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Laura K. Wiley, PhD and Michael G. Kahn, MD, PhD

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.

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What's inside

Syllabus

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.
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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.
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.
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.
Practical Application: Create an ETL Process to Transform a MIMIC-III Table to OMOP
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.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Teaches foundational data science concepts, which are highly relevant to the field of medical research
Taught by experts in the field of clinical data modeling
Provides hands-on experience with real-world clinical data sets
Requires some prior knowledge of SQL and data modeling concepts

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Reviews summary

Challenging but rewarding clinical data course

Learners say this engaging course is difficult, but offers valuable concepts in data quality assessments and clinical data models. Quizzes and assignments, however, have had some confusing questions and technical issues.
Peer review process provides constructive feedback.
"The final assignment is a peer-reviewed project"
Despite some issues, the course provided valuable concepts in data quality assessments and clinical data models.
"I learned new concepts and processes"
"very little programming actually, The information is repeated numerous times Programming/writing on the other hand is not explained clearly and should be repeated more."
Some material is missing or outdated, making it difficult to complete assignments.
"I have found a key website to be out of date"
"Also, files important to finish assignments are missing"
Instructor has engaging lectures but lacks clarity in explaining concepts.
"The instructor is not really engaging."
"some of them are ambiguous or have answers that can be different given the current state of the art"
"Dr. Kahn's presentation and reading ability is on full display, but what is not on full display is how to actually do what he is describing."
Some quizzes and assignments were unclear and didn't always reflect the material.
"The quizzes contained questions on issues never discussed in the course material"
"multiple choice quizzes with only 6 questions where I had some answers marked "incorrect" when they weren't the "most correct" answer."
"Having a student review 3 assignments is incredibly short sighted unless the final grade for ones own assignment is then averaged."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Clinical Data Models and Data Quality Assessments with these activities:
Review Entity Relationship Diagrams
Review these key concepts to ensure a strong foundational understanding of clinical data models.
Show steps
  • Read the course syllabus' section on Introduction to Clinical Data Models and Common Data Models.
  • Review your notes on ERDs from previous coursework or study materials.
  • Complete the practice questions provided in the syllabus.
Review Entity-Relationship Diagrams (ERDs)
Refresh your knowledge of ERDs, which are essential for understanding data models.
Show steps
  • Review course materials on ERDs
  • Study examples of ERDs
  • Practice creating ERDs for simple scenarios
Follow SQL Tutorial
Develop stronger SQL skills. This will greatly aid you throughout the course.
Browse courses on SQL
Show steps
  • Identify a well-regarded online SQL tutorial.
  • Follow the tutorial, completing all exercises and assignments.
  • Complete additional practice problems to solidify your understanding.
15 other activities
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Show all 18 activities
Follow tutorials on data model design
Reinforce your understanding of data model design by following online tutorials.
Show steps
  • Search for tutorials on data model design
  • Follow the tutorials step-by-step
  • Practice creating data models based on the tutorials
Practice data model design exercises
Reinforce your understanding of data model design through hands-on practice.
Show steps
  • Find online exercises or create your own
  • Practice designing data models based on given scenarios
  • Compare your solutions to provided answers or discuss with peers
Discuss data modeling challenges with peers
Engage in discussions with peers to share experiences and insights about the challenges of data modeling in clinical settings.
