NURS 8210 Week 5 Discussion: Data Science Applications and Processes
How might data compiled and analyzed in your healthcare organization or nursing practice help support efforts aimed at patient quality and safety? Why might it be important to consider the how’s and why’s of data collection, application, and implementation? How might these practices shape your nursing practice or even the future of nursing?For this Discussion, you will explore various topics related to data and consider the process and application of each. Reflect on the use of these applications, but also consider the implications of how these applications might shape the future of nursing and healthcare practice.
Resources
Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.
To Prepare
- Review the Learning Resources for this week related to the topics: Big Data, Data Science, Data Mining, Data Analytics, and Machine Learning.
- Consider the process and application of each topic.
- Reflect on how each topic relates to nursing practice.
By Day 3 of Week 5
Post a summary on how predictive analytics might be used to support healthcare. Note: These topics may overlap as you will find in the readings (e.g., some processes require both Data Mining and Analytics).
In your post include the following:
- Describe a practical application for predictive analytics in your nursing practice. What challenges and opportunities do you envision for the future of predictive analytics in healthcare?
SOLUTION NURS 8210 Week 5 Discussion: Data Science Applications and Processes
Main post
Predictive analytics has emerged as a powerful tool in healthcare, offering significant potential to improve patient outcomes, enhance operational efficiency, and reduce costs. It can transform raw data into actionable insights that can inform clinical decision-making and optimize healthcare delivery. One practical application of predictive analytics in nursing practice is in the management of chronic diseases. For example, predictive models can analyze patient data from electronic health records (EHRs), wearable devices, and other sources to identify patterns and risk factors associated with chronic conditions such as diabetes, heart disease, and chronic obstructive pulmonary disease (IDG TECHTalk, 2020). By predicting which patients are at higher risk of exacerbations or hospital readmissions, nurses can implement targeted interventions to prevent these adverse events. This proactive approach not only improves patient outcomes but also reduces the burden on healthcare systems by minimizing the need for emergency care and hospitalizations (IDG TECHTalk, 2020)….
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