Data Characteristics for This Category
Medical insurance settlement data primarily originates from medical insurance bureaus, designated medical institutions, and pharmacies at various levels. It consists mainly of structured and semi-structured data. Structured data includes medical insurance payment policy documents, drug catalogs, treatment item catalogs, disease diagnostic codes (ICD-10), and surgical procedure codes. This data typically exists in XML, JSON, or database table formats. Semi-structured data involves specific settlement statements and expense details. These documents may appear as PDFs, Excel files, or scanned images, containing complex tables and descriptive text. Data update frequency is high; policy documents are usually released quarterly or annually, while drug and treatment catalogs may be adjusted more frequently. Fields often include unified planning areas, reimbursement ratios, deductibles, caps, out-of-pocket ratios, and self-funded items. Units commonly include Yuan, percentages, and counts.
Constraints Imposed by These Characteristics on "Model Integration and Configuration"
The diversity and high update frequency of medical insurance settlement data place specific demands on model integration. Structured data requires efficient parsing and field mapping capabilities to accurately identify and index key medical insurance policy terms. The complex tables and text content of semi-structured settlement statements require models with strong document parsing and information extraction capabilities, especially when processing multi-page, cross-row, and cross-column data. Frequent policy updates mean the knowledge base needs to support rapid incremental updates and version management to prevent the model from providing consultation results based on outdated information. Furthermore, sensitive medical insurance amount and ratio calculations require high numerical processing precision and logical reasoning capabilities from the model. Configuration should focus on the embedding model's ability to understand numbers and percentages, and optimization for precise matching during the recall phase.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 50 MB | Medical insurance policy documents or settlement statements may contain many tables or scanned images, resulting in large file sizes. |
Chunk size | 800–1200 characters | Medical insurance policy terms are often long; reasonable segmentation is needed while ensuring contextual completeness. |
Overlap Length | 100 characters | Ensures contextual continuity, especially for definitions or condition descriptions within policy terms. |
Recall count | Top 8 entries | Medical insurance settlement consultations often require synthesizing multiple policies or terms for judgment, increasing recall coverage. |
Similarity threshold | 0.75 | Medical insurance policy language is precise; a lower threshold might introduce irrelevant terms, while a higher threshold might miss relevant information. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing complex PDF documents takes a long time; sufficient time must be allocated to avoid parsing failures. |
Three Common Mistakes
- Model responses contain outdated medical insurance policy information. This occurs due to an imperfect knowledge base synchronization mechanism, leading the model to use old policy data.
- Medical insurance amount calculation results do not match actual figures. This usually happens because the model fails to accurately identify or extract all expense categories, reimbursement ratios, and out-of-pocket portions when parsing settlement statements.
- Azure OpenAI service integration fails, manifesting as 40X error codes in API calls. This is often due to differences between the Azure service's API protocol and standard OpenAI interfaces, requiring additional configuration for parameters like
api_typeandapi_version.
How to Verify Proper Configuration
- Import recently updated medical insurance policy documents into the knowledge base. Check import logs for parsing errors or timeouts. Randomly select multiple documents and verify that segmented content is complete and logically continuous.
- Simulate various medical insurance consultation scenarios. Ask questions involving reimbursement ratios and deductible calculations. Observe whether the policy terms cited in the model's answers are current and accurate, and check if calculation results meet expectations. Validation thresholds can be set based on official medical insurance bureau calculator results.
- Simulate concurrent requests via API calls. Observe model response speed and stability to ensure timely and accurate consultation services under high load. Check logs for a high volume of failed or timed-out requests.
The values given 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.