How Accurate Is Your MedDRA or WHO-DD Coding Process?

Pharmacovigilance (PV) is the backbone of patient safety in the life sciences industry. Its primary aim is to detect, assess, understand, and prevent adverse effects or any other drug-related problems. A crucial step in this process is the accurate coding of adverse event (AE) data, which enables effective case processing, regulatory reporting, and signal detection. Two widely used coding dictionaries—MedDRA (Medical Dictionary for Regulatory Activities) and WHO-DD (World Health Organization Drug Dictionary)—play a pivotal role in ensuring standardization and consistency.

Yet, despite their importance, coding errors remain a persistent challenge in pharmacovigilance operations. Misclassifications or inconsistencies can compromise regulatory submissions, delay safety signal detection, and impact patient safety. This brings us to a vital question: How accurate is your MedDRA or WHO-DD coding process, and what can be done to improve it?

Understanding MedDRA and WHO-DD Coding

Before analyzing accuracy, let’s revisit the fundamentals:

1. MedDRA

MedDRA is a standardized medical terminology developed under the International Council for Harmonisation (ICH). It is used for coding adverse events, indications, medical histories, and procedures in clinical trials and post-marketing safety surveillance.

Key Features:

• Hierarchical structure with five levels: System Organ Class (SOC), High-Level Group Term (HLGT), High-Level Term (HLT), Preferred Term (PT), and Lowest Level Term (LLT).

• Regular updates (twice yearly) to ensure alignment with current medical knowledge.

• Widely required for regulatory submissions, including EudraVigilance and FDA FAERS.

  
  

2. WHO-DD

WHO-DD, maintained by the Uppsala Monitoring Centre (UMC), is the global standard for coding drug names and drug-related information in Individual Case Safety Reports (ICSRs).

Key Features:

• Classifies drugs by active ingredient, product name, ATC (Anatomical Therapeutic Chemical) codes.

• Ensures standardized drug identification for global pharmacovigilance.

• Supports efficient signal detection for drug safety monitoring.

Both dictionaries are designed to eliminate ambiguity, but their accuracy relies heavily on the coding process used by pharmacovigilance teams.

Why Accuracy in Coding Matters

Accurate coding is the foundation of effective pharmacovigilance for several reasons:

1. Regulatory Compliance: Authorities like the FDA, EMA, and MHRA require standardized MedDRA and WHO-DD coding in safety reports. Coding errors can lead to compliance issues, delayed approvals, or regulatory queries.

   
   

2. Signal Detection and Risk Management: Safety signal detection relies on aggregated coded data. Inaccurate coding may dilute or distort data patterns, potentially causing delayed identification of emerging safety risks.

   
   

3. Data Integrity for Analysis: Clean and consistent coding ensures that downstream processes, such as aggregate reports (DSUR, PBRER) and risk-benefit evaluations, are based on reliable data.

   
   

4. Operational Efficiency: Correct coding minimizes the need for repeated reviews and rework, reducing case-processing timelines and costs.

   
   

Common Challenges in MedDRA and WHO-DD Coding

Despite structured dictionaries, achieving high accuracy is easier said than done. Several operational challenges impact the coding process:

1. Complex Medical Terminology

Medical narratives in ICSRs often contain ambiguous or non-standardized language. For example:

• “Stomach upset” could map to Dyspepsia, Abdominal discomfort, or Nausea depending on context.

• Drug brand names differ across regions, complicating WHO-DD coding.

  
  

2. Frequent Dictionary Updates

Both MedDRA and WHO-DD are updated multiple times per year. Staying current with version changes is critical, as outdated coding can cause inconsistency during audits and regulatory submissions.

3. High Case Volumes

Pharmacovigilance teams often handle thousands of ICSRs monthly. Manual coding under tight timelines can result in human errors, particularly in large post-marketing surveillance studies.

4. Inconsistent Coder Expertise

Accuracy depends on the skills of case processors. Different team members might interpret narratives differently, leading to variability in coding.

