• 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.30 no.4 s.59
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• pp.870-883
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• 1997

The health center has to play an important role in promoting community health and satisfying a variety of community health needs and demands in the decentralized Korea. The nearly enacted Community Health Act compels every health center to make its own health plans which intend to deal with local health problems and plan its future health care. This obligation is obviously a big burden to most health centers. They do not have experiences in and abilities of making local health care plans. In order to establish a systematic community health plan, health centers have to concentrate their efforts on enhancing the ability of making health care plan through gathering and analysing the local health informations. However, it is very difficult in reality. This is simply because it will take long time to accomplish these activities. It seems natural that various professionals and researchers participate in carrying out the process of making community health plan in the initial stage. No standardized methodology and analysing framework exist even in the health professional society. Nonetheless, it is common to introduce survey research methodologies in analysing consumer's health care utilization and cost, and in identifying factors influencing health behaviors. Many researchers and professionals have applied social survey methodologies in obtaining information on providers and health policy makers as well. The authors have found that few studies have ever utilized local health data stored at the self-employed medical insurance society as the data source of planning activities. The purpose of this study is to illustrate the usefulness of the data stored at the Sung-Dong Gu Self-employed Medical Insurance Society in establishing the community health plan. The major contents of this study are as follows ; 1. frequency of utilization by age, area, sex, type of medical care institutions, and some major diseases 2. Medical treatment by type of medical care institutions, by classification of 21 diseases, by frequency of three-character categories 3. Medical treatment of major neoplasm and some chronic diseases by age, sex, and area. The conclusion of this study is that it is of great potentiality to find out the local health problems and to use them in blueprinting the community health plan through comparing the frequency of medical utilization analyzed by a variety of variables with NHI health data or the health data from survey research.

• Health Policy and Management
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• v.24 no.4
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• pp.322-329
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• 2014

Background: As most of people in Korea are covered by National Health Insurance (NHI), the disease information collected in NHI provides high availability for health policy. Nevertheless, the validity of disease codes in NHI data has been controversial till now. So we tried to evaluate the validity of them by comparing the NHI claims data with Korean National Hospital Discharge In-depth Injury Survey (KNHDIIS) data. Methods: We compared the NHI patients sample data (2009) with the KNHDIIS data (2009). We selected the inpatient data of KNHDIIS and NHI patients sample. The weighted number of patients from NHI patients sample was 5,551,210 and the number of patients from KNHDIIS was 5,559,874. We classified the disease codes into principal diagnoses and other diagnoses, and we compared as one, two, three unit level. Also we calculated the agreement rate of each of them. Results: In the comparison of principal diagnoses, NHI claims data had more C code than KNHDIIS data did, whereas KNHDIIS data had more Z code than NHI claims data did. In the comparison of other diagnoses, NHI claims data had 2, 3 more codes than KNHDIIS data did. The overall agreement rate at three unit level was 76.5% in principal diagnoses and 46.8% in other diagnoses. Conclusion: Considering the large difference between the two data, the validity of disease codes in NHI Claims data seems to be low. To increase the validity of them, the definite detail coding indicator, the reinforcement of coding education, and the reform of system are needed.

• The Korean Journal of Health Service Management
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• v.10 no.3
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• pp.63-74
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• 2016

Objectives : There is a need to develop a data quality management algorithm to improve the quality of healthcare data using a data quality management system. In this study, we developed a data quality control algorithms associated with diseases related to hypertension in patients with diabetes mellitus. Methods : To make a data quality algorithm, we extracted the 2011 and 2012 discharge damage survey data from diabetes mellitus patients. Derived variables were created using the primary diagnosis, diagnostic unit, primary surgery and treatment, minor surgery and treatment items. Results : Significant factors in diabetes mellitus patients with hypertension were sex, age, ischemic heart disease, and diagnostic ultrasound of the heart. Depending on the decision tree results, we found four groups with extreme values for diabetes accompanying hypertension patients. Conclusions : There is a need to check the actual data contained in the Outlier (extreme value) groups to improve the quality of the data.

