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- What is Medical Coding?
- Where is it used in clinical trials?
- How does Clinion implement it?
- What does it look like in practice?
- Related Terms
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What is Medical Coding?
Medical Coding is the standardized practice of converting clinical terms reported in a study such as diseases, adverse events, and treatments into predefined medical codes. This process supports uniform data interpretation, facilitates analysis across trials, and ensures that clinical trial data meets regulatory and reporting requirements.
Where is it used in clinical trials?
In clinical trials, medical coding is used during clinical data management to standardize clinical information reported by investigational sites. Verbatim terms related to adverse events, medical history, and concomitant medications are converted into standardized codes using recognized medical dictionaries MedRa and WHODD .This ensures consistency and uniform interpretation of data across subjects, sites, and study phases.
How does Clinion implement it?
Clinion implements medical coding as an integrated feature within its EDC platform, allowing coding to occur alongside data capture and review rather than in a separate system. Using AI, machine learning, and NLP, the platform automatically interprets verbatim clinical terms and suggests standardized codes from dictionaries such as MedDRA (for adverse events) and WHO Drug Dictionary (for concomitant medications).
The solution follows a human-in-the-loop approach, where coders can review, accept, or modify AI-suggested codes, ensuring accuracy and regulatory compliance. Because coding happens earlier and within the same workflow, Clinion helps improve data quality, reduce manual effort, and accelerate database lock and downstream analysis.
What does it look like in practice?
In practice, Clinion’s medical coding works like an AI assistant embedded inside the EDC. When a verbatim term is entered, the AI uses NLP to interpret the clinical context and compares it with dictionary structures and past coding patterns to generate ranked MedDRA or WHO Drug code suggestions directly within the review workflow.
As coders accept or modify these suggestions, the AI learns from those decisions, improving accuracy and consistency for similar terms over time. Routine entries move closer to auto-coding, while complex or ambiguous cases are flagged for human review reducing manual effort while keeping medical oversight firmly in place.
Category
EDC & Data Management