CDM Essentials

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AI, Validation and 21 CFR Part 11: Regulating AI in Clinical Data Management
No separate AI rulebook exists: 21 CFR Part 11, computer system validation and ALCOA govern AI in Clinical Data Management. Where the friction sits, what FDA's 2025 draft guidance proposes,... Read more...
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AI in Clinical Data Management: Skills and Training for 2026
The AI skills that matter for clinical data managers in 2026 are reviewing skills, not building skills. Which five to develop, which common investments to skip, and how to build... Read more...
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AI in Clinical Data Management: 8 Real-World Use Cases
How is AI actually used in Clinical Data Management today? Eight verifiable use cases, from anomaly detection and automated medical coding to EHR-to-EDC data transfer, with examples from Medidata, Oracle... Read more...
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Machine Learning in Clinical Data Management: Applications and Examples
What machine learning actually means in Clinical Data Management, how it differs from the broader AI label, and the four applications that dominate current practice: coding, anomaly detection, reconciliation matching... Read more...
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Automation in Clinical Data Management: What Can (and Can't) Be Automated
A practitioner's guide to the automation boundary in Clinical Data Management: what runs automatically today in setup, conduct and close-out, what must stay human, and how to introduce automation without... Read more...
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AI in Clinical Trial Data Management: From Capture to Database Lock
Follow a data point from patient to database lock and AI now touches every station: capture, validation, coding, reconciliation, quality review and lock readiness. A station-by-station map of AI in... Read more...
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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... Read more...
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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... Read more...
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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... Read more...
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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...
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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... Read more...
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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...