Data mining enables health systems to systemically use data and analytics to identify inefficiencies and best practices that improve care and reduce costs. There needs to be an analysis of large amounts of data to discover patterns and use them to predict future events. The most effective strategy in data mining has a three step approach: Analytics incudes the expertise to gather data, make sense and standardize measurements and aggregating the data into a data warehouse. Content system systematically applies evidence-based best practice to care delivery and the deployment system which drives change through new organizational structures. Implementing all three of these enables a healthcare organization to apply data mining into everyday practice (Crockett, Johnson & Eliason, 2014).
In the United States Chronic Kidney
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The median survival rate is three years and the cost of care is high. Although it cannot be determine the exact reason for the increase, we know there are a number of risk factors that may attribute to the growth. Risk factors include changes in the demographics of the population, differences in disease burden among individuals/groups, prevalence of co-morbidities, medications, medical interventions and the under-recognition of earlier stages of the disease. Despite resources and improvements in technology, the patients continue to experience a reduced quality of life and significant morbidity and mortality. How can we predict what will happen or determine what we can do to improve the prevalence of this chronic disease (Obrador & Brian, 2015).
A good data mining project for my hemodialysis unit would be to collect data on patients who experience intradialytic