Multi-turn Conversation and Prompts for Clinical Trial

Data Characteristics

Clinical trial pre-screening data in medical affairs originates from global clinical trial registries (e.g., ClinicalTrials.gov, WHO ICTRP), institutional investigator-initiated trial (IIT) databases, real-world evidence (RWE) reports, and medical journal literature. Update frequencies vary; registry information typically updates monthly or quarterly, while literature data publishes in real-time. Document structures are often semi-structured or unstructured, including XML-formatted trial protocols, PDF-formatted Investigator's Brochures (IB), textual descriptions of inclusion/exclusion criteria, patient medical record summaries, and physician notes. Key fields include disease diagnosis (ICD-10 codes), gene mutation types, prior treatment history, comorbidities, contraindications, and laboratory indicators (e.g., HbA1c, eGFR) with their units. Data volume is large and contains numerous medical abbreviations.

Constraints Imposed by Data Characteristics on Multi-turn Conversation and Prompts

The semi-structured and unstructured nature of medical affairs clinical trial pre-screening data requires multi-turn dialogue systems to have strong text understanding and entity extraction capabilities. Inclusion/exclusion criteria descriptions are complex and contain many specialized terms. Prompt design must guide the model to accurately identify key medical concepts and avoid semantic drift. The lag in clinical trial data updates means the system must prioritize retrieving the latest data sources during pre-screening and handle differences between versions. Non-standardized expressions in patient medical record summaries and physician notes challenge the model's ability to accurately extract key information. Multi-turn conversations need to support users progressively refining inclusion/exclusion criteria and combining conditions, for example, first filtering by disease type, then limiting by gene mutation, and excluding specific prior treatment histories. This places high demands on context management and complex query construction.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`maxContext`	`8`	Ensures the model covers critical information in multi-turn conversations, such as patient medical history, treatment plans, and inclusion/exclusion criteria, supporting progressive query refinement.
`Chunk size`	`500 characters`	Paragraph lengths vary greatly in clinical trial documents; this length helps preserve the integrity of medical concepts and reduces context loss.
`Recall count`	`10`	Increases the number of recalled items, improving the probability of hitting relevant inclusion/exclusion criteria or patient information from large and complex clinical trial databases.
`Similarity threshold`	`0.75`	Balances recall breadth and precision, effectively identifying similar concepts when medical terms have multiple expressions.
`Rerank result count`	`3`	Selects the most relevant few results to present to medical affairs personnel, reducing information overload and improving decision-making efficiency.
`promptTemplate`	Calibrate by testing	Must include clear instructions guiding the model to identify key entities such as disease diagnosis, treatment history, gene mutations, and laboratory indicators.

Common Pitfalls

Empty or failed conversation logs may result from incorrect API Key configuration for tool calls in the workflow or upstream service response timeouts.
The model's inability to accurately identify patient gene mutation types or laboratory indicators often occurs because the prompt does not explicitly specify the entity types and data formats to extract.
In multi-turn conversations, the model fails to effectively use historical dialogue information, leading to repetitive questions or an inability to perform conditional overlay filtering. This usually indicates maxContext is set too low or context management logic is flawed.

How to Verify Configuration

Perform simulated clinical trial pre-screening, inputting different patient characteristics. Check if the system's output for inclusion/exclusion criteria matches expectations and verify that key medical entities are accurately extracted.
Use FastGPT's debugging interface to observe the complete prompt content during each model call in multi-turn conversations, ensuring historical dialogue information and user intent are correctly passed.
Analyze workflow logs to confirm that tool call request parameters include all necessary historical dialogue context and check if the external service's returned data structure meets expectations.
Conduct stress tests with multiple test cases containing complex inclusion/exclusion criteria. Evaluate the system's response time and accuracy when processing large-scale data and complex queries, then adjust Recall count and Similarity threshold based on the results.

Note: The values provided are common starting points and should be measured 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.