Data Characteristics
Clinical trial pre-screening relies on a mix of structured and unstructured data. Structured data includes patient electronic health records (EHR) such as diagnosis records, medication history, and lab results. This data typically uses HL7 FHIR or CDA standards with fields like patient_id, diagnosis_code, lab_test_name, result_value, and unit. Unstructured data includes physician notes, imaging reports, and genetic testing reports. EHR data updates in real-time. Clinical trial protocols update in batches, typically every few weeks or months. Document structures are complex, involving medical terminology and specialized abbreviations.
Constraints on Tool Calling and Plugins
High-frequency EHR data updates require low-latency and high-concurrency tool calling to ensure real-time pre-screening results. Complex medical terminology and varied field units, such as mmol/L and ng/mL, demand robust data parsing and standardization capabilities from plugins. This requires built-in or integrated medical ontologies (e.g., SNOMED CT, LOINC) for mapping. Integrating diverse data sources, such as linking structured lab results with unstructured imaging reports, requires plugins to incorporate data fusion logic to prevent information silos. Dynamic adjustments to clinical trial protocols mean tool calling logic must be flexible and configurable to adapt to changing inclusion and exclusion criteria.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8192 tokens | Accommodates complex medical records and multi-turn conversations, providing sufficient context for reasoning. |
timeout_seconds | 60 seconds | Balances real-time performance with external API response times, especially when querying large databases or external knowledge bases. |
max_retries | 3 | Handles transient network fluctuations or temporary unavailability of external services, increasing call success rates. |
tool_schema_version | 1.0.0 | Ensures stability and compatibility of tool calling interfaces, preventing failures due to version discrepancies. |
data_refresh_interval | 1 hour | Balances data freshness with system load; most clinical trial protocol updates do not require minute-level refreshing. |
similarity_threshold | 0.75 or calibrated by measurement | Ensures high relevance between retrieved medical text and query intent, reducing misjudgments. |
Common Pitfalls
- External database calls return a 400 status code. This may be due to incorrect SQL statement parameter formats or improper database connection configurations.
- After publishing a text classification workflow, the API call
data.modeldoes not point to the expected model. This may be due to incorrect model selection logic configuration within the workflow. - Custom code calls trigger
AggregateError Code:ETIMEDOUT. This usually indicates an external service timeout or excessive network latency.
Verification Steps
- Simulate various patient medical records. Test whether intelligent triage accurately identifies patients meeting specific clinical trial inclusion/exclusion criteria and outputs the corresponding trial ID.
- Check tool call logs. Confirm successful external API calls and the return of expected data structures. Verify correct parsing of key fields like
patient_idanddiagnosis_code. - Test cases with known medical terminology ambiguities. Observe if the system provides consistent and correct judgments through built-in ontology mapping or external knowledge base queries.
Note: The values provided are common starting points. Measure them against your own samples for optimal performance.
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.