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Week 1 Data-Driven Decision Making in Health Administration

Week 1

Data-Driven Decision Making in Health Administration

 

As a current or future healthcare administration leader, how might you execute data-driven decision making? What types of data might be most useful to you as a healthcare administrator, and how might this data emerge from workflow processes?

 

Healthcare administration leaders face many challenges when managing a health services organization. Not surprisingly, copious amounts of data and reports come across the healthcare administrator’s desk. In an effort to ensure effective and efficient healthcare delivery, healthcare administrators must understand how to interpret, implement, and consider the systems’ perspective on how their health services organization is performing.

 

This week, you examine healthcare leaders and their roles in data-driven decision making. You also explore the challenges of using big data for decision making. You then evaluate distributions for a variable of interest, including how to analyze relationships among variables.

 

Learning Objectives

Students will:

· Analyze healthcare leader roles in data-driven decision making

· Analyze challenges and ethical concerns in use of big data for decision making

· Evaluate the distribution of a single variable

· Analyze relationships among variables

Photo Credit: [filo]/[Digital Vision Vectors]/Getty Images

 

Learning Resources

Note: To access this week’s required library resources, please click on the link to the Course Readings List, found in the Course Materials section of your Syllabus.

 

Required Readings

Albright, S. C., & Winston, W. L. (2017). Business analytics: Data analysis and decision making (6th ed.). Stamford, CT: Cengage Learning.

 

Chapter 1, “Introduction to Business Analytics”

Chapter 2, “Describing the Distribution of a Single Variable”

Chapter 3, “Finding Relationships Among Variables”

 

Kohane, I. S. (2015). Health care policy: Ten things we have to do to achieve precision medicine. Science, 349(6243), 37–38.

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