Can AI Improve Writing Consistency Across Report Sections in Pharmacovigilance?

Introduction

Pharmacovigilance (PV) is one of the most documentation-intensive domains in the life sciences industry. From aggregate reports such as Periodic Safety Update Reports (PSURs) and Development Safety Update Reports (DSURs) to Risk Management Plans (RMPs) and Signal Detection Reports, each document demands precision, scientific accuracy, and—most importantly—consistency.

Consistency in writing across report sections is not a mere stylistic preference; it ensures regulatory clarity, credibility, and traceability. However, maintaining this consistency is easier said than done, especially when multiple authors, reviewers, and cross-functional teams contribute to a single report over time.

With the emergence of Artificial Intelligence (AI) and Natural Language Processing (NLP), the question now arises—can AI truly enhance writing consistency across pharmacovigilance reports? The answer is increasingly becoming a strong “yes.” Let’s explore how.

The Challenge of Consistency in Pharmacovigilance Writing

Pharmacovigilance documents are scientific, regulatory, and narrative-driven at once. Each section must reflect not only accurate data interpretation but also uniform phrasing, terminology, and tone. Yet, several challenges persist:

1. Multiple Contributors: PV reports often involve teams from clinical, safety, regulatory, and medical writing departments, each with their own writing style and interpretation of templates.

2. Dynamic Regulatory Requirements: Guidelines from agencies such as the EMA, FDA, and MHRA evolve regularly. Consistent updates to wording, safety sections, and references become difficult to standardize.

3. Manual Review Dependency: Even with templates and SOPs, consistency checks rely heavily on human proofreading, which is time-consuming and prone to oversight.

4. Repetitive yet Variable Content: Reports such as PSURs and DSURs reuse similar text across cycles, but minor updates or inconsistencies in terminology (e.g., “adverse reaction” vs. “adverse event”) can lead to compliance issues.

5. Versioning and Template Drift: Over time, deviations from master templates accumulate—leading to fragmented report structures or inconsistent tone and style.

These challenges make AI a compelling solution for automating consistency checks, linguistic harmonization, and stylistic alignment.

How AI Enhances Writing Consistency

AI, particularly through Natural Language Generation (NLG) and Language Quality Assurance (LQA) models, can dramatically improve writing consistency in PV reporting.

Below are the key areas where AI demonstrates impact:

1. Harmonizing Terminology and Medical Vocabulary

AI-driven terminology checkers can be trained on organization-specific Medical Dictionaries for Regulatory Activities (MedDRA) and WHO-DD terminologies.By integrating controlled vocabularies and glossaries, AI tools ensure that every mention of a product, adverse event, or indication follows the approved nomenclature.

For example:If one section refers to an adverse event as “elevated hepatic enzymes” while another says “raised liver function tests”, AI models can flag this discrepancy and suggest alignment based on the preferred term.

2. Template-Adherence and Structural Coherence

Regulatory reports follow structured templates prescribed by ICH and regional authorities. AI can automatically compare the draft against the standard structure—checking whether sections like “Summary of Safety Concerns”, “Post-Marketing Experience”, or “Benefit-Risk Evaluation” are consistent in tone and order.

By applying document pattern recognition, AI tools detect deviations in section headings, sequence, or hierarchy and suggest corrections—ensuring every report adheres to the same format and logical flow.

3. Semantic Consistency Across Sections

One of the most challenging aspects of PV report writing is ensuring semantic consistency—that the meaning and interpretation of safety data remain uniform across different sections.

For instance, the “Summary of Safety Concerns” section must align with the “Risk Characterization” section. AI can perform semantic mapping, comparing statements across sections and flagging logical contradictions or data mismatches.

Example:If the signal evaluation section concludes “no causal relationship identified,” but the summary later states “probable association observed,” AI can detect this inconsistency instantly.

4. Automated Style and Tone Checks

Pharmacovigilance writing demands a scientific yet neutral tone. AI-based style engines can analyze readability, passive-to-active voice ratio, sentence complexity, and tone uniformity.

By learning from previous approved reports, AI can automatically adjust stylistic parameters to match the organization’s Medical Writing SOPs—ensuring all contributors produce content that “sounds” consistent even when written by different authors.

5. Cross-Referencing and Data Validation

Consistency also depends on accurate cross-referencing of numerical data—such as case counts, exposure numbers, or frequency percentages—across different report sections.

AI systems can extract tabular data, validate them against other sections, and highlight mismatches automatically.This is especially useful when integrating signal management databases or case line listings into PSURs or PBRERs.

6. Continuous Learning and Version Evolution

Modern AI systems employ machine learning feedback loops. Each time reviewers make corrections, the AI refines its understanding of what “consistent” means within that specific organization’s context.

Over time, it becomes more intelligent—adapting to internal writing conventions, terminology nuances, and regulatory feedback patterns.

The Role of Generative AI in PV Report Authoring

Generative AI, such as large language models (LLMs), goes beyond grammar checks. It can generate first-draft narratives for repetitive sections like:

• Cumulative summary of cases reported

• Actions taken for safety signals

• Conclusion and next steps

When coupled with pharmacovigilance data, AI can ensure that every new report aligns linguistically with the previous submission—providing narrative consistency and continuity across reporting cycles.

