A Five-Course Program Built for Working Professionals

• Introduction to Healthcare Analytics
• With the increasing adoption of electronic health record systems, new forms of data are becoming available that can be used to measure healthcare delivery and improve patient outcomes. In this introductory course designed for healthcare analysts and IT professionals, participants explore the value proposition for clinical intelligence and the role of analytics in supporting a data-driven healthcare system. Through video lectures, reading assignments, written assignments and discussion forums, students learn key concepts in measuring health system performance and leveraging analytics for health improvement. 
• Healthcare Data Acquisition and Management 
• Learn to navigate complex data structures and efficiently retrieve the data needed to answer a question or solve a problem. Explore the types and sources of healthcare data, along with methods for selecting, preparing, querying and transforming healthcare data. Through video lectures, assignments and discussions, clinical operations analysts will examine the range of data sources and the strategies, tools and methods for data preparation and optimizing data quality. By the end of this course, students will understand the new models of healthcare data organization and analytics, such as clinical registries and query health. In addition, you will learn the basics of SQL programming or improve your SQL skills, within the concepts of other course topics.
• Applied Healthcare Statistics
• Successful healthcare analysts require a solid grasp of statistical inference as a foundation of their work. Designed for clinical and operational analysts, this course introduces concepts in healthcare epidemiology, outcomes research and experimental studies. The course focuses on four domains: 1) fundamentals of data and statistics 2) techniques to assess relationships between exposure and outcomes 3) study design and 4) measuring main effects and error. Through online lectures and hands-on practice, students will explore methods to analyze healthcare data, contrast efficacy and effectiveness trials, and learn strategies to adjust for patient, provider and hospital characteristics in a statistical model. All topics will be covered in the context of their direct application to health care. By the end of this course students will be able to design a healthcare study; assess confounding, biases, internal and external validity; and understand variance.
• Data Mining for Healthcare Analytics
• The proliferation of data in the post-EHR era creates opportunities for large-scale data analysis to discover meaningful patterns and trends. Through interactive, online learning, explore the application of data mining techniques for purposes of big data analytics using administrative and clinical systems data. Gain an overview of the data mining process, data mining standards and output protocols, and common techniques used in mining healthcare data. By the end of this course, students will be familiar with data mining techniques and visual representation methods that increase understanding of complex data.
• Quantitative Methods and Decision Analysis
• Health analytics practitioners use an array of quantitative methods to evaluate questions of efficiency and effectiveness in health care. This advanced course for healthcare analysts and other professionals integrates and builds on prior coursework in statistics and data mining and provides additional exposure to advanced methodologies, such as event sequencing, analytical groupers, simulation and predictive modeling. Heavy emphasis is given to the application of methods to contemporary analytic challenges in health care, including demand/utilization forecasting, risk stratification, population health management, quality measurement, fraud detection, and cost containment from readmissions. Throughout this course, students will complete a series of hands-on assignments in the use of specific analytic approaches, as well as a final course project.