Data Characteristics
Pharmacovigilance (PV) data centers on Adverse Drug Reaction (ADR) reports. These reports originate from healthcare institutions, patients, internal pharmaceutical company systems, and regulatory databases. Data documents vary in format. They include structured report forms (e.g., CIOMS I forms), unstructured free text (e.g., case notes, medical literature), and semi-structured Electronic Health Records (EHR). Data updates frequently, especially during the initial post-market phase of new drugs, as regulatory bodies continuously receive and analyze reports. Report fields include patient demographics, drug information (batch number, dosage form, dose), adverse reaction events (description, coding like MedDRA terms), outcomes, and causality assessments. Units for dosage are typically milligrams (mg), grams (g), or milliliters (ml). Time units include hours, days, weeks, and months.
Constraints from "Tool Calling and Plugins"
The high timeliness requirement for pharmacovigilance data demands that tool calling plugins respond and process quickly. This ensures timely capture and analysis of adverse reaction events. Diverse and heterogeneous data formats, particularly large volumes of unstructured text, highlight the need for Natural Language Processing (NLP) and information extraction plugins. The existence of specialized medical coding systems like MedDRA means tool plugins must support or integrate these standard dictionaries for accurate term matching and coding. Additionally, sensitive patient personal information in reports requires data anonymization and privacy protection mechanisms during tool calls. Accurate extraction and comparison of critical information like drug batch numbers and dosages challenge plugin precision and robustness. Any deviation can impact the accuracy of causality assessments.
Configuration Strategy
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 4000–8000 characters | Accommodates lengthy clinical descriptions in ADR reports, ensuring context completeness. |
PARSE_FILE_TIMEOUT_SECONDS | 180–300 seconds | Handles large PDF or Word format ADR report files, preventing parsing timeouts. |
UPLOAD_FILE_MAX_SIZE | 50 MB | Supports uploading report files that include multimedia attachments or high-resolution images. |
similarityThreshold | 0.75–0.85 | Ensures retrieval of highly relevant regulatory clauses or SOP sections related to adverse events. |
Rerank result count | 5 entries | After reranking, focuses on the most relevant core clauses, improving lookup efficiency. |
ExternalAPIAuthentication Method | API Key or OAuth2 | Most internal pharmaceutical company systems or regulatory interfaces use these two mainstream authentication methods. |
Common Pitfalls
- Symptom: External model calls return empty or incomplete results. Cause: The JSON structure in the request body does not match the API's expectation, or critical fields like adverse reaction codes are not mapped correctly.
- Symptom: File parsing succeeds, but key information (e.g., drug batch number, event date) is missing from the result. Cause: The file parsing plugin is not optimized for the specific layout of adverse reaction reports, or regular expressions do not cover all variations.
- Symptom: HTTP request returns a 403 Forbidden error. Cause: The API call's authentication token has expired, permissions are insufficient, or the request header lacks necessary authorization information.
Verification Checklist
- Select a test report containing various typical adverse event descriptions and structured fields. Observe if tool calling accurately extracts all key information.
- For a simulated report containing sensitive information, verify if patient personal information is correctly anonymized in the tool call results.
- Call an external MedDRA coding service. Check if the accuracy of adverse reaction term coding matches expected standards.
- Simulate high-concurrency calling scenarios. Monitor the response time of the tool calling interface to ensure results return within the specified timeframe.
The values provided are common starting points. Measure them 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.