This course is designed for students to understand the issues associated with the use of data management technology and analytics solutions in the health care system. This includes systems and technologies used to generate, harvest, and store clinical data and methods used to create predictive models (including but not limited to methods associated with machine learning). Furthermore, issues related to delivery of predictive analytics and implementation of algorithms in care settings along with clinical, business, and ethical challenges will be explored. In addition, an overview of the issues within the health industry that are driving the use of data, will be reviewed, including population health management, clinical decision support, and advanced research. The goal is for students to be able to gain experience in the description, architecture and implementation planning of data infrastructure in the health care system along with providing a strong foundation in regard to analytics lifecycle and methods.

Objectives: students will enhance abilities to: 1) Describe and conceptualize data infrastructure used in the health care system, including classical and non-classical sources of data and the technologies and methods used to harvest and store clinical data. 2) Utilize statistical and machine learning tools to create and validate predictive models and present analytics results using visualization tools. 3) Identify and problem-solve the organizational, clinical, and ethical implementation challenges associated with predictive algorithms in health care. 4) Gain the ability to position data management and advanced analytics in the context of health system challenges and business models.