HTTP Interface and External Systems for Infectious Disease

Data Characteristics for This Category

Infectious disease clinical trial pre-screening data typically originates from multiple sources. These include Electronic Health Record (EHR) systems, Laboratory Information Systems (LIS), Picture Archiving and Communication Systems (PACS), and public health agency reports. Data types are diverse. They cover patient demographic information, diagnostic codes (e.g., ICD-10), pathogen detection results (including strain type and antimicrobial susceptibility profiles), imaging report descriptions, medication records, and past infection history. Data update frequency is often high. Some laboratory results may update hourly, especially during outbreaks or changes in patient condition. Document structures are primarily semi-structured or unstructured. Examples include handwritten clinical notes from physicians and natural language descriptions in imaging reports. Field and unit specifics are crucial. Pathogen load is often expressed as copies/mL or IU/mL. Antimicrobial susceptibility results use MIC (Minimum Inhibitory Concentration) values. Specific numerical units require strict identification.

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

The high update frequency of infectious disease data requires HTTP interfaces to support efficient polling or webhook reception. This ensures the timeliness of pre-screening results. The presence of semi-structured and unstructured documents means parsing interface return data needs greater flexibility. It cannot rely solely on fixed field mapping. Natural Language Processing (NLP) techniques are necessary. Specific fields and units, such as MIC values, must match precisely during data transmission and parsing. Otherwise, screening logic errors may occur. Data sources involving critical information like pathogen sensitivity often have strict access permissions and data security requirements. This impacts API key configuration strategies and interface access frequency. The heterogeneous nature of data sources also means handling various data formats (e.g., JSON, XML, HL7) and performing standardization.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`requestTimeoutSeconds`	`60 seconds`	Infectious disease data sources may involve large data queries. This avoids interface timeouts due to long data retrieval times.
`maxRetries`	`3`	Addresses temporary network fluctuations or transient external system failures, improving data acquisition success rates.
`apiSecretExpirationHours`	`24 hours`	Balances security and convenience. Regular key rotation reduces leakage risks and aligns with some medical system security standards.
`concurrentRequestsLimit`	`10`	Prevents excessive load on external systems that could lead to denial of service, while maintaining data acquisition efficiency.
`dataParseMode`	`Hybrid (JSON & Regex)`	Infectious disease data is a mix of structured and semi-structured content. This balances efficiency and flexibility.
`customHeaderFields`	`X-API-Version: 2.0`	Explicitly specifies the external API version. This ensures compatibility and prevents interface behavior changes due to version upgrades.

Three Common Mistakes

Pathogen detection result fields in the interface response are empty. This may be due to external system data update delays or interface design not fully covering all test items.
Patient antimicrobial susceptibility judgment in clinical trial pre-screening results is incorrect. This happens because the MIC value unit was not parsed correctly, invalidating the numerical comparison logic.
Frequent triggering of external system API call limits. This manifests as 429 Too Many Requests status codes. This occurs due to improper configuration of concurrent request limits or polling intervals.

How to Confirm Correct Configuration

Simulate multiple patient data requests. Check if the HTTP interface response time is within the requestTimeoutSeconds threshold.
Compare critical patient fields (e.g., pathogen name, MIC value) stored within FastGPT with the original external system data. Confirm the accuracy of data parsing and unit conversion.
Continuously monitor FastGPT's API call logs to external systems. Ensure no 429 or 5xx error status codes appear. Check if the apiSecretExpirationHours configuration triggers key updates as expected.
Randomly select a batch of pre-screened patients. Manually verify if their clinical indicators meet the inclusion criteria. This validates the screening logic, especially the parsing of semi-structured text.

Note: The values given 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.