Data Characteristics for This Category
Medication surveillance data in infection control primarily originates from Hospital Information Systems (HIS), Laboratory Information Systems (LIS), Electronic Medical Records (EMR), and specialized infection control monitoring systems. Data updates frequently. Most critical events, such as medication orders, abnormal lab results, and vital sign changes, update in real-time or near real-time. Other data, like patient demographics and historical medication records, update daily or weekly. Document structures typically follow HL7, CDISC, or other industry standards. However, practical implementations often include numerous custom fields to meet the business needs of different healthcare institutions. Data fields include patient ID, generic drug name, batch number, dosage, administration route, administration time, ordering physician, nurse operator, infection site, pathogen detection results, and antibiotic sensitivity test results. Units for dosage often involve milligrams (mg), grams (g), milliliters (ml), and units (U). Time is precise, down to minutes or even seconds.
Constraints Imposed by These Characteristics on "HTTP Interfaces and External Systems"
The high update frequency of infection control medication surveillance data requires HTTP interfaces to have high throughput and low latency. This ensures timely information synchronization. Real-time requirements make polling or long connections common choices, but server load must be considered. Diverse data sources mean interfaces need to support multiple data format conversions, such as parsing XML to JSON or vice versa. The presence of custom fields challenges interface flexibility. Interfaces need dynamic field extension capabilities or configuration mapping rules to handle these. Large volumes of historical data require efficient conditional filtering and paginated queries for retrieval. Insufficient standardization of fields and units can lead to data parsing errors. Therefore, strict data validation and unit conversion are necessary at the interface layer to ensure downstream models correctly interpret the data.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
requestTimeoutMs | 30000 ms | Most real-time data interface responses should be in seconds. This provides sufficient buffer for network fluctuations or occasional slow queries. |
maxConnections | 100 | Addresses the high concurrency demand for infection control data writing and querying, preventing connection exhaustion. |
payloadSizeLimitMB | 5 MB | Accounts for single requests potentially containing multiple medication records or complex lab results, preventing request failures due to excessive load. |
retryAttempts | 3 | Improves success rates by adding a retry mechanism for transient network fluctuations or occasional external system unavailability. |
successHttpStatusCodes | 200, 201, 202 | Clearly defines HTTP status codes for successful API calls, distinguishing business success from system errors. |
schemaValidationRules | Calibrated by actual measurement | Strictly defines field types, lengths, and mandatory items based on actual received HL7 or custom data structures to ensure data quality. |
Common Pitfalls
- An external API call returns an HTTP status code of 200, but the business fields in the response body are empty. This prevents subsequent processes from executing. This often occurs when the external system returns a successful HTTP status code even when business processing fails, placing error information within the response body.
- In private deployments, uploading large files (e.g., scanned patient pathology reports) via HTTP interfaces causes the agent's backend service to slow down or freeze. This often happens because file uploads do not use chunked transfer or asynchronous processing, directly occupying main thread resources.
- The configured HTTP interface fails to correctly parse specific data formats returned by external systems (e.g., non-standard XML or JSON with specific encodings). This leads to data parsing failures. This can occur if the interface's
contentTypeor character set configuration does not match the external system's.
Verification of Configuration
- Conduct end-to-end testing of critical business processes. Verify the complete pipeline from data acquisition from external systems to FastGPT internal processing and result output. Check that data is correct at all intermediate stages.
- Monitor interface request success rates and average response times. Ensure they are within expected ranges and maintain stable operation.
- Regularly check system logs for HTTP interface-related error messages, especially records of parsing failures, timeouts, or authentication failures. Investigate these promptly.
- In a test environment, simulate abnormal responses from external systems (e.g., returning non-standard data, timeouts, HTTP 500 errors). Verify that FastGPT's error handling mechanisms perform as expected.
Note: The values provided are common starting points. Measure them against your 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.