Clinical Trial Pre-screening Multi-turn Conversations and

Data Characteristics

Clinical trial pre-screening clinical decision support systems primarily use data from Electronic Health Records (EHR), medical imaging reports, laboratory test results, genetic testing data, and clinical trial protocols. This data combines highly structured and semi-structured elements. EHR data is patient-centric, includes diagnoses, medications, and allergy histories, and updates frequently, typically synchronized with patient visits. Laboratory results are mainly numerical, with clear units and reference ranges. Clinical trial protocols are lengthy, unstructured or semi-structured documents detailing inclusion/exclusion criteria and study design. These update less frequently, but each update impacts pre-screening logic. Data fields are complex, involving medical terminology, abbreviations, and specialized units of measurement.

Constraints on Multi-turn Conversations and Prompts

High-frequency updates in EHR and lab data require the dialogue system to access real-time information. Prompt design must account for data recency. The presence of multi-source heterogeneous data (structured, semi-structured, unstructured) means prompts must explicitly specify the required information source and data type to prevent model confusion. For example, extracting features from imaging reports differs from extracting numerical values from lab results and requires different guidance. Complex medical terminology and units of measurement demand that prompts accurately identify and process these during questioning to prevent pre-screening errors due to comprehension discrepancies. Lengthy clinical trial protocol documents impose high demands on the model's context window and information extraction capabilities. Multi-turn conversations must effectively manage context to avoid forgetting key inclusion/exclusion criteria and guide the model to focus on terms matching patient characteristics.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`10`	Retains sufficient multi-turn dialogue history to maintain patient medical history and trial protocol context, preventing information loss.
`Chunk size` (Segment Length)	`800–1200 characters`	Accommodates long documents like clinical trial protocols, ensuring single segments contain complete logical information.
`Recall count` (Recall Count)	`Top 5–8 entries`	Balances recall efficiency and information completeness, ensuring coverage of key inclusion/exclusion criteria and patient characteristics.
`Similarity threshold` (Similarity Threshold)	`0.78–0.85`	Improves the relevance of recall results, filtering out redundant information irrelevant to pre-screening conditions.
`Rerank result count` (Reranked Return Count)	`3–5 entries`	Further refines recall results, prioritizing the most relevant trial protocol terms or patient data.
`JSON_SCHEMA_OUTPUT`	`True`	Ensures the model outputs structured data, facilitating subsequent system parsing and automated processing of pre-screening results.

Common Pitfalls

The dialogue model fails to remember critical patient medical history from previous turns, leading to repeated questioning or inconsistent pre-screening advice in subsequent turns. This occurs due to improper context management configuration, specifically maxContext being set too low, causing early dialogue information to be truncated.
The model extracts patient data with garbled characters or incomplete information. This happens when prompts do not explicitly specify data format or encoding requirements, leading to parsing errors when the model processes non-standard characters or complex data structures.
Some clinical trial inclusion/exclusion criteria are not correctly evaluated by the model in the pre-screening results. This is because the knowledge base segment length is too short, causing critical logic in the clinical trial protocol to be split, preventing the model from understanding the complete inclusion/exclusion conditions.

Verification

Conduct multi-turn dialogue tests to verify the model's ability to continuously track key patient information such as diagnosis, medication, and allergy history, and make judgments based on this information.
Test the model's ability to extract structured and semi-structured data. Check if the completeness of fields and data types in the output results align with expectations.
Select clinical trial protocols with complex inclusion/exclusion criteria. Through simulated dialogues, check if the model can accurately assess whether a patient meets all conditions and provide corresponding reasons.

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.