Clinical Data Models and Data Quality Assessment

Healthcare systems generate vast amounts of clinical data, but unlocking its potential requires understanding how this information is structured and validated. Navigating complex electronic health records and ensuring data integrity is a critical skill for modern health data analysts and clinical researchers. In this text-based course, you will transition from a beginner to a confident practitioner capable of navigating, querying, and evaluating clinical databases. You will gain a solid foundation in standard data architectures and learn how to assess data quality to ensure reliable research outcomes. What you'll learn: - Understand the foundational concepts of clinical data models, including Entity-Relationship Diagrams (ERDs) and relational database designs. - Differentiate between major clinical data models such as the MIMIC-III database and the OMOP Common Data Model. - Write SQL queries to extract, filter, and analyze clinical patient data from structured database environments. - Apply systematic data quality frameworks to assess clinical datasets for completeness, conformance, and plausibility. - Identify common data quality issues in Electronic Health Records and implement strategies to address them. You will start by exploring core database concepts and terminology before moving on to hands-on SQL query design for clinical schemas. The course concludes with practical methodologies for evaluating data quality, preparing you to work with real-world health data. This course is designed for aspiring healthcare data analysts, clinical researchers, and IT professionals new to health informatics. No prior experience with clinical databases or SQL is required. Begin your journey into clinical data analysis and start working with real-world healthcare datasets today.