Usage of Data Analytics and Big Data Analytics in Healthcare

Usage of Data Analytics and Big Data Analytics in Healthcare
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Introduction
Data analytics refers to quantitative and qualitative use of varied techniques in order to
enhance productivity and business profitability. This method enables one to analyze and organize
different data sets, so that it is easily accessible when required (Sivarajah, Kamal, Irani &
Weerakkody, 2017). This method plays a very crucial role in the identification, extraction,
categorization, and analysis of different data to ease the process of management in any
organization.
Big Data can be used to refer to enormous amounts of structured and unstructured data
that is very difficult to process using any conventional or traditional software databases. Thus,
big data is a very complicated process which is used to evaluate and examine varied data sets
(Raghupathi & Raghupathi, 2014).
Big data has been used in the digitization of varied healthcare information, so as to
successfully plan and execute strategic business decisions. This makes it easier to collect
different information, identify them, store as well as analyze them thoroughly, ensuring the
occurrence of no errors in the process. Thus in healthcare, big data analytics includes the
integration and analysis of large amounts of heterogeneous information and data like several
‘omics’ (metabolomics, genomics, proteomics, pharmacogenomics, transcriptomics), electronic
records regarding healthcare, and several biomedical data (Stokes, Rogers, Hertig & Weber,
2016). In healthcare facilities, the staffs, nurses and especially charge-master and charge-master
coordinators are seen to use this method abundantly.

Discussion
Schneeweiss, S. (2018). Automated data-adaptive analytics for electronic healthcare data to
study causal treatment effects. Clinical Epidemiology, Volume 10, 771-788. doi:
10.2147/clep.s166545
This article is an example of a retrospective analysis of the implementation of adaptive-
data analytical techniques in cases of causal diseases, in healthcare sectors. The adaptive data
analysis term refers to the varied developments of the data analytical system and its overall
evolution as well as their practical applications in cases of adaptive approaches. The article also
talks about the usage of high dimensional propensity score (HDPS) and the varied methods used
to analyze different databases related to healthcare. The HDPS is mainly known for its high
quality performance, increased efficiency and for its independent coding systems and data
sources. Thus, these methods can be really beneficial for the decision makers and other
authoritative figures of healthcare sectors, to manage, maintain and evaluate the different
medical products, procedures and other information efficiently and handle them in a safer way.
The article discusses the use of data-adaptive approaches that help in improving the control on
reviewing the effects and properties of causal treatments. The article concludes that the
implementation of semi-automated and enhanced difficult adjustments made in the health care
database analysis has been deemed quite successful across an extensive range of settings.