HTTP Interface and External Systems for Attenuated

Data Characteristics for This Category

Clinical trial data for attenuated inactivated vaccines primarily originates from Electronic Data Capture (EDC) systems, Central Laboratory Systems (CLS), and Pharmacokinetic/Pharmacodynamic (PK/PD) analysis platforms used by clinical research organizations. This data typically exists in structured tabular formats such as CSV, JSONL, or XML. Data update frequency varies across different stages of a clinical trial: early safety data might update daily, while later efficacy data is usually aggregated weekly or monthly. The document structure is rigorous, adhering to ICH GCP and CDISC SDTM/ADaM standards. It includes fields such as subject demographics, vital signs, laboratory test results, adverse events, and concomitant medications. Numerical data often includes clear units, such as mg/dL, IU/mL, mmHg. Textual data includes medical terminology, diagnostic descriptions, and investigator assessments.

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

The diverse sources of attenuated inactivated vaccine clinical trial data require HTTP interfaces to support multi-source data integration, connecting simultaneously to various systems like EDC and CLS. Differences in data update frequency mean external systems must flexibly configure polling intervals when calling the interface; for example, safety data may require a higher call frequency. The rigorous document structure and standardized fields necessitate precise data mapping and transformation logic within the interface to ensure data consistency. This prevents data parsing errors due to unit mismatches (e.g., confusing mmol/L with mg/dL). The large volume of numerical and textual data demands high throughput and data processing capabilities from the interface. This is especially critical when handling large subject cohorts, requiring consideration of batch processing mechanisms. Furthermore, data sensitivity mandates that the interface must have strict authentication and authorization mechanisms, such as OAuth2.0 or API Keys.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for Recommendation
`external_api_url`	`/api/v1/clinical_data` provided by the clinical trial EDC system	Ensures authoritative and real-time data source
`http_method`	`POST`	Most clinical data synchronization interfaces use POST for structured data transfer
`request_timeout_seconds`	`60 seconds`	Accounts for large data transfers and external system processing time, preventing premature timeouts
`auth_header_name`	`Authorization`	Common authentication header field, compatible with most external systems
`data_polling_interval_minutes`	`1440 minutes`	For later efficacy data in attenuated inactivated vaccine clinical trials, poll once daily
`batch_size_records`	`1000 entries`	Balances the amount of data per request and interface processing efficiency, avoiding excessive load

Three Common Mistakes

Symptom: The external system returns an HTTP 401 Unauthorized error. Reason: The API Key or Bearer Token in the Authorization request header is expired or incorrect.
Symptom: Certain critical fields (e.g., adverse_event_severity) are empty in the data obtained from the external interface. Reason: The data structure returned by the interface does not match expectations, or data mapping configuration is incorrect, leading to specific fields not being parsed correctly.
Symptom: When processing file interpretation, the external system receives two requests, and the second request's Authorization header is incorrect. Reason: FastGPT's internal mechanisms may retry in some file processing scenarios, but the retry logic does not correctly reuse the authentication information from the initial request.

How to Verify Configuration

Simulate a complete data request. Check for an HTTP 200 OK status code and verify that the returned data structure and content meet expectations.
Check logs for authentication failures or data parsing errors. Confirm that the request_timeout_seconds configuration is effective.
Select several data samples. Manually compare the original data from the external system with the data stored internally in FastGPT. Verify the accuracy of key fields, especially the unit consistency of numerical fields.

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.