• Health Policy and Management
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• v.28 no.3
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• pp.288-293
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• 2018

In 1994 Korea government began to develop the healthcare industry, since then the government has tried to create opportunities to promote the industry through various political efforts and policies. The biggest achievement was attracting foreign patients from 2009 to 2016 with a cumulative 1.56 million and total revenue of 3 trillion won. But Korea still loses the opportunity to become a global leader in the health care industry due to regulations and various ideological disputes. Accordingly, it is necessary to facilitate policy understanding and present a practical road map so that Korea's healthcare industry become a new growth engine that will lead the trend of global market in the future. It also suggests a national economic development paradigm, the health economy as health and economic value are rotated through a shift in view of health care. At this point, 20 years after the beginning of the healthcare industry development, it is necessary to evaluate the related policies and discuss effective future directions. In this sense, the purpose of this study is to examine the policies and limitations of the healthcare industry by each government division, and based on it, to propose political tasks for the future.

• Convergence Security Journal
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• v.13 no.4
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• pp.101-108
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• 2013

Sensor network configurations are for a specific situation or environment sensors capable of sensing, processing the collected information processors, and as a device is transmitting or receiving data. It is presently serious that sensor networks provide many benefits, but can not solve the wireless network security vulnerabilities, the risk of exposure to a variety of state information. u-Healthcare sensor networks, the smaller the sensor node power consumption, and computing power, memory, etc. restrictions imposing, wireless sensing through the kind of features that deliver value, so it ispossible that eavesdropping, denial of service, attack, routing path. In this paper, with a focus on sensing of the environment u-Healthcare system wireless security vulnerabilities factors u-Healthcare security framework to diagnose and design methods are presented. Sensor network technologies take measures for security vulnerabilities, but without the development of technology, if technology is not being utilized properly it will be an element of threat. Studies suggest that the u-Healthcare System in a variety of security risks measures user protection in the field of health information will be used as an important guide.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.525-536
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• 2021

This study investigated that customer satisfaction based on the effects of perceived value (innovative value, practical value, symbolic value) and innovation characteristics (innovative resistance, visibility) of wearable healthcare device users on purchase attitude. A survey was conducted on actual users who are currently using wearable healthcare devices, and the final 201 copies were analyzed using SPSS 25 and SmartPLS 3.0. The summary of research results is following here. First, it was found that the perceived values, innovative and practical values had a positive (+) effect on the user's purchasing attitude, while symbolic values did not affect the user's purchasing attitude. Second, among the innovation characteristics, innovation resistance was came out negative (-) effect on the user's purchasing attitude, and among the innovation characteristics, visibility did not affect the user's purchasing attitude. Third, it was found that perceived price had a positive (+) effect on the user's purchase attitude. Fourth, it was clarified that the perceived price and purchase attitude had a positive (+) effect on the user's customer satisfaction. Based on these research results, theoretical, practical, and future research directions were proposed.

• Journal of The Korea Institute of Healthcare Architecture
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• v.17 no.2
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• pp.45-52
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• 2011

The visual sensory information in physical environments can induce or reduce occupants' stress. In healthcare settings, positive environmental stimulations can promote patient well-being by reducing their stress: poor health environments work against a patient's health. Changing the color in a patient room is an inexpensive process and thus finding better colors for healthcare settings is a cost effective method of improving healing environments. Color may have important implications for pediatric patients, but the investigation of Korean populations has been non-existent. The purpose of this study was to investigate Korean pediatric patients' color preferences for patient room design. The color preferences from 50 Korean pediatric patients were recorded and investigated for gender effects. A simulation method was used because of its reliability and feasibility, allowing for investigating the value of color in real contexts and controlling confounding variables. The overall color preferences from Korean pediatric patients showed that they preferred blue the most and white the least. Gender differences were found in red and purple. Girls preferred red and purple more than boys. The results from this study can help healthcare providers and designers better understand appropriate colors for Korean pediatric patient populations.

