Multi-Turn Conversations and Prompts for Phase II-III

Data Characteristics

Phase II-III clinical trial pre-screening data primarily originates from sponsor-provided clinical trial protocols, informed consent forms, case report form (CRF) designs, and relevant medical literature and guidelines. These data documents are typically in PDF or Word format. Content includes detailed inclusion/exclusion criteria, investigational drug information, trial procedures, and adverse event definitions. Data update frequency is low, occurring mainly during protocol revisions or version iterations. Document structures are complex, containing numerous specialized terms, abbreviations, charts, and nested logic. Fields and units involve medical indicators (e.g., blood counts, liver and kidney function, imaging results), biomarkers, subject demographic information (age, gender, BMI), disease diagnostic codes (ICD-10), and medication records (dosage, frequency). Units strictly adhere to international standard units or commonly used clinical units, such as mg/kg, mmol/L, ng/mL.

Constraints Imposed by These Characteristics on Multi-Turn Conversations and Prompts

The highly specialized nature and complex document structure of Phase II-III clinical trial pre-screening data require multi-turn dialogue systems to possess deep semantic understanding. The system must accurately parse inclusion/exclusion criteria containing nested logic. Low update frequency means knowledge base construction must focus on historical version management and traceability, ensuring conversations are based on the latest authoritative protocol versions. The large number of specialized medical terms and abbreviations in documents means prompt design must consider terminology standardization and disambiguation. This avoids inaccurate pre-screening results due to misinterpretations of terms. Multi-turn conversations need to support cross-validation and logical judgment across multiple data points (e.g., age, multiple laboratory indicators, concomitant medication status) to match complex enrollment criteria. Strict requirements for fields and units mean the dialogue system must precisely identify and convert different units when extracting and comparing values, for example, converting weight from kg to pounds, or accommodating different dimensions when judging numerical ranges.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`2000 characters`	Clinical protocols are rich in detail; sufficient context length is needed to understand complex logic.
`Similarity threshold` (Similarity Threshold)	`0.85`	Ensures recalled clinical criteria are highly relevant to the user query, reducing false positives.
`Chunk size` (Chunk Length)	`500 characters`	Balances document semantic integrity with recall efficiency, preventing truncation of long texts and loss of critical information.
`Recall count` (Recall Count)	`Top 5`	Considers both information volume and model processing load, covering the main relevant criteria.
`Rerank result count` (Reranked Return Count)	`3`	For pre-screening scenarios, select the few most relevant items as the final basis for judgment.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Clinical documents are often large; parsing can be time-consuming. This prevents import failures due to timeouts.

Common Pitfalls

The conversation results fail to correctly determine if a subject meets enrollment criteria. This happens because prompts do not adequately guide the model to perform multi-condition logical reasoning, or the knowledge base fragments recalled do not completely cover all relevant judgment conditions.
Uploading and parsing a clinical trial protocol PDF file returns a 500 error code. This usually occurs because the PDF file has a complex structure or contains many images, causing the PARSE_FILE_TIMEOUT_SECONDS parameter to be set too low, leading to a parsing timeout.
During a multi-turn conversation, a user asks about the range of a laboratory indicator, and the system replies with an empty response or irrelevant values. This may be because the numerical unit of the indicator in the knowledge base was not correctly identified and extracted, or unit information was lost during embedding.

How to Confirm Correct Configuration

Select a clinical protocol with complex inclusion/exclusion criteria. Simulate multiple typical subject pre-screening processes through multi-turn conversations. Verify that the system's judgment results match manual judgments.
Upload and parse multiple clinical trial-related documents of different formats and sizes. Check if all documents import successfully. Verify that document content is accurately chunked and indexed.
For queries involving numerical indicators (e.g., age, BMI, specific laboratory results), check if the system can precisely extract values and their units. Verify it can make correct range judgments based on user-input values.
Design queries containing specialized medical terms and abbreviations. Verify the system can correctly understand term meanings and maintain terminology consistency in conversations. For example, AST should be understood as Aspartate Aminotransferase.

Note: The values provided are common starting points. Measure performance 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.