Browse courses on Data Models
Show steps
  • Find a peer group or online forum.
  • Discuss specific data modeling challenges you have encountered.
  • Share ideas and solutions with your peers.
Practice SQL statements using Google BigQuery
Practice writing and executing SQL statements to retrieve data from the MIMIC3 clinical data model and the OMOP common data model.
Browse courses on SQL
Show steps
  • Set up a Google BigQuery account and create a project.
  • Load the MIMIC3 and OMOP datasets into your BigQuery project.
  • Write and execute SQL statements to query the datasets.
Participate in peer study groups
Enhance your learning by discussing concepts and solving problems with peers.
Show steps
  • Find a study group or create your own
  • Discuss the course material and ask questions
  • Work together on assignments and projects
Attend a workshop on data modeling
Gain practical experience and insights by attending a workshop on data modeling.
Browse courses on Data Modeling
Show steps
  • Find a relevant workshop and register
  • Attend the workshop and participate actively
  • Take notes and ask questions to enhance your understanding
Tutorial: ETL process to transform MIMIC3 data to OMOP
Follow a tutorial to create an ETL process to transform data from the MIMIC3 clinical data model to the OMOP common data model.
Browse courses on ETL
Show steps
  • Find a suitable tutorial online.
  • Follow the steps in the tutorial to create the ETL process.
  • Test the ETL process to ensure it is working correctly.
Query Clinical Data Models using SQL
Practice querying clinical data models which will prepare you for the hands-on ETL exercise in the course.
Browse courses on SQL
Show steps
  • Find a dataset that is similar to MIMIC3 or OMOP.
  • Write SQL queries to extract data from the dataset.
  • Validate your queries by comparing them to expected results.
Start a data visualization project
Create a data visualization project to solidify your understanding of clinical data models and common data models.
Browse courses on Data Visualization
Show steps
  • Gather data from MIMIC3 and OMOP
  • Clean and transform the data
  • Create data visualizations using Python or R
  • Interpret the results and draw conclusions
Create a data model diagram for a clinical scenario
Create an Entity-Relationship Diagram (ERD) to represent the data model for a clinical scenario, such as patient demographics, diagnoses, and treatments.
Browse courses on Data Models
Show steps
  • Identify the entities involved in the clinical scenario.
  • Determine the relationships between the entities.
  • Create an ERD using a tool such as Lucidchart or draw.io.
Assist fellow learners in the course discussion forums.
Teaching others is one of the best ways to solidify your own learning.
Show steps
  • Identify a discussion forum related to the course.
  • Monitor the forum for questions or discussions where you can offer assistance.
  • Provide helpful and thoughtful responses to other learners.
Create a presentation on data model design
Solidify your knowledge by creating a presentation on data model design principles and best practices.
Show steps
  • Choose a topic related to data model design
  • Research the topic thoroughly
  • Create a presentation using slides or other visual aids
  • Present your findings to your classmates or colleagues
Develop a data quality assessment plan for a clinical dataset
Develop a plan to assess the quality of a clinical dataset, including data completeness, consistency, and accuracy.
Browse courses on Data Quality
Show steps
  • Identify the data quality dimensions to be assessed.
  • Develop data quality rules to measure each dimension.
  • Create a data quality assessment report.
Create an ETL Process to Transform a MIMIC-III Table to OMOP
This hands-on exercise will solidify your understanding of ETL and its application in transforming clinical data.
Browse courses on ETL
Show steps
  • Choose a MIMIC3 table and an OMOP target table.
  • Using the SQL skills you have developed, extract the data from the source table.
  • Transform the extracted data to conform to the target table schema.
  • Load the transformed data into the target table.
  • Test and validate the ETL process to ensure accuracy and completeness.
Create a blog post on data models
Expand your understanding and share knowledge by writing a blog post on data models.
Browse courses on Data Models
Show steps
  • Choose a specific topic related to data models
  • Research the topic and gather information
  • Write a clear and concise blog post
  • Publish your blog post and share it with others

