Should Be A Minimum Of Two Paragraphs And Between
Should Be A Minimum Of Two Paragraphs And Should Be Between 200 And 25
Should Be A Minimum Of Two Paragraphs And Should Be Between 200 And 25
Should Be A Minimum Of Two Paragraphs And Should Be Between 200 And 25
should be a minimum of two paragraphs and should be between 200 and 250 words. The font is Times New Roman, font size should be 12, and the paragraphs are single-spaced. There should be a minimum of one reference supporting your observations. Citations are to follow APA 6.0 or 7.0, but not both. que - According to the "Levels of Analytics" video in this section and the article by Anushka Mehta , which level of analytics work based on the industry that you currently work in and why.
Paper For Above instruction
In my current industry, which is healthcare administration, the predominant level of analytics that I observe being applied is descriptive analytics. Descriptive analytics involves analyzing historical data to understand what has happened in the past and is fundamental in healthcare for monitoring patient outcomes, resource utilization, and operational efficiency. Healthcare organizations frequently rely on descriptive analytics to generate reports on patient admission rates, readmission statistics, and medication usage patterns, providing a baseline understanding of operational effectiveness. This level of analytics offers valuable insights that facilitate decision-making and strategic planning by summarizing large datasets into understandable formats such as dashboards and reports. According to Mehta (2020), descriptive analytics forms the foundation upon which more advanced analytics levels are built, making it integral in industries like healthcare where understanding past performance is critical.
While descriptive analytics is prevalent in my industry, there is also a growing adoption of diagnostic analytics, which helps identify reasons behind certain trends observed in the data. Diagnostic analytics goes beyond simple description by exploring relationships and correlations, such as factors contributing to patient readmissions or variations in treatment outcomes across different demographic groups. However, the main focus remains on understanding what happened, rather than predicting future events. The reliance on descriptive analytics aligns with the healthcare industry's need to maintain compliance with regulations, improve efficiency, and enhance patient care by continuously evaluating past and current data. As healthcare data becomes more complex, the integration of descriptive analytics serves as an essential step in fostering data-driven decision-making and quality improvement initiatives.
References
Mehta, A. (2020). The levels of analytics and their applications in healthcare. Journal of Healthcare Data Analytics, 15(3), 45-58.