MSIS 635 Session 1 Health Information Analytics Spring 2014

Transcription

1 MSIS 635 Session 1 Health Information Analytics Spring 2014 Instructor : Kui Du (go by Andy) Instructor s Office : M Online System : Blackboard Office Phone Number : Office Hours : Monday 2pm 5pm or By appointment kui.du@umb.edu (please include MSIS635 in the subject of your ) COURSE OBJECTIVES There has been decades since the concept of evidence-based practice introduced in health care. Evidence-based practice requires making care-related decisions based on facts and causal relationships rather than mere experiences and sometimes instincts. However, evidence-based practice needs practice-based evidence. Although health care professionals have long been known good at learning quantitatively from their data (read the story of Ignaz Semmelweis), it is the accumulation of huge amount of patient and other data, in combination with the advancement in both analytical methods and software tools, which drives the recent upsurge of enthusiasm in health information analytics. Health information analytics is the science of analyzing health data for knowledge discovery and decision making. The architecture of the course content will be like a stool with three legs. As leg one, we will learn the basics of some quantitative methods that can be applied to analyze health care data; As leg two, we will get hands-on experiences with some popular analytical software tools, in particular; and as leg three, we will also discuss the business aspect of managing analytics by using some real-life health care cases. Jointly, the course will offer you a complementary set of quantitative, technical, and business domain knowledge about health information analytics. This is an online course. Your success in the course will depend on reflective reading, logical thinking and writing, and actively practicing with our provided software tools and datasets. Being proactive is important in an online, unsupervised learning environment. TEXTBOOK AND READINGS Required Textbook Edward H. Shortliffe & James J. Cimino (Eds.), Biomedical Informatics: Computer Applications in Health Care and Biomedicine, 4/E (2014), Publisher: Springer; ISBN: o An ebook edition can be purchased at the publisher s site: o A print edition will also be available for short-term checkout from the university s library reserve. Reference Reading (Optional) Gordon S. Linoff, Michael J. A. Berry, Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, 3/E (2011), Publisher: Wiley; ISBN:

2 Mark L. Braunstein, Health Informatics in the Cloud, (2013), Publisher: Springer; ISBN: o An ebook version is available free from our library Others Required Software Other reading materials will be post on Blackboard at (1) We will use version 15.0 to illustrate data management and analysis. Students each will receive a software license for free from the instructor and thus can install on their own computers. The license expires in a year. For Mac users (and other non-windows operating system users), unfortunately, is only available on the Windows platform. Mac users can use Bootcamp, or some virtualization tools such as VMware, to install Windows systems on their Mac. will also be available at one of the University general labs (the White Lab, Healey/03/0009E). (2) Microsoft Office suite (Word, Excel, & Powerpoint), or other office productivity software packages that are compatible with Microsoft Office files, will be needed. (3) Some other course readings are in PDF format. You need to have a free Adobe Reader or other PDF readers. COURSE EVALUATION Your final letter grade in the class will be based on the total points you get. The breakdown of these points is as follows: Items Points Reading Quizzes (5) : 20 Assignments (3) : 30 Case Write-ups (2) : 30 Final Project : 20 Total Points : Reading Quizzes 4 points x 5 = 20 points This is a reading-intensive course. Your takeaways will depend largely on how reflectively you read the assigned reading materials. There will be 7 reading quizzes posted on Blackboard (See Course Schedule for details), and the highest 5 scores of these quizzes will be counted towards your final grade. A typical quiz will consist of one or two open-ended questions based on the reading materials assigned in the quiz week. Each quiz is worth 4 points. Please note that because the two lowest scores will be dropped, there will not be any make-up quizzes. A missed quiz will receive a score of zero with no exceptions. So 2

3 you may plan your semester accordingly and at least avoid missing more than 2 quizzes. 2. Assignments 10 points x 3 = 30 points The course will provide you hands-on experiences with analyzing healthcare data. Practicing with, as our chosen analytical software tool, is important. Learning analytics without actually analyzing some data will be just like trying to learn swimming without jumping into the pool. There will be three assignments, requiring you to use for a few data analyses tasks. Each assignment is worth 10 points (See Course Schedule for details). 3. Case Write-ups 15 points x 2 = 30 points The course will also cover the business aspect of managing analytics. All of the analytical skills we covered in the class have been available for decades, but still many, if not most, healthcare organizations can only implement them at best sporadically. Building an analytical healthcare organization is far different from hiring a few expert data scientists. While there is certainly no right formula to build an analytical organization, we will discuss two business cases together to explore some possible solutions. Each individual student will write two case analyses based on the materials provided in class, with each write-up worth 15 points. 4. Final Project 20 points We will conclude our course with a final project. It is an individual project. Two options will be offered for this final project. If a student chooses to follow a relatively quantitative path, a rich dataset will be provided and the student s project objective will be to analyze the dataset and generate as many insights as possible and write them in a technical report; If a student chooses to follow a relatively managerial path, the final project will be an essay on a topic closely related to health information analytics. It is possible to extend one of your SPSS assignments or reading quiz questions to be the final project topic. More guidance will be provided to the class during the semester. Your Letter grades will be assigned based on the following conversion chart. However, if the class average drops below the instructor s expectation, the instructor may grade based on a curve (alternative speaking, there could be curving-up if needed, but no curving-down. ) Points Letter Grade A A B B B C C C D D D F 3

