Data Characteristics for This Category
Medical insurance access product data originates from policy documents, drug and consumable catalogs, negotiation announcements, corporate submission materials, and industry association interpretations. These are typically in PDF, Word, and Excel formats. Policy documents update frequently, especially during annual catalog adjustments and negotiation periods. Document structures are complex, containing regulations, technical parameters, clinical data, economic evaluation reports, and payment standards. Fields are diverse, including drug/device names, generic names, dosages, specifications, manufacturers, payment scope, reimbursement ratios, restricted payment conditions, negotiated prices, and agreement periods. Units include yuan, percentages, milligrams, and milliliters.
Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall
Frequent updates to medical insurance access policies require the knowledge base to efficiently update and index documents, ensuring information timeliness. Complex document structures and diverse fields challenge segmentation strategies, requiring a balance between clause completeness and retrieval granularity. Key information like payment scope and restricted payment conditions often appear in nested or table formats. The knowledge base must accurately identify and retain their contextual relationships during parsing. Numerical fields such as negotiated prices and reimbursement ratios require support for exact matches and range queries during retrieval, preventing data distortion from improper tokenization. Common technical terms and abbreviations in documents require the embedding model to accurately understand their semantics, improving retrieval recall accuracy.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Segment Length | 500–800 characters | Balances policy clause completeness and retrieval granularity, preventing long texts from diluting key information. |
Segment Overlap | 50–100 characters | Ensures contextual continuity between paragraphs, improving cross-paragraph information recall. |
Recall Count | Top 5–8 results | Medical insurance access queries often require more relevant context for comprehensive judgment. Increasing recall count improves coverage. |
Similarity Threshold | Calibrate by measurement | Different embedding models and corpora have varying sensitivity to similarity scores. Adjust based on actual performance. |
Rerank Return Count | Top 3 results | Provides a few highly relevant and high-quality results after further filtering by the rerank model. |
Max Concurrent File Parses | 10 | Handles large document imports during periods of intensive medical insurance policy releases, balancing system resource usage. |
Three Common Mistakes
- Query results only display reference links without specific answer content: This can occur if the model lacks sufficient recalled information to generate a coherent answer, or if its confidence does not meet the preset threshold.
- The knowledge base misinterprets imported
docxorxlsxfile content, leading to low answer accuracy: This often happens with complex file formats containing numerous tables, images, or nested structures, where the parser fails to effectively extract core text and structured data. - Inaccurate or missing recall results when querying specific medical insurance payment scopes or restricted conditions: This might be due to a segmentation strategy that fails to effectively retain the association between key conditions and their subjects, leading to incomplete contextual information during retrieval.
How to Confirm Correct Configuration
- Select typical medical insurance access query questions and verify that recall results include all relevant policy clauses, catalog information, and negotiation details.
- Import a batch of medical insurance policy documents containing complex tables and multi-level headings. Check if the knowledge base can correctly parse and retrieve specific values and conditions within the tables.
- For frequently changing information like medical insurance catalog updates and negotiation results, test the accuracy and timeliness of new and old information retrieval after knowledge base updates. Ensure old information is not incorrectly recalled and new information is recalled promptly.
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.