About this Course
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Intermediate Level

Approx. 23 hours to complete

Suggested: 5 weeks of study, 2-4 hours/week...

English

Subtitles: English

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 23 hours to complete

Suggested: 5 weeks of study, 2-4 hours/week...

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
4 hours to complete

Introduction: Clinical Data Models and Common Data Models

This week describes clinical data models and explains the need for and use of common data models in national and international data networks. We will also cover the features of Entity-Relationship Diagrams (ERDs) to describe the key technical features of data models. ...
9 videos (Total 54 min), 4 readings, 1 quiz
9 videos
Clinical Research Data Warehouses9m
Entity Relationship Diagrams (ERDs)4m
Clinical Data Models4m
Why Common Data Models?10m
A Quick Tour of a Common Data Model: i2b26m
A Quick Tour of a Common Data Model: OMOP5m
A Quick Tour of a Common Data Model: Sentinel6m
A Quick Tour of a Common Data Model: PCORNet5m
4 readings
Introduction to Specialization Instructors5m
Course Policies5m
Accessing Course Data and Technology Platform15m
Readings and Course Materials for Module 1s
1 practice exercise
Clinical Data Models and Common Data Models30m
Week
2
3 hours to complete

Tools: Querying Clinical Data Models

We take a deep dive into the technical features of clinical data models using MIMIC3 as our example and research common data models using OMOP as our example....
6 videos (Total 59 min), 1 reading, 1 quiz
6 videos
Querying MIMIC-III9m
A Deep Dive into OMOP Data Model13m
Querying OMOP12m
Comparing the MIMIC and OMOP Data Models10m
The OHDSI Community Ecosystem7m
1 reading
Readings and Course Materials for Module 230m
1 practice exercise
Tools: Querying Clinical Data Models30m
Week
3
3 hours to complete

Techniques: Extract-Transform-Load and Terminology Mapping

This module teaches learners about the processes and challenges with extracting, transforming and loading (ETL) data with real-world examples in data and terminology mapping. ...
6 videos (Total 53 min), 1 reading, 1 quiz
6 videos
Structural versus Terminology Mapping6m
Data Profiling with White Rabbit10m
Data Mapping with the Rabbit in a Hat Tool9m
Terminology Mapping10m
Example mapping of MIMIC Patient to OMOP Person8m
1 reading
Readings and Course Materials for Module 3s
1 practice exercise
Techniques: Extract-Transform-Load and Terminology Mapping30m
Week
4
3 hours to complete

Techniques: Data Quality Assessments

We explore the dimensions of data quality by reviewing its challenges, data quality measurements used to measure it, and data quality rules to assess its acceptability for use....
5 videos (Total 52 min), 1 reading, 1 quiz
5 videos
Data profiling for data quality assessment10m
Data quality assessment using SQL13m
Callahan and Khare rules8m
OHDSI Achilles and Achilles Heel12m
1 reading
Readings and Course Materials for Module 430m
1 practice exercise
Techniques: Data Quality Assessments30m

Instructors

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Laura K. Wiley, PhD

Assistant Professor
Division of Biomedical Informatics and Personalized Medicine, Anschutz Medical Campus
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Michael G. Kahn, MD, PhD

Professor of Clinical Informatics
Department of Pediatrics, Anschutz Medical Campus

About University of Colorado System

The University of Colorado is a recognized leader in higher education on the national and global stage. We collaborate to meet the diverse needs of our students and communities. We promote innovation, encourage discovery and support the extension of knowledge in ways unique to the state of Colorado and beyond....

About the Clinical Data Science Specialization

Are you interested in how to use data generated by doctors, nurses, and the healthcare system to improve the care of future patients? If so, you may be a future clinical data scientist! This specialization provides learners with hands on experience in use of electronic health records and informatics tools to perform clinical data science. This series of six courses is designed to augment learner’s existing skills in statistics and programming to provide examples of specific challenges, tools, and appropriate interpretations of clinical data. By completing this specialization you will know how to: 1) understand electronic health record data types and structures, 2) deploy basic informatics methodologies on clinical data, 3) provide appropriate clinical and scientific interpretation of applied analyses, and 4) anticipate barriers in implementing informatics tools into complex clinical settings. You will demonstrate your mastery of these skills by completing practical application projects using real clinical data. This specialization is supported by our industry partnership with Google Cloud. Thanks to this support, all learners will have access to a fully hosted online data science computational environment for free! Please note that you must have access to a Google account (i.e., gmail account) to access the clinical data and computational environment....
Clinical Data Science

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.