HTTP Interface and External Systems for Patient Assistance

Data Characteristics for This Category

Patient Assistance Program (PAP) clinical trial pre-screening data primarily originates from Electronic Health Record (EHR) systems of medical institutions, patient management platforms of pharmaceutical companies, and third-party data service providers. This data updates frequently, typically with daily or weekly incremental updates, reflecting the latest patient diagnoses, treatment progress, and medication status. Document structures are often semi-structured or structured, such as JSON or XML formats. They include basic patient information, disease diagnosis codes (e.g., ICD-10), medication records (including generic drug names, dosages, frequencies), laboratory test results, imaging report conclusions, and descriptive text related to key inclusion/exclusion criteria. Specific fields include disease staging, genetic test results, and levels of specific biomarkers (e.g., HER2 status, PD-L1 expression). Units strictly adhere to clinical medical norms, such as ng/mL, mmol/L, and mm.

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

The high update frequency of patient assistance clinical trial pre-screening data requires the HTTP interface to have an efficient synchronization mechanism. This prevents data lag from affecting pre-screening accuracy. The mix of semi-structured and structured data means the interface must be compatible with multiple formats for data parsing and capable of flexibly extracting key fields. Standardized fields like disease diagnosis codes and medication records require strict data validation rules to ensure data quality. Specific fields like biomarkers require FastGPT to correctly identify and index them during data ingestion for accurate matching later. Furthermore, due to sensitive patient health information, data transmission must use encrypted protocols. Interface design must also consider access control to ensure data security and compliance. Interface stability and fault tolerance are crucial to handle occasional network fluctuations or abnormal data formats from source systems.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`HTTP_REQUEST_TIMEOUT_SECONDS`	`60` seconds	Handles large data volumes or unstable networks, preventing premature request timeouts.
`MAX_RESPONSE_BODY_SIZE_MB`	`50` MB	Ensures the ability to receive large response bodies containing multiple patient records or detailed medical information.
`DATA_PARSING_STRATEGY`	`JSON_PATH_EXTRACTION`	Precisely extracts key fields from semi-structured JSON data using path expressions.
`KNOWLEDGE_BASE_UPDATE_INTERVAL`	`Daily`	Aligns with the source system's daily update frequency, synchronizing the knowledge base with the latest patient information.
`EMBEDDING_BATCH_SIZE`	`50` records	Balances embedding computation resource consumption with knowledge base update efficiency, suitable for incremental data processing.
`RETRY_ATTEMPTS_ON_FAILURE`	`3` times	Improves interface call robustness, addressing occasional transient network failures or service busyness.

Common Pitfalls

An HTTP request returns a 400 Bad Request error code because some disease diagnosis codes or biomarker field values in the request body do not follow expected enumerations or formats.
After a knowledge base update, a specific patient's latest medication information is not correctly indexed. This occurs because the corresponding JSON Path expression in the data parsing configuration is inaccurate, failing to capture deeply nested fields.
When querying patients in the FastGPT application, the number of returned results is much lower than expected. This happens because the external system interface does not correctly handle offset or limit parameters when returning paginated data, leading to only partial data retrieval.

How to Confirm Correct Configuration

Use FastGPT's HTTP interface debugging tool with simulated patient data requests. Verify the interface returns a 200 OK status code. Check if key fields (e.g., patient_id, diagnosis_code) in the response body are complete and correctly formatted.
In the FastGPT knowledge base management interface, randomly select imported patient documents. Verify their content in the knowledge base matches the source system data, paying close attention to the extraction of specific biomarker fields.
After configuring an incremental synchronization task, manually trigger a synchronization. Observe FastGPT's log output to confirm no data parsing errors or connection timeout exceptions occur.
In the FastGPT application, use patient information known to meet specific clinical trial inclusion/exclusion criteria for a query. Check if the recall results include that patient and evaluate the matching accuracy.

The values provided are common starting points. Measure their effectiveness 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.