• Journal of Preventive Medicine and Public Health
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• v.45 no.3
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• pp.148-155
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• 2012

Since the reformation of the National Health Insurance Act in 2000, the Health Insurance Review and Assessment Service (HIRA) in the Republic of Korea has performed quality assessments for healthcare providers. The HIRA Value Incentive Program (VIP), established in July 2007, provides incentives for excellent-quality institutions and disincentives for poorquality ones. The program is implemented based on data collected between July 2007 and December 2009. The goal of the VIP is to improve the overall quality of care and decrease the quality gaps among healthcare institutions. Thus far, the VIP has targeted acute myocardial infarction (AMI) and Caesarian section (C-section) care. The incentives and disincentives awarded to the hospitals by their composite quality scores of the AMI and C-section scores. The results of the VIP showed continuous and marked improvement in the composite quality scores of the AMI and C-section measures between 2007 and 2010. With the demonstrated success of the VIP project, the Ministry of Health and Welfare expanded the program in 2011 to include general hospitals. The HIRA VIP was deemed applicable to the Korean healthcare system, but before it can be expanded further, the program must overcome several major concerns, as follows: inclusion of resource use measures, rigorous evaluation of impact, application of the VIP to the changing payment system, and expansion of the VIP to primary care clinics.

• 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.

• Journal of Korean Society for Quality Management
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• v.40 no.3
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• pp.372-380
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• 2012

Purpose: The purpose of this research is to develop the methods for evaluating the business value of a company's technical portfolios. In this study, technical portfolios of 10 major manufacturers and e-Biz industries are examined first from a business model perspective. Subsequently, we suggest future direction of R&D for the pharmaceutical industry by deducing the leading industries sharing similar traits with the pharmaceutical industry. Methods: In order to evaluate and analyze the patents of the major leading industries based on the constituents of a business model, the target patents were selected through the following procedure. Results: First, In this study, using the data obtained from the patent analysis, the differences in the technology portfolios of specific business entities based on the constituents of their business models. Second, deduced business rules of particular business entities through classification analysis and role-model of pharmaceutical industry Conclusion: If enterprise discovers technological change and characters of other enterprise or technology, enterprise could judge a direction of technology which will be developed in the near term and a plan which utilized existing technology to increase enterprise's profits.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.940-941
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• 2014

국내 MPI 시스템의 도입을 위하여 한글성명에 대한 유사도 비교 알고리즘이 필요하다. 기존의 영문성명 비교 알고리즘의 경우 조합형 글자를 지원하지 않기 때문에 한글에 적용할 경우 좋은 결과를 내지 못한다. 이러한 문제를 해결하기 위해 한글성명 매칭 알고리즘을 연구하였으며 본 논문에서는 한글 유사도 알고리즘에서 사용되는 여러 가중치의 최적 값을 시뮬레이션을 통해 산정하는 방법에 관하여 연구하였다.

• Journal of the Korean Society of Radiology
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• v.81 no.5
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• pp.1024-1037
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• 2020

The resource-based relative value scale (RBRVS) compares the value of a medical practice to the consumption of resources, which consist of the work of the physician, practice expenses, and professional liability insurance. At the time of the 2nd revision of RBRVS, the fee for radiological examinations had been reduced due to the high preservation rate. In RBRVS, practice expenses account for most of the compensation of radiological examinations, and physicians' work is relatively undervalued. A new healthcare policy (Moon Jae-In care) consists of the expansion of the National Health Insurance (NHI) coverage, reduction of patient charges for the vulnerable class, and support for catastrophic medical expenses. However, Moon Jae-In care is expected to negatively affect the NHI in Korea financially. The expansion of the insurance coverage for ultrasonography and MRI examinations is a significant part of the Moon Jae-In care, and radiological societies should establish fair compensations for physicians' work within the field of radiology while implementing the Moon Jae-In care.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.