Data Characteristics
Medical insurance access regulation data primarily originates from official websites of national and local medical insurance bureaus, centralized drug procurement platforms, and internal management systems of medical institutions. Data update frequencies vary. Policy and regulation documents typically update annually or quarterly. Specific drug payment scopes and reimbursement ratios may change in real-time with adjustments to the medical insurance catalog or negotiation outcomes. Document structures are diverse, including policy texts in PDF, implementation details in Word, and drug lists in Excel. Fields commonly include generic drug name, dosage form, specification, medical insurance payment standard, restricted payment conditions, and indications. Units involve amounts (Yuan), quantities (tablets/boxes), and dosages (mg).
Constraints on Deployment and Upgrade
Medical insurance access regulation data comes from dispersed sources and has inconsistent formats. FastGPT deployment requires robust heterogeneous data processing capabilities. It must be compatible with various document types like PDF, Word, and Excel, and effectively extract structured information. The real-time and high-frequency nature of policy updates challenges knowledge base synchronization. This requires designing flexible incremental update strategies to ensure knowledge base timeliness. The specialized and diverse nature of fields means more effort is needed in the data preprocessing stage for entity recognition, relationship extraction, and standardization. This ensures accuracy of question-answering results. Furthermore, complex restricted payment conditions and indication descriptions demand high precision and contextual understanding from RAG retrieval.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 200 MB | Medical insurance policy documents, especially PDFs with many attachments or charts, can have large file sizes. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing complex PDF or Word documents can be time-consuming. This avoids parsing failures due to timeouts. |
Chunk size (Segment Length) | 800–1200 characters | Medical insurance policy clauses are often lengthy and logically rigorous. Longer segments help maintain contextual completeness and improve comprehension accuracy. |
Recall count (Recall Count) | Top 5 entries | Medical insurance access Q&A demands high precision. Appropriately increasing the recall count can cover more relevant clauses. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | This ensures retrieved clauses are highly relevant to the user's query, avoiding misleading answers. |
Rerank result count (Rerank Return Count) | Top 3 entries | After initial recall, a secondary sort ensures the most relevant core clauses are presented first, improving Q&A efficiency. |
Common Mistakes
- After a knowledge base update, user query results still show old policy information. This occurs when incremental update tasks are not configured correctly, preventing the knowledge base from synchronizing the latest policy documents.
- When processing Excel drug lists, some field data is lost or parsed incorrectly. This happens due to insufficient understanding of table structures, failing to preprocess merged cells or special characters.
- A user asks about a specific drug's reimbursement ratio, but the system returns an empty result. This is due to inaccurate entity recognition for key fields like medical insurance payment standards or a
Similarity threshold(similarity threshold) set too high, preventing relevant information from being recalled.
Verification Steps
- Upload the latest medical insurance policy PDF file. Check if file parsing is successful. Verify that key clauses contained in the file can be retrieved from the knowledge base.
- For medical insurance catalog update files, manually create multiple queries including new and old drugs. Verify that the system's returned results reflect the latest catalog information.
- Select several drugs with complex restricted conditions. Query their medical insurance payment scope or reimbursement conditions. Cross-reference the returned answers with the
indicationsandrestricted payment conditionsdescriptions in the original policy text.
Note: The values provided are common starting points. Measure them against specific 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.