Workflow Orchestration for Pharmacovigilance Quality

Data Characteristics in Pharmacovigilance

Pharmacovigilance data primarily focuses on post-market drug safety monitoring. Quality documents typically include Adverse Drug Reaction (ADR) reports, Periodic Safety Update Reports (PSURs), Risk Management Plans (RMPs), Standard Operating Procedures (SOPs), and relevant regulatory guidelines. Data sources are diverse, encompassing clinical trial data, real-world data, literature search results, and regulatory agency notifications. Update frequency is high; ADR reports may be real-time or event-triggered, PSURs are usually semi-annual or annual, and SOPs are revised based on regulatory changes or internal process optimizations. Document structures are complex, often containing multi-level headings, tables, figures, and attachments. Fields and units are highly specialized, for example, dosage units (mg, μg), time units (days, weeks, months), adverse reaction terms (MedDRA codes), and drug identifiers (CAS numbers, ATC classifications).

Constraints Imposed by These Characteristics on Workflow Orchestration

The real-time requirements of pharmacovigilance quality documents necessitate workflow support for multiple trigger mechanisms, such as file uploads, API calls, or scheduled tasks, to accommodate varying report update frequencies. The complex document structures and specialized fields demand robust structured extraction capabilities in the document parsing stage of the workflow. This ensures accurate identification and extraction of key information like drug names, adverse event descriptions, occurrence times, and severity. The presence of specialized terminology, such as MedDRA codes, places higher demands on knowledge base construction and retrieval, requiring accurate semantic understanding. Furthermore, due to regulatory compliance, every workflow operation may require logging for auditability, necessitating comprehensive audit trail features in the workflow engine. The scenario of multi-source data integration also means workflows must flexibly interact with other systems for data exchange.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`Chunk size` (Segment Length)	`800–1200 characters`	Pharmacovigilance document paragraphs are often long and contain detailed descriptions; this length helps maintain contextual integrity.
`Recall count` (Recall Count)	`Top 8–12 items`	Ensures coverage of different problem dimensions, especially for complex queries.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Pharmacovigilance demands high accuracy; a high threshold reduces interference from irrelevant information.
`Rerank result count` (Rerank Return Count)	`Top 5 items`	After reranking, more precise contexts should appear first, reducing redundancy.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Considering the parsing time for large documents like PSURs, extending the timeout prevents interruptions.
`maxContext`	`4000 characters`	Ensures the model receives a sufficiently long context to understand complex pharmacovigilance queries.

Common Pitfalls

Workflow execution times out, returning a 504 Gateway Timeout error. This often occurs when a large safety update report file is uploaded, and parsing time exceeds the PARSE_FILE_TIMEOUT_SECONDS setting.
Key drug dosage or time units are missing from the answer. This happens when the document parsing stage lacks configured regular expressions or custom extraction rules for specific fields, leading to specialized units being overlooked.
After importing a workflow, some nodes display "Plugin not found." This indicates that the target environment has not installed or enabled specific plugins referenced in the source workflow, such as external service connectors for MedDRA code queries.

Validation Steps

Upload a typical adverse drug reaction report. Verify if the workflow accurately extracts drug names, event descriptions, and report dates.
Submit a complex query (e.g., "common adverse reactions of a certain drug in a specific population"). Check if the answer cites relevant regulations or SOP clauses and verify the accuracy of the cited content.
Simulate a PSUR file upload. Check the workflow execution logs to confirm that all parsing, vectorization, and knowledge base update steps are error-free and within acceptable time limits.
Query a document containing MedDRA codes. Verify if the model correctly interprets the codes and provides corresponding medical terminology explanations.

The values given are common starting points and should be measured against the reader's own samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.