• The Korean Society of Law and Medicine
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• v.13 no.1
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• pp.359-395
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• 2012

In a broad term, health and medical data means all patient information that has been generated or circulated in government health and medical policies, such as medical research and public health, and all sorts of health and medical fields as well as patients' personal data, referred as medical data (filled out as medical record forms) by medical institutions. The kinds of health and medical data in medical records are prescribed by Articles on required medical data and the terms of recordkeeping in the Enforcement Decree of the Medical Service Act. As EMR, OCS, LIS, telemedicine and u-health emerges, sharing and protecting digital health and medical data is at issue in these days. At medical institutions, health and medical data, such as medical records, is classified as "sensitive information" and thus is protected strictly. However, due to the circulative property of information, health and medical data can be public as well as being private. The legal grounds of health and medical data as such are based on the right to informational self-determination, which is one of the fundamental rights derived from the Constitution. In there, patients' rights to refuse the collection of information, to control recordkeeping (to demand access, correction or deletion) and to control using and sharing of information are rooted. In any processing of health and medical data, such as generating, recording, storing, using or disposing, privacy can be violated in many ways, including the leakage, forgery, falsification or abuse of information. That is why laws, such as the Medical Service Act and the Personal Data Protection Law, and the Guideline for Protection of Personal Data at Medical Institutions (by the Ministry of Health and Welfare) provide for technical, physical, administrative and legal safeguards on those who handle personal data (health and medical information-processing personnel and medical institutions). The Personal Data Protection Law provides for the collection, use and sharing of personal data, and the regulation thereon, the disposal of information, the means of receiving consent, and the regulation of processing of personal data. On the contrary, health and medical data can be inspected or delivered of the copies, based on the principle of restriction on fundamental rights prescribed by the Constitution. For instance, Article 21(Access to Record) of the Medical Service Act, and the Personal Data Protection Law prescribe self-disclosure, the release of information by family members or by laws, the exchange of medical data due to patient transfer, the secondary use of medical data, such as medical research, and the release of information and the release of information required by the Personal Data Protection Law.

• Journal of Information Technology Services
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• v.23 no.3
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• pp.39-54
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• 2024

This study explores the barriers to using health and medical data in research and development (R&D) within the healthcare industry and suggests ways to enhance data utilization. As artificial intelligence technology drives transformative changes across industries, there is an increased demand for robust health and medical data, highlighting its critical economic value and utility in fostering innovation. Using qualitative analysis through Grounded Theory, the study involves ten R&D professionals from healthcare industry, including both medical centers and corporations, using surveys and in-depth interviews to gather diverse experiences and perspectives on the challenges and opportunities in health and medical data use. Key findings point to legislative, regulatory, and data quality and integration issues, as well as complexities in patient data access and usage. Technological limitations and inadequate data governance frameworks also emerge as significant obstacles. Recommendations focus on improving regulatory frameworks, enhancing data standardization and quality, and fostering stronger partnerships between data custodians and users. The study concludes that overcoming these obstacles requires a comprehensive strategy involving legislative changes, improved technological infrastructure, and increased stakeholder collaboration. Implementing these recommendations could greatly enhance health and medical data utilization in R&D, significantly advancing medical science and patient care services.

• Korea Journal of Hospital Management
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• v.26 no.1
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• pp.71-82
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• 2021

Purpose: The purpose of this study is to estimate incomes and costs of the medical clinics by using secondary data. Methodology: The medical incomes and costs were estimated from 405 clinics operated by sole practitioner providing out-patient services among all clinics subject to the Medical Cost Survey on National Health Insurance Patients in 2017, excluding dental clinics and oriental medical clinics. The incomes and costs of the medical clinics were reflected with incomes and costs of health insurance benefits and were calculated by types of medical services (i.e., basic care, surgery, general treatment, functional test, specimen test and imaging test). The costs were classified as follows: labor costs, equipment costs, material costs and overhead costs. Secondary data was used to estimate the incomes and costs of the medical clinics. For allocation bases for costs for each type of the medical service, the ratio of revenue from health insurance benefits by types of medical services was applied. However, labor costs were calculated with the activity ratio by types of medical services and occupations, using clinical expert panel data. Finding: The percentage of health insurance income for all medical income was 73.1%. The health insurance cost per clinic was 401,864 thousand won. Labor cost accounted for the largest portion of the health insurance income was 191,229 thousand won (47.6%), followed by management cost was 170,018 thousand won (42.3%), materials cost was 35,434 thousand won (8.8%), and equipment costs was 5,183 thousand won (1.3%). Practical Implications: This study suggests a method of estimating incomes and costs of medical clinic services by using secondary data. It could efficiently provide incomes and costs to assess an appropriate level of the health insurance fee to the clinics.

