About the course

Course Title and Number

HAP 730 Decision Analysis in Healthcare (3:3:0)

Course Description

Students integrate scientific evidence, patients' preferences, and experts' opinions to analyze managerial decisions and identify optimal alternatives. Included are applications to analysis of practice patterns, benchmarking, probabilistic risk assessment, cost analysis, conflict analysis and measurement of severity of illness. Decision analytical tools such as Multi-Attribute Value Models, Bayesian Probability Networks, and Decision Trees are covered.

Course Objectives

We live in a fast changing society where analysis is of paramount importance.  Our hope is to help students solve pressing problems in our organizations and society.  Good decisions based on a systematic consideration of all relevant factors and stakeholder opinions and values lead to good outcomes, both for those involved in the decision making process, and for the customers who are directly impacted by the consequences and effects of such decisions.  At the conclusion of the course participants should be able to:

• Structure decisions
• Measure preferences
• Quantify uncertainty
• Analyze conflict
• Generate new options
• Build mathematical models of decisions

Students are expected to use subjective opinions of experts to construct analytical models that could help managers make important organizational decisions.

Required Textbook

• Decision analysis for healthcare managers.  Alemi F, Gustafson D.  Health Administration Press, 2006. 

Recommended Textbook

• Additional recommended readings are posted under the section titled "More" in each of the lectures.

Teaching Strategy / Methods

Learning analytical tools is difficult, especially for students with limited background in mathematics. To facilitate the learning process, this course is taught based on the concepts of “learn one, do one, teach one.” First the concept is taught by the instructor. Then the student does a rapid analysis project. Next the student using a rubric provided by the instructor comments on the work of others and suggests improvements to other students. In this fashion, the student learns not only by listening to lectures and reading but by doing analytical work and teaching the ideas to others.

Advanced learners such as you learn the underlying concepts better if they teach these concepts.  In order to help you do so, you will review three or four students' bi-weekly projects and provide feelback to authors.   In making your comments, please be positive and follow the recommended structure.  Detailed instructions on how to obtain and review projects will be provided.

Course Requirements

A prior graduate course in statistics or a college level course in algebra is required. To benefit from this course you need to have the following:

• Familiarity with the US health care system. All examples are from the US health care system, though students can apply the ideas to health care organizations within their own country. HAP 678 course is highly recomended prior to taking this clourse.
• Computer, fast internet connection, microphone, and speakers. It is assumed that students will have access to the Internet using Internet Explorer or other browsers. Some of the recorded lectures work only with Internet Explorer browser. If you have problem opening them please contact instructor.
• Previous background in analysis of data and working knowledge of Microsoft Excel. If you do not have experience with use of Excel, web-based tools are available on use of Excel.
• Microsoft Power Point is needed for viewing some portions of reading and lectures. Bi-weekly projects' presentations are to be prepared in Powerpoint. Also, some assignments require recording a narration to accompany slides. To record a narration, students need to have a good quality microphone on their computer. Typically, a microphone that sits on top of the head and moves with the person is preferred to a stationary desk top microphone. An audio guide on how to narrate slides is available. 

Course Evaluation

If you are taking this course as part of a graduate level course, you will receive a grade. Your grade will depend on your participation, quality of your project work and your team work. Participation is key to making the experience of everyone a pleasant one. Internet courses are not only distance learning but also interactive learning. These courses benefit from student participation.  Class participation along with assignments are worth 20% of your grade.  Class participation means that in each section, you should either ask a question, complete the minute evaluation for the session and rate the session, or interact with the instructor in other way (i.e., email). 

If you ask a question, your question will be answered on the same web page within 48 hours and the question and the answer would be available for all students to read and benefit from.  You can also ask questions by sending email to the instructor.

Membership and Participation in a Professional Organization

Grading Policy

Selecting a project, gathering data, and conducting the analysis takes significant amount of time. Please plan well ahead of time and ready the chapter on rapid analysis within the book. It has a great deal of advice on how to complete your work on time. In general, delay in turning in assignments reduces the grade for the assignment by 80%.  

Bi-weekly projects

You are asked to complete 5 bi-weekly projects. In each project you are asked to interview an expert or a decision maker and construct a model of them. These interviews are time consuming and you should arrange for them as soon as possible. In completing these tasks please note the following:

• Select a topic area important to your expert or decision maker and not necessarily to you. Many experts and decision makers are more willing to work with you if it is something that they care about and helps them in their career. You can select topic areas from the place of your work (if you are employed). Your expert can be your friends or spouse or another student in class – but they must have expertise in the topic area. An important skill that we want you to learn is to see the role an analysis can play for an organization, so select your topic carefully to make sure that you can make a reasonable contribution to the organization.
• Your work needs to be reported using narrated slides. The presentation must be reviewed by another students in class and you are required to comment on the analysis conducted by at least one other student (See section on Learn One, Do One and Teach One.
• Look at the work of other students posted on the web but do not assume that these are necessarily correct or best work.  Sometimes you can see instructors comments in these work.
• Look at the rubric used to evaluate a project before you start on the project. 
• Instructions on how to deliver your projects will be provided by the instructor. Narrated slides are usually too large to send by email.

