Tool Calling and Plugins for Clinical Trial Pre-screening in

Data Characteristics

Medical affairs professionals pre-screen clinical trials. Their data primarily comes from public clinical trial registries (e.g., ClinicalTrials.gov, WHO ICTRP), pharmaceutical companies' internal clinical research databases, and medical literature platforms. This data updates frequently. ClinicalTrials.gov records may update weekly. Internal pharmaceutical data changes in real-time with project progress. Document structures vary, including structured trial protocol summaries, unstructured Investigator's Brochures (IB), Case Report Forms (CRF), and medical literature in PDF format. Fields cover disease indications, drug names, dosages, administration routes, inclusion/exclusion criteria, primary/secondary endpoints, and study center geographical locations. Dosages often include units like mg, μg, ml. Time periods use weeks, months, years.

Constraints Imposed by Data Characteristics on Tool Calling and Plugins

High-frequency data updates require tool calling to have real-time or near real-time query capabilities. This ensures pre-screening uses the latest information. Diverse document structures necessitate support for parsing multiple data sources, especially content extraction from unstructured PDF documents. This involves combining OCR and natural language processing. Complex inclusion/exclusion criteria often contain multiple logical conditions and numerical ranges. Tool calling must precisely match or calculate these. For example, it needs to determine if a patient's age is between 18-65 years or if a biomarker level exceeds 10 ng/mL. Geographical location information may require integration with map service plugins for study center accessibility analysis. Consistent handling of field units is crucial to prevent data misinterpretation due to unit mismatches, such as misreading mg as g.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`tool_timeout_seconds`	`60 seconds`	Complex queries and external API calls can be time-consuming; this prevents premature timeouts.
`max_tokens_per_response`	`1500 tokens`	Ensures complete return of medical literature abstracts or detailed inclusion/exclusion criteria.
`function_call_strictness`	`high`	Clinical trial pre-screening demands high parameter precision; strict mode reduces incorrect calls.
`external_api_retry_attempts`	`3 times`	Addresses occasional transient network fluctuations with external clinical databases or literature platforms.
`parse_pdf_timeout_seconds`	`180 seconds`	Allows sufficient time for parsing large Investigator's Brochure PDF files.
`context_window_size`	`4096 tokens`	Accommodates enough inclusion/exclusion criteria descriptions and patient information for comprehensive judgment.

Common Pitfalls

Tool calls return 400 errors or InvalidParameter: This often occurs when the model generates parameter formats or values that do not meet external API expectations. Examples include incorrect date formats or numerical values outside the allowed range.
Pre-screening results show omissions or misjudgments: This typically happens when the knowledge base recalls incomplete inclusion/exclusion criteria, or when tool calls fail to correctly combine multiple conditional logic.
The large language model cannot correctly trigger tool calls: This may be due to function_call_strictness being set too low, or a discrepancy in the model's understanding of the mapping between user intent and available tool functions.

Verification Steps

Perform end-to-end testing with simulated patient data for typical clinical trial pre-screening scenarios. Verify that the output inclusion/exclusion judgments match expectations.
Examine tool call logs. Confirm that external API call parameters and return results meet expectations, especially for field units and numerical ranges.
Select PDF documents containing complex inclusion/exclusion criteria. Test the accuracy of content parsing and key information extraction. Ensure parse_pdf_timeout_seconds meets requirements.
Observe tool_timeout_seconds performance under different query loads. Ensure stable response even during peak periods.

The values provided are common starting points and should be measured against the reader's 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.