Tool Calling and Plugins for Phase I Clinical Trial

Data Characteristics

Phase I clinical trial pre-screening data comes from clinical study protocols, subject screening logs, medical history records, physical examination reports, laboratory test results, and imaging data. This data typically exists as unstructured documents (e.g., PDF, Word) and structured tables (e.g., Excel, CSV). Update frequency varies; subject screening logs and lab results may update daily or weekly, while medical history and imaging data are often acquired once at the initial screening. Document structures are complex and diverse. For example, a lab report may contain multiple test items, each with reference ranges and units. Fields include subject demographic information (age, gender, weight), disease diagnoses, concomitant medications, past medical history, and key biochemical indicators (e.g., ALT, AST, Cr), and complete blood count indicators (e.g., WBC, PLT). Units cover both international and traditional systems; for instance, creatinine units might be mg/dL or umol/L, requiring standardized handling.

Constraints Imposed by Data Characteristics on Tool Calling and Plugins

Phase I clinical trial pre-screening data characteristics place specific demands on tool calling and plugins. First, diverse data sources (mixed unstructured and structured) require multimodal processing capabilities, such as parsing medical report images within PDFs. Second, varying data update frequencies necessitate dynamic data synchronization and incremental processing mechanisms in tool calls to ensure real-time pre-screening results. Complex document structures and diverse field units require robust information extraction and standardization capabilities to unify data from different formats into comparable structured information. For example, standardizing ALT values and their units from different reports enables accurate screening rule judgment. Furthermore, the sensitive nature of subject data demands high standards for data security and privacy protection in tool calls. Finally, the complexity of screening rules and the rigor of medical judgment require tool calls to execute simple logical judgments, support complex nested conditions, and precisely match medical terminology to avoid false positives or false negatives.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`tool_call_max_tokens`	`2048`	Ensures complete transmission of complex screening logic and multi-field parameters to the tool.
`max_parallel_tool_calls`	`2`	Balances query efficiency with tool concurrent processing capacity, preventing single tool bottlenecks.
`parser_config.ocr_enabled`	`true`	Processes image-based text information in unstructured medical reports, such as handwritten annotations or scanned documents.
`extractor_template.unit_mapping`	`{ "mg/dL": "umol/L", ... }`	Standardizes units across different laboratory reports, ensuring numerical comparability.
`retrieval_chunk_size`	`500-800 characters`	Balances contextual completeness with retrieval efficiency, preventing long paragraphs from diluting key information.
`api_timeout_seconds`	`60 seconds`	Accommodates typical response times of external medical system interfaces, preventing call failures due to prolonged waiting.

Common Pitfalls

Tool calls return empty or incomplete data: This occurs when information extraction templates are insufficiently configured, failing to correctly identify key fields from unstructured documents.
Screening results do not match expectations, for example, including subjects who do not meet standards: This happens due to errors in unit conversion or numerical comparison logic, failing to handle unit discrepancies from different data sources.
Frequent timeouts when calling external system APIs: This occurs when api_timeout_seconds is set too short, not adequately accounting for the response latency of external medical system interfaces.

Validation Steps

Run the pre-screening process against simulated subject data in various formats. Verify that the structured data fields returned by the tool call are complete and accurate.
Select known cases of subjects who meet and do not meet screening criteria. Validate that the screening results after tool calls align with human judgment, paying close attention to boundary conditions.
Monitor tool call logs. Observe whether external API response times are within the api_timeout_seconds threshold and check for timeout-related errors.
Review the knowledge base configurations for medical terminology and unit conversions. Ensure that extractor_template.unit_mapping and similar configurations cover common scenarios.

Note: The values provided are common starting points. Measure them 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.