HTTP Interface and External Systems for Telemedicine

Data Characteristics in this Domain

Telemedicine registration documents involve patient records, diagnostic reports, treatment plans, medical device parameters, software version logs, clinical trial data, and various compliance files. Data originates from diverse sources, including Electronic Medical Record (EMR) systems, Laboratory Information Systems (LIS), Picture Archiving and Communication Systems (PACS), and third-party device monitoring platforms. Data updates frequently, especially during clinical trials, where data may update daily or hourly. Document structures are complex, containing both structured data (e.g., patient demographics, diagnostic codes) and unstructured data (e.g., handwritten physician notes, imaging report text). Fields and units must strictly adhere to medical industry standards, such as International Classification of Diseases (ICD) codes, Unified Medical Language System (UMLS) terminology, and various measurement units (e.g., mg/dL, mmHg).

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

The diversity and high update frequency of telemedicine data require HTTP interfaces to support efficient data synchronization. The mix of structured and unstructured data necessitates interface designs that accommodate various data formats like JSON and XML, and can process large volumes of text. The sensitive nature of medical data demands high security for data transmission, mandating HTTPS protocol and potentially involving additional encryption and signing mechanisms. Field and unit standardization requires strict validation logic during data parsing to ensure data consistency and prevent submission errors due to unit confusion. Furthermore, data sources from different medical systems may have varying interface authentication methods, requiring FastGPT's external system integration capabilities to support multiple authentication schemes, such as OAuth 2.0, API Key, or token-based authentication.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`3000` characters	Telemedicine text documents (e.g., medical record summaries) are often long, requiring complete context.
`requestTimeout`	`600` seconds	Accommodates the time needed for transferring large medical imaging reports or complex clinical trial data.
`UPLOAD_FILE_MAX_SIZE`	`500` MB	Covers the upload requirements for medical image files and large clinical data reports.
`PARSE_FILE_TIMEOUT_SECONDS`	`300` seconds	Handles parsing of complex PDF compliance documents, preventing parse timeouts.
`Similarity threshold`	`0.75`–`0.85`	Ensures precise matching for medical terminology and regulatory clauses, reducing false recalls.
`Rerank result count`	`Top 5 entries` (top 5 entries)	Focuses on the most relevant declaration elements and regulatory provisions.

Three Common Pitfalls

A 401 error returns when calling an external medical system interface. This occurs because the Authorization header configured in FastGPT does not match the authentication information forwarded by OneAPI, or OneAPI fails to handle token refresh correctly.
When parsing medical device log files, some critical parameters (e.g., dosage, frequency) are empty. This happens if the file encoding or data separator does not match the preset parsing rules, preventing the parser from correctly extracting data.
When processing clinical trial data, the interface returns an unusually large volume of data, leading to a requestTimeout error. This is because the external system interface lacks pagination or batch data request parameter configurations, causing a single request to exceed the expected data volume.

Verification Steps

Use FastGPT's external system debugging tool to simulate telemedicine data interface requests. Check if the returned data structure, field values, and HTTP status code meet expectations, particularly a 200 status code and data completeness.
Upload a typical telemedicine declaration document, such as a PDF file containing medical image links. Observe FastGPT's file parsing logs to confirm that all critical information (e.g., patient ID, diagnosis results, device model) is correctly extracted and vectorized.
Use FastGPT's knowledge base Q&A function to ask specific questions related to the telemedicine declaration process. Verify that the system accurately recalls relevant regulatory provisions, clinical guidelines, or technical standards, and provides coherent answers.

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.