Adverse Event Assessment & Regulatory Report Generator
Classify adverse events and generate FDA/ICH-compliant pharmacovigilance reports
What You Can Do
You can rapidly triage adverse events, extract and structure clinical narratives, apply standardized causality assessment algorithms, and generate audit-ready regulatory reports that meet FDA and EMA submission standards. The skill systematizes complex judgment calls in pharmacovigilance by automating event classification, severity coding, causality scoring, and compliance verification—reducing clerical variability while flagging missing critical data before submission.
Features
automatically categorizes events as serious/non-serious and expected/unexpected based on defined safety profiles
applies Naranjo and WHO-UMC standardized algorithms to temporal relationships, dose-response patterns, and dechallenge/rechallenge data
assigns standardized medical terminology and severity scales for regulatory consistency
clusters related adverse events, identifies outlier patterns, and flags emerging safety trends across cohorts
produces ICH E2A case narratives and FDA MedWatch-formatted reports ready for submission
validates presence of critical data elements (dates, outcomes, concomitant medications) and flags gaps before regulatory filing
parses unstructured clinical text to capture patient demographics, timeline, medication details, and outcome information
Example Output
Input: Clinical narrative describing chest pain onset 2 days after starting an antihypertensive medication in a 67-year-old patient with hypertension history.
Output:
- Event Classification: Serious (hospitalization required); Unexpected (not listed in safety profile)
- Causality Score (Naranjo): 7/13 (Probable relationship)
- WHO-UMC Category: Probable
- MedDRA Coding: Chest pain (PT10008479), Hypertension (PT10020772)
- Safety Signal Flag: Potential signal—temporal clustering of 3 similar cardiothoracic events within first week of dosing in current quarter
- ICH E2A Narrative: "Patient is a 67-year-old female with medical history of hypertension who received [drug name] at [dose] on [date]. Two days post-administration, patient experienced acute onset chest pain, shortness of breath, and diaphoresis requiring emergency department evaluation and hospitalization. Troponin levels elevated; EKG showed ST changes. No dechallenge attempted during report period. Alternative causes include acute coronary syndrome from underlying atherosclerosis."
What's Included
- SKILL.md instruction file with prompt engineering templates for event extraction and algorithm application:
- AE Intake Checklist: structured form capturing all required data elements (dates, concomitant medications, outcomes, causality factors)
- Naranjo & WHO-UMC Algorithm Workflow: step-by-step prompts for applying causality scoring with example reasoning
- Regulatory Report Templates: pre-formatted ICH E2A, FDA MedWatch, and EudraVigilance narrative templates
- Safety Signal Detection Framework: clustering and trend analysis prompts for identifying outlier event patterns
- Completeness Verification Checklist: validation rules flagging missing critical data before submission
Who It's For
- Pharmacovigilance Specialists conducting initial adverse event triage and causality assessment
- Regulatory Affairs Professionals preparing submissions to FDA, EMA, and other authorities
- Clinical Trial Safety Teams processing adverse event reports from investigator sites
- Medical Safety Scientists performing signal evaluation and trend analysis across product portfolios
- Quality Assurance & Compliance Managers validating regulatory report completeness before filing
Best For
- Initial triage and coding of clinical adverse event narratives from sites or call centers
- Rapid causality assessment using standardized algorithms (Naranjo, WHO-UMC) with documented reasoning
- Generation of FDA MedWatch and ICH E2A regulatory narratives from unstructured clinical data
- Safety signal detection and clustering across multiple adverse event reports
- Compliance validation and gap identification before regulatory submission
- Batch processing of recurring event types with consistent terminology and scoring
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