AI-Powered
Pharmacovigilance Case Processing
Growing Safety-Case Volume Requires Faster Processing Without Compromising Compliance
Pharmacovigilance teams must process adverse-event reports from multiple channels, standardize patient and product information, code medical concepts, identify potential safety signals, prepare narratives and maintain regulatory timeliness. Manual case assembly creates effort, variability and scale pressure in an environment where traceability and validated controls are essential.
Common challenges include:
- Multiple intake sources: Adverse-event information and supporting documents can enter through different channels and formats.
- Manual medical-data extraction: Safety teams must identify patient, product, event and medical-history information from case documents.
- Triage complexity: Cases require consistent classification, prioritization and evaluation against safety rules.
- Narrative preparation effort: Case narratives require structured synthesis of source information before specialist review.
- Regulatory traceability: Safety review, validation and submission need a controlled process with clear human accountability and a complete case record.
GxP-Governed Pharmacovigilance Workflow with NLP, Case Triage and Human Safety Review
The workflow uses intelligent document processing and NLP to capture case evidence, extract medical entities, support coding and triage, detect signal indicators and generate a draft case narrative. Safety professionals validate the evidence, medical assessment and reportability decision before the case moves to the safety database or regulatory submission path.
The solution uses:
- Automated adverse-event intake: Capture incoming safety cases into a consistent processing workflow.
- Intelligent case-document capture: Collect and organize source documents required for processing.
- Medical-data extraction: Extract patient, product, event and relevant clinical information from unstructured case content.
- Case triage and signal detection: Apply safety rules to prioritize cases and highlight patterns requiring attention.
- AI-generated narrative: Prepare a structured narrative draft using the information available in the case.
- Safety review and validation: Keep medical and regulatory judgment with qualified safety specialists.
- Regulatory submission: Move validated case information into the appropriate PV system and submission process.
Quantified Evidence for Scalable Pharmacovigilance
93% portfolio-level processing-time reduction
Tecnoprism delivery evidence from a regulated pharma automation portfolio that included Drug Safety workflows.
50–60% lower case-processing time
A biopharma adverse-event automation deployment reduced case-processing time per case by 50–60%.
65–75% annual case-processing cost savings
The same cognitive case-processing deployment produced 65–75% annual savings in case-processing cost.
From Manual Case Assembly to Traceable Exception-Based Safety Processing
How AI-Powered Pharmacovigilance Case Processing Works
The workflow progresses from adverse-event intake and document capture through medical extraction, triage, narrative drafting, specialist validation and regulatory submission.
Adverse Event Intake
An adverse-event report enters the pharmacovigilance workflow and the case is registered for processing.
- Event source and case identifier
- Initial patient and product context
- Received date and source information
Technology / System: Safety Case Intake / Workflow Automation
Case Document Capture
Supporting documents are collected and organized so the case can be processed consistently.
- Source documents
- Attachments and correspondence
- Available medical or product records
Technology / System: IDP / OCR
Medical Data Extraction
Relevant case information is extracted from structured and unstructured content.
- Patient and event details
- Product information
- Medical history and relevant clinical facts
Technology / System: NLP / Medical Entity Extraction
Case Triage & Signal Detection
Rules evaluate the case for priority, completeness and safety indicators that require attention.
- Case classification
- Safety / seriousness indicators
- Potential signal or escalation flags
Technology / System: Safety Triage / Signal Analytics
GenAI Narrative Generation
Generative AI organizes the extracted facts into a structured case narrative draft for safety review..
- Chronology of the event
- Relevant patient and product context
- Key supporting case facts
Technology / System: GenAI Narrative Draft
Safety Review & Validation
A PV specialist reviews the extracted data and narrative, resolves issues and confirms the case for the next step.
- Validate medical and product data
- Review narrative accuracy and completeness
- Resolve or escalate discrepancies
Technology / System: Safety Specialist Review / Validation
Regulatory Submission
The validated case is updated in the pharmacovigilance environment and prepared for the applicable reporting process.
- Final case record
- Submission status
- Regulatory / PV system update
Technology / System: Safety Database / Regulatory Submission
