Multi-turn Conversation and Prompts for CRO Clinical Trial

Data Characteristics

Data used in CRO (Contract Research Organization) clinical trial pre-screening originates from sponsor-provided medical research protocols, subject inclusion/exclusion criteria, prior clinical data, genomic data, biomarker reports, and potential subjects' electronic health records (EHR). This data typically exists as unstructured text (e.g., clinical reports, medical record summaries), semi-structured data (e.g., lab results, imaging reports), and structured data (e.g., demographic information, disease diagnosis codes). Data update frequencies vary; sponsor protocols are relatively stable, while potential subject information may update in real-time. Document structures are complex, involving extensive medical terminology and abbreviations, diverse fields, and units spanning various biological and clinical measurements, such as mmol/L, ng/mL, mmHg, and μg/kg/day.

Constraints on Multi-turn Conversation and Prompts

The complexity and diversity of CRO data impose specific requirements on multi-turn conversation and prompts. Medical terminology and abbreviations in unstructured text require strong semantic understanding for accurate context parsing during multi-turn interactions. Numerical fields in semi-structured and structured data, along with their units and normal ranges, are critical for pre-screening logic, demanding accurate numerical comparison and logical judgment from the model during conversations. Uncertain data update frequencies mean that RAG (Retrieval Augmented Generation) mechanisms must quickly index and update the knowledge base to ensure real-time query results. Furthermore, the strictness and detail of clinical trial protocols require prompt design to precisely guide the model, preventing vague or medically unethical responses, especially when determining subject inclusion criteria.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`maxContext`	`8000 tokens`	Clinical trial protocols and medical record summaries are often long, requiring a larger context window to maintain conversational coherence and information completeness.
`Recall count` (Number of Retrieved Items)	`Top 10 entries` (Top 10)	Ensures retrieval of sufficient relevant subject data and trial criteria from the knowledge base, improving pre-screening accuracy.
`Similarity threshold` (Similarity Threshold)	`0.78`	The specialized nature of clinical data demands high semantic matching to avoid irrelevant information interfering with judgment.
`Chunk size` (Segment Length)	`500 characters` (500 characters)	Balances text semantic integrity and retrieval efficiency, preventing information loss or fragmentation due to segments being too long or too short.
`Rerank result count` (Number of Reranked Items)	`Top 5 entries` (Top 5)	Further filters the most relevant document snippets, reducing the model's burden of processing irrelevant information and improving response speed.
`LLM_MODEL_NAME`	`gpt-4o`	Advanced LLM models excel in medical terminology understanding and complex logical reasoning, suitable for the rigorous requirements of clinical pre-screening.

Common Pitfalls

Multiple AI conversation nodes in a workflow output simultaneously, leading to redundant information in the chat interface. This occurs when intermediate AI conversation node outputs are not hidden or filtered, and all node responses are displayed in the final conversation by default.
User-specific historical conversations are not isolated, leading to data leakage or confusion. This happens when the user system is not effectively integrated with FastGPT's session management module, failing to store and manage conversation records independently for each user.
The system cannot ask follow-up questions or clarifications in multi-turn Q&A, interrupting the conversation flow. This results from prompt design that fails to effectively guide the model to proactively ask questions or seek further clarification based on user feedback, limiting the conversation to one-way information provision.

Verification of Configuration

Simulate multi-turn conversations for a set of typical clinical trial inclusion/exclusion criteria. Verify that the model's judgments on key subject indicators (e.g., hemoglobin concentration, liver function indicators) align with expectations.
Randomly select multiple medical records containing complex medical terminology and abbreviations. Through conversation, verify if the model can accurately parse them and map them to pre-screening criteria, checking recall and precision.
Test whether historical conversation records for different users are correctly isolated and accessible only to their own records, verifying via the user_id field.
During simulated pre-screening, deliberately introduce vague or incomplete information. Observe whether the model proactively asks follow-up questions and provides specific content for those questions.

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.