This course is designed to train students in the fundamentals of manipulating and conducting analyses of clinical data. Basic statistical principles and big data analytics will be discussed briefly, though the main focus of this course is understanding and working with "raw" clinical and administrative data sources to develop structured data sets suitable for analysis by more advanced techniques. Students will gain insight into the unique idiosyncrasies of the healthcare setting that impact the interpretation of analyses of clinical data. This class will explore in detail an anonymized patient data set including demographics, clinical encounters, diagnoses, medications, and laboratory results. The class will analyze clinical data sets with special focus on medication management, patient adherence, healthcare disparities, trends in health utilization, disease management, and quality measurement. Challenging issues in dealing with "dirty data" will be addressed. The course will also explore the impact of data modeling decisions and clinical ontology choices on the conclusions that can be drawn from clinical analytics. Through the course, students will learn the many assumptions that are incorporated into even seemingly straightforward queries, and the impact of these assumptions on the interpretation of results. Students also will be able to test the generalizability of their knowledge through analyses of additional publicly available clinical data sets.
Level Registration Restrictions: Must be enrolled in one of the following Levels: Graduate.
Repeatability: This course may not be repeated for additional credits.