Multiturn Conversation and Prompts for Clinical Trial

Data Characteristics

Medical insurance access clinical trial pre-screening data primarily originates from the National Medical Products Administration (NMPA) drug approval documents, the National Healthcare Security Administration (NHSA) medical insurance catalog (including Class A and Class B drugs), and provincial supplementary catalogs. Data update frequencies vary. NMPA data updates with new drug approvals, while the medical insurance catalog adjusts annually. Document structures are mainly structured or semi-structured. For example, the medical insurance catalog is typically a table containing fields such as drug name, dosage form, specification, and payment scope. Unstructured data includes clinical trial protocols, drug inserts, and pharmaceutical company application materials. Field names often involve generic drug names, brand names, ATC classification codes, indications, reimbursement ratios, and payment restrictions. Units are typically milligrams, grams, milliliters, percentages, or unitless.

Constraints from these Characteristics on Multiturn Conversation and Prompts

The structured nature of the medical insurance catalog makes field-based precise retrieval critical for multiturn conversations. Avoid generalized queries. The complexity of drug names and indications requires prompt design to effectively handle synonyms, aliases, and medical acronyms, improving recall. Payment restrictions often involve complex logical judgments, such as "limited to second-line use" or "limited to patients with specific gene mutations." This requires the dialogue system to guide users to ask in-depth questions, progressively clarify restrictions, and integrate multiple pieces of information for reasoning. Varying document update frequencies mean the knowledge base needs a version management mechanism. Prompts should specify querying particular data versions to avoid interference from outdated information. Additionally, the presence of unstructured data like drug inserts increases information extraction difficulty, requiring more complex prompts to guide the model in extracting key information.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	8000–16000 tokens	Covers complex payment restrictions in the medical insurance catalog and multiturn conversation history.
`Recall count` (Recall Count)	Top 10–15 items	Ensures coverage of relevant drugs and policies in the medical insurance catalog, increasing hit rate.
`Similarity threshold` (Similarity Threshold)	0.75	Balances precision and recall, avoiding interference from irrelevant information.
`Rerank result count` (Rerank Return Count)	Top 5 items	Focuses on the most relevant information, improving user experience.
`Chunk size` (Segment Length)	500 characters	Balances information completeness and semantic coherence, suitable for medical insurance catalog entries.
`temperature`	0.3	Ensures the rigor and accuracy of responses, reducing hallucinations.

Common Pitfalls

When calling DeepSeek-R1, the file parsing tool fails to activate. Common reasons include insufficient file storage path permissions or the PARSE_FILE_TIMEOUT_SECONDS parameter being set too short, causing large file parsing to time out.
The history contains instructions for specific reply plugins, leading to redundant historical memory. This typically occurs when the historyIgnorePlugins configuration item is not correctly set, failing to filter out plugin instructions.
Incorrect or expired API key configuration leads to unavailable dialogue functionality, displaying Unauthorized or Invalid API Key error codes in the interface or logs.

Verification Steps

Test querying drug names and indications. Verify if the returned results include payment scope and restrictions from the medical insurance catalog and cross-reference with official catalogs.
Conduct multiturn conversations, simulating a user progressively providing restrictions (e.g., "limited to second-line use"). Check if the system correctly filters and provides drug information that meets the conditions.
Upload typical drug inserts. Check if the system can accurately extract key information, such as dosage and administration, and adverse reactions, and compare it with the original text.

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.