HTTP Interface and External Systems for Patient Assistance

Data Characteristics for This Category

Patient Assistance Program (PAP) registration data is diverse. Key data types include patient recruitment and screening criteria, medication usage records, adverse event reports, follow-up data, and program compliance documents. Data sources typically include Hospital Information Systems (HIS), Clinical Trial Management Systems (CTMS), pharmacy management systems, and patient-submitted paper or electronic forms. Data update frequencies vary; for example, medication usage records might update daily, while adverse event reports are submitted in real-time or on demand. Document structures are complex, encompassing structured database records (e.g., patient ID, dosage, follow-up date) and unstructured text documents (e.g., physician diagnoses, patient consent forms, adverse event descriptions). Field names may include medical abbreviations, and units involve dosage (mg), frequency (times/day), and time (days, months).

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

The complexity of patient assistance data requires HTTP interfaces to have robust data parsing and validation capabilities. Diverse, heterogeneous data sources necessitate support for multiple data formats (JSON, XML, CSV, and even PDF text) and data standardization. High-frequency data updates (e.g., medication usage records) demand high concurrency processing and low-latency responses from the interface. Integrating unstructured text documents requires the AI Agent to call external OCR services for text extraction and use natural language processing techniques for information extraction. The specialized medical nature of fields means that data mapping and validation require pre-configured or dynamically loaded medical dictionaries to ensure data accuracy. Strict compliance requirements also mandate end-to-end encryption for data transmission and fine-grained access control for external systems.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`HTTP_REQUEST_TIMEOUT_SECONDS`	`600 seconds`	OCR and NLP tasks for large amounts of unstructured text can be time-consuming; this prevents request timeouts.
`MAX_FILE_SIZE_MB`	`100 MB`	Patient consent forms and diagnostic certificates may contain multiple pages, leading to large individual file sizes.
`BATCH_PROCESS_SIZE`	`500 entries`	Balances single request efficiency with external system processing capacity, suitable for batch data like medication usage records.
`AUTH_HEADER_TYPE`	`Bearer Token`	The biomedical field has high data security requirements, so standard security authentication mechanisms are used.
`RETRY_INTERVAL_SECONDS`	`10, 30, 60 seconds`	External systems may fail due to momentary high load; stepped retries improve success rates.
`DATA_ENCODING`	`UTF-8`	Ensures correct transmission and parsing of mixed Chinese and English patient information and medical terms, preventing garbled characters.

Three Common Mistakes

HTTP request not executed, system log shows "Host '10.100.3.144' is not allowed to connect to this M": This indicates a security policy restriction from the external system or firewall, typically due to an unconfigured IP whitelist or insufficient API key permissions.
Garbled content after uploading a CSV file: This usually occurs when the CSV file encoding does not match the encoding expected by the interface, for example, if the file is GBK encoded and the interface parses it as UTF-8.
Frequent HTTP request timeouts when processing large amounts of patient follow-up data: This might be because HTTP_REQUEST_TIMEOUT_SECONDS is set too short, or the external system's processing capacity is insufficient, leading to delayed responses.

How to Verify Correct Configuration

Use FastGPT's debugging interface to simulate HTTP requests with different data types (structured, unstructured). Check if the returned status code is 200 OK and verify that the returned data structure matches expectations.
Upload a CSV file containing patient information with special characters and multi-language text. Check if the data is correctly parsed and imported without garbled characters.
During peak hours or simulated high-concurrency scenarios, continuously call the interface. Check if the request response time is within an acceptable range and if there are no numerous timeout errors.
Cross-reference external system logs to confirm that the data content of each HTTP request matches what FastGPT sent and that data processing is error-free.

The values given 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.