Autoimmune Pharmacovigilance: HTTP API and External Systems

Data Characteristics in this Category

Autoimmune disease pharmacovigilance data comes from diverse sources. These include clinical trial reports, real-world evidence (RWE), physician case reports, patient self-reports, and social media monitoring. Data update frequencies vary. Clinical trial data typically updates when study milestones are published. RWE and spontaneous reports may flow in continuously. Document structures are complex. They include unstructured text descriptions (e.g., adverse event details, patient history), semi-structured medical terminology (e.g., ICD-10 codes, MedDRA dictionary), and structured laboratory indicators and drug dosage information. Field specificity is high. For example, systemic lupus erythematosus may require tracking changes in antinuclear antibody (ANA) titers. Rheumatoid arthritis requires recording inflammatory markers like C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR). Units vary. Dosage often uses milligrams (mg) or international units (IU). Test results have specific units based on the item.

Constraints Imposed by these Characteristics on the "HTTP API and External Systems" Component

Diverse data sources require highly flexible HTTP API design. The API must adapt to different data formats and authentication mechanisms. Unpredictable update frequencies, especially continuous inflow of spontaneous reports, mean the system needs to support high-concurrency real-time data ingestion. POST request processing capability is critical. Unstructured text, such as narratives in adverse event reports, demands high requirements for API payload size and character encoding. It needs UTF-8 encoding support and large payload allowance. The mix of semi-structured and structured data, like MedDRA codes and laboratory indicators, requires the API to perform initial field validation and type conversion upon data receipt. This ensures data quality. Specific fields and units, such as ANA titer or CRP values, mean that when transmitting via HTTP API, a clear data model definition is necessary. For example, using JSON Schema for specification avoids parsing errors or unit confusion.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxRequestPayloadSize`	`20 MB`	Accommodates the text length of unstructured adverse event reports, ensuring complete transmission.
`timeoutSeconds`	`600 seconds`	Handles potential large data packet transfers and external system response delays, especially for bulk data import.
`concurrencyLimit`	`Calibrate by actual measurement`	Balances system resources and real-time data ingestion needs, preventing service overload due to high concurrency.
`requestHeader`	`Content-Type: application/json`	Ensures data transmission in standard JSON format, facilitating parsing and processing.
`authenticationMethod`	`OAuth 2.0` or `API Key`	Matches the strict requirements for data security and access control in the biomedical field.
`dataSchemaValidation`	`Enabled`	Validates the structure and type of incoming data, reducing dirty data entry, especially for specific fields and unit validation.

Three Common Pitfalls

The HTTP API returns a 400 Bad Request error with content like "Invalid JSON format" or "Missing required field". This happens when the validation of the incoming data body's structure or fields is too strict, failing to accommodate different report formats from various sources.
Adverse event report text received by the system is truncated or garbled. Logs show Payload Too Large or Unsupported Character Encoding. This occurs when the maximum request body size or character encoding for the interface is not configured correctly, preventing complete transmission of long texts or special characters.
Data is sent by the external system, but no update appears in FastGPT, and there is a long period of unresponsiveness. This can be due to an HTTP request timeout setting that is too short, failing to wait for the external system to process and return confirmation, especially when handling complex logic or batch data.

How to Confirm Correct Configuration

Send POST requests containing different types of adverse event reports using a simulation tool. Verify that the HTTP status code is 200 OK and check if the data in the FastGPT knowledge base is complete and correctly parsed.
Use test data containing specific biomarkers (e.g., ANA titer, CRP value) and measurement units. Verify that FastGPT accurately identifies and stores these fields and their values, for example, by querying ANA 1:1280.
Perform stress tests for high-concurrency scenarios, simulating the submission of a large number of adverse event reports in a short period. Monitor system logs and performance metrics to confirm that the interface remains stable under heavy load, without timeouts or data loss.

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.