• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.2
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• pp.134-143
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• 2022

'Data' is the key component that leads Digital Healthcare. Most of the Healthcare Data is personal information of data subject and includes Sensitive Information. It is very important for companies to use data lawfully and safely during the lifecycle of data collection, use, provision, and destruction. However, small and medium-sized enterprises(SMEs), ventures, and startups, which account for 78% of the Healthcare Services Industry, have had difficulties in performing tasks related to personal information protection. The personal Information Protection Act's requirements depending on the purpose of using Personal Information are different. Also, the requirements for each personal information lifecycle are varied. Therefore, this study suggests six purposes for companies to use healthcare data. It examines the considerations during the lifecycle in which personal information is collected to be destroyed.

• Design Convergence Study
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• v.15 no.4
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• pp.1-16
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• 2016

Healthcare service helps users' health management by collecting an individual user's activity and biometric data from mobile devices and by providing them to the user. As a result, it has become necessary to perform a research on how to show the collected data. According to information visualization, the same data can have various interpretations depending on how they are represented. Healthcare data must be elivered to information acceptors without errors or distortion as they are directly related to people's health. With the expansion of healthcare service by the development of technology, this study could measure various data from users and was started to provide a necessary guideline for the visualization of measured numerical data. To propose a specific visualization by applying the visualization direction, 5 types of data including present value, measured value, relative value, relation data, and prediction value were set as the values necessary for the continuous use of mobile healthcare. Visualization was proposed concretely by applying clarity, variable comparison, brevity, relation, reliability, independence, and contextuality, which are the criteria for vitalizing the healthcare service.

• The Korean Journal of Food & Health Convergence
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• v.8 no.6
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• pp.19-29
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• 2022

Business intelligence (BI) is a process for turning data into insights that inform an organization's strategic and tactical decisions. BI aims to give decision-makers the information they need to make better decisions Patient safety analysis, illness surveillance, and fraud identification are just a few healthcare decision-making processes that can be supported by data mining. Thus, the purpose of the current research is to outline the need if BI as an essential factor in the healthcare sector by reviewing various scholarly materials and the findings. The present author conducted one of the most famous qualitative literature approach which has been called as PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) statement. The selecting criteria for eligible prior studies were estimated by whether studies are suitable for the current research, identifying they are peer-reviewed and issued by notable publishers between 2017 and 2022. According to the result based on the PRISMA analysis, BI plays a vital role in the healthcare sector and there are four business intelligence factors (Data, Analytic, Reporting, and Visualization) that will ensure that the healthcare sector provides the right healthcare services to the customers to be addressed in this section include; data, analytics, reporting, and visualization.

• Korea Journal of Hospital Management
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• v.28 no.2
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• pp.66-78
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• 2023

Purposes: This study explored the difference analysis of financial performance and technological innovation according to the ESG management activities of healthcare companies based on the time before and after the mandatory ESG management reporting of listed Chinese healthcare companies in China. Methodology: This study collected ESG management activities, corporate financial performance, and technological innovation data of Chinese listed healthcare companies by using Bloomberg Database and China-listed company reports to collect data for analyzing differences between groups through T-test. Findings: ESG activities in the healthcare industry have a certain impact on corporate financial performance, but have no impact on corporate technological innovation. Like the world trend, the ESG activities and financial results of China's healthcare industry have shown a positive development direction in recent years, and ESG scores are rising. Practical Implication: Since 2018, ESG activities in China's healthcare industry have flourished, and ESG activities and financial performance have developed in a positive direction.

• Journal of Convergence for Information Technology
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• v.8 no.3
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• pp.63-69
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• 2018

Recently, healthcare services are using cloud services to efficiently manage users' healthcare data. However, research to ensure the stability of the user's healthcare data processed in the cloud environment is insufficient. In this paper, we propose a partial random encryption scheme that efficiently encrypts healthcare data in a cloud environment. The proposed scheme generates two random keys (p, q) generated by the user to optimize for the hospital medical service and reflects them in public key and private key generation. The random key used in the proposed scheme improves the efficiency of user 's healthcare data processing by encrypting only part of the data without encrypting the whole data. As a result of the performance evaluation, the proposed method showed 21.6% lower than the existing method and 18.5% improved the user healthcare data processing time in the hospital.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1238-1249
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• 2020

Throughout the world, aging populations and doctor shortages have helped drive the increasing demand for smart healthcare systems. Recently, these systems have benefited from the evolution of the Internet of Things (IoT), big data, and machine learning. However, these advances result in the generation of large amounts of data, making healthcare data analysis a major issue. These data have a number of complex properties such as high-dimensionality, irregularity, and sparsity, which makes efficient processing difficult to implement. These challenges are met by big data analytics. In this paper, we propose an innovative analytic framework for big healthcare data that are collected either from IoT wearable devices or from archived patient medical images. The proposed method would efficiently address the data heterogeneity problem using middleware between heterogeneous data sources and MapReduce Hadoop clusters. Furthermore, the proposed framework enables the use of both fog computing and cloud platforms to handle the problems faced through online and offline data processing, data storage, and data classification. Additionally, it guarantees robust and secure knowledge of patient medical data.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.1-12
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• 2022

