Real-World Study Pharmacovigilance Tool Calling and Plugins

Data Characteristics in This Category

Real-world study data in pharmacovigilance primarily originates from Electronic Health Records (EHR), medical insurance claims databases, patient registries, and wearable devices. Data update frequencies vary; EHR data may update in real-time, while medical insurance claims data typically aggregates quarterly or annually. Document structures are diverse, including unstructured clinical notes, semi-structured medical reports, and structured diagnostic codes (e.g., ICD-10), drug codes (e.g., ATC), and adverse event reports (e.g., MedDRA). Field units show variability; for example, dosage may be expressed in milligrams (mg), grams (g), or International Units (IU), and timestamps can range from second-level records to only dates. The data often contains extensive free-text descriptions, requiring Natural Language Processing (NLP) for structured extraction.

Constraints Imposed by These Characteristics on "Tool Calling and Plugins"

The diversity of data sources requires FastGPT's tool calling to flexibly integrate with various external APIs or data interfaces, such as Hospital Information System (HIS) interfaces or medical insurance data platforms. The varying update frequencies, especially for real-time or near real-time data streams, demand specific requirements for tool calling trigger mechanisms and response speeds. This necessitates support for polling or webhook mechanisms to capture new adverse event reports promptly. The presence of unstructured documents means that when calling external text analysis tools, the full document content must be passed, and the structured entity recognition results must be received. The non-standardization of fields and units implies that during data conversion or parameter passing, units must be preset or dynamically identified to ensure data consistency. For instance, when calling an external dosage calculation tool, the dosage unit must be explicitly passed.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale for This Value
`maxContext`	`3000 Tokens`	Accommodates the context length of complex medical texts, ensuring critical information is not truncated.
`tool_timeout`	`120 seconds`	Most external APIs have longer response times when processing complex medical texts or large datasets.
`plugin_input_schema`	Explicitly define MedDRA Preferred Term	Ensures standardized input for adverse event descriptions, facilitating accurate identification by external tools.
`plugin_output_schema`	Include `event_id` and `severity_score` fields	Facilitates tracking specific adverse events and quantifying their severity.
`retry_attempts`	`3 times`	Addresses transient external service failures or network fluctuations, improving call success rates.
`parameter_mapping_rules`	Calibrate based on actual measurements	Maps FastGPT internal parameters to the format required by external systems, based on specific external system API documentation.

Three Common Pitfalls

External service calls return status code 500 or time out because an excessively large text block was passed, exceeding external processing capacity, or tool_timeout was set too short.
After plugin execution, the expected event_id field is empty because the external tool failed to extract key entities from unstructured text, or the plugin_output_schema definition does not match the actual return.
Performance significantly degrades when iteratively calling external APIs to process lists of adverse events because AI sessions within the loop were not optimized for batch processing, leading to frequent single requests.

How to Verify Configuration

Check FastGPT's log system to confirm that tool calls return a 200 OK status code, indicating a successful response from the external service.
Verify the severity_score field in the plugin output, ensuring it falls within the expected numerical range and logically corresponds to the input adverse event description.
Select multiple representative real-world adverse event reports as input for end-to-end testing. Check if the data structure and key fields (e.g., drug_name, adverse_event) returned by each tool call are complete and accurate.
Monitor FastGPT's resource utilization during runtime. Confirm that CPU and memory usage remain within acceptable limits when processing batch data, avoiding performance bottlenecks caused by frequent tool calls.

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.