Classification and Clustering
Throughout this course, you have examined multiple analytic techniques, which can be used to enhance your decision making as a current or future healthcare administration leader. While no single analytic tool can be used to address a healthcare administration practice issue, a good understanding of which tools to use to inform decision making is invaluable.
As you complete this course, this week focuses on classification and clustering. What is classification and clustering, and how might it impact your role as a current or future healthcare administration leader?
1. This week, you explore classification and clustering techniques for health services organizations. You consider how these techniques might inform your practice as a current or future healthcare administration leader and apply these techniques to a healthcare administration practice problem.
· Evaluate classification and clustering techniques for health services organizations
· Apply clustering techniques to healthcare administration practice
Photo Credit: [erhui1979]/[Digital Vision Vectors]/Getty Images
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.
Albright, S. C., & Winston, W. L. (2017). Business analytics: Data analysis and decision making (6th ed.). Stamford, CT: Cengage Learning.
Chapter 17, “Data Mining”
Chapter 17.4, “Classification Methods”
Chapter 17.5, “Clustering”
Grande, T. [Todd Grande]. (2015, September 21). K-means cluster analysis in SPSS [Video file]. Retrieved from https://www.youtube.com/watch?v=YRue69W-dYU
Lee, C., Famoye, F., & Shelden, B. (2008c). (2015, September 21). SPSS training workshop: Logistic regression [Video file]. Retrieved from http://calcnet.mth.cmich.edu/org/spss/V16_materials/Video_Clips_v16/23logistic/23logistic.swf.
Classification and Clustering
Classifying and clustering are important methods that may assist healthcare administrators in identifying specific issues or challenges in healthcare delivery or general management of a health services organization. For example, how would you determine if a patient is likely to pay on time, pay late, or not pay a bill? Using classification and clustering techniques can help administrators describe characteristics of their patients.
1. For this Discussion, review the resources for this week. Then, reflect on how you might apply classification and clustering techniques for your health services organization or one with which you are familiar.
By Day 3
2. Post a brief description of one of the techniques on classification and clustering examined this week. Explain how the technique you described might apply to your health services organization or one with which you are familiar. Be specific, and provide examples.
By Day 5
Continue the Discussion and respond to your colleagues in one or more of the following ways:
· Ask a probing question, substantiated with additional background information, evidence, or research.
· Share an insight from having read your colleagues’ postings, synthesizing the information to provide new perspectives.
· Offer and support an alternative perspective, using readings from the classroom or from your own research in the Walden Library.
· Validate an idea with your own experience and additional research.
· Make a suggestion based on additional evidence drawn from readings or after synthesizing multiple postings.
· Expand on your colleagues’ postings by providing additional insights or contrasting perspectives based on readings and evidence.
What is “k-means clustering,” and how might it help healthcare administration leaders in their health services organization?
One of the more common methods of clustering is the use of k-means, where “k” is the number of clusters that are meant to describe the population of interest. For example, what if you were interested in segmenting patients based on satisfaction levels? K-means clustering could be used in the same way to help describe this given population.
This technique might assist leaders in determining what characteristics need to be considered in order to study crime and unemployment. In striving to address the problem, k-means clustering is an effective tool for leadership to consider.
1. For this Assignment, review the resources for this week, and examine the k-means clustering approach. Reflect on how you might apply this approach to an issue or challenge in a health services organization. Then, complete the problems for the Assignment.
For Chapter 17, problem 21, you will need to download the file P17_21.xlsx from the textbook companion website http://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781305947542. Under “Book Resources”, click on “Student Downloads” to view the downloadable files. Click “Problem Files” and download the zipped file 1305947541_538885.zip. Open the zipped file, and select folder “Problem Files” and then select folder “Chapter 17” to access the file P17_21.xlsx.
The Assignment: (3 pages)
Complete Problem 21 on page 938 of your course text.
Page 938 on Textbook
Using the data in the file P17_21.xlsx, the same data as in Problem 19, Use the method in Example 17.4 to find four clusters of cities. Note that there are two text variables, Crime_Trend and Unemployment_Threat. You can ignore them for this problem. Write a short report about the clusters you find. Does the clustering make sense? Can you provide descriptive, meaningful names for clusters?
By Day 7
Submit your answers and embedded analysis as a Microsoft Word management report.
Submission and Grading Information
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Week 11 Assignment Rubric
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Submit Your Assignment by Day 7
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