Data Characteristics in This Category
Data in Site Management Organizations (SMO) pharmacovigilance primarily comes from Serious Adverse Event (SAE) and Suspected Unexpected Serious Adverse Reaction (SUSAR) reports. Clinical Research Coordinators (CRCs) and investigators submit these reports. Data exists in both structured formats (e.g., CSV, XML from EDC systems) and unstructured formats (e.g., PDF medical reports, scanned handwritten doctor's notes). Update frequency varies with trial progress and event occurrence. High-risk drugs may have daily updates. Routine reports might be weekly or monthly. Document structures are complex. They include patient demographics, medication history, adverse event descriptions, diagnostic results, lab data, and causality assessments. Standardizing medical terminology, units of measure (e.g., mg/kg, mmol/L), and timestamps (e.g., 2023-10-26 14:30:00) is critical.
Constraints on Workflow Orchestration from These Characteristics
The diverse and heterogeneous nature of SMO pharmacovigilance data requires data preprocessing as a primary consideration for workflow orchestration. Structured data needs field mapping and standardization, especially for drug names and adverse event codes (e.g., MedDRA codes). Unstructured PDF reports require OCR and information extraction. This ensures accurate extraction of key information like patient IDs, event times, and drug dosages. Varying report update frequencies necessitate support for multiple trigger mechanisms. For example, timed triggers for batch processing of periodic reports and event-driven triggers for immediate SAE/SUSAR processing. Specialized medical terminology demands knowledge base construction and retrieval support for medical ontologies and synonym matching to improve recall accuracy. Sensitive information (e.g., patient names) requires anonymization. This means integrating privacy protection components into the workflow.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Data Source Type | Multiple File Upload and API Interface | Accommodates both periodic batch imports and real-time SAE/SUSAR reporting. |
Unstructured Document Parsing Mode | OCR+Table Recognition+Text Extraction | Ensures effective recognition and extraction of text and tabular data from PDF reports. |
Recall Count | Top 10 | Reduces unnecessary processing while maintaining information completeness, improving efficiency. |
Similarity Threshold | 0.75 | Balances recall accuracy and false positive rate, mitigating the risk of missing critical adverse events. |
Segment Length | 500 characters | Adapts to long descriptions in medical reports, ensuring semantic integrity and controlling token consumption. |
Rerank Return Count | Top 3 | Refines key information, providing a more focused reference for manual review. |
Three Common Mistakes
- Workflow logs show "Call failed," but the model backend has response logs. This usually indicates a data format mismatch between workflow components. For example, an upstream component outputs a JSON array, but a downstream component expects a string.
- After calling the workflow, the conversation log is empty. This might be because a critical node in the workflow (e.g., knowledge base query or large model call) uses an incorrect output variable name. This prevents the final result from being correctly passed.
- The "Question Optimization" component is always placed at the end of the workflow. This stems from a misunderstanding of "Question Optimization." This component is better suited for rewriting the user's original query before knowledge base retrieval, improving recall quality.
How to Confirm Correct Configuration
- Simulate submitting an SAE report containing both structured and unstructured data. Observe if the workflow triggers correctly and completes end-to-end processing. Verify if the final output fields match expectations.
- Examine logs for all data extraction and transformation nodes within the workflow. Confirm that intermediate data formats and content conform to expectations, especially for medical terminology standardization and unit conversions.
- Perform multiple end-to-end tests for several typical adverse event reporting scenarios. Compare the workflow's summarized output or judgment with expert judgments. Define an acceptable deviation threshold based on business requirements.
The values provided are common starting points. Measure them against your 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.