Foundations of Real World Data Science: Electronic Health Records

Graduate / Professional

The course covers scientific principles, best practices, and limitations of using observational data from administrative records, including hypothesis generation, feasibility assessments, and causal inference. Students learn pragmatic skills required to prepare analytic data from large, complex transactional databases. We cover methods for data quality characterization and profiling for study planning. Coursework includes application of methods for creation and validation of computable phenotypes, electronic clinical quality measures, and derived analytic variables. Skills include preparation of real-world data for visualization and reporting in business intelligence tools commonly used in population health and health administration. Students reproduce results from published literature using existing databases for predictive modeling, public health, and outcomes research. Completion of this course positions students for externships in healthcare analytics and health data science. Prerequisites: Course must be approved by the instructor BIS 638 , Clinical Database Management Systems and Ontologies (or equivalent); proficiency in SQL and Python, or R; HIPAA and HSR training; YNHHS Research Basic Access; COS550 or equivalent; execution of DUAs for public and proprietary databases; demonstrated use of YU-YNHHS data science platform. HTBA