Ki Kim, Ho-Young Yhim, Junshik Hong, Soo-Mee Bang
      Journal of Korean Medical Science.2022;[Epub]     CrossRef
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      Chulhan Kim, Sung-Youn Chun, Sun Jung Kim, Ki Hwa Yang, Ji Hyeon Baek, Ji Hyeon Shin, Ji Won Yoo, Young Woo Chang, Keon Wook Kang, Jinwook Hwang
      Journal of Korean Medical Science.2022;[Epub]     CrossRef
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      Masar J. Jari, Ghanim A. Abbas, Meraim A. Kazaal
      Bionatura.2022; 7(2): 1.     CrossRef
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      In-Hyuk Ha, Eun-San Kim, Sook-Hyun Lee, Yoon Jae Lee, Hyun Jin Song, Younhee Kim, Koh-Woon Kim, Jae-Heung Cho, Jun-Hwan Lee, Byung-Cheul Shin, Jinho Lee, Joon-Shik Shin
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    • Clinically refined epidemiology of nontuberculous mycobacterial pulmonary disease in South Korea: overestimation when relying only on diagnostic codes
      Jae Hyeon Park, Sue Shin, Taek Soo Kim, Hyunwoong Park
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    • Risk of infective endocarditis associated with invasive dental procedures in patients with cardiac rhythm devices
      Ju Youn Kim, Seung-Jung Park, Sang Hwa Lee, Gi Hyeon Seo, Sung-Won Jang
      EP Europace.2022; 24(12): 1967.     CrossRef
    • Postoperative Hormone Replacement Therapy and Survival in Women with Ovarian Cancer
      Eunjeong Ji, Kidong Kim, Banghyun Lee, Sung Ook Hwang, Hee Joong Lee, Kyungjin Lee, Minkyung Lee, Yong Beom Kim
      Cancers.2022; 14(13): 3090.     CrossRef
    • Analysis of Scoliosis-related Medical Services in South Korea Between 2010 and 2018
      Doohyun Hong, Yu-Cheol Lim, Sook-Hyun Lee, Won-Suk Sung, Yoon Jae Lee, Eun-Jung Kim, In-Hyuk Ha
      Spine.2022; 47(12): E499.     CrossRef
    • Cost-Benefit Analysis of Tafenoquine for Radical Cure of Plasmodium vivax Malaria in Korea
      Jiyeon Suh, Jung Ho Kim, Jong-Dae Kim, Changsoo Kim, Jun Yong Choi, Jeehyun Lee, Joon-Sup Yeom
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    • Association of pyogenic spondylitis with congestive heart failure in Korea: a nationwide longitudinal cohort study
      Chong Jin Park, Seung Hun Sheen, Jeong Gyun Kim, Hakyung Kim, Yeo Song Kim, In-bo Han, Je Beom Hong, Chun Kee Chung, Seil Sohn
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    • Long-term risk of uterine malignancies in women with uterine fibroids confirmed by myomectomy: a population-based study
      Jin-Sung Yuk, Myounghwan Kim
      Journal of Obstetrics and Gynaecology.2022; 42(7): 3067.     CrossRef
    • The disease burden of migraine patients receiving prophylactic treatments in Korea: a population-based claims database analysis
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    A guide for the utilization of Health Insurance Review and Assessment Service National Patient Samples
    A guide for the utilization of Health Insurance Review and Assessment Service National Patient Samples
    Sample type Computation standard
    HIRA - National Inpatient Sample (2009-2011) 700,000 inpatients per year (13%), approximately 400,000 outpatients per year (1%)
    HIRA - National Patient Sample (2010-2011) 1.4 million patients overall per year (3%)
    HIRA - Adult Patient Sample (2010-2011) Approximately 1 million patients over the age of 65 per year (20%)
    HIRA - Pediatric Patient Sample (2010-2011) Approximately 1.1 million patients under the age of 20 per year (10%)
    Country-based comparison Korea (HIRA) US (AHRQ) Taiwan (NHIRD)
    Sampling unit Patient sampling Hospital sampling Patient sampling
    Unit of data provided Patient based Institution case-based (discharge data) Patient data
    Stratification variable Demographic characteristic (sex, age group) Hospital characteristics Geographic location Simple random sampling
    Data providee All researchers All researchers National research institutions and researchers (general public uses the educational data set)
    Sampling unit Inpatients, all patients, pediatric and teen patients, elderly patients under 1.5 million people per category Approximately 7 million institutional cases (inpatient data) Health-plan registrees, 1 million people
    Sample type Sample size (%) Estimated population Actual population
    HIRA - National Patient Sample 1,375,842 (3) 45,861,321 47,026,505
    HIRA - National Inpatient Sample 765,564 (13) 5,888,921 6,026,063
    HIRA - Adult Patient Sample 1,073,183 (19) 5,365,917 5,650,511
    HIRA - Pediatric Patient Sample 1,026,648 (10) 10,266,474 10,681,503
    Table Variables
    Table 20 (general specification) Billing statement identification code (key ID), patient ID, provider’s ID, stratification variables, strata, age, gender, sample weight, DRG billing number, claims types, date of admission, insurance type, hospital arrival path way, major diagnosis, secondary diagnosis, injury from public service, days of care, initial date of care, final date of care, days in hospitalization, payer’s amount, patient’s out of pocket cost, total amount, surgical status
    Table 30 (healthcare services) Billing statement identification code (Key ID), service category, classification type, unit price, total price, daily dosages, days of supply, quantity of supply, service codes, drug codes
    Table 40 (diagnosis information) Billing statement identification code (key ID), indicator for major diagnosis, department, diagnosis
    Table 53 (outpatient prescriptions) Billing statement identification code (key ID), classification type, unit price, total price, daily dosages, days of supply, quantity of supply, service codes, drug codes
    Table of Providers Provider ID, type of providers, presence of special equipment (CT, MRI, PET), location, number of beds, number of staff per 50 beds - physicians, dentists, acupuncturists, and nurses
    Table 1. Sample sizes and computation of each sample data

    The size of each sample data is either 1.5 million patients or 20% of the total number in the corresponding area of the original data

    HIRA, Health Insurance Review and Assessment Service.

    Table 2. Comparison of the nations’ sample datasets

    HIRA, Health Insurance Review and Assessment Service; AHRQ, Agency for Healthcare Research and Quality; NHIRD, National Health Insurance Research Database.

    Table 3. Comparison between Patient Samples and the actual population (unit: person)

    The percentage of the total patients in claims data.

    Based on data from 2011.

    Table 4. Main variables in the Patient Samples

    IDs are given an alternative ID to protect private information.

    Table 1.

    Table 2.

    Table 3.

    Table 4.

    Epidemiol Health : Epidemiology and Health
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