Pharmacovigilance Quality Documents: HTTP Interface and

Data Characteristics

Pharmacovigilance data originates from post-market adverse event reports, clinical trial data, literature, and regulatory safety information. Data updates are frequent, especially for adverse event reports, which can be real-time. Document types are diverse, including adverse event report forms (CIOMS I, MedWatch 3500A), Risk Management Plans (RMP), product inserts, drug quality standards, and Standard Operating Procedures (SOPs). These documents often mix structured and unstructured information. For example, adverse event reports contain patient demographics, drug information, free-text event descriptions, and medical terminology codes (e.g., MedDRA). Fields include general text fields, drug batch numbers, manufacturing dates, expiry dates, patient age, gender, weight, event dates, and reporting dates. Some fields require specific medical coding systems. Units involve dosage (mg, g, IU), frequency (times/day), and time (hours, days, years).

Constraints on HTTP Interfaces and External Systems

High-frequency data updates require real-time HTTP interfaces with high throughput. The system must support concurrent requests and fast responses to avoid data delays. Complex document structures demand flexible data parsing capabilities from HTTP interfaces. These interfaces must handle various formats like JSON, XML, PDF, and DOCX, extracting both structured and unstructured information. For instance, the system needs to accurately identify and extract MedDRA codes from PDF reports or identify key adverse event information from free-text descriptions. The specialized medical nature of fields and the strictness of units require interfaces to maintain data integrity and accuracy during transmission. This prevents data distortion from format conversion or unit mismatches. Given data sensitivity, interfaces must support encrypted transmission (e.g., HTTPS) and strict authentication and authorization mechanisms to ensure data security.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`HTTP_REQUEST_TIMEOUT_SECONDS`	`60 seconds`	Accommodates complex document parsing and slow external system responses, preventing premature timeouts.
`MAX_FILE_SIZE_MB`	`50 MB`	Covers most report file sizes, allowing transfer of documents with images or detailed appendices.
`CONCURRENT_REQUEST_LIMIT`	`20`	Balances system resource utilization with real-time requirements, supporting a degree of concurrent data ingestion.
`AUTH_HEADER_NAME`	`Authorization`	Follows industry standards, facilitating unified authentication and authorization by external systems.
`ERROR_RETRY_COUNT`	`3 times`	Addresses transient external system failures, increasing data transmission success rates.
`PARSE_FILE_TYPE_LIST`	`pdf, docx, xml, json`	Covers mainstream document formats in pharmacovigilance, ensuring broad compatibility.

Common Pitfalls

Symptom: API calls succeed, but key fields in the returned data are empty or incorrectly formatted. Cause: External system data structure changes, and interface configurations are not updated promptly, leading to invalid data mapping or parsing rules.
Symptom: When batch uploading adverse event reports, some files fail to process, and logs show "connection timeout." Cause: HTTP_REQUEST_TIMEOUT_SECONDS is set too short, insufficient to handle the time required by external systems for large files or complex parsing tasks.
Symptom: External systems report an inability to receive valid API requests or authentication failures. Cause: Authentication parameters like AUTH_HEADER_NAME or API_KEY are configured incorrectly, or the authentication method required by the external system (e.g., OAuth 2.0) does not match the current interface configuration.

Verification Steps

Send pharmacovigilance document samples in different formats (PDF, XML, JSON) via the FastGPT console or API. Verify successful import and correct parsing of key fields.
Simulate external system response delays or transient failures. Observe FastGPT logs for retry records and eventual successful data processing.
Use API testing tools provided by external systems. Combine with FastGPT's HTTP interface configuration to verify that the authentication mechanism works and test data is successfully sent.

The values given are common starting points and should be measured against your 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.