This blog features:
- An overview and definition of MedDRA coding in pharmacovigilance
- Understanding MedDRA encoding and auto-encoding processes
- Key considerations, challenges, and common pitfalls in MedDRA coding
Introduction
MedDRA coding is a fundamental component of pharmacovigilance and clinical trial safety processing. Every adverse event, medical history term, indication, and product-related event captured in a safety report must be accurately coded using the Medical Dictionary for Regulatory Activities (MedDRA) to ensure consistent reporting, analysis, and regulatory compliance.
As the volume of safety reports continues to increase, many organizations rely on auto-encoding tools within their safety databases to improve efficiency and reduce manual effort. While these tools can significantly accelerate case processing, they are not without limitations. Incorrect coding, inappropriate term selection, and a lack of contextual understanding can lead to data quality issues, regulatory findings, and inaccurate safety evaluations.
Understanding how MedDRA encoding works, how auto-encoding systems assign terms, and the common pitfalls associated with automated coding is essential for every pharmacovigilance professional. In this article, we explore the fundamentals of MedDRA encoding, the role of auto-encoding, and the critical considerations required to maintain coding accuracy and case quality.
MedDRA Coding
MedDRA coding is one of the most important aspects of pharmacovigilance and clinical trial case processing. Without proper MedDRA coding, a safety case is incomplete and cannot be effectively analyzed, reported, or aggregated. It is also one of the most critical and challenging tasks in case processing, where many case processors receive errors, queries, and action items due to coding inaccuracies.
The importance of MedDRA coding extends throughout the lifecycle of a case, especially before case completion and regulatory submission. At the same time, not every processor has direct access to the MedDRA Browser, making coding decisions even more challenging.
During routine case processing, you may have noticed that many adverse events, medical conditions, indications, and medical history terms are already coded when they appear in the source documents or initial reports. In many situations, processors simply accept these coded terms without further review. However, this approach can introduce significant coding errors and data quality issues.
In this article, we will explore MedDRA encoding, auto-encoding, and the common pitfalls that every pharmacovigilance professional should be aware of.
“In pharmacovigilance, accurate coding is not just about selecting a term—it is about preserving the true clinical meaning behind every reported event.”
What is MedDRA Encoding?
Encoding in the context of MedDRA refers to the process of mapping a reported verbatim term (free-text adverse event or medical condition reported by a patient, healthcare professional, or investigator) to a standardized MedDRA term.
The objective is to select the most appropriate Lowest Level Term (LLT) that accurately reflects the reported concept. The selected LLT is then linked to its corresponding Preferred Term (PT), which is generally used for reporting and analysis.
MedDRA Hierarchy (Five Levels)
- SOC – System Organ Class
Example: Gastrointestinal disorders - HLGT – High Level Group Term
Example: Gastrointestinal signs and symptoms - HLT – High Level Term
Example: Abdominal pains - PT – Preferred Term
Example: Upper abdominal pain - LLT – Lowest Level Term
Example: Upper abdominal pain
Note: Although coding begins by selecting the most appropriate LLT, regulatory reporting and analysis are generally performed at the PT level.
Auto-Encoding
Auto-encoding, also known as auto-coding, is a software-assisted process where a safety database automatically suggests or assigns MedDRA terms without direct manual intervention by the coder.
Most modern pharmacovigilance databases include some form of auto-encoding functionality to improve efficiency and reduce manual effort.
How Auto-Encoding Works
1. Exact Match
The verbatim term exactly matches a MedDRA LLT.
Example:
Verbatim: “Headache”
Result:
LLT: Headache
PT: Headache
In such cases, the code is usually applied automatically.
2. Fuzzy Matching or NLP-Based Matching
The system attempts to identify the closest LLT or PT based on spelling similarity, keyword matching, or natural language processing algorithms.
3. AI/ML-Based Matching
Advanced systems use machine learning models trained on historical coding data to predict the most appropriate MedDRA term for a given verbatim description.
Challenges with Auto-Encoding
Although auto-encoding can significantly improve productivity, its confidence level varies depending on the quality and complexity of the reported term. Automated suggestions should never be accepted blindly.
Many coding errors occur because processors assume that automatically assigned terms are always correct.
Key Considerations for MedDRA Auto-Encoding
Regulatory Expectations
Even when a term is auto-coded, regulatory authorities expect appropriate medical review, particularly for serious adverse events, medically significant events, and cases requiring expedited reporting.
Version Control
MedDRA is updated twice each year. Organizations must maintain proper version control and ensure that encoded terms are associated with the correct MedDRA version.
Synonyms and Multiple LLTs
A single medical concept can be represented by multiple LLTs. The selected term should always be the most specific and medically accurate representation of the reported event.
Clinical Context Matters
The same verbatim term may require different coding approaches depending on the context of the study, indication, or medical history.
Common Auto-Encoding Pitfalls
One of the biggest risks of auto-encoding is the selection of inappropriate terms due to a lack of contextual understanding.
For example:
Verbatim: “Allergic to CAT scan”
Auto-encoded as:
LLT: Allergic to cats
This coding is clearly incorrect because the term “CAT” refers to a computed axial tomography scan, not the animal.
Another example:
Verbatim: “Myocardial infarction in the fall of 2000”
Auto-encoded as:
LLT: Myocardial infarction
LLT: Fall
In this case, the system incorrectly interprets the word “fall” as an adverse event instead of recognizing it as a reference to the season.
These examples demonstrate why all auto-encoded terms should be carefully reviewed before finalization. While automation can improve efficiency, accurate MedDRA coding still requires human judgment, medical understanding, and attention to context.
Conclusion
MedDRA coding is much more than a routine data entry activity—it is a critical process that directly impacts case quality, safety signal detection, regulatory reporting, and patient safety. While auto-encoding technologies have improved efficiency and reduced manual workload, they cannot fully replace human judgment and medical understanding.
Every auto-coded term should be carefully reviewed to ensure that the selected MedDRA term accurately reflects the reported clinical concept and context. Even seemingly simple verbatim terms can be misinterpreted by automated systems, leading to incorrect coding and potentially misleading safety data.
By understanding the MedDRA hierarchy, the principles of encoding, and the common pitfalls of auto-encoding, pharmacovigilance professionals can improve coding consistency, reduce quality issues, and contribute to more reliable safety assessments. Ultimately, accurate MedDRA coding remains one of the most important foundations of effective pharmacovigilance practice.
