Data Characteristics in SMO Pharmacovigilance
Site Management Organizations (SMOs) handle diverse data from clinical trial sites for pharmacovigilance and adverse event monitoring. This data includes Adverse Event (AE) reports, Serious Adverse Event (SAE) reports, laboratory results, vital sign measurements, and concomitant medication information. Data sources are varied, primarily Electronic Data Capture (EDC) systems, scanned paper Case Report Forms (CRFs), or exports from internal hospital systems. Data updates frequently, especially during clinical trials, where AE/SAE reports may be generated in real-time or submitted within specified periods. Document structures are complex. Adverse event reports typically follow ICH GCP and regulatory agency (e.g., FDA, EMA) guidelines, containing structured fields (e.g., event name, date of occurrence, severity, outcome, causality assessment) and extensive unstructured text descriptions (e.g., event narrative, treatment measures, physician assessment). Fields and units must strictly adhere to medical terminology and international standards, such as using MedDRA for adverse event coding and WHO-DD for drug coding. Laboratory results require unit specification and comparison with normal ranges.
Constraints Imposed by Data Characteristics on Deployment and Upgrade
The diversity and high update frequency of SMO pharmacovigilance data impose specific requirements on FastGPT deployment and upgrades. First, large volumes of unstructured text (adverse event descriptions, physician notes) require efficient text segmentation and vectorization capabilities to ensure accurate information retrieval. Second, the mix of structured and unstructured data necessitates a knowledge base that flexibly supports ingesting and indexing different data types. High update frequency means the knowledge base must support incremental updates and real-time indexing to avoid data latency affecting decision-making. The formality of documents and the specialized nature of medical terminology require careful consideration during model fine-tuning or prompt engineering to reduce hallucinations and improve answer precision. Additionally, data sensitivity demands that the deployment environment meets strict data security and compliance standards, particularly regarding data isolation and access control. During upgrades, changes in new versions to data structures or indexing mechanisms require thorough compatibility testing and data migration plans to ensure business continuity.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | SMO adverse reaction reports may contain many images or scanned documents, leading to large file sizes. |
maxContext | 3000 characters | Adverse event descriptions and physician assessment texts are long, requiring a larger context window to capture complete information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing complex PDFs or scanned documents, OCR recognition, and text extraction can be time-consuming. |
Chunk size | 800–1000 characters | Medical texts have strong contextual relevance; longer segments help retain key semantic information. |
Recall count | Top 8 entries | Ensures coverage of multiple potentially relevant AE/SAE reports, improving relevance. |
Similarity threshold | 0.75 | The medical field demands high precision; a higher threshold filters out low-quality matches. |
Common Pitfalls
- The login interface or knowledge base content displays in English: This typically occurs when container images, after deployment or upgrade, fail to correctly load or apply language configuration files, leading to the system's default language settings being overwritten.
- Quoted file download failures or content parsing anomalies: This may be due to API adjustments in new versions for file storage or parsing services, or
UPLOAD_FILE_MAX_SIZE,PARSE_FILE_TIMEOUT_SECONDSparameters set too low, causing large file processing timeouts or failures. - Slow response or errors when calling a locally deployed large model: This could be related to the local large model service not starting correctly, incorrect API address configuration, or
maxContextand other parameters not matching the model's actual capabilities.
Verification Steps
- Upload a typical adverse event report PDF (containing structured tables and unstructured text). Verify successful upload, correct parsing, and expected segmentation content.
- Ask questions using medical terminology about the uploaded report. Verify the AI's answer accuracy, its ability to cite correct original text snippets, and check for hallucinations.
- Add or update an adverse event record in the knowledge base, then immediately query it. Confirm that incremental data is timely indexed and retrievable.
- Check system logs for any errors or warnings related to file processing, model calls, or knowledge base indexing.
These values are common starting points and should be measured against specific 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.