Why Are Case Narratives Still Inconsistent Between Reviewers?

Pharmacovigilance, the science of detecting, assessing, understanding, and preventing adverse effects of medicines, is fundamental to ensuring patient safety and the efficacy of therapeutics. Among the critical tasks in pharmacovigilance is the preparation and review of case narratives—detailed reports that describe individual adverse drug reactions (ADRs) or other safety-related events. Despite advances in standardization and technology, inconsistencies in case narratives between reviewers remain a persistent challenge. Understanding why these discrepancies occur and exploring solutions is essential for regulatory compliance, improved patient safety, and operational efficiency.

Understanding Case Narratives in Pharmacovigilance

A case narrative is more than a mere summary of adverse events. It is a structured document that includes detailed patient demographics, medical history, concomitant medications, description of the adverse event, clinical outcomes, and causality assessments. Case narratives are used by regulatory authorities, pharmacovigilance teams, and safety experts to evaluate the risk-benefit profile of a drug.

The creation of a case narrative is not purely mechanical. While standardized guidelines such as ICH E2B(R3) and MedDRA coding provide frameworks, the narrative must convey clinical nuances clearly. This requires combining structured data with clinical judgment, making the process susceptible to human interpretation.

Sources of Inconsistency

Several factors contribute to inconsistencies between reviewers in pharmacovigilance case narratives.

1. Variability in Clinical Interpretation

Even among experienced reviewers, clinical interpretation of the same set of data can vary. For instance, reviewers may differ in how they assess the severity of an adverse event or the relationship between a drug and the observed reaction. Differences in clinical training, therapeutic area experience, and prior exposure to similar cases can all influence the way a narrative is framed.

2. Differences in Writing Style

Case narratives require both precision and readability. However, individual writing styles—choice of language, sentence structure, and level of detail—can differ significantly. One reviewer may provide a concise narrative focusing only on key clinical details, while another may include extensive context, leading to variations in length, emphasis, and perceived importance of information.

3. Subjectivity in Causality Assessment

Causality assessment, determining whether the drug caused the adverse event, is inherently subjective. While scales like WHO-UMC or Naranjo provide guidance, the final judgment can vary between reviewers. These differences propagate into the narrative, affecting how the event is described and how strongly the drug-event association is emphasized.

4. Incomplete or Ambiguous Source Data

Case narratives are only as accurate as the data they are based on. Often, source documents such as patient charts, lab reports, or spontaneous reports from healthcare providers are incomplete, ambiguous, or inconsistent. Reviewers may interpret these gaps differently, leading to discrepancies in the final narrative.

5. Evolving Regulatory and Company Guidelines

Regulatory requirements and company-specific Standard Operating Procedures (SOPs) for case narratives can vary across regions and evolve over time. A reviewer working under one set of guidelines may approach the narrative differently from another working under updated or region-specific protocols. This leads to inconsistencies not only in content but also in the organization, formatting, and level of detail.

6. Time Constraints and Workload Pressure

Pharmacovigilance teams often operate under tight timelines, especially when dealing with expedited or serious adverse event reports. High workload can lead to hurried reviews, less attention to subtle details, and variations in how thoroughly each reviewer assesses and documents a case. Over time, this can introduce systematic inconsistencies in case narratives.

7. Differences in Training and Experience

Even within the same organization, reviewers may have different levels of training, experience, and exposure to therapeutic areas. Less experienced reviewers may struggle to capture critical clinical nuances or misinterpret medical jargon, while seasoned reviewers may inject their clinical judgment more confidently, resulting in narratives that vary in both depth and style.

8. Limitations of Existing Tools

Many pharmacovigilance teams rely on legacy tools or manual processes for narrative writing. While electronic case report forms (eCRFs) and safety databases standardize some data entry, narrative writing often requires manual synthesis. Without advanced guidance or automation, inconsistencies between reviewers are inevitable.

Implications of Inconsistent Case Narratives

Inconsistent case narratives are not merely an academic concern—they have real-world implications for patient safety, regulatory compliance, and business operations.

• Regulatory Risks: Regulatory authorities require accurate, consistent, and high-quality narratives to evaluate drug safety. Inconsistent narratives can trigger queries, audits, or even delays in drug approval and reporting timelines.

• Impact on Signal Detection: Pharmacovigilance relies on accurate narratives for signal detection and risk assessment. Variations in how events are described can affect data aggregation, pattern recognition, and ultimately, the identification of emerging safety risks.

