One of the today's most exciting and important applications of Business Analytics is Healthcare, thanks to the rise of Data Science and the Patient Protection and Affordable Care Act. Every day, more data on provider performance is becoming available to consumers to help them make better informed decisions about their healthcare. Hospital revenues are being driven more and more by clinical results through incentive programs for improving hospital readmissions, patient safety, costs, and patient outcomes. At the same time, population health is improving as Big Data is being used to learn what treatments are most effective at an unprecedented pace and scale. These forces are transforming the healthcare industry and public health.

In this class, you will learn how data analytics drives the business of healthcare. The course combines lecture and discussion with hands-on work with large, real-world healthcare datasets. You will learn the underlying logic and calculations of value-based hospital reimbursement, outcomes measurement, and benchmarking, working directly with patient-level claims datasets from CMS (Centers for Medicare and Medicaid Services) and elsewhere.

Students will use state-of-the-art commercial software tools that permit data preparation and collaboration on datasets too large to work with efficiently using spreadsheets. Data manipulation and analyses will be done using a combination of both point-and-click recipes and pre-prepared analysis scripts in the statistical software package R. By the end of the course, students will be prepared to conduct and/or participate in a real-world data analysis project at a healthcare institution or a consultancy.

Students interested in the Healthcare Analytics Laboratory (Bus 40721) are strongly encouraged to enroll in this class. While this course is designed to complement the Healthcare Analytics Lab, it is a standalone offering and can be taken independently. Students seeking exposure to healthcare data analytics who are unable to make the commitments the Lab requires will find it useful preparation for future endeavors.

Basic statistics and introductory exposure to R programming. (The later can be achieved through various online introductory tutorials, if needed.) Students will not be required to write code, but occasionally you will be required to run or slightly edit pieces of code provided by the instructor. Almost all numerical work will be conducted using "point-and-click" recipes in Data Science Studio by Dataiku. Thus, students should have a willingness and interest in learning powerful new software tools and working with real data to transform it into usable management insights.

All Non-Booth students require instructor approval:  strict. Cannot enroll in 40206 if you have taken 40205 or 40201: strict. 

Students will work with real-world healthcare data that is protected by HIPAA, on a secure, private server housed at Chicago Booth. As such, students will receive training and certification in HIPAA as part of their first class assignment.

Required Textbook: Risk Adjustment for Measuring Health Care Outcomes, by Lisa I. Iezzoni, 4th Edition, 2013.

Supplementary articles and reading materials.

Students will be required to bring their internet-ready laptop to every class session.

Based on a series of mini-projects and class participation. Can be taken pass/fail.

No auditors. Provisional grades will be given to graduating students.