The current progression in the Internet of Things (IoT) and Machine Learning (ML) based technologies converted the traditional healthcare system into a smart healthcare system. The incorporation of IoT and ML has changed the way of treating patients and offers lots of opportunities in the healthcare domain. In this view, this research article presents a new IoT and ML-based disease diagnosis model for the diagnosis of different diseases. In the proposed model, vital signs are collected via IoT-based smart medical devices, and the analysis is done by using different data mining techniques for detecting the possibility of risk in people's health status. Recommendations are made based on the results generated by different data mining techniques, for high-risk patients, an emergency alert will be generated to healthcare service providers and family members. Implementation of this model is done on Anaconda Jupyter notebook by using different Python libraries in it. The result states that among all data mining techniques, SVM achieved the highest accuracy of 0.897 on the same dataset for classification of Parkinson's disease.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.35-57
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• 2021

Fog computing aims to provide the solution of bandwidth, network latency and energy consumption problems of cloud computing. Likewise, management of data generated by healthcare IoT devices is one of the significant applications of fog computing. Huge amount of data is being generated by healthcare IoT devices and such types of data is required to be managed efficiently, with low latency, without failure, and with minimum energy consumption and low cost. Failures of task or node can cause more latency, maximum energy consumption and high cost. Thus, a failure free, cost efficient, and energy aware management and scheduling scheme for data generated by healthcare IoT devices not only improves the performance of the system but also saves the precious lives of patients because of due to minimum latency and provision of fault tolerance. Therefore, to address all such challenges with regard to data management and fault tolerance, we have presented a Fault Tolerant Data management (FTDM) scheme for healthcare IoT in fog computing. In FTDM, the data generated by healthcare IoT devices is efficiently organized and managed through well-defined components and steps. A two way fault-tolerant mechanism i.e., task-based fault-tolerance and node-based fault-tolerance, is provided in FTDM through which failure of tasks and nodes are managed. The paper considers energy consumption, execution cost, network usage, latency, and execution time as performance evaluation parameters. The simulation results show significantly improvements which are performed using iFogSim. Further, the simulation results show that the proposed FTDM strategy reduces energy consumption 3.97%, execution cost 5.09%, network usage 25.88%, latency 44.15% and execution time 48.89% as compared with existing Greedy Knapsack Scheduling (GKS) strategy. Moreover, it is worthwhile to mention that sometimes the patients are required to be treated remotely due to non-availability of facilities or due to some infectious diseases such as COVID-19. Thus, in such circumstances, the proposed strategy is significantly efficient.

• Journal of Korean Academy of Nursing
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• v.43 no.6
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• pp.770-780
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• 2013

Purpose: The purpose of this study was to identify the factors affecting the unmet healthcare needs of married immigrant women. Methods: This study was a secondary data analysis using data from the 2009 National Survey of Multicultural Families. Data collected from 58,735 married immigrant women who had spouses were analyzed using descriptive statistics, Chi-square test, and logistic regression. Results: Overall, 9.9% of married immigrant women have unmet healthcare needs. The significant predictors related to unmet healthcare needs were young age, high level of education, employed, country of origin, long period of residence, low income, uninsured, urban area, low level of subjective health status, and illness experience over past two weeks. In particular, four variables (long period of residence, low income, subjective health status, and illness experience over past two weeks) significantly predicted unmet healthcare needs for women from all countries of origin. Conclusion: The results of the study indicate that common predictors related to unmet healthcare needs of married immigrant women are a long period of residence, low income, subjective health status, and illness experience over past two weeks. Therefore intervention strategies to decrease unmet healthcare needs should focus on these significant predictors.

• The Journal of Bigdata
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• v.3 no.1
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• pp.73-82
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• 2018

At the core of future medicine is the realization of Precision Medicine centered on individuals. For this, we need to have an open ecosystem that can view, manage and distribute healthcare data anytime, anywhere. However, since healthcare data deals with sensitive personal information, a significant level of reliability and security are required at the same time. In order to solve this problem, the healthcare industry is paying attention to the blockchain technology. Unlike the existing information communication infrastructure, which stores and manages transaction information in a central server, the block chain technology is a distributed operating network in which a data is distributed and managed by all users participating in the network. In this study, we not only discuss the technical and legal aspects necessary for demonstration of healthcare data distribution using blockchain technology but also introduce KOREN SDI Network-based Healthcare Big Data Distribution Demonstration Study. In addition, we discuss policy strategies for activating blockchain technology in healthcare.