Clinical Data Models and Data Quality Assessments

University of Colorado System via Coursera

Go to Course: https://www.coursera.org/learn/clinical-data-models-and-data-quality-assessments

Introduction

### Course Review: Clinical Data Models and Data Quality Assessments #### Overview The "Clinical Data Models and Data Quality Assessments" course on Coursera is an essential program for healthcare professionals, data scientists, and anyone interested in understanding the complexities of clinical data management. This course focuses on teaching learners about clinical data models, the significance of common data models, and the critical evaluation of data quality—all fundamental in supporting clinical care and advancing data science. #### Course Syllabus Breakdown 1. **Introduction: Clinical Data Models and Common Data Models** This introductory week sets the stage for understanding clinical data models and the need for standardized frameworks in clinical data management. You will learn about the significance of common data models in enhancing data interoperability across national and international networks. The use of Entity-Relationship Diagrams (ERDs) is emphasized, helping learners visualize the key technical features of various data models. 2. **Tools: Querying Clinical Data Models** Diving deeper into the technical aspects, this module focuses on querying clinical data models, with MIMIC3 as a case study and OMOP as a research common data model. This segment equips learners with hands-on experience in understanding data structures, preparing them for practical applications in real-world scenarios. 3. **Techniques: Extract-Transform-Load (ETL) and Terminology Mapping** This section tackles the essential processes behind data management - extracting data, transforming it to fit operational needs, and loading it into data warehouses. The module offers real-world examples, providing insights into terminology mapping challenges, which are particularly relevant in clinical settings where precise data transformation is necessary. 4. **Techniques: Data Quality Assessments** Recognizing that data quality is paramount, this part of the course explores various dimensions of data quality. Key concepts include the challenges of maintaining data quality, measurable criteria, and the application of data quality rules to ensure the acceptability of data for clinical use. 5. **Practical Application: Create an ETL Process to Transform a MIMIC-III Table to OMOP** This capstone project allows learners to consolidate their knowledge by developing their own ETL process. You will apply what you've learned to transform clinical data from MIMIC3 into the OMOP model, enhancing your practical skills and preparing you for challenges in the field. #### Recommendations I highly recommend the "Clinical Data Models and Data Quality Assessments" course for anyone looking to deepen their understanding of clinical data management. Here are a few reasons why: - **Comprehensive Curriculum:** The course provides a thorough exploration of both foundational concepts and advanced techniques essential for managing clinical data. - **Hands-On Learning:** Practical components, like the ETL process project, not only reinforce theoretical knowledge but also cultivate skills applicable in real-world settings. - **Diverse Applications:** The skills you acquire can be beneficial in various roles—be it clinical informatics, healthcare analytics, or data science. - **Expert Instructors:** Taught by industry professionals, the course offers insights backed by experience in the field, enhancing the learning experience. In conclusion, this course stands out as a valuable asset for individuals aiming to excel in the ever-evolving landscape of clinical data and data science. Completing it will arm you with the knowledge and skills to navigate the complexities of clinical data models and ensure data quality in practice. Whether you're a budding data analyst, a healthcare professional, or simply interested in the intersection of medicine and data science, this course is a stepping stone towards your goals.

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.

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.