HDM-Chain: A Secure Blockchain-based Healthcare Data Management framework to ensure Privacy and Security in the Health Unit

2021 ◽  
Author(s):  
Md. Jobaer Hossain ◽  
Md. Anwar Hussen Wadud ◽  
Md. Alamin
Keyword(s):  
Data Management ◽  
Health Unit ◽  
Privacy And Security ◽  
Management Framework ◽  
Healthcare Data

Survey of Cloud Computing in Healthcare Sector

2017 ◽  
Vol 7 (7) ◽  
pp. 337
Author(s):  
S. Karthiga Devi ◽  
B. Arputhamary
Keyword(s):  
Cloud Computing ◽  
Healthcare Providers ◽  
High Volume ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Cloud Infrastructure ◽  
Healthcare Organizations ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Number Of Patients

Today the volume of healthcare data generated increased rapidly because of the number of patients in each hospital increasing.  These data are most important for decision making and delivering the best care for patients. Healthcare providers are now faced with collecting, managing, storing and securing huge amounts of sensitive protected health information. As a result, an increasing number of healthcare organizations are turning to cloud based services. Cloud computing offers a viable, secure alternative to premise based healthcare solutions. The infrastructure of Cloud is characterized by a high volume storage and a high throughput. The privacy and security are the two most important concerns in cloud-based healthcare services. Healthcare organization should have electronic medical records in order to use the cloud infrastructure. This paper surveys the challenges of cloud in healthcare and benefits of cloud techniques in health care industries.

A review on Privacy-Preserving Data Preprocessing

2020 ◽  
pp. 16-30
Author(s):  
Mukesh Soni ◽  
◽  
◽  
◽  
YashKumar Barot ◽  
...  
Keyword(s):  
Health Care ◽  
Information Security ◽  
Social Insurance ◽  
Disease Outbreaks ◽  
Security And Privacy ◽  
Health Care Information ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Security Issues ◽  
Care Information

Health care information has great potential for improving the health care system and also providing fast and accurate outcomes for patients, predicting disease outbreaks, gaining valuable information for prediction in future, preventing such diseases, reducing healthcare costs, and improving overall health. In any case, deciding the genuine utilization of information while saving the patient's identity protection is an overwhelming task. Regardless of the amount of medical data it can help advance clinical science and it is essential to the accomplishment of all medicinal services associations, at the end information security is vital. To guarantee safe and solid information security and cloud-based conditions, It is critical to consider the constraints of existing arrangements and systems for the social insurance of information security and assurance. Here we talk about the security and privacy challenges of high-quality important data as it is used mainly by the healthcare structure and similar industry to examine how privacy and security issues occur when there is a large amount of healthcare information to protect from all possible threats. We will discuss ways that these can be addressed. The main focus will be on recently analyzed and optimized methods based on anonymity and encryption, and we will compare their strengths and limitations, and this chapter closes at last the privacy and security recommendations for best practices for privacy of preprocessing healthcare data.

An Integrated Data Management Framework for Drug Discovery – From Data Capturing to Decision Support

2012 ◽  
Vol 12 (11) ◽  
pp. 1237-1242 ◽  
Author(s):  
Walter Cedeno ◽  
Simson Alex ◽  
Edward P. Jaeger ◽  
Dimitris K. Agrafiotis ◽  
Victor S. Lobanov
Keyword(s):  
Decision Support ◽  
Drug Discovery ◽  
Data Management ◽  
Management Framework ◽  
Data Capturing

scholarly journals DIRAC Data Management Framework

2017 ◽  
Author(s):  
Andrei Tsaregorodstsev ◽  
Keyword(s):  
Data Management ◽  
Management Framework

Exploring Factors Influencing Big Data and Analytics Adoption in Healthcare Management

2020 ◽  
pp. 413-428
Author(s):  
Sampson Abeeku Edu ◽  
Divine Q. Agozie
Keyword(s):  
Big Data ◽  
Data Management ◽  
Patient Management ◽  
Technological Development ◽  
Healthcare Management ◽  
Quality Cost ◽  
Healthcare Data ◽  
Management Quality ◽  
Factors Influencing ◽  
Quality Decision

