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Clinical Data Models and Data Quality Assessments

Clinical Data Models and Data Quality Assessments

This course is part of Clinical Data Science Specialization

Instructors: Laura K. Wiley, PhD

8,832 already enrolled

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5 modules

Gain insight into a topic and learn the fundamentals.

66 reviews

Intermediate level

Some related experience required

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

66 reviews

Intermediate level

Some related experience required

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments

Taught in English

91%

of learners achieved a positive career outcome

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Build your subject-matter expertise

This course is part of the Clinical Data Science Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 5 modules in this course

This course aims to teach the concepts of clinical data models and common data models. Upon completion of this course, learners will be able to interpret and evaluate data model designs using Entity-Relationship Diagrams (ERDs), differentiate between data models and articulate how each are used to support clinical care and data science, and create SQL statements in Google BigQuery to query the MIMIC3 clinical data model and the OMOP common data model.

Module details

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.

What's included

9 videos5 readings1 assignment

9 videosTotal 54 minutes

Welcome to Clinical Data Models and Data Quality Assessments2 minutes
Clinical Research Data Warehouses9 minutes
Entity Relationship Diagrams (ERDs)4 minutes
Clinical Data Models4 minutes
Why Common Data Models?10 minutes
A Quick Tour of a Common Data Model: i2b27 minutes
A Quick Tour of a Common Data Model: OMOP6 minutes
A Quick Tour of a Common Data Model: Sentinel6 minutes
A Quick Tour of a Common Data Model: PCORNet6 minutes

5 readingsTotal 150 minutes

Get help and meet other learners in this course. Join your discussion forums!5 minutes
Introduction to Specialization Instructors5 minutes
Course Policies5 minutes
Accessing Course Data and Technology Platform15 minutes
Readings and Course Materials for Module 1120 minutes

1 assignmentTotal 30 minutes

Clinical Data Models and Common Data Models30 minutes

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.

What's included

6 videos1 reading1 assignment

6 videosTotal 59 minutes

A Deep Dive into the MIMIC-III Data Model5 minutes
Querying MIMIC-III10 minutes
A Deep Dive into OMOP Data Model14 minutes
Querying OMOP12 minutes
Comparing the MIMIC and OMOP Data Models11 minutes
The OHDSI Community Ecosystem7 minutes

1 readingTotal 90 minutes

Readings and Course Materials for Module 290 minutes

1 assignmentTotal 30 minutes

Tools: Querying Clinical Data Models30 minutes

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.

What's included

6 videos1 reading1 assignment

6 videosTotal 53 minutes

The ETL Task7 minutes
Structural versus Terminology Mapping6 minutes
Data Profiling with White Rabbit10 minutes
Data Mapping with the Rabbit in a Hat Tool9 minutes
Terminology Mapping11 minutes
Example mapping of MIMIC Patient to OMOP Person9 minutes

1 readingTotal 120 minutes

Readings and Course Materials for Module 3120 minutes

1 assignmentTotal 30 minutes

Techniques: Extract-Transform-Load and Terminology Mapping30 minutes

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.

What's included

5 videos1 reading1 assignment

5 videosTotal 52 minutes

Data quality dimensions / fitness for use7 minutes
Data profiling for data quality assessment10 minutes
Data quality assessment using SQL13 minutes
Callahan and Khare rules9 minutes
OHDSI Achilles and Achilles Heel13 minutes

1 readingTotal 90 minutes

Readings and Course Materials for Module 490 minutes

1 assignmentTotal 30 minutes

Techniques: Data Quality Assessments30 minutes

In this module, you gather everything you’ve learned to complete a real-world hands-on exercise using ETL methods to convert MIMIC3 data into the OMOP common data model.