• The Journal of Society for e-Business Studies
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• v.24 no.1
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• pp.17-27
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• 2019

With the occurrence of the 4th Industrial Revolution, environmental change is happening in the healthcare industry as overall flow of Industry heads to ICT-based business environment. Healthcare Industry, which has the characteristic of public goods, is requiring a reliability and continuity of healthcare industry, however, the introduction of security is being delayed due to the problem of compatibility and extendability of existing system. Accordingly, in this research, we have built a section and role for stakeholders to be concerned in order to induce, analyze and introduce a needed security technology for rapidly building a security system in a smart healthcare environment. We have suggested a possibility of extendability regarding a multi-dimensional effort of stakeholders for establishing a healthcare security system.

• Asia pacific journal of information systems
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• v.29 no.4
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• pp.817-837
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• 2019

The medical industry is rapidly evolving into a combination of artificial intelligence (AI) and ICT technology, such as mobile health, wireless medical, telemedicine and precision medical care. Medical artificial intelligence can be diagnosed and treated, and autonomous surgical robots can be operated. For smart medical services, data such as medical information and personal medical information are needed. AI is being developed to integrate with companies such as Google, Facebook, IBM and others in the health care field. Telemedicine services are also becoming available. However, security issues of medical information for smart medical industry are becoming important. It can have a devastating impact on life through hacking of medical devices through vulnerable areas. Research on medical information is proceeding on the necessity of privacy and privacy protection. However, there is a lack of research on the practical measures for protecting medical information and the seriousness of security threats. Therefore, in this study, we want to confirm the research trend by collecting data related to medical information in recent 5 years. In this study, smart medical related papers from 2014 to 2018 were collected using smart medical topics, and the medical information papers were rearranged based on this. Research trend analysis uses topic modeling technique for topic information. The result constructs topic network based on relation of topics and grasps main trend through topic.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1484-1502
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• 2023

The medical sector actively changes and implements innovative features in response to technical development and revolutions. Many of the most crucial elements in IoT-connected health services are safeguarding critical patient records from prospective attackers. As a result, BlockChain (BC) is gaining traction in the business sector owing to its large implementations. As a result, BC can efficiently handle everyday life activities as a distributed and decentralized technology. Compared to other industries, the medical sector is one of the most prominent areas where the BC network might be valuable. It generates a wide range of possibilities and probabilities in existing medical institutions. So, throughout this study, we address BC technology's widespread application and influence in modern medical systems, focusing on the critical requirements for such systems, such as trustworthiness, security, and safety. Furthermore, we built the shared ledger for blockchain-based healthcare providers for patient information, contractual between several other parties. The study's findings demonstrate the usefulness of BC technology in IoHT for keeping patient health data. The BDSA-IoHT eliminates 2.01 seconds of service delay and 1.9 seconds of processing time, enhancing efficiency by nearly 30%.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.18-26
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• 2009

In this paper, we proposed multipurpose signal detection methods for ECG (electrocardiogram) based u-healthcare systems. For ECG based u-healthcare system, QRS signal extraction for cardiovascular disease diagnosis is essential. Also, for security and convenience reasons, it is desirable if u-healthcare system support biometric identification directly from user's bio-signal such as ECG for this case. For this, from Lead II signal, we developed QRS signal detection method and also, we developed signal extraction method for biometric identification using Lead II signal which is relatively robust from signal alteration by aging and diseases. For QRS signal detection capability from Lead II signal, ECG signals from MIT-BIH database are used and it showed 99.36% of accuracy and 99.68% of sensitivity. Also, to show the performance of signal extraction capability for biometric diagnosis purpose, Lead III signals are measured after drinking, smoking, or exercise to consider various monitoring conditions and it showed 99.92% of accuracy and 99.97% of sensitivity.

• 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 Korea Society of Industrial Information Systems
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• v.15 no.3
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• pp.23-32
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• 2010

U-healthcare provides people with many conveniences and keeps their health. As facility centered medical environment is being changed to patient centered one, however, various problems are emerging. These problems range from institutional problems to technological, producer related, and user related ones. The researcher took interested in problems originating from users, in particular, elderly patients and guardians taking care of an elderly patient. The main focus of this study is the direction of serious contents design for training skills in HMI of U-healthcare equipment and developing emergency coping abilities. In this study, I started from research on elderly cognitive psychology and elders' motor reactions and interconnected the characteristics of various digital content forms. The results of this study are expected to be useful for the rapidly expanding elderly population and in the U-healthcare industry.

