NURSING > EXAM > NR 599 / NR599 Nursing Informatics for Advanced Practice Final Exam Review | Highly Guide | Latest 2 (All)

NR 599 / NR599 Nursing Informatics for Advanced Practice Final Exam Review | Highly Guide | Latest 2020 / 2021 | Chamberlain College

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NR 599 / NR599 Nursing Informatics for Advanced Practice Final Exam Review | Highly Guide | Latest 2020 / 2021 | Chamberlain College 1. Data mining - a method in computer science that is used to discover patterns and trends within large data sets. 2. Anomaly detection - a data mining technique - pattern detection of data errors or unusual deviations from the norm - ex: detection of disease outbreaks. 3. Association rule learning - a data mining technique that identifies association between variables to predict outcomes. - Identifies relationships in variables associated with an outcome of interest; can be preliminary work to predictive modeling 4. Cluster Analysis - Discovering groups or structures in the data - a data mining technique that discovers groups or structures in the data, such as clusters of patients who tend to go to one hospital in a given ZIP code or county. 5. Classification a data mining technique - Generalizing known structure to new data or information - Classifying patient safety errors related to HIT can support taxonomy development 6. Regression Modeling a data mining technique - Modeling data for prediction or explaining some phenomenon with the least amount of error as possible - often used for predictive analytics such as predicting factors that are associated with mortality or 30-day readmission. 7. Summarization - a data mining technique - business intelligence (BI) tools that aggregate cubic views of data or report certain outcomes. - tools allow an end user to drag and drop and quickly identify patterns and trends in the data based on the summarization of tables. 8. Big Data - many different types - including indexes; images and videos; social networks such as Twitter and Facebook; surveillance data; company records including medical records; and data heavy fields such as astronomy; genetics and economics. - the masses of unstructured textually rich data within the EHR are among the prime examples. 9. Uses of Big data in the healthcare industry - Big data explorations and mining techniques to improve decision making. - 360-degree view of the customer, extending the ability to view the healthcare consumer by internal and external data sources. - Security and intelligence to lower risk, detect fraud and monitor cybersecurity. - Operational and clinical analysis to improve healthcare outcomes, quality and cost. - Ability to augment data warehouse capabilities to integrate and use big data to increase efficiencies and improve outcomes. 10. Foundation of knowledge Model and Telehealth

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