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- Big Data in Health Care: Applications and Challenges
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- Big data in healthcare: management, analysis and future prospects
Big Data in Health Care: Applications and Challenges
Big data has changed the way we manage, analyze, and leverage data across industries. One of the most notable areas where data analytics is making big changes is healthcare. In fact, healthcare analytics has the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases, and improve the quality of life in general. Health professionals, just like business entrepreneurs, are capable of collecting massive amounts of data and look for the best strategies to use these numbers. What are the obstacles to its adoption? We will then look at 18 big data examples in healthcare that already exist and that medical-based institutions can benefit from. Big data in healthcare is a term used to describe massive volumes of information created by the adoption of digital technologies that collect patients' records and help in managing hospital performance, otherwise too large and complex for traditional technologies.
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Healthcare systems are being digitally transformed by technological enhancements in medical information systems, electronic medical records, wearable and smart devices, and handheld devices. This increase in medical big data, alongside the development of computational techniques in the field of healthcare, has enabled researchers and practitioners to extract and visualize medical big data in a new spectrum. Scientific programming will play a key role in providing solutions to existing and future problems involved in the management of large scale data in healthcare, such as by assisting in the processing of huge data volumes, complex system modelling, and sourcing derivations from healthcare data and simulations. Programming tools, including Apache Hadoop, Informatica PowerCenter, and Tableau, analyze data extremely efficiently and enable the visualization of meaningful insights extracted from big data. Visualization will be a key tool in creating images, diagrams, or animations by which to communicate healthcare messages and enhance understanding.
Received 17th September, ; Received in revised form 28th October, ; Accepted 22nd November, ; Published online 26th December, This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Big Data analytics in healthcare is emerging as a promising field and is most trusted technology today that is used to determine significant insights of huge data sets with minimized costs and improved results. Big data is defined as very large volume of high velocity, complex and variable data that needs innovative techniques to enable the data or information capture, storing, dissemination, management and analysis. The data volume of healthcare systems in enormous and is gaining fast momentum with respect to almost every area of research as well as industry. Nonetheless it provide appropriate storage and access platform for the healthcare systems also.
PDF | On Jan 1, , Ashwin Belle and others published Big Data Analytics in Healthcare | Find, read and cite all the research you need on ResearchGate.
Big data in healthcare: management, analysis and future prospects
This book includes state-of-the-art discussions on various issues and aspects of the implementation, testing, validation, and application of big data in the context of healthcare. The concept of big data is revolutionary, both from a technological and societal well-being standpoint. It also features a multifaceted and state-of-the-art literature review on healthcare data, its modalities, complexities, and methodologies, along with mathematical formulations. Skip to main content Skip to table of contents. Advertisement Hide.
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Raghupathi and V. Raghupathi , V.
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 concept of Big Data is popular in a variety of domains. The purpose of this review was to summarize the features, applications, analysis approaches, and challenges of Big Data in health care. Big Data in health care has its own features, such as heterogeneity, incompleteness, timeliness and longevity, privacy, and ownership. These features bring a series of challenges for data storage, mining, and sharing to promote health-related research. To deal with these challenges, analysis approaches focusing on Big Data in health care need to be developed and laws and regulations for making use of Big Data in health care need to be enacted. From a patient perspective, application of Big Data analysis could bring about improved treatment and lower costs. In addition to patients, government, hospitals, and research institutions could also benefit from the Big Data in health care.
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