Data Characteristics
Data for Clinical Research Organizations (CSOs) in clinical trial pre-screening primarily originates from pharmaceutical company-commissioned clinical trial protocols, patient recruitment criteria, historical patient data, real-world data (RWD), and clinical guidelines. This data typically exists as unstructured documents (e.g., PDF trial protocols, investigator brochures, patient informed consent forms), semi-structured tables (e.g., Excel or CSV patient screening forms, medical imaging reports), and structured databases (e.g., diagnostic records, medication history in electronic health record systems). Data update frequencies vary; trial protocols are usually finalized before launch, but patient recruitment criteria may be adjusted based on actual conditions. Real-world data updates continuously. Documents contain extensive medical terminology, abbreviations, dosage units (e.g., mg, g, mL), time units (e.g., days, weeks, months), and biomarker indicators.
Constraints Imposed by These Characteristics on Multiturn Conversation and Prompts
The highly specialized and diverse nature of CSO clinical trial pre-screening data imposes specific requirements on the design of multiturn conversations and prompts. The complexity of medical terminology demands that prompts accurately understand and generate professional content, avoiding semantic deviations. The high proportion of unstructured documents means a Retrieval-Augmented Generation (RAG) system must efficiently extract key information from long texts. The strictness of patient recruitment criteria requires multiturn conversations to perform precise conditional screening and logical judgments, such as handling "and/or" relationships and numerical range evaluations. The uncertainty of data updates necessitates that prompts possess dynamic adaptability, adjusting to the latest trial protocols or guidelines. Additionally, multiturn conversations frequently require referencing specific fields and units for verification, demanding the system maintain the integrity and accuracy of this information during retrieval and generation.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 8–10 turns | Clinical pre-screening requires multiple rounds of questions to fully understand patient conditions and trial criteria, while avoiding performance degradation from excessively long contexts. |
maxToken | 1500–2000 | Ensures the model has sufficient output space to explain screening criteria, potential risks, or next steps in detail, accommodating the complexity of medical content. |
prompt | Includes role definition, task objective, and key constraints | Clearly instructs the model to act as a clinical pre-screening assistant, strictly adhering to inclusion/exclusion criteria based on knowledge base content, and requiring output of the judgment basis. |
temperature | 0.3–0.5 | Clinical pre-screening demands accuracy and consistency of results; lower temperature values help reduce the randomness of model-generated content, increasing reliability. |
Recall count (Retrieval Count) | Top 5–7 items | Ensures coverage of multiple dimensions such as patient symptoms, medical history, and examination results, improving screening accuracy. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Increases the precision of retrieved content, reducing interference from irrelevant or ambiguous information in pre-screening judgments, accommodating the rigor of medical terminology. |
Three Common Mistakes
- Symptom: The AI repeatedly asks for information already provided or fails to correctly apply given screening conditions during the conversation. Reason:
maxContextis set too low, causing the model to lose critical historical conversation information and fail to maintain conversational coherence. - Symptom: AI responses are too short or lack critical information, for example, failing to fully list all requirements for a specific inclusion criterion. Reason:
maxTokenis insufficient, limiting the model's ability to generate complete and detailed responses. - Symptom: The AI misunderstands patient information, leading to inaccurate screening results, or its explanation of medical terms does not align with professional understanding. Reason: Insufficient constraints in the prompt regarding the model's role, task objective, or professional terminology; the model may deviate from professional requirements when generating content freely.
How to Confirm Proper Configuration
- Conduct multiple simulated conversations to test whether the AI can accurately understand and apply complex inclusion/exclusion criteria, especially in scenarios involving multiple conditional combinations.
- Check whether the AI's references to key medical terms, dosage units, and time units are accurate and consistent with the original knowledge base text.
- Validate whether the AI's pre-screening judgments and their basis highly align with clinical expert judgments across different patient cases.
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.