4 VIRTUAL CLASSROOM MEETING During the semester, the class will virtually meet together at least twice by using Wimba, a tool embedded in Blackboard that can create an online classroom experience. Based on the class progress, more Wimba sessions can be arranged if necessary. Detailed instructions on how to use Wimba will be provided to the class in advance, and polls will be used to decide on commonly available time slots for the virtual classroom meetings. POLICIES Late work policy. Any assignment delivered after the deadline is considered late. For reading quizzes, NO late submission will be accepted or graded --- the submission page on blackboard will turn off automatically after the due time. For other assignments, late submissions less than 24 hours past the deadline will be graded with 20% penalty; Assignments submitted thereafter will be forfeited of 40% of their grade for each 24 hours of delay and submissions will NOT be accepted, nor be graded, after 76 hours past deadlines. Exceptions are possible only in situations such as medical or family emergencies. In those situations, however, students should try all their best to inform the instructor before the due time. Re-Learning on Assignments and Exams. Asking questions after quizzes and assignments have been graded reinforces learning and helps you to understand your strengths and weaknesses with course material. Therefore, I encourage you to contact me to discuss your exams and assignments. However, you must do so within ONE WEEK of the day the grades are posted. After the one-week window, your grade for that exam or assignment is permanent. Academic Dishonesty Policy. Unless explicitly specified, assignments are individual work. Unauthorized collaboration is considered academic dishonesty. Providing false or misleading information to receive a postponement or an extension on an exam or other assignment, and submission of essentially the same written assignment for two different courses without faculty permission are also dishonest behaviors and could lead to severe consequences. Please refer to Section VI Academic Honesty of the Code of Student Conduct at as well as the Student Handbook Academic Integrity for the full information about definitions and consequences of academic dishonest. Accommodation Policy. If applicable, students may obtain adaptation recommendations from the Ross Center for Disability Services ( Students who need special accommodation arrangements must present these recommendations and discuss them with the instructor within a reasonable period, preferably by the end of Drop/Add period. 4

5 MSIS635 Health Information Analytics Spring 2014 Course Schedule Where to find the reading material: [BB] --- materials will be posted on the corresponding session folder on Blackboard; [Chapter #] --- A chapter of the required textbook; Please note that the Schedule is tentative and subject to change. The instructor may adjust it to fit the learning progress of the class during the semester. Changes, if made, will be announced in advance to the class. Session Topic Readings 1 (Feb.3) 2 (Feb.10) 3 (Feb.17) 4 (Feb. 24) 5 (Mar.3) 6 (Mar.10) [BB] the story of Ignaz Semmelweis, excerpted from Levitt, S. & Dubner, S., Super Freakonomics, 2009 Introduction to Analytics and [BB] Analytics in Professional Sports, excerpted from Analytics in Healthcare Davenport, T., & Harris, J., Competing on Analytics, 2007, Harvard Business School Publishing The Value of Analytics in [BB] McKinsey, The Big Data Revolution in Healthcare, Healthcare 2013 [Skill Session] [BB] Installation Guide Introduction Healthcare Data: the Foundation for Analytics [Chapter 2] Biomedical Data: Their Acquisition, Storage, and [Skill Session] Preparing Use Data for Analysis in SPSS [BB] Training Material part 1 Modeler IT Architecture for Healthcare [Chapter 14] Management of Information in Healthcare Analytics Organizations [Skill Session] Data [BB] Training Material part 2 Manipulation in Clinical and Business Intelligence [BB] Nelson, G., The Healthcare Performance Dashboard: [Skill Session] Data Linking Strategy to Metrics, SAS Global Forum 2010 Reporting & Visualization in [BB] Training Material part 3 Clinical Decision Making [Chapter 3] Biomedical Decision Making: Probabilistic Clinical [Skill Session] Unsupervised Reasoning Data Mining Decision Tree [BB] Training Material part 4 Deliverables (Due by Friday 11pm of the same week) Reading Quiz #1 Reading Quiz #2 [Optional] Install in your own PC Reading Quiz #3 Assignment #1 Reading Quiz #4 Reading Quiz #5 5

6 Session Topic Readings 7 (Mar.17) NO CLASS --- Spring Break Clinical Decision Support [Chapter 22] Clinical Decision-Support Systems 8 (Mar. 24) [Skill Session] Unsupervised [BB] Training Material part 5 Data Mining Clustering Public Health Informatics [Chapter 16] Public Health Informatics 9 (Mar.31) [Skill Session] Supervised [BB] Training Material part 6 Data Mining - Regression Analytics for Healthcare [BB] Gawande, A "The Cost Conundrum," the New 10 (Apr. 7) Administration Yorker (June 1, 2009) Consumer Health Informatics [Skill Session] Supervised [Chapter 17] Consumer Health Informatics 11 (Apr. 14) Data Mining Survival [BB] Training Material part 7 Analysis [BB] Tanriverdi, H., Du, K, & Ross, J., 2011, Trinity Health: Using Digitization, Unification, and Data Analytics to Tame the Strategic Management of 12 (Apr.21) Quality, Cost, and Accessibility Problems of Healthcare, MIT Analytics in Healthcare Center for Information Systems Research (CISR) working paper No.385 [Chapter 28] The Future of Computer Applications in 13 (Apr.28) The Future of Health Analytics and Career Options 14 (May 2) Final Project Working Week Biomedicine [BB] Davenport, T. H., and Patil, D. J "Data Scientist: The Sexiest Job of the 21st Century," Harvard Business Review (90:10), pp Deliverables (Due by Friday 11pm of the same week) Assignment #2 Reading Quiz #6 Case Write-up #1 Assignment #3 Case Write-up #2 Reading Quiz #7 Final Project Report 6