• Journal of Korea Entertainment Industry Association
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• v.14 no.3
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• pp.75-82
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• 2020

It is important to secure patient healthcare information in medical institutions. Education can enhance healthcare information security practice. The purpose of this study is to investigate the effect size of the correlation between healthcare information security education and healthcare information security practice in medical institutions. Systematic Review and Meta-Analysis were used for this study. Data were collected from January 1, 2010 to July 31, 2019 through DBpia, RISS, NDSL. Four studies were eligible for inclusion in the analysis. Data were analyzed with R. The results of the Meta-Analysis demonstrated statistically significant large effect size of correlation with education and practice. Based on the results of this study, we will be able to understand the importance of healthcare information security education in medical institutions and use them as a basis for developing healthcare information security education programs.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.8
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• pp.3383-3389
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• 2014

Background: Prostate cancer is rapidly increasing in Korea and professional societies have requested adding prostate specific antigen (PSA) testing to the National Cancer Screening Program (NCSP), but this started a controversy in Korea and neutral evidence on this issue is required more than ever. The purpose of this study was to provide economic evidence to the decision makers of the NCSP. Materials and Methods: A cost-utility analysis was performed on the adoption of PSA screening program among men aged 50-74-years in Korea from the healthcare system perspective. Several data sources were used for the cost-utility analysis, including general health screening data, the Korea Central Cancer Registry, national insurance claims data, and cause of mortality from the National Statistical Office. To solicit the utility index of prostate cancer, a face-to-face interview for typical men aged 40 to 69 was conducted using a Time-Trade Off method. Results: As a result, the increase of effectiveness was estimated to be very low, when adopting PSA screening, and the incremental cost effectiveness ratio (ICER) was analyzed as about 94 million KRW. Sensitivity analyses were performed on the incidence rate, screening rate, cancer stage distribution, utility index, and treatment costs but the results were consistent with the base analysis. Conclusions: Under Korean circumstances with a relatively low incidence rate of prostate cancer, PSA screening is not cost-effective. Therefore, we conclude that adopting national prostate cancer screening would not be beneficial until further evidence is provided in the future.

• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.131-141
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• 2021

The present study is following a series of research investigations on design resources coming from collected data referring to users' awareness and preferences. The aim of this research is to test the Healthcare Environmental Color Index as a basis for practitioners in the field of healthcare design. An array of color samples selected from previous research, have been presented to the respondents via an online survey, in order to identify the preferences of the two groups on the relation between environmental color and health. As a result of the first experiment and through the comparison of processed data, the maximum percentage of respondents from each group is validating the relation between environmental color and health. For the second experiment we intend to highlight the patterns of color preferences for each group, and thus to test the color samples healing function. The compared data also showed a higher awareness of Koreans than Romanians on the potential of color applied to healing environment. Last but not least in the third experiment we show the top five color samples preferred by each group. It is significant that the comparison of the results validated once more some of our previous findings related to the healthcare environment, such as: the general preference for the green hue (associated to fatigue relax according to color psychology) and the blue hue (sedation release effect) but also the yellow hue - associated to bright energy. Three out of the top five preferred color samples have been identical to both groups while the other two samples have shown characteristic variations. These results show that similarities are strong and can be used in a glocal design strategy as an accessible tool for any practitioner. Based on the Healthcare Environmental Color Index and users' preferences analysis, a new design culture for healthcare can be established and developed.

• Journal of Korea Entertainment Industry Association
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• v.14 no.2
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• pp.225-235
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• 2020

Purpose: This study is a descriptive and secondary analytical study that uses panel data to analysis of unmet healthcare needs and risk factors for improving life care of osteoporosis patients. Methods: The subjects of this study were 941 patients who were diagnosed with osteoporosis using Korea Medical Panel 2015 data(β-version 1.0). Data analysis was performed using Chi-Square and logistic regression using SPSS/win 22.0. Results: The unmet healthcare needs of osteoporosis patients were 22.6%. The factors of unmet healthcare needs were education level and age in Model I of demographic factors, and eating problems, memory problems, activity limitation, and disability in Model II. In Model III, which added socio-psychological factors, eating problems, memory problems, Total family income, and pain/Discomfort were identified. Conclusion: Based on the results of this study, it should be considered in the planning of medical policies to improve the life care of osteoporosis patients, and it is necessary to improve access to medical services and to prevent and mediate realistically to reduce unmet healthcare needs.

• Health Policy and Management
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• v.18 no.4
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• pp.111-124
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• 2008

New ways of work-shift and learning programs, which were based on the concept of 'performance improvement through people', have been introduced to healthcare organizations. I analyzed the performance of the changes and the performance differences. Data were collected through interview and survey. I discussed that modification of management practices which were developed in manufacturing organizations is important for successful implementation in healthcare organizations.

