HTTP Interface and External Systems for Telemedicine

Data Characteristics in this Category

Telemedicine clinical trial pre-screening data originates primarily from patient-completed electronic health questionnaires, physiological metrics collected by wearables (e.g., heart rate, blood pressure, blood glucose), and remote consultation records. This data is typically structured (JSON, XML) or semi-structured (free text) and may contain sensitive information such as patient medical history, medication details, and family medical history. Data update frequency depends on patient interaction and device collection cycles, usually periodic (e.g., daily, weekly) or event-driven (e.g., patient completes a questionnaire, doctor updates a diagnosis). Document structures often adhere to medical data exchange standards like the HL7 FHIR resource model. Field names involve specialized terminology from classifications such as ICD-10 and ICF, and frequently include specific units of measurement (e.g., mmHg, mmol/L).

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

The highly sensitive nature of telemedicine data mandates that HTTP interfaces enforce HTTPS protocol and implement strict authentication and authorization mechanisms, such as OAuth 2.0 or JWT. The heterogeneous nature of multi-source data means interfaces must support parsing various data formats and possess robust data cleaning and standardization capabilities to unify data from different sources and formats. Frequent data updates demand high real-time performance and concurrent processing capabilities from interfaces to ensure the pre-screening process can promptly respond to the latest patient status. Accurate recognition and processing of specialized medical terminology and units of measurement require interfaces to integrate medical terminology services at the data parsing layer, preventing inaccurate pre-screening results due to misunderstandings of units or terms. Additionally, given potentially large data volumes, interface design needs to consider pagination and incremental synchronization mechanisms to reduce unnecessary network load.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`API_ENDPOINT`	`https://api.example.com/patient_data/v2`	Enforces HTTPS and clear version control, ensuring data transmission security and interface stability.
`HTTP_METHOD`	`POST` or `GET`	`POST` for submitting questionnaires or device data; `GET` for querying patient historical records.
`AUTH_HEADER`	`Bearer <JWT_TOKEN>`	Uses JWT for authentication, ensuring request legitimacy and user authorization.
`TIMEOUT_SECONDS`	`30 seconds`	Balances network latency and data processing time, preventing long waits or connection interruptions.
`RATE_LIMIT_PER_MINUTE`	`120 times`	Balances system load and data update requirements, preventing malicious or overloaded requests.
`CONTENT_TYPE`	`application/json`	Telemedicine data is often transmitted in JSON format, ensuring data parsing compatibility.

Common Pitfalls

An HTTP interface returning a 400 status code with an invalid image error typically indicates an issue during encoding or transmission of multimodal data (e.g., medical images), resulting in corrupted image files or a format that does not meet interface requirements.
A workflow running correctly in FastGPT but only interacting with the first dialogue node after integration with an external system may be due to the external system not correctly parsing instructions like next_node_id or action_payload returned by FastGPT, failing to drive the dialogue flow forward.
SQL query errors in data linking tools are commonly caused by SQL statements not adapting to the specific dialect of the external database (e.g., MySQL, PostgreSQL), or by table or field names in the query not matching the external system's data structure.

Verification Steps

Send test requests using Postman or curl with the configured API_ENDPOINT and AUTH_HEADER. Check for an expected 200 status code and structured data response.
Simulate a patient submitting a complete electronic health questionnaire. Observe if the FastGPT workflow fully receives and processes all fields. Compare with the patient's record in the external system to confirm data consistency.
Configure a test dialogue in FastGPT that includes multimodal input (e.g., uploading medical images). Check if the interface correctly processes image data and confirm no invalid image errors appear in the logs.

The values provided are common starting points and should be measured against specific 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.