Retail Chain Pharmacovigilance: HTTP Interface and External

Data Characteristics

Retail chain pharmacovigilance data originates from pharmacy sales systems, pharmacist reporting systems, and consumer feedback channels. This data updates frequently. Sales data is often real-time or near real-time, while adverse event reports may be aggregated daily or weekly. Data document structures vary. Structured sales records and drug batch information exist alongside semi-structured adverse event descriptions and patient medication histories. Key fields include drug generic name, batch number, manufacturer, sales date, basic patient information, adverse event occurrence time, symptom description, and severity. Units involve quantity (e.g., tablets, boxes), monetary value (Yuan), time (year, month, day, hour, minute), and severity grading (e.g., mild, moderate, severe).

Constraints Imposed by "HTTP Interface and External Systems"

The characteristics of retail chain pharmacovigilance data impose specific requirements on HTTP interfaces and external system integration. High-frequency sales data updates require interfaces to support batch or stream transfer to prevent data backlog and delays. Semi-structured adverse event reports demand flexible data parsing capabilities from interfaces. These interfaces must handle non-standardized text information and map it to predefined knowledge base structures. Patient privacy information necessitates strict adherence to data security and compliance requirements during data transmission. For example, HTTPS protocol encryption is mandatory. Diverse fields and units require interfaces to validate different data formats and perform necessary unit conversions or standardization. This ensures data consistency and prevents misjudgments due to inconsistent units.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`HTTP_TIMEOUT_SECONDS`	`60 seconds`	Balances data volume and real-time needs, prevents long connections from consuming system resources.
`MAX_RECORDS_PER_BATCH`	`2000 records`	Accommodates batch uploads of retail chain sales data, balancing transfer efficiency and single-pass processing load.
`DATA_SCHEMA_VERSION`	`v1.2`	Ensures consistency with external system data structure definitions, facilitating data parsing.
`ERROR_RETRY_INTERVAL`	`300 seconds`	Provides a reasonable retry interval for occasional external system failures.
`AUTHENTICATION_METHOD`	`API Key`	Suitable for inter-system calls, facilitating permission management and tracking.
`LOG_LEVEL`	`INFO`	Records key operations and errors, aiding in troubleshooting.

Common Pitfalls

The interface returns 500 Internal Server Error, and logs show field parsing failures. This may occur if the data format transmitted by the external system does not match the DATA_SCHEMA_VERSION expected by the interface.
Some adverse event reports fail to import. The status code shows 202 Accepted, but data is missing. This may occur if semi-structured text is not effectively encoded during transmission, leading to the loss of special characters.
Data synchronization experiences severe delays, despite a reasonable HTTP_TIMEOUT_SECONDS setting. This may occur if the external system has insufficient processing capacity during peak request periods, causing a large queue of requests.

Verification Steps

Simulate data reporting from a retail chain sales system. Observe FastGPT logs to confirm complete capture of sales records and related drug batch information.
Export simulated adverse event reports, including various symptom descriptions and severity gradings, from a pharmacist reporting system. Upload these reports via the HTTP interface. Check if FastGPT's knowledge base accurately identifies and classifies this information.
Monitor interface error logs. Ensure no 5xx error codes or data parsing exceptions appear after a period of continuous operation.

The values provided are common starting points. Measure them 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.