Multi-turn Conversations and Prompts for Medical Insurance

Data Characteristics for This Category

Medical insurance settlement data primarily comes from policy documents, drug catalogs, treatment item catalogs, consumable catalogs, and internal settlement rules published by national and local medical insurance bureaus. This data updates frequently, typically with policy adjustments, such as annual medical insurance catalog changes or quarterly/biannual payment standard updates. Document structures vary, including PDF policy texts, Excel spreadsheets for drug/treatment item lists, and structured data exported from databases. Fields and units involve generic drug names, dosages, specifications, medical insurance payment standards (yuan), reimbursement ratios (%), payment scopes, restriction conditions, fee codes, disease diagnosis codes (e.g., ICD-10), and settlement categories.

Constraints Imposed by These Characteristics on "Multi-turn Conversations and Prompts"

Frequent updates to medical insurance policies require the knowledge base to quickly synchronize the latest data, preventing outdated information in responses. Diverse document formats, especially numerous unstructured policy texts, challenge knowledge extraction and semantic understanding. This necessitates a focus on PDF parsing and text summarization capabilities. The accuracy of numerical fields like medical insurance payment standards and reimbursement ratios is critical. Multi-turn conversations must ensure precise numerical transfer and calculation, avoiding vague answers. Additionally, medical insurance settlements involve complex restrictions and payment scopes. Prompt design must guide the model to deeply understand these rules and accurately determine applicability. The specialized nature of disease diagnosis codes and fee codes requires the model to possess domain knowledge for correct identification and association in multi-turn conversations.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`6`	Medical insurance policy queries often require multiple follow-up questions for refinement, balancing performance with information completeness.
`Chunk size` (Segment Length)	`500 characters` (500 characters)	Policy document paragraphs are long; this ensures single-segment information completeness and prevents critical information truncation.
`Similarity threshold` (Similarity Threshold)	`0.75`	Medical insurance terminology and policy statements are rigorous; this improves recall precision and reduces false positives.
`Recall count` (Recall Count)	`Top 8 entries` (Top 8)	Complex queries may involve multiple intersecting policies; increasing recall covers more relevant information.
`Rerank result count` (Reranked Return Count)	`Top 3 entries` (Top 3)	Selects the most relevant content from the recalled items, reducing redundant information for the model.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (600 seconds)	Parsing large PDF policy files can be time-consuming; this provides sufficient time for processing.

Three Common Pitfalls

Excessive conversation response time, exceeding 10 seconds (10 seconds): This may be due to a large volume of knowledge base data or a high Recall count (Recall Count) setting, leading to a significant increase in the context information the model needs to process, impacting inference speed.
User asks about reimbursement ratios, model gives uncertain or generic answers: This often occurs when Similarity threshold (Similarity Threshold) is set too low, recalling many irrelevant document snippets and diluting the model's focus on core information.
Model cannot provide specific restrictions for queries about reimbursement conditions for particular drugs or treatment items: This is common when Chunk size (Segment Length) is set too short, causing restriction conditions within policy texts to be split across different segments, making it difficult for the model to fully understand.

How to Confirm Proper Configuration

Select multiple typical medical insurance settlement scenarios and conduct multi-turn conversation tests. Observe whether the model accurately identifies and links to the correct policy clauses.
For newly adjusted drugs or treatment items in the medical insurance catalog, verify whether the model provides the latest payment standards and reimbursement ratios, and cross-reference with officially published data for consistency.
Input queries containing professional terminology and codes. Check if the model correctly parses their meaning and guides the user to further refine the question.

Note: 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.