Data Characteristics in this Category
Monitoring devices generate data during clinical trial pre-screening primarily from real-time sensor collection of physiological parameters. Examples include electrocardiograms (ECG), blood pressure, blood oxygen saturation (SpO2), body temperature, and respiratory rate. This data typically exists as continuous time series with high update frequencies. For instance, ECG sampling rates can reach 500 Hz or higher, and blood pressure is measured every few minutes. Data storage formats vary, commonly including HL7 FHIR resources, DICOM WLM, or proprietary vendor binary formats. This data may ultimately convert to CSV or JSON for analysis. Document structure often includes metadata such as device model, calibration information, patient ID, and measurement timestamps. Field names frequently follow industry standards, for example, observation.code.coding.code identifies the measurement type, and observation.valueQuantity.value stores the measured value. Units strictly adhere to International System of Units (SI units) or common clinical units like mmHg, bpm, Celsius, or percentages. Some data may contain anomaly flags or alarm information, indicating data quality issues or potential physiological abnormalities.
Constraints Imposed by these Characteristics on Tool Calling and Plugins
The real-time nature and high-frequency updates of monitoring device data require low-latency processing capabilities for tool calls. This prevents data accumulation from delaying pre-screening. Large volumes of time-series data challenge storage and transmission bandwidth. Plugins must effectively handle data batch uploads or streaming to avoid single-request overload. Diverse data formats, especially proprietary ones, mean tools need flexible parsers and conversion modules to ensure unified data processing. High-sampling-rate data contains extensive detail, demanding advanced model understanding and feature extraction. This may necessitate customized data preprocessing steps, such as downsampling, filtering, or time/frequency domain feature extraction. Metadata and unit information within the data are crucial for tool call accuracy. Plugins must correctly parse and utilize this contextual information to prevent errors caused by unit confusion. Anomaly flags and alarm information require tools to integrate data quality assessment into pre-screening logic, filtering or specially handling anomalous data to improve pre-screening reliability.
Configuration Strategy
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
HTTP_REQUEST_TIMEOUT_SECONDS | 120 seconds | Large monitoring data packets, network transmission, and backend processing can take time. This avoids premature timeouts. |
MAX_PAYLOAD_SIZE_MB | 20 MB | Accommodates the size of single uploaded monitoring data files, such as CSV or JSON containing several hours of high-frequency physiological data. |
TOOL_RESPONSE_MAX_TOKENS | 2000 characters | Pre-screening results typically include structured judgments, risk levels, and key indicator summaries. This provides sufficient space for presentation. |
PARSE_FILE_TYPE_WHITELIST | csv, json, hl7, dicom | Ensures handling of common structured data formats from monitoring devices. |
MAX_CONCURRENT_CALLS | 5–10 | Adjust based on backend processing capabilities and data concurrency. This balances response speed with system load. |
API_KEY_ROTATION_INTERVAL | Calibrate by actual measurement | Consider security and external API provider policies. Regularly rotate API keys to mitigate security risks from key compromise. |
Common Pitfalls
- Symptom: Tool call node hangs or remains unresponsive for an extended period. Cause: The backend data processing service times out due to receiving an excessively large file or performing complex calculations, exceeding the
HTTP_REQUEST_TIMEOUT_SECONDSsetting. - Symptom: Physiological parameter values are incorrect or units are confused in the model's pre-screening output. Cause: The plugin failed to correctly parse the unit field in the input data or did not perform unit conversion, leading the model to make judgments based on incorrect values.
- Symptom: Some monitoring data files fail to upload or cause parsing errors. Cause: The file format is not in the
PARSE_FILE_TYPE_WHITELIST, or the file content contains non-standard encoding or corruption, preventing the parser from recognizing it.
Verification Steps
- Upload typical monitoring data sample files. Check if the tool call succeeds and verify the backend service logs for any exceptions.
- In the model output, examine the key physiological parameter values in the pre-screening results. Compare them with the original data to confirm numerical and unit accuracy.
- Simulate concurrent requests. Observe the response time of the tool call node to ensure no significant delays occur under expected load.
- Attempt to upload monitoring data files of different formats (e.g., CSV, JSON) and sizes. Confirm that all supported formats are processed correctly and that large file uploads do not trigger timeouts.
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.