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Health Care Student’s Name Institutional Affiliation Health Care Real-time Data Analytics Abstract Big data is a good strategy for extracting, keeping and performing an analysis of data to reveal information unknown in the past. This is the age of big data, and the healthcare sector must keep stride with the growth in the data produced by the industry. Data analytics in the health sector refers to the need to take advantage of the expanding patient and health sector systems’ data accessibility to produce healthcare novelty (Wang & Hajli, 2017). By the development of new ways of interpreting data, it can be possible to obtain new insights. In healthcare, such eventualities could have a more substantial implication rather than just mining the records of patients, diagnostic reports or medical images. Useful ideas can involve diagnoses, decision-support systems and endless analyses of the data channels generated by patients in a health institution, at home, or even perhaps through mobile services (Lv et al., 2017). Today, a large part of healthcare analytics occurs through regular refreshing of data using distributed databases that generate pre-developed reporting documents. There is a gaping hole concerning events such as lab test reports, which can be available after a certain number of days. The future of data analytics in healthcare requires spot moments and not reports in pre-processed formats. Aims/Goals The health sector is demanding a lot from the efforts of data analytics as it requires instant insights that can be instrumental in driving critical decisions. In response, health sector players are keen to adopt new technologies, such as the
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