HAP 752 Advanced Health Information Systems Course
About the instructor: This course is taught by Dr. Janusz Wojtusiak whose background in computer and computational sciences combined with work on hard healthcare problems lead to his engagement in the field of health informatics. Currently he is an assistant professor in the GMU Department of Health Administration and Policy. He is the Chief of GMU’s Health Informatics program, and also serves as the Director of Machine Learning and Inference Laboratory and the Director of Center for Discovery Science and Health Informatics. He teaches HAP 730 (Healthcare Decision Analysis), HAP 752 (Advanced Health Information Systems) and HAP 780 (Data Mining in Health Care). Previously he also taught HAP 709 (Healthcare Databases).
Dr. Wojtusiak conducts novel research in the areas of artificial intelligence, machine learning and intelligent systems with special focus on solving complex healthcare problems. To large extent his research areas are currently referred to as BIG DATA. Dr. Wojtusiak authored/co-authored about 80 publications or presentations. To see the full bio, please see his website.
If the above video does not work, download this file: HAP 752 - 0 - Overview.mp4
About this course: HAP 752 (Advanced Health Information Systems) provides in-depth analyses of heath information systems including Electronic Health Records, Personal Health Records, and Decision Support Systems. Analyzes architectural trends, workflow redesign, and implementation strategies. Describes new trends in computing technologies and infrastructure in health applications. Laboratory time provides learning experience and practical skills in various allied situations.
Objectives: Upon completion of the course, students will be able to:
Additional reading in journals and magazines
Laptop Devices: This is a technology course and we will use electronic devices. Students need to be equipped with Laptops or desktop computers in order to access course content, work on practical exercises, listen to lectures, and complete assignments. The computers need to be running Windows XP or later, have MS office 2007 or later, and be capable of running virtual machines. The minimum configuration is 2GB or RAM or more 2-core processor, and at least 50GB of free space. It is recommended to have at least 4GM RAM. Please contact the instructor for details. Fast internet connection and ability to open video files are required to listen to recorded lectures.
Note that it is possible to run Windows on Apple computers using Boot Camp or virtualization software such as Parallels, VMWare, or VirtualBox. Contact your computer technical support for details.
If you do not have a suitable computer, you can access one of computer labs at GMU campus. You can also get access to Health Informatics Learning Lab.
Partial List of Software that will be used in Class
(*) HAP department holds license to Microsoft software for education purposes
Teaching Learning Strategies:
The course combines lectures with learning practical data skills.Before registering for this class, students are expected to complete HAP 700 and HAP 709 we will use technical skills obtained in the earlier courses. Most of weekly assignments, midterm exam, and the final project will require demonstration of both theoretical knowledge and practical skills. Students will query databases, integrate data/systems, and build technology solutions.
Each week reading material will be assigned and discussed in the following week.
Expectations: Students are responsible for assigned readings, class content and material. You are expected to arrive to class on time, stay for the duration of the class, and contribute actively.
Students are responsible for checking GMU email daily, including SPAM/JUNK folders. Assignments, comments and additional materials are distributed through email. Clean your mailbox weekly so it is not over quota even if you forward emails.
Evaluation: Participation is key to making the experience of everyone a pleasant one. For online course it means that you need to be engaged in self-study, interact with instructor when needed, and complete all exercises provided on a timely basis (every Sunday following the class), ask questions about the topic, be involved in class discussions, etc.
Late submissions of may be penalized up to 20%.
Final grade is calculated based on percentages provided below. If at any time during the semester you are not aware of your partial grades, contact your instructor.The process is transparent, and the calculation is done using the formula below.
The final exam may be waived by the instructor based on very good results of research presentation, project, and assignments. This is decided on the individual basis after all other requirements are completed.