Clinical Data Models and Data Quality Assessments
Master clinical data modeling, ETL processes, and quality assessment using MIMIC-III and OMOP frameworks.
Master clinical data modeling, ETL processes, and quality assessment using MIMIC-III and OMOP frameworks.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Clinical Data Science Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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English
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What you'll learn
Create and analyze Entity-Relationship Diagrams
Implement ETL processes for clinical data
Assess data quality using multiple dimensions
Query MIMIC-III and OMOP data models
Perform terminology mapping and data transformation
Skills you'll gain
This course includes:
4.3 Hours PreRecorded video
4 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course focuses on clinical data models and common data models in healthcare. Students learn to interpret and evaluate data model designs using Entity-Relationship Diagrams (ERDs), work with MIMIC-III and OMOP frameworks, and perform ETL processes. The course covers data quality assessments, terminology mapping, and practical applications in clinical data transformation. Using Google BigQuery, students gain hands-on experience in querying and manipulating healthcare data models.
Introduction: Clinical Data Models and Common Data Models
Module 1 · 3 Hours to complete
Tools: Querying Clinical Data Models
Module 2 · 2 Hours to complete
Techniques: Extract-Transform-Load and Terminology Mapping
Module 3 · 3 Hours to complete
Techniques: Data Quality Assessments
Module 4 · 2 Hours to complete
Practical Application: Create an ETL Process to Transform a MIMIC-III Table to OMOP
Module 5 · 4 Hours to complete
Fee Structure
Instructors
Leader in Biomedical Informatics and Precision Medicine at the University of Colorado Anschutz Medical Campus
Dr. Laura K. Wiley is an Associate Professor in the Department of Biomedical Informatics at the University of Colorado Anschutz Medical Campus, where she also serves as Chief Data Scientist for Health Data Compass. Her research focuses on leveraging electronic health record (EHR) data to enhance precision medicine through the development of computational phenotyping algorithms and innovative approaches to clinical data science.Dr. Wiley has led significant projects, including work on precision dosing algorithms for warfarin in African Americans and serving as the lead informatician on a comprehensive tobacco cessation service funded by the NIH Cancer Moonshot initiative. She is a principal investigator in the Colorado Center for Personalized Medicine and has published extensively on topics related to health informatics and medical technology.In addition to her research, Dr. Wiley is actively involved in the American Medical Informatics Association (AMIA), having chaired various summits and served on the board of directors. She has co-developed the Coursera Clinical Data Science Specialization, which includes courses designed to teach essential skills in clinical research informatics.Her courses on Coursera include "Introduction to Clinical Data Science," "Advanced Clinical Data Science," and "Predictive Modeling and Transforming Clinical Practice," aimed at equipping students with the knowledge necessary for data-driven healthcare solutions
Leader in Clinical Informatics and Pediatric Research
Dr. Michael G. Kahn is a distinguished Professor of Pediatrics at the University of Colorado Denver, where he also serves as the Biomedical Informatics Core Director for the Colorado Clinical and Translational Sciences Institute and co-Director of the Colorado Center for Personalized Medicine. As the Director of Research Informatics at Children’s Hospital Colorado, he spearheads initiatives that enhance data integration and research capabilities in pediatric care. Dr. Kahn leads Health Data Compass, a cloud-based research data warehouse that aggregates data from multiple clinical, financial, and research institutions, as well as state and federal sources, facilitating advanced research in healthcare. His research interests primarily focus on data model harmonization and the development of sharable data quality measures within distributed research networks. Through his involvement in various regional, national, and international clinical data research networks, Dr. Kahn is committed to improving healthcare outcomes by leveraging informatics to enhance data accessibility and quality for research purposes.
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