Cardiovascular Interventional Clinical Trial Pre-screening

Data Characteristics in this Domain

Clinical trial pre-screening data in cardiovascular intervention primarily originates from hospital Electronic Health Record (EHR) systems, Picture Archiving and Communication Systems (PACS), and Laboratory Information Systems (LIS). Data update frequency varies by source. For example, patient vital signs may update in real-time, while laboratory results typically update within hours. Document structures are predominantly semi-structured and unstructured, including physician notes, imaging reports, and lab reports. Data transmission often uses HL7 v2 or FHIR standards. Key fields include patient ID, diagnostic information (e.g., ICD-10 codes), surgical records (e.g., CPT codes), imaging measurements (e.g., percentage of vascular stenosis), biomarker results (e.g., Troponin I in ng/mL; NT-proBNP in pg/mL), and medication records.

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

The diversity and complexity of cardiovascular interventional data require HTTP interface compatibility. Semi-structured and unstructured data necessitate interfaces that support flexible data formats, such as JSON or XML, and can handle nested structures. Varying data update frequencies mean interfaces need to support multiple trigger mechanisms, such as webhook pushes for real-time data and scheduled polling for periodic updates. Standardized encodings (HL7, FHIR, ICD-10, CPT) in document structures require interfaces to possess semantic understanding capabilities during data parsing, ensuring accurate field mapping. The precise units for imaging measurements and biomarker results demand that interfaces maintain data type and precision integrity during data transmission and storage, preventing precision loss or unit confusion due to type conversion.

Configuration Guidelines

Configuration Item	Recommended Approach	Rationale for this Approach
`externalApi.url`	External system API address	Configure based on the actual API endpoint of the hospital EHR/PACS/LIS system, e.g., `https://ehr.hospital.com/api/v2/patients`.
`request.method`	`POST` or `GET`	Most data pushes use `POST`; data queries use `GET`.
`request.headers.Content-Type`	`application/json` or `application/xml`	Adapts to different system data exchange formats. FHIR standard often uses `application/fhir+json`.
`request.body.template`	Custom JSON/XML template	Define the request body structure according to the target system's API documentation, including fields like patient ID, diagnosis, and key indicators.
`response.timeout`	`60000` milliseconds	Allows sufficient response time, considering potential high load on clinical systems, to prevent data synchronization failures due to timeouts.
`data.extraction.jsonPath`	`$.patients[*].data`	Accurately extracts required patient information and examination results from the returned JSON data using JSONPath expressions.

Three Common Pitfalls

HTTP status code 400 Bad Request or 500 Internal Server Error occurs because required fields are missing from the request body or data format does not comply with external system requirements.
After data synchronization, some key indicator fields in FastGPT are empty because data.extraction.jsonPath is configured incorrectly, failing to accurately parse the nested JSON structure returned by the external system.
FastGPT does not reflect the latest status after external system data updates because webhook listening or scheduled tasks are not configured correctly, resulting in insufficient data synchronization frequency.

Verification Steps

Use FastGPT's HTTP interface debugging tool to simulate a complete patient data query or push operation. Check if the returned HTTP status code is 200 OK.
Create a test patient in the FastGPT knowledge base. Manually import a simulated cardiovascular interventional data entry from an external system. Verify that key fields (e.g., vascular stenosis percentage, Troponin I) and their units match the source data.
Configure a scheduled task, for example, to synchronize the latest laboratory results every 30 minutes. Subsequently, check the data update time for relevant patients in the FastGPT knowledge base to confirm the synchronization mechanism is working correctly.
Monitor the external system's API logs to see if requests initiated by FastGPT are sent at the expected frequency and format, and verify that the external system successfully processes these requests.

The values provided in this document 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.