Tool Calling and Plugins for CRO Pharmacovigilance

Data Characteristics in This Category

Contract Research Organizations (CROs) handle pharmacovigilance data primarily from clinical trial reports, real-world evidence (RWE) data, spontaneous reporting systems, and medical literature. This data typically exists as unstructured text (e.g., case narratives, physician handwritten notes), semi-structured data (e.g., CIOMS I forms, MedWatch forms), and structured data (e.g., patient information, drug information, adverse event codes in databases). Data updates frequently, potentially daily or even in real-time, especially during clinical trials. Document structures are complex, containing extensive medical terminology, abbreviations, and specific coding systems (e.g., MedDRA, WHO-ART). Fields are diverse, covering patient demographics, medical history, adverse event descriptions, severity, outcomes, and causality assessments. Units use international standards, but descriptive text may contain non-standard expressions.

Constraints Imposed by These Characteristics on Tool Calling and Plugins

The heterogeneous nature and high update frequency of CRO pharmacovigilance data impose specific requirements on tool calling and plugins. For instance, processing unstructured text requires calling external Natural Language Processing (NLP) services to extract key entities and relationships. The real-time nature of data updates demands efficient data synchronization capabilities from plugins, ensuring models always base decisions on the latest information. The presence of multiple document structures means plugins must adapt to different data parsing logic. For example, a PDF CIOMS I form might require calling an OCR service for text recognition before structured extraction. The specialized nature of medical terminology and coding necessitates integrating or querying professional medical dictionary services when calling tools, ensuring accuracy in entity recognition and standardization to prevent misclassification of adverse events due to misinterpretations of terminology.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`3000–4000 token`	Pharmacovigilance reports are often lengthy, requiring more context to understand the full scope of an event.
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	Accommodates clinical report files that include images or complex formats.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Parsing large PDFs or scanned documents can take a long time.
`Chunk size`	`500 characters`	Ensures each segment contains sufficient information while avoiding excessive length that could disperse semantic meaning.
`Similarity threshold`	`0.78`	The pharmacovigilance domain demands high accuracy; a high threshold reduces false positives.
`Rerank result count`	`Top 10 entries`	Increases the breadth of relevant information available to the model for more comprehensive analysis.

Three Common Pitfalls

When calling an external HTTP interface to write to a Feishu multi-dimensional table, the interface returns a 4xx status code. This often happens because the request body format does not meet Feishu API requirements, or the Authorization header for authentication is incorrectly set.
After a file upload API call succeeds, the model cannot immediately retrieve the file content, or the retrieval results are incomplete. This can occur because the file indexing process is asynchronous and time-consuming, requiring a wait for the background indexing task to complete, or because file parsing failed, leading to some content not being ingested.
The model provides a summary answer without citing original database snippets. This usually means the data returned by Function Call was not effectively integrated into the model's context, or the prompt did not explicitly instruct the model to reference the raw data returned by tool_code.

How to Verify Configuration

Upload a PDF report containing a typical adverse event description. Call the file parsing plugin and check the backend logs to confirm file_parse_status is SUCCESS.
Configure a tool that calls an external medical terminology query API. Input a non-standard medical abbreviation and check if the tool's returned result includes the correct standard term and definition.
Simulate an adverse event report entry process. Use tool calling to write data to a target database or table. Then, check if the record fields and content in the target system match the input.

The values provided 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.