HCI 660 What is the purpose of data analytics in health care?
Data Analytics refers to the process of collecting and analyzing raw datasets to get trends, derive insights, identify potential for improvement, and support decision making at the business and patient levels. The major purpose of data analysis is to discover meaning in data and use the knowledge obtained from the analysis to support informed decisions (Sousa et al., 2019). The knowledge derived from data analysis can be used in healthcare to foster improvements in patient care, reduce health care costs, facilitate accurate diagnosis, predict outbreak of diseases, evade preventable diseases, foster personalized treatment, and simplify internal operations. Taken together, data analysis is essential in supporting informed decision making process to improve the overall quality of care and life of patients and health care as business.
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Structured Query Language (SQL) refers to a programming language developed to manage data in a relational database (Epstein & Dexter, 2017). In analytics, SQL is used to access, read, maneuver, and appraise the data in the database and generate essential insights to support an informed process of decision making (Epstein & Dexter, 2017). Also, SQL is used to retrieve subsets of data from database to help in processing transactions and application of analytics, and to adjust index structures and tables in the database. SQL can also be used to add, renew, and erase rows of data.
Moreover, Tomar et al., (2019) claims that SQL is popularly used as a base infrastructure to create its simple dashboard alongside reporting tools. Since it is generally easy to commune intricate instructions to database and maneuver data instantly, SQL creates sensitive dashboards with ability to display information in different ways. In addition, SQL is easy to access, clearly organized, and effective in interaction, which makes it an exceptional tool to create data warehouses.
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Epstein, R. H., & Dexter, F. (2017). Development and validation of a structured query language implementation of the Elixhauser comorbidity index. Journal of the American Medical Informatics Association, 24(4), 845-850. https://doi.org/10.1093/jamia/ocw181
Sousa, M. J., Pesqueira, A. M., Lemos, C., Sousa, M., & Rocha, Á. (2019). Decision-making based on big data analytics for people management in healthcare organizations. Journal of medical systems, 43(9), 1-10. https://doi.org/10.1007/s10916-019-1419-x
Tomar, D., Bhati, J. P., Tomar, P., & Kaur, G. (2019). Migration of healthcare relational database to NoSQL cloud database for healthcare analytics and management. In Healthcare Data Analytics and Management (pp. 59-87). Academic Press. https://doi.org/10.1016/B978-0-12-815368-0.00002-6