HTTP Interface and External Systems for Pharmacovigilance

Data Characteristics in this Category

Pharmacovigilance registration documents primarily include adverse event reports, safety update reports, and risk management plans. Data sources are diverse, encompassing clinical trial data, post-marketing surveillance data, literature reviews, and real-world data. These data update frequently, especially post-marketing surveillance data, which may involve continuous event reporting. Document structures typically adhere to international standards like ICH E2B and CIOMS I. Data fields are highly standardized, including adverse event descriptions, drug information, patient demographic information, event start/end dates, and causality assessments. Units strictly follow medical and pharmaceutical norms, such as dosage units (mg, μg) and time units (days, weeks, months). Data volume can be extensive and often includes a large amount of unstructured text.

Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"

The multi-source nature and high update frequency of pharmacovigilance data require HTTP interfaces to support high concurrency and real-time or near real-time synchronization mechanisms. Standardized document structures and fields necessitate strict adherence to specific data models, such as JSON Schema or XML Schema, during interface design to ensure accurate data parsing and transmission. The presence of unstructured text demands support for UTF-8 encoding and long text fields for interface input parameters. Due to data sensitivity, secure authentication and encrypted transmission (e.g., TLS) are mandatory for interfaces to comply with data privacy regulations. Large data volumes may lead to prolonged single-request response times or the need for data chunking, requiring interfaces to support pagination or streaming.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`requestTimeout`	`60000 ms`	Ensures sufficient response time for complex queries and large data uploads
`contentType`	`application/json` or `application/xml`	Complies with data exchange formats like ICH E2B standards
`maxPayloadSize`	`10 MB`	Accommodates larger report files containing unstructured text and attachments
`authMethod`	`Bearer Token` or `OAuth2`	Provides secure authentication mechanisms, complying with data security regulations
`retryCount`	`3`	Addresses transient network fluctuations or temporary external system unavailability, improving data transmission reliability
`tlsVerify`	`true`	Ensures communication link encryption and server identity verification, preventing man-in-the-middle attacks

Three Common Pitfalls

HTTP request returns tls: failed to verify certificate. This usually occurs because the external system's TLS certificate is invalid, expired, or not recognized by the FastGPT runtime environment's trust chain.
Interface call succeeds but returns empty data fields or incorrect format. This may be due to request parameters not matching the external system's expected data model, such as misspelled field names or data type mismatches.
Request times out after a long wait. This can result from insufficient processing capacity of the external system, high network latency, or an interface design that did not account for the processing time required by excessively large data volumes.

How to Verify Correct Configuration

Use standard API testing tools to send a minimal dataset conforming to ICH E2B or CIOMS I specifications, and check if the HTTP response status code is 200 OK.
Verify that critical pharmacovigilance data fields obtained from the external system, such as adverseEventTerm and patientAgeUnit, match expected values and units.
Simulate high-concurrency requests to evaluate the interface's response time and stability during peak data loads, ensuring the requestTimeout setting is appropriate.
Examine the parsing logic for returned data within the FastGPT workflow to ensure all required fields are correctly extracted and used for subsequent processing.

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.