CDM Essentials

AI in Clinical Data Management: How Artificial Intelligence Is Transforming Clinical Trials (2026)
Artificial intelligence is reshaping how clinical trial data is captured, cleaned, and reviewed. From automated anomaly detection to AI-assisted query generation, the routine, repetitive work that once consumed a clinical data manager's day is increasingly handled by machine learning — freeing professionals for the higher-value work of oversight, interpretation, and strategy. This guide explains what AI in clinical data management actually means in practice, where it is being used today, what it can and cannot do, and what it means for the professionals who build their careers in this field.... Read more...
Quality Control and Audit Trails in Clinical Data Management
Quality control proves the data meets the required standard, and the audit trail makes data integrity demonstrable. This guide covers QC checks, quality metrics, the audit trail, and the ALCOA principles. Read more...
Data Validation in Clinical Data Management: Edit Checks, Queries and Cleaning
Data validation is the engine of data cleaning — edit checks, query management, discrepancy resolution, and manual review. This guide covers how it works, and how it differs from computer system validation. Read more...
The Clinical Data Management Plan: Contents, Structure and Purpose
The data management plan is the central document that defines how a trial's data will be captured, cleaned, coded, reconciled, and locked. This guide explains its purpose, structure, and contents. Read more...
Clinical Data Management Roles and Responsibilities: Who Does What
Clinical data management is a team discipline. This guide explains the core roles — data manager, coordinator, database programmer, medical coder, and reviewers — and how sponsor and CRO responsibilities divide. Read more...
Medical Coding in Clinical Data Management: MedDRA and WHODrug Explained
Medical coding standardises adverse events and medications using MedDRA and WHODrug. This guide explains the dictionaries, the coding workflow, auto-coding versus manual coding, and quality control. Read more...
Reconciliation in Clinical Data Management: SAE, Lab, and Vendor Data
Reconciliation confirms that data held in separate systems agrees before database lock. This guide covers the three most common types in clinical data management: SAE, laboratory, and external vendor reconciliation. Read more...
The Clinical Data Management Process: A Step-by-Step Guide (2026)
The clinical data management process turns raw trial observations into a clean, locked, analysis-ready dataset. This step-by-step guide follows the three stages used in practice: set-up, conduct, and close-out. Read more...