• The Korean Society of Law and Medicine
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• v.22 no.3
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• pp.31-55
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• 2021

The Personal Information Protection Act, one of the revised 3 Data Laws, established a special cases concerning pseudonymous data. As a result, a personal information controller may process pseudonymized information without the consent of data subjects for statistical purposes, scientific research purposes, and archiving purposes in the public interest, etc. In addition, as a follow-up to the revised Personal Information Protection Act, a 'Guidelines for Utilization of Healthcare Data' was prepared, which deals with the pseudonymization in the medical sector. The guidelines are meaningful in that they provide practical criteria for accomplices by defining specific interpretations and examples that take into account the characteristics of healthcare data. However, the guidelines need to clarify the purpose of using pseudonymous data and strengthen the fairness of the composition of the data deliberation committee. The guidelines also require establishing a healthcare data compensation framework and strengthening the protection of rights for vulnerable subjects. In addition, the guidelines need to be adjusted for inconsistency with the Bioethics and Safety Act and the Medical Service Act. It is expected that this study will contribute to the creation of a safe environment for the utilization of healthcare data as well as the improvement of related laws and systems.

• The Journal of Information Systems
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• v.30 no.3
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• pp.21-45
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• 2021

Purpose This paper analyzed the impacts of domestic stock market by a global pandemic such as COVID-19. We investigated how the overall pattern of the stock market changed due to the impact of the COVID-19 pandemic. In particular, we analyzed in depth the pattern of stock price, as well, tried to find what factors affect on stock market index(KOSPI) in the healthcare industry due to the COVID-19 pandemic. Design/methodology/approach We built a data warehouse from the databases in various industrial and economic fields to analyze the changes in the KOSPI due to COVID-19, particularly, the changes in the healthcare industry centered on bio-medicine. We collected daily stock price data of the KOSPI centered on the KOSPI-200 about two years before and one year after the outbreak of COVID-19. In addition, we also collected various news related to COVID-19 from the stock market by applying text mining techniques. We designed four experimental data sets to develop decision tree-based prediction models. Findings All prediction models from the four data sets showed the significant predictive power with explainable decision tree models. In addition, we derived significant 10 to 14 decision rules for each prediction model. The experimental results showed that the decision rules were enough to explain the domestic healthcare stock market patterns for before and after COVID-19.

• Journal of Korean society of Dental Hygiene
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• v.3 no.2
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• pp.169-182
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• 2003

The present study attempts to investigate the knowledge, belief, attitude and behavior of healthcare major students and non-healthcare counterparts concerning their oral hygiene. The purpose is to provide basic data for positive oral health activities to the students with non-healthcare major, who tend to have insufficient information on oral hygiene. A survey was conducted to 400 students in K college in Incheon from May 1-30, 2003. A total of 384 surveys were analyzed using the SPSS program Version 10.0. The result is as follows: 1. There was a statistically significant difference in the knowledge on oral hygiene between the healthcare(M=3.08) and non-healthcare(M=2.78) students(pE0.05). 2. As for the beliefs and attitudes toward oral health behaviors, 56.9% of the healthcare students and 60.6% of non-health care counterparts responded "moderate" to the question asking if they liked tooth-brushing. The reason they liked tooth-brushing were cleanliness(60.3% of healthcare and 71.9% of non-healthcare students). They didn't like brushing their teeth because they felt it was a nuisance(60.6% of healthcare and 54.5% of non-healthcare students). 90.6% of healthcare students and 90.1% of their non-healthcare counterparts said they wanted to keep their oral health intact. Most of the subjects seemed to acquire information on oral hygiene through mass media(62.2% of healthcare and 55.3% of non-healthcare students). The persons who give them oral health information are their friends or neighbors(26.8% of healthcare and 22.8% of non-healthcare students), and dental hygienists were the last in the list of the sources of information(3.4% of healthcare and 2.5% of non-healthcare students). 3. Their oral health behaviors were also considered, 64.4% of the healthcare students and 53.7% of the non-healthcare counterparts brush their teeth once or twice a day, 51.4% of the former brush their teeth for 2 minutes and 44.8% of the latter for 3 minutes. Some of them use oral health measures other than tooth-brushing(13.3% of healthcare and 14.3% of non-healthcare students). Not many of them used oral health products(6.6% of healthcare and 5.9% of non-healthcare), and the difference was statistically significant(pE0.05). The largest number of healthcare students brush their teeth right before going to bed(29.9%), while their counterparts do it after breakfast(25.8%)

• 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.

• Advanced Industrial SCIence
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• v.1 no.1
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• pp.7-15
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• 2022

To adopt the development in the medical scenario IoT developed towards the advancement with the processing of a large amount of medical data defined as an Internet of Medical Things (IoMT). The vast range of collected medical data is stored in the cloud in the structured manner to process the collected healthcare data. However, it is difficult to handle the huge volume of the healthcare data so it is necessary to develop an appropriate scheme for the healthcare structured data. In this paper, a machine learning mode for processing the structured heath care data collected from the IoMT is suggested. To process the vast range of healthcare data, this paper proposed an MTGPLSTM model for the processing of the medical data. The proposed model integrates the linear regression model for the processing of healthcare information. With the developed model outlier model is implemented based on the FinTech model for the evaluation and prediction of the COVID-19 healthcare dataset collected from the IoMT. The proposed MTGPLSTM model comprises of the regression model to predict and evaluate the planning scheme for the prevention of the infection spreading. The developed model performance is evaluated based on the consideration of the different classifiers such as LR, SVR, RFR, LSTM and the proposed MTGPLSTM model and the different size of data as 1GB, 2GB and 3GB is mainly concerned. The comparative analysis expressed that the proposed MTGPLSTM model achieves ~4% reduced MAPE and RMSE value for the worldwide data; in case of china minimal MAPE value of 0.97 is achieved which is ~ 6% minimal than the existing classifier leads.