Week 11 Assignment Data-Driven Decision Making for Health care Administration

Week 11 Assignment Data-Driven Decision Making for Health care Administration

Week 11 Assignment Data-Driven Decision Making for Health care Administration

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.

Learning Objectives

Students will:

· Evaluate classification and clustering techniques for health services organizations

· Apply clustering techniques to healthcare administration practice

Photo Credit: [erhui1979]/[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 17, “Data Mining”

Chapter 17.4, “Classification Methods”

Chapter 17.5, “Clustering”

Required Media

Grande, T. [Todd Grande]. (2015, September 21). K-means cluster analysis in SPSS [Video file]. Retrieved from /orders/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.

Discussion Part: 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.

Assignment Part: K-Means Clustering

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

To submit your completed Assignment for review and grading, do the following:

Please save your Assignment using the naming convention “WK11Assgn+last name+first initial.(extension)” as the name.

Click the Week 11 Assignment Rubric to review the Grading Criteria for the Assignment.

Click the Week 11 Assignment link. You will also be able to “View Rubric” for grading criteria from this area.

Next, from the Attach File area, click on the Browse My Computer button. Find the document you saved as “WK11Assgn+last name+first initial.(extension)” and click Open.

If applicable: From the Plagiarism Tools area, click the checkbox for I agree to submit my paper(s) to the Global Reference Database.

Click on the Submit button to complete your submission.

Grading Criteria

To access your rubric:

Week 11 Assignment Data-Driven Decision Making for Health care Administration

Check Your Assignment Draft for Authenticity

To check your Assignment draft for authenticity:

Submit your Week 11 Assignment draft and review the originality report.

Submit Your Assignment by Day 7

To submit your Assignment: Week 11 Assignment

Course: MAT210 – Data-Driven Decision Making Week 3 Assignment 1: The Foundation of Data-Driven Decisions

Due: Week 3 Points: 85

Skill(s) Being Assessed: Problem Solving – Data Analysis (Mathematical Reasoning)

What to Submit / Deliverables: Word Document uploaded to the Blackboard Assignment

What is the value of doing this assignment? You’ll practice your problem solving skill by identifying foundational statistical concepts and explaining how data-driven decision making can help inform real world problems. This will provide you with a solid foundation that will help you to not only be successful in this course, but to learn to make smarter, data-driven decisions in your personal and professional life.

Your goal for this assignment is to: Practice your problem solving skill by answering questions about statistical concepts and the benefits and uses of data-driven decision making.

Steps to Complete:

STEP 1: Answer the questions below in a Word document.

STEP 2: Save and submit your Word document in the Assignment link in the Week 3 Submit page in BlackBoard.

1. Explain the difference between descriptive and inferential statistical methods and give an example of how each could help you draw a conclusion in the real world.

2. You would like to determine whether eating before bed influences your sleep patterns. Describe the steps you would take to conduct a statistical study on this topic.

· What is your hypothesis on this issue?

· What type of data will you be looking for?

· What methods would you use to gather information?

· How would the results of the data influence decisions you might make about eating and sleeping?

3. A company that sells tea and coffee claims that drinking two cups of green tea daily has been shown to increase mood and well-being. This claim is based on surveys asking customers to rate their mood on a scale of 1–10 after days they drink/do not drink different types of tea. Based on this information, answer the following questions:

· How would we know if this data is valid and reliable?

· What questions would you ask to find out more about the quality of the data?

· Why is it important to gather and report valid and reliable data?

4. Identify two examples of real world problems that you have observed in your personal, academic, or professional life that could benefit from data driven solutions. Explain how you would use data/statistics and the steps you would take to analyze each problem. You may also choose topics below (or examples from the weekly content) to help support your response:

· Productivity at work.

· Financial decisions and budgeting.

· Health and nutrition.

· Political campaigns.

· Quality testing in products.

· Human resource policies.

· Algorithms for programming/coding.

· Accounting & financial policies.

· Crime reduction and trends.

· Environmental protection / Emergency preparedness.

5. How does analyzing data on these real world problems aid in problem solving and drawing conclusions? Be sure to note the value and benefits of data-driven decision making. Week 11 Assignment Data-Driven Decision Making for Health care Administration

ADDITIONAL INSTRUCTIONS FOR THE CLASS

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• Discussion Questions (DQ)

Initial responses to the DQ should address all components of the questions asked, including a minimum of one scholarly source, and be at least 250 words. Successful responses are substantive (i.e., add something new to the discussion, engage others in the discussion, well-developed idea) and include at least one scholarly source. One or two-sentence responses, simple statements of agreement or “good post,” and responses that are off-topic will not count as substantive. Substantive responses should be at least 150 words. I encourage you to incorporate the readings from the week (as applicable) into your responses.

• Weekly Participation

Your initial responses to the mandatory DQ do not count toward participation and are graded separately. In addition to the DQ responses, you must post at least one reply to peers (or me) on three separate days, for a total of three replies. Participation posts do not require a scholarly source/citation (unless you cite someone else’s work). Part of your weekly participation includes viewing the weekly announcement and attesting to watching it in the comments. These announcements are made to ensure you understand everything that is due during the week. Week 11 Assignment Data-Driven Decision Making for Health care Administration

• APA Format and Writing Quality

Familiarize yourself with the APA format and practice using it correctly. It is used for most writing assignments for your degree. Visit the Writing Center in the Student Success Center, under the Resources tab in Loud-cloud for APA paper templates, citation examples, tips, etc. Points will be deducted for poor use of APA format or absence of APA format (if required). Cite all sources of information! When in doubt, cite the source. Paraphrasing also requires a citation. I highly recommend using the APA Publication Manual, 6th edition.

• Use of Direct Quotes

I discourage over-utilization of direct quotes in DQs and assignments at the Master’s level and deduct points accordingly. As Masters’ level students, it is important that you be able to critically analyze and interpret information from journal articles and other resources. Simply restating someone else’s words does not demonstrate an understanding of the content or critical analysis of the content. It is best to paraphrase content and cite your source.

• LopesWrite Policy

For assignments that need to be submitted to Lopes Write, please be sure you have received your report and Similarity Index (SI) percentage BEFORE you do a “final submit” to me. Once you have received your report, please review it. This report will show you grammatical, punctuation, and spelling errors that can easily be fixed. Take the extra few minutes to review instead of getting counted off for these mistakes. Review your similarities. Did you forget to cite something? Did you not paraphrase well enough? Is your paper made up of someone else’s thoughts more than your own? Visit the Writing Center in the Student Success Center, under the Resources tab in Loud-cloud for tips on improving your paper and SI score.

• Late Policy

The university’s policy on late assignments is a 10% penalty PER DAY LATE. This also applies to late DQ replies. Please communicate with me if you anticipate having to submit an assignment late. I am happy to be flexible, with advance notice. We may be able to work out an extension based on extenuating circumstances. If you do not communicate with me before submitting an assignment late, the GCU late policy will be in effect. I do not accept assignments that are two or more weeks late unless we have worked out an extension. As per policy, no assignments are accepted after the last day of class. Any assignment submitted after midnight on the last day of class will not be accepted for grading. Week 11 Assignment Data-Driven Decision Making for Health care Administration

• Communication

Communication is so very important. There are multiple ways to communicate with me: Questions to Instructor Forum: This is a great place to ask course content or assignment questions. If you have a question, there is a good chance one of your peers does as well. This is a public forum for the class. Individual Forum: This is a private forum to ask me questions or send me messages. This will be checked at least once every 24 hours. Week 11 Assignment Data-Driven Decision Making for Health care Administration

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