Multi-Turn Conversations and Prompts for High-Value

Data Characteristics for This Category

High-value consumable clinical trial data primarily originates from hospital clinical departments' Electronic Health Record (EHR) systems, Picture Archiving and Communication Systems (PACS), and Laboratory Information Systems (LIS). This data exists in a mixed structured and unstructured format. It includes patient basic information, diagnostic records, surgical records, post-operative follow-up results, imaging reports, and laboratory reports. Data updates frequently, especially during a trial, as patient follow-up data is continuously entered at pre-set intervals. Document structures are complex. For example, surgical records are typically free text, while laboratory reports contain numerous numerical fields. Field and unit specificity is evident in the frequent use of specialized medical terminology, such as "femoral head necrosis staging," "vascular stent diameter (mm)," and "insertion depth (cm)." These fields demand strict precision and unit adherence.

Constraints on Multi-Turn Conversations and Prompts

The data characteristics of high-value consumable clinical trial pre-screening impose multiple constraints on multi-turn conversation and prompt design. First, diverse and complex data sources require prompts to effectively guide the model in extracting key information from unstructured text and integrating it with structured data for comprehensive judgment. An example is identifying consumable models and implantation sites from free-text surgical records. Second, the specialized nature of medical terminology demands prompts with a high degree of domain knowledge to avoid screening errors due to misinterpretation. Precise units and numerical values are critical components of screening criteria. Prompts must ensure the model accurately identifies and compares these values. High-frequency data updates mean the model must process temporal information in multi-turn conversations, such as tracking changes in patient physiological indicators at different time points. This requires the conversational system to maintain contextual coherence and dynamically adjust screening strategies based on the latest data.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8000 tokens`	Ensures coverage of key information like patient history and current test results in multi-turn conversations, supporting complex logical judgments.
`temperature`	`0.3`	Reduces the randomness of model-generated content, ensuring the rigor and reproducibility of screening results.
`systemPrompt`	Includes professional terminology definitions and screening logic	Pre-embeds medical knowledge and screening criteria for high-value consumable clinical trials, improving accuracy.
`recall_top_k`	`10`	Recalls enough relevant document snippets from the knowledge base to handle complex queries and multi-condition screening.
`similarity_threshold`	`0.75`	Filters out low-relevance document blocks, avoiding noise information and improving retrieval accuracy.
`max_tokens_per_response`	`500 tokens`	Ensures the model provides detailed screening criteria and suggestions, but avoids verbose or unnecessary responses.

Common Mistakes

Symptom: The model fails to accurately identify temporal terms like "this year" or "this month" in multi-turn conversations, leading to incorrect time range judgments. Reason: The prompt does not explicitly inform the model of the current date or provide sufficient contextual information for inference.
Symptom: After configuring the workflow for multi-turn conversations, the model still fails to recall previous information, with each response resembling a new conversation. Reason: The maxContext parameter is set too low, causing historical conversation information to be truncated when passed to the API.
Symptom: When calling the application via an API, the AI responds slowly or returns null values, even if it tests normally within the platform. Reason: Necessary parameters like appId or userId are not correctly passed during the API call, preventing the application from correctly identifying the conversation session or user identity.

Verification

Conduct simulated clinical case conversations. Check if the model accurately identifies key patient clinical features and indications for high-value consumables.
Randomly select multiple patient records. Perform multi-turn conversation tests at different time points. Verify if the model correctly handles time-related screening conditions.
Call the application via API. Observe if the returned results include all expected fields. Check if response_time is within an acceptable range.
Use a test set similar to actual clinical data. Verify the consistency between the model's pre-screening conclusions after multi-turn interaction and expert judgment.

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.