Career center

Learners who complete Clinical Data Models and Data Quality Assessments will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. A Clinical Data Models and Data Quality Assessments course may be helpful for this role, as it provides the skills to work with clinical data models and common data models. This can help Data Analysts to understand the structure of clinical data and to extract meaningful insights from it.
Quality Assurance Analyst
Quality Assurance Analysts are responsible for ensuring that data meets the required standards for quality. A Clinical Data Models and Data Quality Assessments course may be useful for this role, as it provides the skills to evaluate and interpret data model designs using Entity-Relationship Diagrams (ERDs). This can help Quality Assurance Analysts to identify and correct errors in data, ensuring that it is accurate and reliable.
Data Scientist
Data Scientists are responsible for developing and implementing data-driven solutions to business problems. A Clinical Data Models and Data Quality Assessments course may be helpful for this role, as it provides the skills to work with clinical data models and common data models. This can help Data Scientists to understand the structure of clinical data and to develop models that can accurately predict outcomes.
Clinical Research Associate
Clinical Research Associates are responsible for managing clinical trials and ensuring that they are conducted in accordance with good clinical practice (GCP) guidelines. A Clinical Data Models and Data Quality Assessments course may be helpful for this role, as it provides the skills to work with clinical data models and common data models. This can help Clinical Research Associates to understand the structure of clinical data and to ensure that it is collected and managed in a way that meets the requirements of GCP.
Healthcare Consultant
Healthcare Consultants provide advice and guidance to healthcare organizations on a variety of topics, such as clinical data management, quality improvement, and strategic planning. A Clinical Data Models and Data Quality Assessments course may be helpful for this role, as it provides the skills to understand the challenges and opportunities associated with clinical data.
Healthcare Data Manager
Healthcare Data Managers are responsible for managing the collection, storage, and analysis of healthcare data. A Clinical Data Models and Data Quality Assessments course may be helpful for this role, as it provides the skills to work with clinical data models and common data models. This can help Healthcare Data Managers to understand the structure of clinical data and to develop systems for managing it effectively.
Medical Writer
Medical Writers are responsible for writing and editing medical documents, such as clinical trial protocols, patient information sheets, and scientific papers. A Clinical Data Models and Data Quality Assessments course may be helpful for this role, as it provides the skills to understand the structure of clinical data and to communicate it clearly and accurately.
Data Architect
Data Architects are responsible for designing and implementing data architectures for organizations. A Clinical Data Models and Data Quality Assessments course may be helpful for this role, as it provides the skills to work with clinical data models and common data models. This can help Data Architects to understand the structure of clinical data and to develop architectures for managing it effectively.
Clinical Data Manager
Clinical Data Managers are responsible for managing the collection, storage, and analysis of clinical data. A Clinical Data Models and Data Quality Assessments course may be helpful for this role, as it provides the skills to work with clinical data models and common data models. This can help Clinical Data Managers to understand the structure of clinical data and to develop systems for managing it effectively.
Medical Librarian
Medical Librarians are responsible for providing access to and managing medical information resources. A Clinical Data Models and Data Quality Assessments course may be helpful for this role, as it provides the skills to understand the structure of clinical data and to develop systems for organizing and retrieving it.
Health Informatics Specialist
Health Informatics Specialists are responsible for designing, implementing, and managing health information systems. A Clinical Data Models and Data Quality Assessments course may be helpful for this role, as it provides the skills to work with clinical data models and common data models. This can help Health Informatics Specialists to understand the structure of clinical data and to develop systems for managing it effectively.
Database Administrator
Database Administrators are responsible for designing, implementing, and managing databases. A Clinical Data Models and Data Quality Assessments course may be helpful for this role, as it provides the skills to work with clinical data models and common data models. This can help Database Administrators to understand the structure of clinical data and to develop systems for managing it effectively.
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. A Clinical Data Models and Data Quality Assessments course may be helpful for this role, as it provides the skills to work with clinical data models and common data models. This can help Software Engineers to understand the structure of clinical data and to develop applications for managing it effectively.
Data Engineer
Data Engineers are responsible for designing, building, and maintaining data pipelines. A Clinical Data Models and Data Quality Assessments course may be helpful for this role, as it provides the skills to work with clinical data models and common data models. This can help Data Engineers to understand the structure of clinical data and to develop pipelines for managing it effectively.
Biostatistician
Biostatisticians are responsible for designing and conducting statistical analyses of medical data. A Clinical Data Models and Data Quality Assessments course may be helpful for this role, as it provides the skills to work with clinical data models and common data models. This can help Biostatisticians to understand the structure of clinical data and to develop models for analyzing it effectively.

Reading list

We've selected 13 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Clinical Data Models and Data Quality Assessments.
Provides a comprehensive overview of algorithms for computational biology, covering the principles and practices used to analyze and interpret biological data. It valuable resource for anyone involved in the analysis or interpretation of biological data.
Provides a comprehensive overview of statistical methods for bioinformatics, covering the principles and practices used to analyze and interpret biological data. It valuable resource for anyone involved in the analysis or interpretation of biological data.
Provides a comprehensive overview of Spark. It valuable resource for anyone working with big data.
Provides a comprehensive overview of dimensional modeling, including the principles, techniques, and tools used to create and manage dimensional data models.
Provides a comprehensive overview of natural language processing with Python. It valuable resource for anyone working with natural language processing.
Provides a comprehensive overview of data governance, including the principles, techniques, and tools used to manage data as an asset.
Provides a comprehensive overview of computer vision with Python. It valuable resource for anyone working with computer vision.
Provides a comprehensive overview of Python for data analysis. It valuable resource for anyone working with data.
Provides a comprehensive overview of statistical learning. It valuable resource for anyone working with statistical learning.

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