• Journal of Preventive Medicine and Public Health
• /
• v.50 no.5
• /
• pp.294-302
• /
• 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
• /
• v.52 no.4
• /
• pp.234-241
• /
• 2019

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

• The Journal of Information Systems
• /
• v.31 no.2
• /
• pp.217-238
• /
• 2022

Purpose This study aims to develop a work-related injury and illness monitoring geographic information system that analyzes and visualizes the types of work-related injury and illness based on workers' compensation insurance big data. Design/methodology/approach Using the developed system, we explained the process of monitoring the areas of the applied workplace, medical care application, index, and medical care institution. We also showed examples of analyzing the index and medical care institution area. By applying the system, we can intuitively recognize the current status of workers' compensation insurance and confirm the basic information necessary for managing the current status of workers' compensation insurance. Findings We generated more helpful information by combining workers' compensation insurance data and designated medical care institution data. We were able to apply the severity score and the vulnerability index of work-related injury and illness to the system as a demonstration. To efficiently manage workers' compensation insurance, it was necessary to integrate workers' compensation insurance and designated medical care institution data, as well as the data from various sources.

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

• The Journal of Bigdata
• /
• v.2 no.2
• /
• pp.17-31
• /
• 2017

With the development of the big data environment, public institutions also have been providing big data infrastructures. Public data is one of the typical examples, and numerous applications using public data have been provided. One of the cases is related to the employment insurance. All employers have to make contracts for the employment insurance for all employees to protect the rights. However, there are abundant cases where employers avoid to buy insurances. To overcome these challenges, a data-driven approach is needed; however, there are lacks of methodologies to integrate, manage, and analyze the public data. In this paper, we propose a methodology to build a predictive model for identifying whether employers have made the contracts of employment insurance based on public data. The methodology includes collection, integration, pre-processing, analysis of data and generating prediction models based on process mining and data mining techniques. Also, we verify the methodology with case studies.

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

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

• Archives of Plastic Surgery
• /
• v.47 no.2
• /
• pp.118-125
• /
• 2020

Since April 2015, post-mastectomy breast reconstruction has been covered by the Korean National Health Insurance Service (NHIS). The frequency of these procedures has increased very rapidly. We analyzed data obtained from the Big Data Hub of the Health Insurance Review and Assessment Service (HIRA) and determined annual changes in the number of breast reconstruction procedures and related trends in Korea. We evaluated the numbers of mastectomy and breast reconstruction procedures performed between April 2015 and December 2018 using data from the HIRA Big Data Hub. We determined annual changes in the numbers of total, autologous, and implant breast reconstructions after NHIS coverage commenced. Data were analyzed using Microsoft Excel. The post-mastectomy breast reconstruction rate increased from 19.4% in 2015 to 53.4% in 2018. In 2015, implant reconstruction was performed in 1,366 cases and autologous reconstruction in 905 (60.1% and 39.8%, respectively); these figures increased to 3,703 and 1,570 (70.2% and 29.7%, respectively) in 2018. Free tissue transfer and deep inferior epigastric perforator flap creation were the most common autologous reconstruction procedures. For implant-based reconstructions, the rates of directto-implant and tissue-expander breast reconstructions (first stage) were similar in 2018. This study summarizes breast reconstruction trends in Korea after NHIS coverage was expanded in 2015. A significant increase over time in the post-mastectomy breast reconstruction rate was evident, with a trend toward implant-based reconstruction. Analysis of data from the HIRA Big Data Hub can be used to predict breast reconstruction trends and convey precise information to patients and physicians.

• International journal of advanced smart convergence
• /
• v.8 no.2
• /
• pp.102-108
• /
• 2019

Big data was associated with three key concepts, volume, variety, and velocity. Securities and investment services produce and store a large data of text/numbers. They have also the most data per company on the average in the US. Gartner found that the demand for big data in finance was 25%, which was the highest. Therefore securities and investment companies produce the largest data such as text/numbers, and have the highest demand. And insurance companies and credit card companies are using big data more actively than banking companies in Korea. Researches on the use of big data in securities and investment companies have been found to be insignificant. We surveyed 22 major securities and investment companies in Korea for activating big data. We can see they actively use AI for investment recommend. As for big data of securities and investment companies, we studied open API. Of the major 22 securities and investment companies, only six securities and investment companies are offering open APIs. The user OS is 100% Windows, and the language used is mainly VB, C#, MFC, and Excel provided by Windows. There is a difficulty in real-time analysis and decision making since developers cannot receive data directly using Hadoop, the big data platform. Development manuals are mainly provided on the Web, and only three companies provide as files. The development documentation for the file format is more convenient than web type. In order to activate big data in the securities and investment fields, we found that they should support Linux, and Java, Python, easy-to-view development manuals, videos such as YouTube.

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

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

• Journal of the Korea Society of Computer and Information
• /
• v.22 no.9
• /
• pp.99-105
• /
• 2017

In this paper, we propose to design and implement the database management system for analyzing vehicle accidents through utilizing integration of the public big data. And the paper aims to provide valuable information for recognizing seriousness of the vehicle accidents and various circumstances at the accident time, and to utilize the produced information for the insurance company policies as well as government policies. For analysis of the vehicle accidents the system utilizes the integrated big data of National Indicator System, the Meteorological Office, National Statistical Office, Korea Insurance Development Institute, Road Traffic Authority, Ministry of Land, Infrastructure and Transport as well as the National Police Agency, which differentiates this system from the previous systems. The system consists of data at the accident time including weather conditions, vehicle models, age, sex, insurance amount etc., by which the database system users are able to obtain the integral information about vehicle accidents. The result shows that the vehicle accidents occur more frequently in the clear weather conditions, in the vehicle to vehicle conditions and in crosswalk & crossway. Also, it shows that the accidents in the cloudy weather leads more seriously to injury and death than in the clear weather. As well, the vehicle accident information produced by the system can be utilized to effectively prevent drivers from dangerous accidents.