Ai For Clinical Data Management: Edc, Coding & Lock
 

AI for EDC, queries, coding, reconciliation, and database lock
What you'll learn
Understand how AI can support EDC review, query workflows, coding preparation, reconciliation, and database lock readiness
Use AI more safely for data review, issue detection, query drafting, and operational tracking in clinical data management
Recognize where human oversight, data integrity, traceability, and ALCOA+ thinking must remain central
Apply practical AI workflows to improve consistency, organization, and confidence across real CDM tasks
Requirements
No programming experience is required
Basic familiarity with clinical research, clinical data, or trial workflows is helpful
An interest in AI, data quality, and regulated healthcare operations
A willingness to verify outputs carefully and use AI responsibly in real-world work
Description
This course contains the use of artificial intelligence.
Clinical Data Management sits at the center of trial quality, consistency, and database readiness. Teams are expected to review data carefully, manage queries clearly, support coding and reconciliation workflows, and prepare studies for clean database lock, all while maintaining traceability, oversight, and data integrity. This course is designed to help learners understand how artificial intelligence can support those responsibilities in a practical, responsible, and job-relevant way.
In this course, you will learn where AI can fit into real CDM work across protocol-to-CRF thinking, edit check support, EDC review, data cleaning, query drafting, coding preparation, reconciliation logic, issue tracking, and lock readiness. The goal is not to replace professional judgment, but to help you use AI more effectively while protecting quality, consistency, and operational control.
We also cover the limits of AI in regulated clinical data work. You will learn why verification matters, where hallucinations and over-automation can create risk, how ALCOA+ thinking connects to AI-supported workflows, and why human oversight must remain central when reviewing data, resolving issues, and preparing for lock.
By the end of the course, you will be able to explain how AI can support clinical data management tasks, use prompting more effectively for CDM-related work, identify where traceability and review are essential, and apply structured AI workflows to improve organization and confidence across real study operations. Whether you are already working in clinical data management, preparing for a CDM role, or trying to understand how AI fits into modern trial data workflows, this course gives you a clear and practical foundation.
Who this course is for
Clinical data management professionals and aspiring CDM associates who want practical AI skills for real trial workflows
Clinical research, data operations, and life-science professionals interested in AI-supported data review and query work
Beginners who want a structured introduction to AI in EDC, queries, coding, reconciliation, and database lock
Learners who want to understand how AI can support CDM without losing data integrity, oversight, or compliance focus