Importance of Big Data In Healthcare System A Survey Approach

2020 ◽  
pp. 47-56
Author(s):  
M. Pavithra ◽  
E. S. Shamila ◽  
G. Krishna Priya ◽  
G. VijiPriya ◽  
R. Ashwini
Keyword(s):  
Big Data ◽  
Healthcare Providers ◽  
Public Healthcare ◽  
Healthcare Industry ◽  
Healthcare Organizations ◽  
Public And Private ◽  
Healthcare Data ◽  
System A ◽  
Efficient Management ◽  
Public And Private Sector

<p>‘Big data’ is massive amounts of information that can work wonders. It has become a topic of special interest for the past two decades because of a great potential that is hidden in it. Various public and private sector industries generate, store, and analyze big data with an aim to improve the services they provide. In the healthcare industry, various sources for big data include hospital records, medical records of patients, and results of medical examinations, and devices that are a part of internet of things. Biomedical research also generates a significant portion of big data relevant to public healthcare. This data requires proper management and analysis in order to derive meaningful information. Otherwise, seeking solution by analyzing big data quickly becomes comparable to finding a needle in the haystack. There are various challenges associated with each step of handling big data which can only be surpassed by using high-end computing solutions for big data analysis. That is why, to provide relevant solutions for improving public health, healthcare providers are required to be fully equipped with appropriate infrastructure to systematically generate and analyze big data. An efficient management, analysis, and interpretation of big data can change the game by opening new avenues for modern healthcare. That is exactly why various industries, including the healthcare industry, are taking vigorous steps to convert this potential into better services and financial advantages. With a strong integration of biomedical and healthcare data, modern healthcare organizations can possibly revolutionize the medical therapies and personalized medicine.</p>

Big Data in Healthcare

2014 ◽  
pp. 35-58 ◽  
Author(s):  
Yiannis Koumpouros
Keyword(s):  
Big Data ◽  
Healthcare Professionals ◽  
Healthcare Providers ◽  
Open Data ◽  
Healthcare Systems ◽  
Tipping Point ◽  
Healthcare Industry ◽  
Healthcare Data ◽  
Big Data Technologies ◽  
Legal Barriers

The era of open data in healthcare is under way. The progress in technologies along with their adoption by the healthcare providers and the maturity of the citizens has brought the healthcare industry to the tipping point. An unprecedented amount of healthcare data is being generated today. This data comes from researchers, healthcare professionals and organizations, and patients. If we can harness this data, it can help us improve our understanding of disease and pinpoint new and improved therapies more efficiently than ever before. Big Data technologies are coming to market in a rapid way. The challenges, however, are still there due to fragmented systems and databases, semantic differences, legal barriers, and others. The hidden and unexploited knowledge is hindered by these barriers. The big data revolution promises a solution both to this situation, as well as to act as a catalyst to the viability of the healthcare systems. This is supported by the numerous efforts and explored in this chapter.

Big Data in Healthcare

2015 ◽  
pp. 23-46 ◽  
Author(s):  
Yiannis Koumpouros
Keyword(s):  
Big Data ◽  
Healthcare Professionals ◽  
Healthcare Providers ◽  
Open Data ◽  
Healthcare Systems ◽  
Tipping Point ◽  
Healthcare Industry ◽  
Healthcare Data ◽  
Big Data Technologies ◽  
Legal Barriers

The era of open data in healthcare is under way. The progress in technologies along with their adoption by the healthcare providers and the maturity of the citizens has brought the healthcare industry to the tipping point. An unprecedented amount of healthcare data is being generated today. This data comes from researchers, healthcare professionals and organizations, and patients. If we can harness this data, it can help us improve our understanding of disease and pinpoint new and improved therapies more efficiently than ever before. Big Data technologies are coming to market in a rapid way. The challenges, however, are still there due to fragmented systems and databases, semantic differences, legal barriers, and others. The hidden and unexploited knowledge is hindered by these barriers. The big data revolution promises a solution both to this situation, as well as to act as a catalyst to the viability of the healthcare systems. This is supported by the numerous efforts and explored in this chapter.

scholarly journals Big Data in Healthcare

2020 ◽  
Vol 8 (6) ◽  
pp. 2127-2131
Keyword(s):  
Big Data ◽  
Healthcare Providers ◽  
Information Storage ◽  
Healthcare Industry ◽  
Positive Health ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Data Volume ◽  
Effective Decision ◽  
Effective Decision Making