• Safety and Health at Work
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• v.13 no.1
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• pp.112-116
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• 2022

Background/Aims: The Gachon Regional Occupational Cohort Study (GROCS) is a large-scale longitudinal study of occupational safety and health data (covering Work Environment Monitoring, Workers' Health Surveillance, and Occupational Health Service) conducted by the Gachon University Gil Medical Center (GUGMC) in Incheon, Republic of Korea. We conducted GROCS to identify the health effects of workers' occupational risks, behavior, socioeconomic status, and life style. Methods: The GROCS includes data from Work Environment Monitoring, Workers' Health Surveillance, and Occupational Health Service. The baseline year for all data collection was 2018. Work Environment Monitoring was conducted in 240 companies located in Incheon. General Health Examination and Special Health Examination were performed on 32,725 and 9,504 workers, respectively. Occupational Health Services were provided to 16,883 workers in 171 companies. These data have been collected and operated at an external data management institution and were provided as a retrospective cohort after removing personal identification information. Results: In 2018, the total number of companies was 2,854, among which which 488 special Health Examination, 171 Work Environment Monitoring, and 240 Occupational Health Service. The proportion of companies undergoing Special Health Examination was 17.1%, the proportion of companies undergoing Work Environment Monitoring was 8.4%, and the proportion of Companies undergoing Occupational Health Service was 6.0%. Conclusion: GROCS expects researchers to utilize its useful and reliable resource for occupational health and surveillance with for academic or political purposes to lead to improved workers' health and working environment.

• Health Policy and Management
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• v.25 no.2
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• pp.80-89
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• 2015

Background: This study aims to identify the monthly average medical expenses of public pension recipients, and analyze the determinants of total health and medical expenses and Western and Oriental medicine expenses, medical service expenses, and medical supplies expenses. Methods: This study used the fifth year data of 2013 out of the raw data of the Korean Retirement and Income Study collected by the National Pension Research Institute. This study conducted t-test, analysis of variance, and linear regression to verify publicly the relevance between pension recipients' general characteristics and health and medical expenses status using IBM SPSS ver. 21.0 for data analysis. Results: It was analyzed that there is a difference in the spending of expenditure and health care costs according to public pension recipients. Medical expenses of the national pensioners was higher compared to the special corporate pensioners. The national pensioner is related expenditure size, education level, family members living together, residential areas, status of spouse, number of chronic illness, and status of limitation in daily life with psychological health status. Conclusion: Therefore, fairness does not occur fire to the medical use between the special corporate pensioners and national pensioners, aggressive of government such as health policy and financial support for the retiree pension policy that reflects the reality intervention would be required.

• Journal of the Health Care and Life Science
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• v.10 no.1
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• pp.1-14
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• 2022

This study is a study on the possibility of using big data-related education programs in health and medical administration-related departments using health and medical big data. This paper intends to examine the health and medical big data from five perspectives. 1st, in addition to the aforementioned 'Health and Medical Big Data Open System', I would like to examine the characteristics and application technologies of public big data disclosed by 'Korea Welfare Panel', 'Public Big Data', 'Seoul City Big Data', 'Statistical Office Big Data', etc. 2nd, it is intended to examine the appropriateness of whether the applicable health and medical big data can be used as living data in regular subjects of health and medical administration and health information related departments of junior colleges. 3rd, we want to select the most appropriate tool for classroom lectures using existing statistical processing packages and programming languages. Fourth, finally, by using verified health and medical big data and appropriate tools, we want to test the possibility of expressing graphs, etc. in class and the steps from writing a report. 4th, I would like to describe the relative advantages of R language that can satisfy portability, installability, cost effectiveness, compatibility, and big data processing potential.

• The Korean Journal of Health Service Management
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• v.12 no.4
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• pp.203-217
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• 2018

Objectives : This study aimed to investigate the clinical practice ability and satisfaction of clinical training of health-medical information management major students. Methods : The data were collected from 68 persons from students finished clinical training at medical record (information) team using self administered questionnaires. The data were analyzed using t-test, ANOVA and correlation with SPSS 22.0 version. Results: Performance of data collection, data management, and data analysis were analyzed in three areas of the job area. In terms of academic characteristics and correlation, they were not related to the level of satisfaction with the practical experience. Conclusions : Research on a virtuous cycle clinical practice program that analyzes the factors by assessing the satisfaction level of clinical practice in each area of health care information management will be conducted continuously.

• 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.1
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• pp.13-23
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• 2014

Background: A need arises to efficiently control health expenditure for medical aid due to a sharp increase in medical aid expenditure. This study experimently analyzes the impact of physician behavior on medical use for medical aid beneficiaries using supplier induced demand (SID) theory. Methods: This study looks into analyze SID effect using expenditure factor analysis of medical aid for the years between 2003 and 2010 in comparison with health insurance. Moreover, this study analyzes the existence and scale of SID using econometrics modeling with panel data on 16 cities and provinces's health expenditure data for medical aid from 2003 1/4 to 2010 4/4. Results: This study finds that the growth rate of visit days per capita and treatment amount per visit days for medical aid is higher than health insurance. Furthermore, the result of econometrics modeling analysis shows the existence of SID in general hospital, hospital, clinic, oriental clinic. Conclusion: In order to efficiently control expenditure for medical aid, it is required to reinforce macro polices such as the introduction of 'target management' and micro policies such as the strengthen of management on medical institutes in the perspective of suppliers as well as regulations of demanders.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.6
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• pp.303-308
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• 2017

Nowadays, living standard is improved and people have high interest to the personal health care problem. Accordingly, people desire to know the personal physical condition and the related medical treatment. Thus, there is the necessary of the personalized medical treatment, and there are many studies about the automatic disease diagnosis and the related services. Those studies focus on the particular disease prediction which is based on the related particular data. However, there is no studies about the medical treatment prediction. In our study, national health data based medical treatment predictor is built by using SVM, and the performance is evaluated by comparing with other prediction methods. The experimental results show that the health data based medical treatment prediction resulted in the average accuracy of 80%, and the SVM performs better than other prediction algorithms.