Balancing Auto-coding and Manual Coding in MedDRA: Risks and Benefits

Introduction to Auto-coding and Manual Coding

Adverse event reporting in clinical trials depends heavily on MedDRA coding. Coders and pharmacovigilance staff transform investigator-reported verbatim terms into standardized Lowest Level Terms (LLTs) and Preferred Terms (PTs). Two primary approaches exist: auto-coding and manual coding. Both methods are widely used, and most sponsors employ a hybrid approach to balance efficiency and accuracy.

Auto-coding refers to the use of software algorithms that automatically map verbatim terms to MedDRA LLTs and PTs. This process improves speed and consistency but carries risks of misclassification. Manual coding, by contrast, requires trained coders to review verbatim terms and select the most accurate PT, ensuring clinical accuracy but requiring more time and resources.

Regulatory authorities, including the FDA, EMA, MHRA, and CDSCO, accept either method, provided coding is accurate, consistent, and traceable. Inspections often focus on whether sponsors have controls to minimize auto-coding errors and whether manual coding is performed with adequate SOPs and training.

Benefits of Auto-coding

Auto-coding offers several advantages:

• Speed: Automated mapping allows high-volume processing of adverse events, especially in late-phase or large-scale trials.
• Consistency: Ensures identical verbatim terms are mapped to the same PT, reducing variability between coders.
• Efficiency: Minimizes manual workload for straightforward terms, freeing safety teams for complex coding tasks.
• Scalability: Particularly useful in global pharmacovigilance databases handling thousands of SAE and AE reports daily.

For example, common terms such as “headache,” “nausea,” or “fever” can be reliably auto-coded to their respective PTs with little risk of error. In such scenarios, auto-coding significantly improves throughput without compromising accuracy.

Risks of Auto-coding

Despite its advantages, auto-coding presents risks:

• Misclassification: Verbatim terms that are ambiguous or unusual may be incorrectly coded.
• Lack of clinical context: Algorithms may select PTs that miss subtle nuances in the investigator’s description.
• False confidence: Users may rely too heavily on automated systems without appropriate review.
• Regulatory findings: Incorrect PT assignments discovered during inspections can be classified as major findings.

A common example is the investigator term “fainting.” An auto-coding algorithm may map this to “Loss of consciousness,” while the clinically correct PT should be “Syncope.” Without manual review, the coding would be inaccurate and potentially misleading in safety analyses.

Benefits of Manual Coding

Manual coding by trained professionals provides several advantages over automation:

• Clinical judgment: Coders apply medical knowledge to interpret ambiguous or complex terms.
• Accuracy: Reduces risk of misclassification by considering clinical context and study-specific nuances.
• Flexibility: Allows handling of rare events not typically recognized by auto-coding algorithms.
• Audit readiness: Demonstrates human oversight in coding processes, which regulators value during inspections.

For example, the term “liver swelling” might not have a straightforward auto-coded PT. A trained coder would correctly assign “Hepatomegaly,” ensuring data accuracy.

Limitations of Manual Coding

Manual coding, however, has its drawbacks:

• Time-consuming: Large datasets with thousands of AEs require significant manpower.
• Inter-coder variability: Different coders may select different PTs for the same term without clear conventions.
• Resource intensive: Requires continuous training and staffing.

For global trials, where thousands of SAE reports may be received monthly, manual-only coding can strain resources and delay reporting timelines.

Hybrid Approach: Best of Both Worlds

Most sponsors adopt a hybrid approach that combines the efficiency of auto-coding with the accuracy of manual coding:

• Auto-coding: Used for common, low-risk terms like “headache” or “nausea.”
• Manual coding: Applied to ambiguous, rare, or complex terms requiring clinical interpretation.
• Quality checks: Safety departments conduct routine audits to identify and correct auto-coding errors.

This balanced method ensures that large volumes of routine data are processed efficiently, while complex cases receive the clinical oversight they require. Many sponsors implement a “70/30 split,” where 70% of coding is auto-coded and 30% is manually reviewed.

Regulatory Expectations and Inspections

Regulators expect sponsors to demonstrate oversight in both auto-coding and manual coding processes. Common inspection findings include:

• Over-reliance on auto-coding without adequate review.
• Failure to document manual coding decisions.
• Lack of SOPs governing auto-coding thresholds and exceptions.
• Inconsistent coding across studies due to inter-coder variability.

To mitigate these risks, sponsors should maintain detailed SOPs, perform reconciliation checks, and train coders on both methods. Reference registries like the Japan Registry of Clinical Trials highlight the importance of coding accuracy in safety reporting worldwide.

Key Takeaways

The choice between auto-coding and manual coding in MedDRA is not binary. Clinical trial sponsors should:

• Leverage auto-coding for routine terms to improve speed and consistency.
• Apply manual coding for complex, ambiguous, or high-risk terms.
• Adopt hybrid models with built-in quality controls.
• Ensure SOPs and conventions are updated with each MedDRA release.
• Maintain inspection readiness by documenting coding workflows and training logs.

By balancing the benefits and risks of both methods, sponsors can ensure that safety data is coded efficiently, accurately, and in line with global regulatory expectations.