Purposes of Data Analytics in Health Care and SQL in Analytics

DQ: Data Analytics
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DQ: Data Analytics
The last decade saw a significant revolution in the adoption of data analytics in healthcare mainly due to the wide adoption of electronic health records (EHRs) and the emergence of machine learning and big data analytical technologies (Agrawal, 2018). Aided by artificial intelligence, data analytics can be used to develop predictive algorithms allowing physicians to make individualized prognostic or diagnostic risk predictions (Van Calster, Wynants, Timmerman, Steyerberg & Collins, 2019). Used appropriately, data analytics can improve care and patient safety and outcome. In particular, the University of Illinois at Chicago (UIC) observes that data analytics can be used to evaluate the effectiveness and performance of health care workers, reduce patient cost, and predict risks to chronic diseases using new software and technologies (UIC, n.d). Belle et al (2015) observe that big data analytics has provided technologies for collecting, managing, analyzing, and assimilating large junks of disparate, structured and unstructured data in healthcare organizations.
Structured Query Language (SQL) is crucial in creating, maintaining, and extracting relational databases (IBM Cloud Education, 2019). Large companies employ the standard database language, SQL in solving incredibly challenging issues. Major database man...