Data Characteristics for Medical Insurance Settlement
Medical insurance settlement data typically originates from official documents, policy interpretations, and operational guidelines published by national and local medical insurance bureaus. These documents are released in various formats, including PDF, Word, and HTML. Update frequencies vary; core policies may change annually, while detailed rules or local regulations update irregularly based on actual conditions. Document structures usually include original policy texts, attachments, and Q&A sections, with clear hierarchical organization. Fields involve reimbursement ratios, payment scopes, deductibles, caps, special drug catalog codes, and medical service item codes. Units include percentages, monetary amounts (Yuan), and time (days). This data is highly standardized and specialized, demanding extreme accuracy.
Constraints on Tool Calling and Plugins from Data Characteristics
The standardized nature of medical insurance settlement data requires tool calls to precisely match key fields and values within policy clauses. Uncertain update frequencies necessitate flexible data source synchronization mechanisms to ensure information timeliness. Diverse document formats challenge plugin data extraction capabilities, requiring support for multiple document types. The specialized nature of fields and rigorous units demand that tool calls accurately understand and process concepts like "Class A," "Class B," and "self-payment ratio" when constructing queries and parsing results, preventing settlement errors due to semantic misunderstandings. For example, querying reimbursement details for a specific medical service item requires precise identification of the service code and corresponding payment regulations.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 tokens | Medical insurance policy documents are often lengthy; sufficient context is needed to understand complex rules. |
similarityThreshold | 0.75–0.85 | Ensures recalled policy clauses are highly relevant to the query intent, preventing misinterpretation. |
recallTopK | 8–12 items | Medical insurance rules are interconnected; multiple recalls provide a more comprehensive reference. |
toolCallTimeout | 600 seconds | Handles complex queries or slow responses from external systems. |
maxRetryAttempts | 3 times | Addresses occasional external interface failures, increasing stability through retries. |
outputFormat | JSON or XML, with schema specified | Ensures structured output for easier parsing and display, clarifying data types. |
Common Pitfalls
- Tool calls return XML or JSON code blocks but do not render charts. This may be due to incorrect rendering plugin configuration or a mismatch between the returned data structure and the plugin's expectations.
- Queries for specific medical insurance items yield empty or inaccurate results. This typically occurs when the knowledge base lacks corresponding codes or policy clauses, or the recall strategy fails to retrieve relevant content.
- HTTP calls within a workflow enter a loop. This may stem from poorly designed conditional logic leading to infinite requests, or from the external API's design having recursive dependencies.
Verification of Configuration
- Simulate user queries for typical medical insurance settlement questions. Check if tool calls accurately identify intent and trigger corresponding external interfaces.
- Validate data returned by tool calls. Check if their structure and content align with the expected medical insurance policy data schema, especially for key fields like reimbursement ratios and payment amounts.
- Test edge cases, such as querying obsolete policies or specific local regulations. Confirm the system correctly returns "no relevant information" or provides guidance to the latest policies.
The values provided are common starting points and should be measured 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.