• 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 Academic Society of Home Health Care Nursing
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• v.26 no.1
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• pp.72-80
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• 2019

Purpose: This study aimed to measure health-related quality of life (HRQoL) and investigate the relevant factors for middle aged women using the 2014 Korea Health Panel data. Methods: The Korea Health Panel data 2014 were collected from February to August 2014 by the Korea Institute for Health and Social Affairs and the National Health Insurance Corporation and included 2,075 people who responded to the questionnaire. Using SPSS WIN program, the data were analyzed by t-test, ANOVA, Pearson correlation coefficient, and multiple regression analysis. Results: Limited activity was the most influential factor for the health-related quality of life of middle-aged women. For health behavior, the factors affecting HRQoL were drinking, sleeping time, and physical activity. For physical health, factors affecting HRQoL were vision problems, eating problems, and hearing problems. For mental health, the factors affecting HRQoL were suicidal impulse, stress, and frustration. Conclusion: These results indicated that to improve HRQoL for middle-aged women, limited activity and suicidal impulses should be addressed.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.239-250
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• 2019

An individual's health data is very sensitive and private. Such data are usually stored on a private or community owned cloud, where access is not restricted to the owners of that cloud. Anyone within the cloud can access this data. This data may not be read only and multiple parties can make to it. Thus, any unauthorized modification of health-related data will lead to incorrect diagnosis and mistreatment. However, we cannot restrict semipublic access to this data. Existing security mechanisms in e-health systems are competent in dealing with the issues associated with these systems but only up to a certain extent. The indigenous technologies need to be complemented with current and future technologies. We have put forward a method to complement such technologies by incorporating the concept of blockchain to ensure the integrity of data as well as its provenance.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1496-1506
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• 2020

With the progress of miniaturization and high performance of various sensors, a lot of data is generated in various fields and being collected in real-time, but the use of such large-capacity data is often unable to keep up with the collection technology. In the medical field, health data is collected and managed by platform, which causes inconvenience to users in searching their own health data and receiving medical services. In this paper, in order to solve these problems, we designed and implemented PHDItemReader to improve the speed of data query in a batch application environment that can integrate and process health data having various data expression formats. The experiment compared and analyzed 3 types of query speed based on 1,000,000 hypothetical health data, and as a result of the experiment, it was verified that the PHDItemReader implemented in this paper improved up to about 21% compared to the existing one.

• Korean Journal of Health Education and Promotion
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• v.24 no.3
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• pp.119-128
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• 2007

The purpose of this study is to develop Web database for healthy city that contains healthy city indicators for making city health plans, setting project priorities, monitoring projects, and evaluating healthy city projects, effectively. Using Delphi survey method for identifying indicator domains and indicators, we extracted nine domains with thirty-four healthy city indicators. Based on the appraisals of DB users about the contents of DB, a web database for healthy city Wonju was constructed. We developed a web database system for the purposes of sharing high quality health related data for managing and evaluating healthy city projects. The web database currently provides variety data in the web address, http://healthycity.wonju.go.kr/index.html. The web DB comprised with major healthy city indicators that are the most important indicators, healthy city indicator data that have a variety data set for encompassing all domain areas such as city infrastructure, health medicine, economies, and all other related areas and qualitative data that contains policy reports, research results, healthy city information and all other tips. A database of healthy city is very essential and important because it makes healthy city projects alive by managing and sharing healthy city related data effectively. But we need to fill out some blank cells in DB because there are currently unavailable data for some indicators. In conclusion, we expect the web DB contributes information sharing of healthy city project teams and improving healthy city project quality at Wonju city in Korea.

• Health Policy and Management
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• v.21 no.1
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• pp.77-92
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• 2011

Objectives : This study examined the health policy researchers' needs and their accessibility towards health insurance claim datasets according to their academic capacity. Methods : An online questionnaire to capture relevant proxy variables for academic needs, accessibility, and research capacity was constructed based on previous studies. The survey was delivered to active health policy researchers through three major scholarly associations in South Korea. Seven-hundred and one scholars responded while the survey as open for 12 days (starting on December 20th, 2010). Descriptive statistics and logistic regression analysis were carried out. Results : Regardless of the definition for operational needs, the prevalent needs of survey respondents were not met with the current provision of claim data. Greater research capacity was shown to be correlated with increased demand for claim data along with a positive correlation between attempts to obtain claim datasets and research capacity. A greater research capacity, however, was not necessarily correlated with better accessibility to the claim data. Conclusions : The substantial unmet need for claim data among the healthcare policy research community calls for establishing proactive institutions which could systematically prepare and make available public datasets and provide call-in services to facilitate proper handling of data.