Advantages Of Big Data Analytics

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Abstract--This paper provides a brief idea about additional value from health information used in health care centers using a new information management approach called as big data analytics .Including big data analytics in health sector provides stakeholders, the new insights that have the capacity to advance personalized care improve patient outcomes and avoid unnecessary costs. This paper describes big data analytics and its characteristics, advantages and challenges in health care. Keywords--Big data, Analytics, Hadoop, Healthcare, Framework, Methodology, challenges; future applications I. INTRODUCTION Big data is “a term that includes large volumes of complex, high velocity, and variable data that wants advanced techniques and technologies…show more content…
New types include content, metrics, mobile, physical data points, process, location or geo-spatial, hardware data points, machine data, radio frequency identification (RFID), search, and web. It also includes unstructured data. Veracity: It is defined as the accuracy of data. Incorrect data can cause a lot of problems for organizations. Hence, organizations need to ensure that the data is correct as well as the analyses performed on the data are correct. In automated decision-making, where no human is involved we need to be sure that both the data and the analyses are correct. II. BIG DATA IN HEALTH CARE Big data in healthcare can come from internal (e.g., electronic health records, clinical decision support systems, CPOE, etc.) and external sources (government sources, pharmacies, insurance companies etc.), often in multiple formats (flat files, relational tables, etc.) and residing at many locations (geographic as well as in different healthcare providers’ sites) in numerous legacy and other applications (transaction processing.). Resources and data types…show more content…
Machine-to-machine data: Readings from meters, sensors, and other devices. 3. Big transaction data: Health care claims and other billing records increasingly available in semi-structured and unstructured formats. 4. Biometric data: Fingerprints, genetics, retinal scans, and similar to this types of data. This also includes X-rays and other medical images. 5. Human-generated data: Unstructured and semi-structured data such as electronic medical records (EMRs), physician’s notes, email, and paper documents. In recent years, BDA has become increasingly apparent that multiple streams of data like these can be leveraged with powerful new collection, aggregation, and analytics technologies and techniques to improve the delivery of health care at the level of individual patients as well as at the levels of disease- and condition-specific populations. A conceptual architecture of big data analytics. III. Platforms & tools for big data analytics in

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