To improve quality in healthcare, it is very much important to store, manage and retrieve as well as use the data & information properly as it has great potential to help leaders in effective decision making. Managing data in healthcare is not easy task as it has many associated risk. As per new trends in healthcare industry, it is observed that the data volume generation in healthcare is growing rapidly. Big Data is substantial, less organized and mixed in nature. In addition to that, big data is considered one of the best tools to reduce the associated and functional cost of healthcare providers worldwide. While income should not be only a main or prime indicator, it is equally important for healthcare providers to gather the most valuable present tools and techniques and setup to force or inculcate big data effectively otherwise it can harm or risk organization to lose money in business as well as profit. Objective: This paper is focusing on the special factors of Big Data in healthcare. The main aim of this study is to find the roles of big data in healthcare and know how big data is helping in data transaction in healthcare industry. Methods: More than 30(n=30) published papers have been reviewed and suitable papers (n=18) have been included to make the conclusion. Information was condensed utilizing distinct measurable assessment & techniques. Findings: According to investigation of published articles, it has established that the role of big data is very much unique and important as well as it is helping healthcare providers to improve the patient safety and quality by providing smooth health information storage and exchange with high privacy and security. Conclusion: Big Data in healthcare is a new concept introduced in healthcare data analytics and management which is basically focusing in improving the drug and disease discovery, personal healthcare record, electronic health record, effective decision in diagnosis and treatment by healthcare practitioners and at most helps in getting desired and positive health outcome. The data is one of the crucial factors in healthcare and it is high time for healthcare providers to look into those matters in enormous way.

scholarly journals Benefits and challenges of Big Data in healthcare: an overview of the European initiatives

2019 ◽  
Vol 29 (Supplement_3) ◽  
pp. 23-27 ◽  
Author(s):  
Roberta Pastorino ◽  
Corrado De Vito ◽  
Giuseppe Migliara ◽  
Katrin Glocker ◽  
Ilona Binenbaum ◽  
...  
Keyword(s):  
Public Health ◽  
Big Data ◽  
Best Practice ◽  
Clinical Care ◽  
Healthcare Providers ◽  
Big Data Analytics ◽  
Well Being ◽  
Ageing Population ◽  
Healthcare Organizations ◽  
Healthcare Data

Abstract Healthcare systems around the world are facing incredible challenges due to the ageing population and the related disability, and the increasing use of technologies and citizen’s expectations. Improving health outcomes while containing costs acts as a stumbling block. In this context, Big Data can help healthcare providers meet these goals in unprecedented ways. The potential of Big Data in healthcare relies on the ability to detect patterns and to turn high volumes of data into actionable knowledge for precision medicine and decision makers. In several contexts, the use of Big Data in healthcare is already offering solutions for the improvement of patient care and the generation of value in healthcare organizations. This approach requires, however, that all the relevant stakeholders collaborate and adapt the design and performance of their systems. They must build the technological infrastructure to house and converge the massive volume of healthcare data, and to invest in the human capital to guide citizens into this new frontier of human health and well-being. The present work reports an overview of best practice initiatives in Europe related to Big Data analytics in public health and oncology sectors, aimed to generate new knowledge, improve clinical care and streamline public health surveillance.

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.

scholarly journals Public and private sectors collective response to combat COVID-19 in Malaysia

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Ching Siang Tan ◽  
Saim Lokman ◽  
Yao Rao ◽  
Szu Hua Kok ◽  
Long Chiau Ming
Keyword(s):  
Insurance Coverage ◽  
Healthcare Providers ◽  
Screening Tests ◽  
Public Healthcare ◽  
Private Hospitals ◽  
Public And Private ◽  
The Public ◽  
Private Healthcare ◽  
Care Services ◽  
Challenges And Opportunities

AbstractOver the last year, the dangerous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly around the world. Malaysia has not been excluded from this COVID-19 pandemic. The resurgence of COVID-19 cases has overwhelmed the public healthcare system and overloaded the healthcare resources. Ministry of Health (MOH) Malaysia has adopted an Emergency Ordinance (EO) to instruct private hospitals to receive both COVID-19 and non-COVID-19 patients to reduce the strain on public facilities. The treatment of COVID-19 patients at private hospitals could help to boost the bed and critical care occupancy. However, with the absence of insurance coverage because COVID-19 is categorised as pandemic-related diseases, there are some challenges and opportunities posed by the treatment fees management. Another major issue in the collaboration between public and private hospitals is the willingness of private medical consultants to participate in the management of COVID-19 patients, because medical consultants in private hospitals in Malaysia are not hospital employees, but what are termed “private contractors” who provide patient care services to the hospitals. Other collaborative measures with private healthcare providers, e.g. tele-conferencing by private medical clinics to monitor COVID-19 patients and the rollout of national vaccination programme. The public and private healthcare partnership must be enhanced, and continue to find effective ways to collaborate further to combat the pandemic. The MOH, private healthcare sectors and insurance providers need to have a synergistic COVID-19 treatment plans to ensure public as well as insurance policy holders have equal opportunities for COVID-19 screening tests, vaccinations and treatment.

Big Data Analytics in Healthcare

2021 ◽  
pp. 85-98
Author(s):  
Sam Goundar ◽  
Karpagam Masilamani ◽  
Akashdeep Bhardwaj ◽  
Chandramohan Dhasarathan
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Health Data ◽  
Massive Data ◽  
Healthcare Industry ◽  
Health Records ◽  
Healthcare Data ◽  
Data Formats ◽  
Analytical Tools ◽  
Different Sources

This chapter provides better understanding and use-cases of big data in healthcare. The healthcare industry generates lot of data every day, and without proper analytical tools, it is quite difficult to extract meaningful data. It is essential to understand big data tools since the traditional devices don't maintain this vast data, and big data solves the major issue in handling massive healthcare data. Health data from numerous health records are collected from various sources, and this massive data is put together to form the big data. Conventional database cannot be used in this purpose due to the diversity in data formats, so it is difficult to merge, and so it is quite impossible to process. With the use of big data this problem is solved, and it can process highly variable data from different sources.

