Data Characteristics for This Category
Medical insurance settlement data originates from medical insurance bureaus, healthcare institutions, and commercial insurance companies. Data updates are frequent, typically incremental daily or weekly, with full data archiving and release annually. Document structures are complex, including information on designated institutions, drug catalogs, diagnostic and treatment codes, medical service prices, personal account details, and settlement statements. This data often exists as a mix of structured (e.g., XML, JSON, CSV) and semi-structured formats (e.g., PDF reports, scanned policy documents). Fields include patient_id, service_code, charge_amount, and reimburse_ratio. Units include CNY, percentages, and quantity units (e.g., boxes, times). Field definitions and encoding standards can vary across different provinces, cities, and policies.
Constraints on Deployment and Upgrade Due to These Characteristics
The complexity and high update frequency of medical insurance settlement data impose high demands on data synchronization mechanisms and model training during deployment and upgrade processes. Structured data requires efficient parsing and ingestion strategies to handle daily incremental data in the tens of millions, ensuring data consistency. Semi-structured documents require robust text recognition and information extraction capabilities to convert them into retrievable and analyzable knowledge points. Regional differences in medical insurance policies mean that knowledge bases must support multi-version, multi-region configurations during deployment and seamlessly switch or merge different knowledge versions during upgrades. Furthermore, archiving historical data and injecting new data necessitate flexible data version management and traceability features to ensure the accuracy and auditability of consultation results.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Accommodates large policy documents or bulk settlement statement uploads. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles complex PDF documents or structured text with many tables. |
embedding_model | m3e-large | Provides strong semantic understanding for medical terminology and policy texts. |
Chunk size | 800–1200 characters | Balances context completeness and segment processing efficiency, preventing information loss. |
Recall count | Top 10 entries | Ensures coverage of potential matches across multi-dimensional medical insurance policies and settlement rules. |
Similarity threshold | Calibrate by testing | Adjusts based on characteristics of medical insurance data from different regions to balance precision and recall. |
Common Pitfalls
- Knowledge base query results include non-medical insurance content. This can occur if the embedding model's recognition accuracy for medical insurance terminology is insufficient, or if the similarity threshold is set too low, introducing noise.
- A
workflow error {"message":"Dangerous behavior"}message appears during workflow debugging. This can happen if the model triggers a security policy when processing medical insurance policy queries, requiring adjustments to prompts or filtering rules. - After upgrading FastGPT, some medical insurance query results show trailing symbols related to traceability display rules. This can be due to format adjustments in the new version, making older post-processing logic incompatible.
How to Verify Configuration
- Select at least 5 typical medical insurance settlement consultation scenarios. Test each scenario and verify the accuracy of key information such as policy terms, reimbursement ratios, and required documents in the returned results.
- Upload a policy document containing complex tables and specific medical insurance terminology. Check if the file parser correctly extracts all core fields and key paragraphs.
- Simulate medical insurance policy queries for different regions and years. Verify that the system accurately calls the corresponding knowledge base version based on input conditions to provide answers.
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.