HTTP Interface and External Systems for Clinical Trial

Data Characteristics in This Category

Data for clinical trial pre-screening in nursing management primarily originates from Electronic Health Record (EHR) systems, nursing notes, patient self-reported questionnaires, and wearable device data. This data typically exists as a mix of unstructured text, structured tables, and time-series data. Unstructured text includes nursing assessment reports, physician order execution records, and patient communication logs, containing extensive free-text descriptions of patient symptoms, adherence, lifestyle habits, and potential risk factors. Structured data, such as vital signs, medication records, and laboratory test results, is stored in standardized fields and units. Wearable device data provides continuous physiological indicators like heart rate, sleep patterns, and activity levels. Data update frequency is high, potentially hourly during acute phases or treatment adjustments, and multiple times a week even for chronic disease management.

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

The mixed structure of nursing management data poses challenges for HTTP interface design. Parsing unstructured text requires robust natural language processing capabilities, while standardized fields in structured data demand precise mapping by the interface. High update frequency necessitates HTTP requests that support high concurrency and low latency to ensure real-time pre-screening results. Common medical terminology, abbreviations, and mixed units (e.g., blood pressure in mmHg, kPa; blood glucose in mmol/L, mg/dL) in documents require the interface to standardize data before transmission or convert units upon reception. Simultaneously, patient privacy regulations (e.g., HIPAA, GDPR) mandate that all data transfers occur over encrypted channels with strict access control, directly influencing the choice of HTTP security protocols and authentication mechanism implementation.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for Recommendation
`HTTP_TIMEOUT_SECONDS`	`30 seconds`	Accounts for potential remote EHR system response delays and network fluctuations, preventing frequent timeouts.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accommodates the time required to parse large nursing record PDF documents, especially those with complex charts and tables.
`maxContext`	`8000 Tokens`	Ensures that critical information from a single nursing record, including free-text descriptions and structured data, can be fully loaded.
`Chunk size` (Chunk Length)	`500 characters`	Balances semantic completeness with subsequent retrieval efficiency, effectively processing long paragraphs in nursing records.
`Similarity threshold` (Similarity Threshold)	`0.75`	Improves matching accuracy, avoiding misjudgments due to similar medical terms with different meanings.
`API_KEY`	Calibrate based on actual measurements	Required by external systems to ensure the security and authentication of API calls.

Three Common Mistakes

Symptom: API request returns 401 Unauthorized or 403 Forbidden. Reason: API_KEY configuration is incorrect or expired, or the external system's IP whitelist does not include the FastGPT deployment address.
Symptom: Knowledge base file upload parsing progress is stalled for a long time, eventually showing Parsing Failed. Reason: The uploaded PDF nursing record is too large or has a complex internal structure, leading to PARSE_FILE_TIMEOUT_SECONDS being insufficient to complete parsing.
Symptom: Web page data obtained via HTTP request is incomplete or empty. Reason: The target web page content is dynamically loaded, and FastGPT's HTTP request cannot execute JavaScript to retrieve the final rendered data, or the request frequency is limited by the target website.

How to Confirm Proper Configuration

Call the external system's provided test interface or health check endpoint to confirm correct HTTP connectivity and authentication configuration.
Upload a typical nursing record PDF file containing various data types (text, tables), observe if its parsing status is successful, and check if key information has been extracted into the knowledge base.
Simulate a complete pre-screening process, including data acquisition, knowledge base querying, and result generation, to verify the smooth operation of the entire data pipeline and whether response times are within expectations.
Review logs to confirm the absence of frequent connection timeouts, parsing failures, or API call errors, especially those related to interactions with external systems.

Note: The values provided are common starting points. Measure against specific samples to determine optimal settings for individual use cases.

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.