• Operational Inefficiencies: Reviewers may spend additional time reconciling differences between narratives, duplicating effort and increasing operational costs.

• Compromised Communication: Inconsistent narratives can hinder effective communication between pharmacovigilance teams, medical monitors, regulatory agencies, and external stakeholders, potentially affecting decision-making in critical safety matters.

  
  

Addressing Inconsistency: Best Practices

To reduce variability between reviewers, organizations adopt multiple strategies:

1. Standardized Templates: Using structured narrative templates aligned with regulatory guidelines ensures that all essential sections are consistently captured.

2. Training and Calibration: Regular training sessions and calibration exercises help align reviewers’ understanding of causality, severity, and reporting expectations.

3. Peer Review and Quality Checks: Implementing multi-level review processes and quality control checks helps identify and correct inconsistencies before narratives are submitted.

4. Clear SOPs: Maintaining up-to-date Standard Operating Procedures that define narrative content, style, and regulatory requirements reduces subjective interpretation.

5. Leveraging Technology: Automation tools and AI-assisted solutions can standardize narrative structure, suggest appropriate phrasing, and reduce human variability.

   
   

How Tesserblu Can Help

Tesserblu is emerging as a transformative solution in the pharmacovigilance space, particularly in addressing inconsistencies in case narratives. By combining AI-driven automation with domain expertise, Tesserblu enables pharmacovigilance teams to create more consistent, accurate, and regulatory-compliant narratives efficiently.

1. Automated Narrative Drafting

Tesserblu can automatically generate case narrative drafts from structured adverse event data. By analyzing patient demographics, medical history, lab results, and concomitant medications, it produces standardized narratives that capture all critical information. This reduces subjectivity and ensures consistency across reviewers.

2. Guided Causality and Severity Assessment

Through embedded clinical algorithms and regulatory guidelines, Tesserblu provides guidance for causality and severity assessment. It offers evidence-based suggestions for wording and classification, helping reviewers align their interpretation and minimize discrepancies.

3. Template-Based Consistency

Tesserblu uses standardized templates aligned with global regulatory standards. Every narrative generated follows the same structure, ensuring uniformity in content and format while still allowing for necessary customization based on individual cases.

4. Audit Trail and Version Control

One of the challenges in narrative review is tracking changes and reconciling reviewer differences. Tesserblu maintains a full audit trail and version control, allowing teams to see what changes were made, by whom, and why. This transparency reduces miscommunication and ensures compliance with regulatory expectations.

5. Training and Knowledge Transfer

Tesserblu serves as a continuous learning tool for reviewers. By analyzing prior narratives, it highlights best practices and provides examples of high-quality narratives. New reviewers can quickly learn organizational standards, reducing variability due to experience levels.

6. Integration with Safety Databases

Tesserblu integrates seamlessly with pharmacovigilance safety databases and electronic case report systems, ensuring that data flows accurately into narratives. This eliminates manual data entry errors and further enhances consistency.

Looking Ahead

While human judgment will always play a role in pharmacovigilance case narratives, technology like Tesserblu is reshaping the landscape. By reducing subjectivity, enforcing standardization, and streamlining review processes, it helps pharmacovigilance teams achieve higher consistency and quality in narratives.

In the coming years, we can expect broader adoption of AI-assisted narrative generation and advanced data analytics, transforming how pharmacovigilance teams manage safety data. With these tools, organizations can focus more on clinical insights and proactive safety management rather than reconciling narrative inconsistencies.

Conclusion

Inconsistencies in pharmacovigilance case narratives are driven by a combination of subjective clinical interpretation, variable writing styles, incomplete data, evolving guidelines, and human factors such as experience and workload. While these challenges are inherent to the narrative review process, they are not insurmountable.

Standardization, training, clear SOPs, and technological solutions like Tesserblu provide practical pathways to reducing variability. By leveraging AI-driven narrative generation, template standardization, and guided assessments, pharmacovigilance teams can ensure high-quality, consistent narratives that meet regulatory standards, support accurate signal detection, and ultimately enhance patient safety.

Consistency in case narratives is no longer just an operational goal-it is a critical element in safeguarding patients and upholding the integrity of the pharmacovigilance process. With the right combination of human expertise and technological innovation, organizations can bridge the gap between variability and quality, ensuring that every narrative reflects a clear, accurate, and clinically meaningful account of each adverse event. Book a meeting if you are interested to discuss more.