27 Sep Data Science: Applications for Practice
Order Instructions
Week 5: Data Science: Applications for Practice
Data drives innovations in healthcare. Whether through exploring patient care practices, introducing new care techniques, or providing new lifesaving medicine, data drives the ability to offer these solutions in practice. Data is not compiled, applied, or analyzed using only one approach—therefore, it is important to explore the various strategies used and consider implications, barriers, and impact of data science on nursing practice.
This week, you will analyze the use of data science applications and processes for healthcare organizations and nursing practice. You will also consider and examine approaches for implementation of data science.
This week also serves as the first week in which you will submit a portion of your small nursing informatics project. You will submit Part 1 of your project, and you will begin working on Part 2. Remember, while using project management skills and techniques, the goal of this project is to demonstrate your understanding of nursing informatics through the implementation, or potential implementation, of your proposed small nursing informatics project.
Learning Objectives
Students will:
Analyze data science applications and processes for healthcare organizations and nursing practice
Evaluate approaches for implementation of data science applications and processes for nursing practice
Analyze use of predictive analytics for clinical practice
Develop a small nursing informatics project
Identify a small nursing informatics project
Develop a project scope and charter for a nursing informatics project
Perform a SWOT analysis related to a small nursing informatics project
Create a GAP analysis for a nursing informatics project
Analyze the work breakdown structure related to a small nursing informatics project
Construct a project timeline for a small nursing informatics project
Identify responsibility roles in small nursing informatics projects
Develop a communication plan for small nursing informatics projects
Develop a change management plan for small nursing informatics projects
Develop a risk management plan for small nursing informatics projects
Learning Resources
Required Media (click to expand/reduce)
Required Readings (click to expand/reduce)
Data Analytics (click to expand/reduce)
Optional Resources (click to expand/reduce)
Discussion: Data Science Applications and Processes
Data mining has been cited as one of the advantages scientists used in the creation of the COVID-19 vaccinations. Data mining was used in the trials of these vaccinations to signal safety concerns and trends more quickly in the trial groups. As a result, these vaccinations were quickly available to support the effort in combatting the COVID-19 pandemic.
Photo Credit: Colin Anderson / Blend Images / Getty Images
Thinking beyond the scope of a major vaccination effort and pandemic, 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.
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 succinct summary on how each topic might apply to nursing practice. Be specific. 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:
Explain how you see the data concepts presented aligning with your current practice. What do you need to know to apply these concepts?
Do you currently use one of these processes in your healthcare organization or nursing practice? If so, how and in what context?
If you do not currently use one of these processes in your healthcare organization or nursing practice, what would it take to implement it? What do you see as a benefit for use?
How is predictive analytics applied to clinical practice? Be specific and provide examples.
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