Compare and contrast data analytics, specifically the use of explanatory or predictive analysis
Data analytics is applied to discover trends and patterns in health care data, and it predicts future events based on the discoveries. Compare and contrast data analytics, specifically the use of explanatory or predictive analysis, that you might employ for analyzing health data.
Data Analytics Comparison and Contrast
Analytics of data is critical in healthcare processes. The process entails manipulating data to identify trends and patterns in healthcare processes. Trends can reveal or inform decision-making processes aimed at improving efficiency and patient outcomes. During the research process, methodologies that are relevant and can guide the investigation processes are always identified. The propositions of healthcare studies are typically reflected in a literature review, research questions, and the researchers’ skills and proficiency. Theoretical orientation is always used to enhance the analysis as well as the reexamination of different interpretations in explanatory studies that include hypothesis testing.
Explanatory data analysis frequently involves critical approaches to conducting preliminary investigations on a given data set in order to discover trends or patterns and to identify anomalies in testing hypotheses (Cox, 2017). Explanatory data analysis also aids in the verification of assumptions through the use of graphical representation and summary statistics.
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Predictive data analysis is the process of using data to create models that help predict future trends in the healthcare system (Acharjya & Anitha, 2017). In predictive data analysis, various software is always used to determine accurate outcomes. There is also the use of various statistical analyses, such as regression analysis, to develop effective models for predicting future operational processes. Predictive models frequently forecast the future and Help healthcare professionals and policymakers in understanding the evolution of patient care. Predictive data analytics frequently adds significant value to processes carried out in intensive care units (Daily & Peterson, 2017). Explanatory and predictive data analysis are both important in healthcare research processes because they help to provide the information needed to improve healthcare processes.
