About the course
Course Title and Number
HAP 730 Decision Analysis in Healthcare (3:3:0)
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.
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:
Students are expected to use subjective opinions of experts to construct analytical models that could help managers make important organizational decisions.
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 need to obtain a bi-weekly project from one of the students in the class and using the rubric provided comment on the assignment . Your comments should be emailed within 48 hours of receiving the assignment. If you cannot keep this time frame you need to alert the student who has provided you with the assignment as well as the instructor. In making your comments, please be positive and follow the recommended structure.
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:
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 is worth 10% of
your grade. Class participation means that in each section, you should
either ask a question (see
how) or complete the minute evaluation for the session
and rate the session.
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. Class participation also means that you would become a member of a professional organization such as HIMSS, ACHE, IHI or other local or national organizations focused on your career.
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%.
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:
Enter Your Email Address
If you are enrolled in this course, you may receive communications from the course faculty. In order to make sure that you receive the information on time, please provide us with your email address.
: “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).
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).
See more information about course instructors. For more information contact Farrokh Alemi, Ph.D. 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 10/21/2011 © Copyright protected.