Big Data Analysis in Healthcare

Editorial This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. are being developed and introduced all over the world. Seton Healthcare, working with predictive analytics, sought to address the need to reduce the occurrence of high-cost conges-tive heart failure readmissions [2]. The European Commission has developed 'MediSys' , an information scanning tool, to reinforce the network for the surveillance of communica-ble diseases and the early detection of bioterrorist activities. Breaking news can be detected by algorithm of MediSys using over 20,000 Internet articles per day produced by the Eu-rope Media Monitor and can be sent to key persons by email and SMS. Many successful big data applications are being introduced [3]. Many healthcare institutions and countries have done many trials and made many successful cases of big data analysis to solve traditional problems in healthcare, such as reducing readmissions, increasing the effectiveness and efficiency of healthcare, improving the quality of care, and forecasting demands for healthcare services. There are several factors that must be taken into consideration in using big data analytics for healthcare, although it has the potential to provide insights to enhance our understand of changing phenomena. Big data must be prepared systematically and must be of good quality. Regarding big data analytics, we should remember the popular saying 'gar-bage in, garbage out'. Proper big data analytics using highly qualified big data would produce useful and valuable results for understanding contexts and forecasting the future of healthcare. Next, we have not to think and use the results of big data analytics as being one and powerful information to understand and forecast something interested. The interpretation of results must be done cautiously by experts who come from diverse fields of expertise and have a wide variety of perspectives. We have to use big data analytics cautiously even after strategic consideration, though it has much potential to improve our understanding of phenomena and strengthen our ability to accurately forecast the future. The rise of big data is promising new opportunities to understand and forecast contexts or problems in the healthcare area. Big data related to healthcare has explosively expanded with the development of the Internet. It is not only accumulated in purely healthcare service settings but also in cyber-digital space, such as traditional …