Moreover, advanced LLMs can even rephrase repetitive content intelligently while maintaining compliance and stylistic coherence, reducing the “copy-paste fatigue” commonly faced by PV writers.

Regulatory Compliance and Transparency

Regulatory bodies encourage clarity and consistency. AI-assisted writing supports compliance by:

• Maintaining traceability of textual changes

• Enforcing controlled vocabularies and templates

• Supporting audit-ready documentation with version history and justification for edits

Tools equipped with explainable AI (XAI) frameworks ensure that every suggestion is transparent—reviewers can see why the AI flagged a phrase or proposed a change, maintaining accountability and trust.

Real-World Example: Harmonizing a DSUR

Consider a global safety team preparing an annual DSUR involving contributions from multiple affiliates.

Without AI:

• Teams manually merge sections, leading to inconsistencies in section titles and terminology.

• The safety conclusion uses a slightly different tone than the executive summary.

• Repeated proofreading rounds consume days of reviewer time.

With AI:

• The AI scans all sections, identifies inconsistent usage (e.g., “subjects” vs. “participants”), and harmonizes terminology.

• It checks alignment between “Summary of Important Risks” and “Conclusion.”

• It generates a consistency score for the report and recommends edits accordingly.

Result: A uniform, regulator-ready document with a fraction of the manual effort.

Integrating AI into Existing PV Workflows

Adoption does not require a complete overhaul. AI can integrate seamlessly into existing document management or PV authoring systems.

Common integration pathways include:

• Microsoft Word or Excel plug-ins for real-time suggestions

• APIs that connect AI models to PV document repositories

• Custom dashboards that generate consistency and style reports for each document

Organizations can start small—by automating terminology checks or cross-section alignment—and gradually expand to full AI-assisted authoring.

How Tesserblu Can Help

Tesserblu is at the forefront of applying AI to pharmacovigilance documentation. Its capabilities are designed specifically for consistency, compliance, and collaboration in medical and regulatory writing.

Here’s how Tesserblu empowers PV teams:

1. AI-Powered Consistency Engine

Tesserblu’s core algorithm uses context-aware NLP to analyze all sections of a report, detecting subtle variations in phrasing, terminology, or tone. It doesn’t just flag grammatical differences—it understands the scientific intent behind each sentence.

• Detects semantic drift between sections

• Harmonizes drug names, event terms, and safety statements

• Suggests standardized language based on previous approved reports

• 
  

2. Smart Templates and Controlled Vocabulary Integration

Tesserblu integrates with standard templates (e.g., ICH E2C, E2F, E2A) and in-house SOPs. It enforces adherence to structure and terminology, reducing the risk of template drift.

The platform’s controlled vocabulary management ensures uniformity in:

• MedDRA terms

• WHO-DD drug names

• Company-defined glossary terms

  
  

3. Automated Review and Quality Metrics

Tesserblu provides a “Consistency Score” and “Linguistic Quality Index” for every report, quantifying how aligned the document is with organizational standards.Reviewers can instantly identify inconsistent sections, saving hours of manual review time.

Additionally, it tracks review comments and accepted changes, feeding them back into the AI model—making it smarter with every project.

4. Collaboration and Version Control

Pharmacovigilance writing involves multiple contributors and reviewers across geographies. Tesserblu’s cloud-based collaboration environment ensures everyone works on the same version while maintaining full version traceability.

It automatically reconciles input from multiple users without overwriting style or structure—ensuring that the final report remains consistent across all sections.

5. Regulatory-Ready Audit Trail

Every AI suggestion in Tesserblu is logged and traceable.This audit-ready transparency allows regulatory teams to justify text revisions with documented rationale—meeting both internal QA and external regulatory expectations.

6. Seamless Integration

Tesserblu can integrate with:

• Document Management Systems (DMS) like Veeva Vault

• Signal detection tools

• Safety databases (Argus, ArisG, etc.)

This allows automated cross-checking of data-driven sections—ensuring narrative text aligns with safety data tables and case summaries.

The Future of AI-Driven Consistency in PV

AI will not replace pharmacovigilance writers—it will enhance them. The goal is not automation for its own sake but augmentation: enabling medical writers to focus on interpretation, analysis, and scientific storytelling, while AI manages the repetitive, linguistic, and consistency-related tasks.

As regulatory agencies begin to accept AI-assisted reports, tools like Tesserblu will play a pivotal role in ensuring standardized, compliant, and high-quality safety documentation across the product lifecycle.

Conclusion

Writing consistency is the backbone of regulatory credibility in pharmacovigilance. Yet, achieving uniformity across multi-author, multi-country safety documents has long been a challenge.

AI brings the solution—combining linguistic intelligence, semantic analysis, and automation to make pharmacovigilance writing consistent, compliant, and efficient.

With platforms like Tesserblu, organizations can bridge the gap between human expertise and machine precision—ensuring that every safety report tells the same clear, consistent, and scientifically sound story. Book a meeting if you are interested to discuss more.