• Health Policy and Management
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• v.33 no.3
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• pp.273-294
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• 2023

Background: Most developed countries are working to improve their universal health coverage systems. This study investigates regional disparities in unmet healthcare needs and their causes in South Korea. Additionally, it compares the unmet healthcare needs rate in South Korea with that of 33 European countries. Methods: The analysis incorporates information from 13,359 adults aged 19 or older, using data from the Korea Health Panel. The dependent variables encompass the experience of unmet healthcare needs and the three causes of occurrence: "burden of medical expenses," "time constraints," and "lack of care." The primary variable of interest is the region of residence, while control variables encompass 14 socio-demographic, health, and functional characteristics. Multivariable binary logistic regression analysis, accounting for the sampling design, is conducted. Results: The rate of unmet healthcare needs in Korea is 11.7% (95% confidence interval [CI], 11.0%-13.3%), which is approximately 30 times higher than that of Austria (0.4%). The causes of unmet healthcare needs, ranked in descending order, are "lack of care," "time constraints," and "burden of medical expenses." Predictive probabilities for experiencing unmet healthcare needs and each cause differ significantly between regions. For instance, the probability of experiencing unmet healthcare needs due to "lack of care" is approximately 10 times higher in Gangwon-do (13.5%; 95% CI, 13.0%-14.1%) than in Busan (1.3%; 95% CI, 1.3%-1.4%). The probability due to "burden of medical expenses" is approximately 14 times higher in Seoul (4.1%; 95% CI, 3.6%-4.6%) compared to Jeollanam-do (0.3%; 95% CI, 0.2%-0.4%). Conclusion: Amid rapid sociodemographic transitions, South Korea must make significant efforts to alleviate unmet healthcare needs and the associated regional disparities. To effectively achieve this, it is recommended that South Korea involves the National Assembly in healthcare policy-making, while maintaining a centralized financing model and delegating healthcare planning and implementation to regional authorities for their local residents-similar to the approaches of the United Kingdom and France.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.161-168
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• 2018

Recently, the bio-sensing information systems for collecting and analysing human body information of a patient in real time in the field of medical information and healthcare information service are continuously increasing. Specially, various wearable devices such as a wrist, a garment, and a skin attachment type for supporting health information of a mobile user are rapidly increasing. Until now, there is no patch-type biometric information service model. Therefore, this paper presents a biometric information system model and the application examples to support biometric information sensing and health information service of mobile user with digital patch system as a new biometric information system. As a result, through this research, research issues based on digital patch system are searched to suggest the direction of continuous research.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.5
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• pp.31-40
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• 2016

This paper is to set up the autoregressive distributed lag model in order to estimate factor elasticities of the service export in the medical and healthcare service industry and to analyze the determinants for its export performance during 1975-2015. The empirical results suggest that industrial markup, exchange rate, and interest rate have had more decisive effects on the industrial export performance in the medical and healthcare service industry, in particular, during the declining period in factor prices since the foreign currency crisis beginning in late 1997.

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.35 no.7
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• pp.9-25
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• 2017

In this paper, we surveyed state-of-the-art health and wellness platforms. The motivation of this paper is to review the state-of-the-art health and wellness platforms and their maturity with respect to adoption of latest enabling technologies. The is review is classified into four categories: healthcare systems, AI-assisted healthcare, wellness platforms, and open source health and wellness initiatives. From this comprehensive review, it can be stated that the contemporary healthcare systems are well-adopting wellness due to the concentration shift towards prevention. Thus, the gap between health and wellness is slowly yet carefully entering gray area. Where both the domains can freely invoke each other's services, and supporting enabling technologies. Furthermore, the biomedical researchers and physicians are no longer carrying the myopic views of trusting their knowledge for diagnosis. AI-assisted technologies based on machine learning and big data are influencing today's prognosis with trust and confidence.