Data Characteristics
Pharmacovigilance data in II-III clinical phases primarily originates from clinical research organizations, subject reports, laboratory test results, and investigator safety reports. Data updates rapidly, especially during ongoing trials, where adverse events (AEs) and serious adverse events (SAEs) typically require immediate reporting and recording. Document structures are diverse, including Case Report Forms (CRFs), medical reports, laboratory reports, imaging reports, subject diaries, and investigator brochures. These documents are often unstructured or semi-structured text, containing extensive medical terminology, abbreviations, and specialized descriptions. Fields and units involve subject demographic information, disease diagnoses, medication use, AE occurrence time, duration, severity, outcome, and drug-relatedness assessment. Laboratory data includes various biochemical and hematological indicators, usually with clear units of measurement and normal ranges.
Constraints on Workflow Orchestration from these Characteristics
The multi-source, rapidly updating, and complex nature of II-III clinical pharmacovigilance data imposes specific requirements on workflow orchestration. First, the system must efficiently integrate data from different systems and formats, such as PDF medical reports, structured CRF data, and unstructured subject interview records. Second, due to the high timeliness requirements for adverse event reporting, workflows must support near real-time data ingestion and processing to ensure timely detection of potential safety signals. Medical terminology and abbreviations in documents require text processing modules within the workflow to have strong semantic understanding capabilities to accurately extract key information. Furthermore, with many fields and complex relationships, the workflow needs a flexible rule engine to adapt to evolving safety assessment standards and regulatory requirements. For laboratory data, the workflow should identify and process numerical data with units, performing standardization for subsequent trend analysis and outlier detection.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale for this Value |
|---|---|---|
maxContext | 32000 | Clinical reports are often lengthy, requiring a larger context window to maintain semantic integrity and prevent loss of critical information. |
Chunk size (Chunk Length) | 800–1200 characters (characters) | Medical text sentences are typically long and contain multiple modifiers; this length helps maintain semantic coherence within a single chunk. |
Similarity threshold (Similarity Threshold) | 0.75 | In pharmacovigilance reports, similar adverse event descriptions may have subtle differences; a higher threshold helps ensure precise matching. |
Recall count (Recall Count) | 10 entries (items) | Ensures that initial retrieval covers a sufficient number of relevant adverse events and case reports, improving information comprehensiveness. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Parsing large clinical documents, especially scanned copies or complex PDFs, takes a long time, requiring a longer timeout to prevent parsing interruptions. |
Rerank result count (Rerank Return Count) | 5 entries (items) | After recalling many documents, reranking selects a small number of the most relevant key pieces of information, reducing the burden on subsequent processing. |
Three Common Pitfalls
- File parsing node unresponsive or erroring for extended periods: This usually occurs when an excessively large or overly complex clinical trial report file is uploaded, leading to
PARSE_FILE_TIMEOUT_SECONDSbeing set too short, preventing complete file parsing. - Missing or misinterpreted key medical terms in workflow execution results: This may stem from an inappropriate
Chunk size(Chunk Length) setting, causing medical concepts to be truncated, or insufficientmaxContext, failing to provide enough contextual information for the language model to make accurate judgments. - Workflow template not functioning after import: Check code execution nodes within the workflow to confirm if they depend on a specific Python environment version or external libraries that are not provided in the local deployment environment.
Verification Steps
- Upload a typical clinical study report (e.g., a multi-MB PDF) and observe the file parsing status to ensure successful parsing within the expected time frame.
- Execute queries involving complex medical terminology and check if the returned results accurately identify and reference relevant medical concepts and data from the report, performing manual verification for semantic accuracy.
- Run a simulated adverse event reporting process to validate data flow and information extraction between different data sources (structured data, unstructured text) within the workflow, and check the accuracy of key field extraction (e.g., adverse event type, severity).
Note that the values provided 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.