HTTP Interface and External Systems for Medical Record

Data Characteristics

Medical record quality control data originates primarily from Hospital Information Systems (HIS), Electronic Medical Record (EMR) systems, and physician order systems. This data is typically structured or semi-structured. It includes patient demographics, diagnoses, treatment plans, medication records, lab and imaging results, surgical records, and nursing notes. Outpatient records update immediately after a visit. Inpatient records update continuously during hospitalization, with final quality control performed post-discharge. Medical record quality control documents follow standardized medical industry templates, such as admission records, discharge summaries, and surgical records. These documents contain extensive text descriptions and standardized coded fields. Field content includes medical terminology, units of measurement (e.g., mg, ml, mmol/L), and diagnostic codes (e.g., ICD-10) and surgical codes.

Constraints on HTTP Interface and External Systems

Diverse data sources and varying update frequencies for medical record quality control data require HTTP interfaces to support high concurrency and real-time or near real-time data synchronization. The standardized structure of medical record documents and the extensive use of medical terminology mean interfaces must support complex JSON or XML formats with nested structures for data transfer. Fields containing ICD-10 codes and various units of measurement require external systems to perform strict data type validation and unit conversion during parsing to avoid ambiguity. The large volume of historical medical record data demands efficient query capabilities and pagination from the interface. Due to the sensitive nature of medical record data, HTTP interfaces must enforce HTTPS, strict authentication, and access control to ensure data transmission security and compliance. Documents may include images (e.g., imaging reports), requiring interfaces to support binary file transfer or provide stable file storage services.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`requestTimeout`	`60000 ms`	Ensures sufficient time for parsing and structuring medical record documents, preventing timeouts due to large data volumes.
`maxConnections`	`Benchmark against actual usage`	Based on concurrent request volumes from HIS/EMR systems, ensuring data synchronization efficiency.
`payloadSizeLimit`	`10 MB`	Medical record documents may contain large amounts of text and embedded images, requiring support for larger file transfers.
`authHeaderName`	`Authorization`	Adheres to mainstream authentication standards like OAuth 2.0 or JWT, ensuring interface security.
`responseSchema`	`JSON Schema`	Defines a strict response data structure, ensuring the completeness and accuracy of medical record data fields for downstream system parsing.
`fileUploadMethod`	`multipart/form-data`	Supports uploading image files like imaging reports, ensuring complete file content transfer.

Common Pitfalls

An API call returns 400 Bad Request because the request body lacks the required patientId field or has an incorrect ICD-10 code format.
After uploading an image file, the external system receives an inaccessible file link. This occurs because FastGPT's internal file storage path is relative, requiring additional BASE_URL configuration or file proxying.
When processing large volumes of historical medical record data, the HTTP interface frequently encounters 504 Gateway Timeout errors. This happens when a single API call processes too much data or involves overly complex business logic, exceeding the default timeout limits of the gateway or server.

Verification Steps

Use Postman or curl with sample data containing a complete medical record structure and standard codes to call the interface. Check if the returned status code is 200 OK and verify that the response data structure matches the expected JSON Schema.
Upload a medical record document with an attached image. Check if the file link in FastGPT is accessible and confirm that the external system can correctly parse and display the image.
Simulate high-concurrency requests using a stress testing tool. Observe if the interface response time is within an acceptable range. Check error logs for timeout or connection refused messages to determine appropriate maxConnections and requestTimeout thresholds.

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.