HTTP Interface and External Systems for Pharmacovigilance

Data Characteristics in this Category

Pharmacovigilance data during clinical trial pre-screening primarily originates from clinical trial protocols, subject medical records, adverse event reports, and laboratory test results. This data exists as unstructured text, semi-structured tables, and structured database records. Update frequency varies: adverse event reports are real-time and can occur at any moment; subject medical records and laboratory test results typically update after each visit, with frequencies ranging from several days to several weeks. Document structure: Clinical trial protocols are normative texts, containing trial design, inclusion/exclusion criteria, and usually exist in PDF format. Adverse event reports may include free-text descriptions, medical terminology codes (e.g., MedDRA codes), and occurrence times. Specificity of fields and units is evident in the standardization of medical terminology, dosage units (e.g., mg/kg, IU), time units (e.g., days, weeks, months), and adverse event severity grading.

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

The diversity and update frequency of pharmacovigilance data impose specific requirements on HTTP interface design. Real-time adverse event reports necessitate support for high-concurrency event push mechanisms, such as Webhooks or high-frequency polling API interfaces. Unstructured text data, like subject medical records and adverse event descriptions, requires transmission via interfaces and support for subsequent Natural Language Processing (NLP) analysis. This demands interfaces capable of handling large data payloads and clear specifications for text encoding, typically UTF-8. Semi-structured and structured data require interfaces to define clear data models and fields to ensure accurate data transmission. For example, MedDRA codes for adverse events must be correctly transmitted via the interface for subsequent risk signal detection. Furthermore, time information in the data, such as adverse event occurrence time and reporting time, requires interfaces to support standardized timestamp formats, like ISO 8601, to avoid timezone and format conversion issues.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for Recommendation
`HTTP_REQUEST_TIMEOUT`	`600 seconds`	Allows sufficient transmission and processing time for requests that may contain large amounts of free text, such as adverse event reports.
`MAX_PAYLOAD_SIZE_MB`	`50 MB`	Accommodates potentially long text descriptions or attachments in clinical medical records and adverse event reports, ensuring sufficient data payload capacity per request.
`API_AUTHENTICATION_METHOD`	`OAuth 2.0`	Ensures the security of clinical trial data transmission; OAuth 2.0 provides robust authorization and authentication mechanisms.
`WEBHOOK_RETRY_INTERVAL`	`10 seconds`	For adverse event pushes, if the initial attempt fails, a short retry interval ensures timely data delivery.
`DATA_ENCODING`	`UTF-8`	Ensures correct transmission and parsing of medical terminology, patient descriptions, and various languages and special characters.
`DATE_FORMAT_STANDARD`	`ISO 8601`	Standardizes time representation to prevent data parsing errors and timezone issues caused by inconsistent date formats.

Common Pitfalls

HTTP interface returns a 400 Bad Request status code with a message indicating "missing field or incorrect format." This typically occurs when an external system sends an adverse event report without providing all required fields as per interface documentation, or when a field's value does not conform to the expected data type or enumeration range, for example, leaving the MedDRA_CODE field empty.
After an external system pushes data, no corresponding data updates are observed in FastGPT, or data updates are delayed. This might be due to an incorrect Webhook callback URL, preventing event notifications from being delivered; or the external system's push frequency is too high, exceeding the FastGPT interface's processing capacity, leading to some requests being dropped or queued.
Query interface returns fewer data entries than expected, or certain key fields are empty. This could be because the interface's matching of medical terminology is not precise enough when handling complex query conditions, leading to some relevant data being filtered out; or during synchronization from the source system, some non-mandatory field data failed to transmit successfully, resulting in incomplete data received by FastGPT.

How to Verify Configuration

Use FastGPT's interface testing tool to simulate sending an HTTP POST request containing complete pharmacovigilance data. Check if the returned status code is 200 OK and verify if the response body contains a success message.
In the FastGPT knowledge base, search for medical terms or subject identifiers included in the test data to confirm that the data has been successfully ingested and is retrievable. Check if the values of key fields (e.g., ADVERSE_EVENT_TERM, ONSET_DATE) match those sent.
Configure an external system Webhook to simulate triggering an adverse event report push. Then, check FastGPT's internal logs or relevant modules for records of receiving this event and confirm the data content is correct.
For data sources expected to update frequently, such as laboratory test results, continuously observe the update frequency and timeliness of related data in the FastGPT knowledge base over a period to ensure the synchronization mechanism with external systems meets expectations.

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