Can GenAI Really Replace Manual Narrative Writing?
- Sushma Dharani
- Oct 4
- 5 min read
The field of pharmacovigilance (PV) has long relied on detailed medical case narratives to capture the essence of adverse event reports. These narratives—carefully crafted summaries of a patient’s experience with a medicinal product—are vital for regulators, pharmaceutical companies, and healthcare providers to assess causality, seriousness, and potential risks. Traditionally, narrative writing has been a labor-intensive, manual process requiring medical expertise, clinical judgment, and significant time investment.
With the advent of Generative AI (GenAI), however, the question has gained prominence: Can AI truly replace manual narrative writing in pharmacovigilance?
This blog explores the possibilities, limitations, and future directions of GenAI in PV narrative generation, while highlighting how platforms like Tesserblu are bridging the gap between human expertise and AI efficiency.
The Role of Narratives in Pharmacovigilance
In pharmacovigilance, an adverse event (AE) report is more than just structured fields in a safety database. Narratives provide the “human story” behind the data, describing how the adverse event unfolded, patient history, suspect medications, clinical interventions, outcomes, and reporter’s observations.
Regulatory authorities such as the FDA and EMA often require these narratives in case submissions to understand the clinical context. A well-written narrative:
Provides a coherent chronology of events.
Summarizes relevant medical history and concomitant medications.
Highlights causality assessments and outcomes.
Maintains clarity, conciseness, and neutrality.
Because of the medical nuances involved, manual narrative writing has historically been considered a specialized skill combining scientific accuracy with clear communication.
Why Automate Narrative Writing?
Manual narrative writing presents several challenges:
Time-Intensive: Writing each narrative can take 20–40 minutes depending on complexity. For large safety databases with thousands of cases, the workload is enormous.
Human Error: Inconsistent styles, omissions, or errors in chronology can creep into manually drafted narratives.
Scalability Issues: During product launches or safety spikes, case volumes surge, putting strain on PV teams.
Cost: Skilled medical writers, while essential, add significant costs to safety operations.
Automation through GenAI promises to:
Reduce turnaround time from hours to minutes.
Ensure consistency in style and structure.
Free up human experts to focus on complex medical judgment instead of repetitive drafting.
GenAI and Narrative Writing: How Does It Work?
Generative AI models, trained on large datasets of language and medical content, can generate coherent text when given structured inputs. In PV, this typically involves:
Data Extraction: Pulling relevant fields from a case safety report (ICSR) such as patient demographics, medical history, adverse event details, drug information, and outcomes.
Template or Prompting: Feeding structured case data into an AI model with prompts to create a readable narrative.
Quality Review: Human experts reviewing and validating the AI-generated output for accuracy, completeness, and compliance.
For example, a GenAI system can take the following structured fields:
Patient: 58-year-old male
Medical history: Hypertension, Type 2 Diabetes
Suspect drug: Drug X, started on 10-Jan-2025
Adverse event: Severe hypoglycemia on 15-Jan-2025
Concomitant medication: Metformin
Outcome: Recovered after glucose infusion
The model can produce a narrative:
A 58-year-old male with a history of hypertension and Type 2 Diabetes, on concomitant Metformin, initiated Drug X on 10-Jan-2025. On 15-Jan-2025, the patient experienced severe hypoglycemia and required glucose infusion. The event resolved, and the patient recovered.
This level of automation can save hours across hundreds of cases.
The Limitations of GenAI in Pharmacovigilance
While promising, GenAI cannot fully replace manual narrative writing—at least not yet. The limitations are significant:
Medical Nuance and Judgment: Narratives aren’t just factual recounts; they require interpretation of relevance. Which medical history elements are pertinent? Should certain concomitant medications be emphasized? AI can struggle with this prioritization.
Regulatory Compliance: Narratives must adhere to global regulatory expectations for accuracy, neutrality, and completeness. An AI-generated narrative that omits a critical element or introduces biased phrasing could compromise compliance.
Data Quality Dependency: AI relies heavily on the quality of structured inputs. Missing or inconsistent data can result in flawed narratives. Human writers often resolve these gaps by contacting reporters or inferring clinically relevant details.
Contextual Interpretation: For complex cases (e.g., multi-drug therapies, overlapping adverse events, comorbidities), AI may generate oversimplified or misleading narratives. Human expertise is essential to synthesize context.
Auditability: Regulators often demand traceability—why was a particular detail included or excluded? Human rationale is easier to document than AI’s opaque “black box” reasoning.
Language Risks: AI sometimes produces “hallucinations” (inventing details) or over-polished language that may unintentionally alter medical meaning. In pharmacovigilance, such errors are unacceptable.
The Hybrid Future: Human-in-the-Loop Model
Rather than full replacement, the future of narrative writing lies in AI-human collaboration.
GenAI drafts the first version using structured data.
Medical writers/physicians review, edit, and validate for compliance and accuracy.
Automation platforms ensure consistency while humans handle nuance.
This human-in-the-loop model combines efficiency with clinical judgment, ensuring both speed and safety.
How Tesserblu Can Help
Tesserblu, a next-generation pharmacovigilance automation platform, is at the forefront of enabling this hybrid future. It empowers PV teams to leverage AI while maintaining compliance and clinical integrity.
Key Ways Tesserblu Helps with Narrative Writing:
Automated Draft Narratives: Tesserblu integrates with safety databases to extract structured case data and generate AI-assisted narrative drafts in seconds. This reduces manual effort by 50–70%.
Customizable Templates: Narratives generated align with client-specific and regulatory formatting requirements. Teams can define custom rules, phrasing, and styles to ensure compliance.
Quality Control Layer: The platform embeds a review workflow, where medical writers validate AI outputs, flag inconsistencies, and finalize narratives. This ensures the final product meets global standards.
Scalability During Case Surges: During spikes in AE reporting (e.g., after product launch or safety alerts), Tesserblu scales narrative generation capacity without compromising turnaround.
Audit Readiness: Every AI-generated narrative in Tesserblu includes traceability logs showing which data fields were used and how the narrative was constructed—critical for regulatory audits.
Continuous Learning: With feedback from medical writers, the system learns and improves over time, enhancing narrative quality and reducing rework.
End-to-End PV Automation: Beyond narratives, Tesserblu also supports case intake, triage, data entry, signal detection, and reporting, making it a unified platform for modern PV operations.
Can GenAI Truly Replace Humans in Narrative Writing?
The short answer: Not completely.
GenAI is excellent at handling structured, repetitive cases, drafting quick narratives, and ensuring consistency. But for complex, nuanced, or incomplete cases, human expertise remains irreplaceable. The real value lies in combining both:
AI for speed and scalability.
Humans for judgment, nuance, and compliance.
Think of GenAI not as a replacement, but as a co-pilot for PV professionals—augmenting their productivity while leaving critical decision-making in human hands.
Conclusion
Generative AI represents a transformative shift in pharmacovigilance, particularly in narrative writing. While it cannot fully replace manual medical writing, it can significantly enhance efficiency, reduce repetitive work, and standardize outputs. The key is adopting a human-in-the-loop approach—where AI accelerates the process and human experts ensure accuracy and compliance.
Platforms like Tesserblu are pioneering this balanced future, providing PV teams with the tools to embrace automation without compromising safety or regulatory standards.
As case volumes continue to grow and regulatory scrutiny intensifies, the integration of GenAI into pharmacovigilance is not a question of if, but how fast. By harnessing AI responsibly, PV organizations can shift from being overwhelmed by manual tasks to focusing on what truly matters—protecting patients and ensuring drug safety. Book a meeting if you are interested to discuss more.
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