5. Limited Automation

Many organizations still rely heavily on manual coding processes with limited technological support. This increases the risk of missing the exact LLT or misclassifying a drug in WHO-DD.

Evaluating the Accuracy of Your Coding Process

To understand how accurate your coding process truly is, consider the following evaluation methods:

1. Quality Audits and Peer Reviews: Conduct regular audits of coded cases by senior coders or medical reviewers to detect patterns of errors or deviations from SOPs.

2. Error Rate Tracking: Track key performance indicators (KPIs) like:

   • Coding error rate (number of incorrect codes per 100 cases)

   • Inter-coder agreement percentage

   • Rework frequency

3. Consistency Checks with Historical Data: Evaluate if similar adverse events or drug entries are coded consistently across cases and over time.

4. Cross-Dictionary Validation: If using MedDRA for AEs and WHO-DD for drugs, ensure that drug-event relationships are harmonized in the database.

   
   

Best Practices for Improving Coding Accuracy

Once you’ve assessed the accuracy, the next step is to optimize your coding process. Some best practices include:

1. Comprehensive Coder Training

Regular training and refresher programs ensure coders stay updated with:

• Latest MedDRA and WHO-DD versions

• Regional regulatory requirements

• Case processing SOPs

  
  

2. Version Control and Impact Assessment

Always upgrade to the latest dictionary versions and perform an impact analysis to identify which cases require recoding.

3. Implement Coding Guidelines

Maintain clear internal coding guidelines, including:

• Hierarchies for resolving ambiguous terms

• Handling synonyms, abbreviations, and colloquial expressions

• Standardized practices for combination drugs

  
  

4. Leverage Technology for Semi-Automation

Adopting coding tools with auto-suggest functionality can improve speed and reduce manual errors. These tools can pre-populate likely LLTs or ATC codes, leaving the final review to human coders.

5. Regular Quality Control

Implement multi-level QC for high-volume case processing. Early detection of inconsistencies prevents error propagation.

How Tesserblu Can Help

Improving coding accuracy in pharmacovigilance requires more than just manual effort—it calls for intelligent automation. This is where Tesserblu comes in.

Tesserblu is an advanced pharmacovigilance automation platform designed to streamline MedDRA and WHO-DD coding processes while enhancing accuracy and compliance.

Key Benefits of Tesserblu:

1. AI-Powered Auto-CodingTesserblu leverages natural language processing (NLP) and machine learning algorithms to auto-suggest the most appropriate MedDRA LLTs or WHO-DD entries, significantly reducing manual effort and error rates.

   
   

2. Real-Time Dictionary Updates: The platform stays synchronized with the latest MedDRA and WHO-DD versions, ensuring coders always work with the most up-to-date terms.

   
   

3. Error Detection and Consistency Checks: Built-in quality control flags inconsistencies, such as conflicting drug-event pairings or repeated misclassifications, enabling faster corrections.

   
   

4. Scalability for High-Volume Processing: Whether you are processing hundreds or thousands of ICSRs daily, Tesserblu’s automation allows your team to scale without compromising quality.

   
   

5. Audit-Ready Reporting: Tesserblu provides transparent logs of coding decisions, simplifying regulatory audits and inspections.

By combining automation, version management, and built-in quality controls, Tesserblu empowers PV teams to achieve higher coding accuracy, improve operational efficiency, and enhance patient safety outcomes.

Conclusion

Accurate MedDRA and WHO-DD coding is not just a regulatory checkbox—it is central to effective pharmacovigilance. Errors in coding can have cascading effects, from delayed safety signal detection to regulatory non-compliance.

Organizations that rely solely on manual coding often struggle with accuracy, consistency, and efficiency. By adopting structured coding guidelines, regular training, and intelligent tools like Tesserblu, pharmacovigilance teams can transform their coding process into a highly accurate, compliant, and future-ready operation.

In a world where patient safety is paramount and regulatory scrutiny is intensifying, the question is no longer whether you can afford to improve your coding accuracy—but how quickly you can make it happen. Book a meeting, if you are interested to discuss more.