Improving Healthcare with Data-Driven Track-and-Trace Systems

2015 ◽  
pp. 65-82 ◽  
Author(s):  
Eldar Sultanow ◽  
Alina M. Chircu
Keyword(s):  
Quality Assurance ◽  
Big Data ◽  
Value Added ◽  
High Volume ◽  
Data Capture ◽  
Data Driven ◽  
Healthcare Organizations ◽  
Efficient Management ◽  
Internal Operations ◽  
Pharmaceutical Supply Chains

This chapter illustrates the potential of data-driven track-and-trace technology for improving healthcare through efficient management of internal operations and better delivery of services to patients. Track-and-trace can help healthcare organizations meet government regulations, reduce cost, provide value-added services, and monitor and protect patients, equipment, and materials. Two real-world examples of commercially available track-and-trace systems based on RFID and sensors are discussed: a system for counterfeiting prevention and quality assurance in pharmaceutical supply chains and a monitoring system. The system-generated data (such as location, temperature, movement, etc.) about tracked entities (such as medication, patients, or staff) is “big data” (i.e. data with high volume, variety, velocity, and veracity). The chapter discusses the challenges related to data capture, storage, retrieval, and ultimately analysis in support of organizational objectives (such as lowering costs, increasing security, improving patient outcomes, etc.).

Role of Urban Big Data in Travel Behavior Research

2020 ◽  
pp. 036119812097502
Author(s):  
Chihuangji Wang ◽  
Daniel Baldwin Hess
Keyword(s):  
Big Data ◽  
Survey Data ◽  
Travel Behavior ◽  
Data Science ◽  
Location Based Service ◽  
Labor Costs ◽  
Public And Private ◽  
Public And Private Sector ◽  
Data Analytic

Understanding urban travel behavior (TB) is critical for advancing urban transportation planning practice and scholarship; however, traditional survey data is expensive (because of labor costs) and error-prone. With advances in data collection techniques and data analytic approaches, urban big data (UBD) is currently generated at an unprecedented scale in relation to volume, variety, and speed, producing new possibilities for applying UBD for TB research. A review of more than 50 scholarly articles confirms the remarkable and expanding role of UBD in TB research and its advantages over traditional survey data. Using this body of published work, a typology is developed of four key types of UBD—social media, GPS log, mobile phone/location-based service, and smart card—focusing on the features and applications of each type in the context of TB research. This paper discusses in significant detail the opportunities and challenges in the use of UBD from three perspectives: conceptual, methodological, and political. The paper concludes with recommendations for researchers to develop data science knowledge and programming skills for analysis of UBD, for public and private sector agencies to cooperate on the collection and sharing of UBD, and for legislators to enforce data security and confidentiality. UBD offers both researchers and practitioners opportunities to capture urban phenomena and deepen knowledge about the TB of individuals.

scholarly journals A balanced scorecard for assessing a strategic plan in a clinical laboratory

2019 ◽  
Vol 29 (2) ◽  
pp. 284-291
Author(s):  
Luisa Alvarez ◽  
Anna Soler ◽  
Leonor Guiñón ◽  
Aurea Mira
Keyword(s):  
Balanced Scorecard ◽  
Clinical Laboratory ◽  
Theoretical Perspective ◽  
Strategic Plan ◽  
Reference Laboratory ◽  
Healthcare Organizations ◽  
Public And Private ◽  
The Balanced Scorecard ◽  
Definition Of ◽  
Public And Private Sector

The Balanced Scorecard (BSC) is a tool for strategic management that is used in many companies and organizations worldwide, both in the public and private sector. With this purpose it has also been used in healthcare organizations and institutions but there are not many studies on the implementation of BSC methodology in the day-to-day clinical laboratory. This review shows the strategy for the development of a BSC, which includes theoretical perspective objectives, as well as some indicators and goals with which the monitoring and quantitative measurement of the achievements of a strategic plan in a clinical laboratory can be done. Moreover, the results of the indicators allow the prioritization of the initiatives to be implemented each year. The methodology for the development of the proposed BSC includes the following steps: definition of theoretical objectives of each of the perspectives most used in the management of a clinical laboratory (customers, financial, internal processes and learning) taking into account the vision and the organizational model of the laboratory; creation of a strategic map of perspective objectives; definition of the relevant indicators to follow up on the objectives in a quantitative manner and establishment of the goals. Whether or not the laboratory is a reference laboratory, in which specific and infrequent analysis and health population programs are performed, is another fact to take into account. In this review a BSC for a reference clinical laboratory of the Spanish public sector is shown.

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