HTTP Interface and External Systems for Clinical Trial

Data Characteristics for This Category

Data for clinical trial pre-screening in hospital operations primarily originates from hospital internal systems. These include Electronic Medical Record (EMR/EHR) systems, Laboratory Information Systems (LIS), Picture Archiving and Communication Systems (PACS), and Clinical Data Management Systems (CDMS). Data exists in a mixed format, combining structured data (e.g., patient demographics, diagnoses, medication records, lab results) and unstructured data (e.g., handwritten physician notes, imaging reports). Data updates frequently. Patient visits, examinations, and medication events can trigger real-time or near real-time updates. Data document structures typically adhere to healthcare industry standards like HL7 and DICOM. However, specific fields and encodings may vary by hospital information system vendor. Field names can be a mix of Chinese and English. Units strictly follow international standard units or common clinical units, such as mg/dL, mmol/L, kPa.

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

The data characteristics of clinical trial pre-screening in hospital operations impose specific requirements on FastGPT's HTTP interface and external system integration. High-frequency updates and real-time needs require interface designs that support efficient data synchronization mechanisms, such as incremental updates or event-driven approaches. Mixed data structures necessitate interfaces capable of processing complex JSON or XML bodies and effectively parsing both structured and unstructured components. Healthcare industry standards, such as HL7 v2 or FHIR resources, require FastGPT to perform appropriate parsing and mapping upon data reception, ensuring semantic accuracy. Furthermore, sensitive patient information demands robust data security and privacy protection mechanisms, including Transport Layer Security (TLS) and access control. The standardization of fields and units requires validation during data ingestion to prevent pre-screening result discrepancies due to inconsistent units.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`requestTimeout`	`30000 ms`	Allows sufficient time for responses, considering the complexity and volume of medical data queries.
`maxConnections`	`20`	Balances concurrent processing capability with hospital information system load to prevent overload.
`authHeader`	`Bearer <JWT_TOKEN>`	Uses industry-standard JWT authentication to secure the interface.
`jsonParseMode`	`lenient`	Addresses potential non-standard JSON formats or special characters in medical data.
`retryAttempts`	`3`	Handles transient network fluctuations or occasional backend system failures, improving data transmission stability.
`dataValidationSchema`	Based on actual data source's HL7/FHIR structure or custom JSON Schema	Ensures the completeness and standardization of received data, such as the format of the `patientId` field.

Three Common Pitfalls

The interface returns HTTP 500 Internal Server Error because the external system timed out while processing a complex query and failed to respond promptly.
Some critical fields (e.g., diagnosisCode or labResultValue) are empty in the pre-screening results. This occurs because the HL7 or FHIR message body returned by the external system was not parsed correctly, indicating an incorrect field mapping configuration.
The AI assistant's pre-screening recommendations deviate from actual clinical situations because the units of laboratory results provided by the external system were not correctly identified and standardized, leading to misinterpretation of values.

How to Verify Configuration

Use FastGPT's interface testing tool to simulate sending multiple query requests for different patient types. Verify that each request returns an HTTP 200 OK status code within the requestTimeout parameter.
Randomly select at least 10 returned patient data sets. Manually cross-check critical fields such as patientId, diagnosisCode, and medicationName to ensure their values match the external source system records and that units are standardized.
Configure pre-screening rules in FastGPT. Run the pre-screening process against patient data known to meet or not meet specific clinical trial criteria. Evaluate the concordance between the AI assistant's pre-screening conclusions and expected outcomes to assess the reasonableness of the similarity threshold.

The values provided 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.