Demand for improvement in healthcare management in the areas of quality, cost, and patient care has been on the upsurge because of technology. Incessant application and new technological development to manage healthcare data significantly led to leveraging on the use of big data and analytics (BDA). The application of the capabilities from BDA has provided healthcare institutions with the ability to make critical and timely decisions for patients and data management. Adopting BDA by healthcare institutions hinges on some factors necessitating its application. This study aims to identify and review what influences healthcare institutions towards the use of business intelligence and analytics. With the use of a systematic review of 25 articles, the study identified nine dominant factors driving healthcare institutions to BDA adoption. Factors such as patient management, quality decision making, disease management, data management, and promoting healthcare efficiencies were among the highly ranked factors influencing BDA adoption.

For Better Healthcare Mining Health Data

2017 ◽  
pp. 135-158
Author(s):  
Güney Gürsel
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Healthcare Management ◽  
Support Vector ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Management Fraud ◽  
Use Of Data ◽  
Customer Relation Management ◽  
Vector Machines

Data mining has great contributions to the healthcare such as support for effective treatment, healthcare management, customer relation management, fraud and abuse detection and decision making. The common data mining methods used in healthcare are Artificial Neural Network, Decision trees, Genetic Algorithms, Nearest neighbor method, Logistic regression, Fuzzy logic, Fuzzy based Neural Networks, Bayesian Networks and Support Vector Machines. The most used task is classification. Because of the complexity and toughness of medical domain, data mining is not an easy task to accomplish. In addition, privacy and security of patient data is a big issue to deal with because of the sensitivity of healthcare data. There exist additional serious challenges. This chapter is a descriptive study aimed to provide an acquaintance to data mining and its usage and applications in healthcare domain. The use of Data mining in healthcare informatics and challenges will be examined.

scholarly journals Healthcare data management issues and the eCert solution

2011 ◽  
Author(s):  
Lisha Chen-Wilson ◽  
Xin Wang ◽  
Gary B Wills ◽  
David Argles ◽  
Charles Shoniregun
Keyword(s):  
Data Management ◽  
Healthcare Data ◽  
Management Issues

scholarly journals Big data and analytics in tourism and hospitality: opportunities and risks

2020 ◽  
Vol 6 (3) ◽  
pp. 257-262 ◽  
Author(s):  
Anca Yallop ◽  
Hugues Seraphin
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Governance ◽  
Privacy And Security ◽  
Content Type ◽  
Data Ethics ◽  
Technological Advances ◽  
International Tourist ◽  
Tourism And Hospitality Industry ◽  
Tourism And Hospitality

Purpose The purpose of this paper is to examine and provide insights into one of the most influential technologies impacting the tourism and hospitality industry over the next five years, i.e. big data and analytics. It reflects on both opportunities and risks that such technological advances create for both consumers and tourism organisations, highlighting the importance of data governance and processes for effective and ethical data management in both tourism and hospitality. Design/methodology/approach This paper is based on a review of academic and industry literature and access to trends data and information from a series of academic and industry databases and reports to examine how big data and analytics shape the future of the industry and the associated risks and opportunities. Findings This paper identifies and examines key opportunities and risks posed by the rising technological trend of big data and analytics in tourism and hospitality. While big data is generally regarded as beneficial to tourism and hospitality organisations, there are extensively held ethical, privacy and security concerns about it. Therefore, the paper is making the case for more research on data governance and data ethics in tourism and hospitality and posits that to successfully use data for competitive advantage, tourism and hospitality organisations need to solely expand compliance-based data governance frameworks to frameworks that include more effective privacy and ethics data solutions. Originality/value This paper provides useful insights into the use of big data and analytics for both researchers and practitioners and offers new perspectives on the debate on data governance and ethical data management in both tourism and hospitality. Because forecasts from the UNWTO indicate a significant increase in international tourist arrivals (1.8 billion tourist arrivals by 2030), the ways tourism and hospitality organisations manage customers’ data become important.

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