Pharmacoeconomics and Pharmacovigilance: HTTP Interface and

Data Characteristics in this Domain

Pharmacoeconomics applied to pharmacovigilance primarily uses data from Real-World Evidence (RWE), clinical trial data, post-market surveillance reports, and adverse drug reaction (ADR) databases from national regulatory agencies. This data often exists in structured or semi-structured formats like CSV, JSON, and XML files. It also includes extensive unstructured text, such as physician diagnostic records, patient self-reports, and medical literature. Data updates are frequent, especially for post-market surveillance data, which may update weekly or monthly. Document structures are complex, commonly including fields like generic drug name, brand name, ATC classification code, indications, adverse event, severity, frequency, treatment cost, hospital days, and patient QALY (Quality-Adjusted Life Year). Field units vary; for example, costs are in local currency, frequencies are percentages, and time is in days or years.

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

The high update frequency of pharmacoeconomics data requires HTTP interfaces to have efficient data synchronization mechanisms. This ensures the timeliness of model analysis results. The diversity of data sources and complex structures mean external system interfaces must support parsing multiple data formats. For example, the system must process XML-formatted adverse reaction reports from different national regulatory agencies while also parsing JSON-formatted cost-benefit data from clinical studies. The variety of field units, especially currency and time units, necessitates clear unit conversion or annotation during interface calls to prevent data confusion. Large volumes of unstructured text data require the interface to efficiently transmit and process long texts, collaborating with backend text processing services (e.g., entity recognition, sentiment analysis). Data volumes are typically large, demanding interface stability and response speed, particularly during bulk data import or complex queries.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`requestTimeout`	`120000 ms`	Pharmacoeconomics data volumes are large. Complex queries or bulk imports can take significant time. Ample time prevents timeout interruptions.
`maxPayloadSize`	`20 MB`	External systems transmitting raw adverse reaction reports or clinical trial data files may have large single request payloads.
`contentType`	`application/json` or `application/xml`	Accommodates mainstream structured data formats, supporting parsing of pharmacoeconomics data documents from various sources.
`retryAttempts`	`3`	External systems may fail requests due to network fluctuations or temporary service unavailability. Multiple retries increase data synchronization success rates.
`authHeaderName`	`Authorization`	Follows common API authentication mechanisms, ensuring data transmission security, for example, using a Bearer Token.
`dataSchemaVersion`	`V2023-09-01`	Specifies the data interface version to manage changes in pharmacoeconomics fields over time and maintain compatibility.

Three Common Pitfalls

An API call returns a 400 status code with an "Invalid JSON format" error. This occurs when data sent by the external system does not conform to the interface's expected JSON structure, for example, missing required fields or mismatched data types.
The interface call succeeds, but returned data fields are empty or values are abnormal. This typically results from inconsistencies between the external system's data units and FastGPT's internal processing logic, such as un-converted currency units or un-standardized time units.
An application calling the interface experiences a long delay without a response, eventually leading to a timeout error. This may be because the external system takes too long to process the request, or FastGPT's requestTimeout parameter is set too short, failing to wait for the external system to complete data processing.

How to Verify Configuration

Use FastGPT's debugging tools or logs to check if the HTTP request's response status code is 200 and confirm the returned data structure matches expectations.
For critical pharmacoeconomics fields, such as cost_value and adverse_event_rate, verify that the values and units in the returned data are correct. Compare them against the original data source to confirm data has been converted as expected.
Simulate high concurrency or large data volume requests. Monitor the interface's response time and stability. This ensures no timeouts or performance bottlenecks occur in real-world application scenarios. Adjust the requestTimeout parameter if necessary.
Review the processing logic for data returned by external systems within the FastGPT workflow. Ensure all relevant fields, especially long text content and nested structures, are correctly parsed and utilized.

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.