Topical Outline

The schedule below is approximate and may be changed to adapt to students' needs and requests, new material, closing of the university and for other reasons. In the beginning of each week, you will receive detailed instructions about the material to be covered, assignments, etc. Specific due dates for all assignments and projects will be provided with assignments.

Week Starting on Date	Reading	Bi-weekly project Due approx. 2 weeks after listed date.	Assignments Due within 5 days of listed date
Session 1	About the course (this page) Review Excel if you do not know how to use this tool
	Read Introduction to Decision Analysis pages 1 through 20.  Read Rapid Policy Analysis pages 319 to 329.		Complete "What do you know?" section. Rate this lecture. Ask questions Complete "What do you know?" section. Rate this lecture. Ask questions
Session 2	Read Modeling Preferences pages 21 through 64	Work on project on modeling experts' values.  Here is a recent example of a student's work.	Complete "What do you know?" section. Rate this lecture. Ask questions
Session 3	Read Measuring Uncertainty pages 67 through 90.		Complete "What do you know?" section in Measuring Uncertainty. Rate this lecture. Ask questions
Session 4	Teach One:  Discussion of Student Project for Modeling Preferences	Comment on work of other students using the rubric provided.
Session 5	Read Modeling Uncertainty pages 91 through 116.	Work on biweekly project on modeling experts' uncertainty	Complete "What do you know?" section in "Modeling Uncertainty." Rate this lecture. Ask questions
Session 6	Teach One:  Discussion of Student Project for Modeling Uncertainty	Comment on work of other students using the rubric provided.
Session 7	Read Decision Trees pages 117 through 148.	Work on biweekly project on Decision Trees	Complete "What do you know?" section in "Decision Trees." Rate this lecture.  Ask questions
Spring Break
Session 8	Teach One:  Discussion of Student Project for Decision Trees	Comment on work of other students using the rubric provided.
Session 9	Read Root Cause Analysis pages 169 through 186	Work on biweekly project on Root Cause Analysis	Answer What Do You Know questions Rate this lecture.  Ask questions.
Session 10	Read Cost Effectiveness of Clinics pages 187 through 214.		Complete your analysis for the "What do you know?" section on cost effectiveness.  (Optional) Rate this lecture.  Ask questions.
Session 11	Teach One:  Discussion of Student Project for Root Cause Analysis	Comment on work of other students using the rubric provided.
Session 12	Read Benchmarking Clinicians pages 299 through 319	Work on biweekly project on benchmarking clinicians	Complete "What do you know?" section on benchmarking clinician's performance.  Rate this lecture.  Ask questions.
Session 13
Session 14	Teach One:  Discussion of Student Project for Benchmarking	Comment on work of other students using the rubric provided.
Session 15	Exit Interview Take final exam if invited by the instructor to do so	Catch up with your work. Meet with the instructor and review your progress in projects to date. Show that you have corrected assignments based on input from peers and the instructor.   Present a portfolio of all of your work.  Jeopardy Game for Decision Analysis

Your Email Address

If you are enrolled in this course, you will receive communications from the course faculty.  Please send a confirmatory email to your instructor. You may loose up to 5% of grade if not checking your email daily. Please use your GMU email address. Also, check SPAM folder as some class related emails may be incorrectly classified as spam.

Honor Code

: “To promote a stronger sense of mutual responsibility, respect, trust, and fairness among all members of the George Mason University community and with the desire for greater academic and personal achievement, we, the student members of the university community, have set forth this honor code: Student members of the George Mason University community pledge not to cheat, plagiarize, steal, or lie in matters related to academic work” (George Mason University Catalog, 2006-2007, p. 31).

Disability Accommodations

If you are a student with a disability and you need academic accommodations, please see Debbie Wyne and contact the Disability Resource Center at 703 993-2474.  All academic accommodations must be arranged through the Disability Resource Center.

George Mason University is committed to complying with the Rehabilitation Act of 1973 and the Americans with Disabilities Act of 1990 by providing reasonable accommodations for disabled applicants for admission, students, applicants for employment, employees, and visitors. Applicants for admission and students requiring specific accommodations for a disability should contact the Disability Resource Center at 703-993-2474, or the Equity Office at 703-993-8730. Applicants for employment and employees should contact Human Resources at 703-993-2600 or the Equity Office. Students and employees are responsible for providing appropriate documentation and requesting reasonable accommodation in a timely manner (George Mason University Catalog, 2006-2007, p. 55).

More information

See more information about course instructors.  This page is part of the course on Decision Analysis, the section on "About the course."  It was first created in 2004.  It was last edited on 01/20/2011 © Copyright protected.