Data Characteristics for This Category
R&D documents related to medical insurance access typically originate from policy documents, drug catalogs, negotiation rules, and payment standards published by national and provincial medical insurance departments and various pharmaceutical industry associations. These documents update on a relatively fixed schedule, usually annually or quarterly, but may update ad-hoc if policies change. Document formats vary, including scanned PDFs, Word documents, Excel spreadsheets, and web content. Structurally, they often contain extensive policy clauses, drug lists (including ATC codes, generic names, dosages, specifications, manufacturers, and medical insurance coverage), pricing information, summaries of clinical trial data, and historical negotiation reviews. Field and unit specificities include strict definitions for drug names, dosages, and specifications. Medical insurance payment standards are often expressed as "yuan/unit" or "percentage," involving complex reimbursement ratios and restricted payment conditions.
Constraints Imposed by These Characteristics on Multi-Turn Conversations and Prompts
The authoritative nature and update frequency of medical insurance access document data necessitate that knowledge base construction prioritizes data timeliness and accuracy. This prevents the introduction of outdated or incorrect information. The diversity of document formats, especially the presence of numerous scanned PDFs, demands high OCR recognition and structured extraction capabilities from the document parsing module. The extensive professional fields and units in drug lists require prompt designs to accurately identify and associate these entities. For example, multi-turn conversations must precisely distinguish between different dosages and specifications of the same drug. The complex logic within negotiation rules and payment conditions means multi-turn conversations must understand complex conditional judgments to answer questions like "Is a certain drug included in medical insurance under specific conditions?" and trace back to specific policy bases. Furthermore, the need for historical data queries requires the knowledge base to effectively manage different data versions and provide version traceability in conversations.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size | 800–1200 characters | Medical insurance policy clauses and drug descriptions often contain long, logically complete paragraphs. Overly short segments can break context, while overly long segments add irrelevant information. |
Recall count | Top 8–12 entries | Policy interpretation and drug comparison typically require multiple pieces of related information for cross-verification, ensuring comprehensive and accurate answers. |
Similarity threshold | 0.75–0.85 | Medical insurance access information demands high precision. A threshold that is too low may retrieve irrelevant information, while one that is too high may miss critical details. |
Rerank result count | Top 5 entries | Re-ranking more accurately filters the top information most relevant to the user's intent, reducing the model's processing burden. |
maxContext | 4096 tokens | Medical insurance policy questions often involve complex conditions and comparisons of multiple pieces of information, requiring a longer conversation context to maintain logical coherence. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large policy files or scanned documents takes a long time. Allocating sufficient time prevents parsing failures due to timeouts. |
Three Common Mistakes
- After uploading documents, the conversation cannot access the latest policy information, or answers are based on outdated data. This occurs when the knowledge base is not updated promptly or document parsing fails to correctly identify document versions and effective dates.
- When users ask, "Is drug XX included in the medical insurance catalog?", the AI provides vague answers or cannot give a clear conclusion. This happens when prompt design fails to effectively guide the model to extract the drug's medical insurance status from structured data, or the drug's medical insurance status field is missing in the knowledge base.
- When "reimbursement ratio" or "restricted payment conditions" are mentioned in the conversation, the AI cannot provide specific numbers or detailed rules. This occurs when document parsing fails to accurately extract these fields, or the prompt does not emphasize the identification and citation of numbers and conditional logic.
How to Confirm Correct Configuration
- Upload the latest medical insurance policy documents. Ask questions about the medical insurance status and payment scope of newly added drugs in the document. Verify if the model can provide accurate answers based on the latest data, with source citations.
- Construct multi-turn conversation scenarios for complex medical insurance access conditions. Verify if the model can understand and apply conditional logic in multi-turn interactions and provide expected results.
- Extract key fields such as specific drug names, dosages, specifications, and payment standards from documents. Verify through questioning if the model can precisely identify and cite this information, and if units and numerical expressions are correct.
The values provided are common starting points and should be measured 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.