Data Characteristics
Medical insurance settlement data originates from policy documents, notices, interpretations, and operational guidelines issued by national and local medical security bureaus. These documents are typically in PDF, Word, or HTML format. Updates are frequent, especially during policy adjustments or annual summaries. Document structures include regulations, implementation rules, fee codes, payment standards, reimbursement ratios, special disease certifications, and out-of-area medical settlement processes. Fee codes (e.g., ICD-10, C-DRG/DIP) and payment standards are core components, involving substantial numerical data. Monetary units are in "yuan," percentages are "%," and time units include "days" and "months." Differences exist across regions and medical service items.
Constraints from Data Characteristics on Workflow Orchestration
Medical insurance settlement document characteristics impose specific workflow orchestration requirements. Policy updates are frequent. The knowledge base needs regular incremental updates. The workflow must trigger document parsing and vectorization processes. Documents contain significant structured and semi-structured data, such as fee lists and reimbursement ratio tables. Dedicated extraction nodes are necessary to identify and extract key information like fee codes, reimbursement ratios, and deductibles. For numerical data like amounts and percentages, accurate calculation or comparison is required during Q&A to prevent large model hallucinations. Complex scenarios such as out-of-area medical treatment and special diseases involve multi-level judgments and conditional branching. The workflow must support flexible logical judgments and multi-path execution to adapt to settlement rules under different query conditions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 600 characters (600 characters) | Medical insurance policy documents often have long paragraphs with multiple clauses. This length helps maintain contextual completeness and reduces semantic discontinuity. |
Recall count (Recall Count) | Top 8 entries (Top 8) | Medical insurance settlement questions often involve multiple overlapping policies. Increasing the recall count appropriately improves the coverage of relevant policies and ensures comprehensive information. |
Similarity threshold (Similarity Threshold) | 0.78 | Medical insurance clauses are precisely worded. A higher threshold filters out semantically less relevant segments, focusing on core policy content. |
maxContext | 4000 Token | Medical insurance policy explanations are complex. A longer context is needed to understand user intent and provide detailed answers, preventing information loss. |
Parsing Type | Automatic Recognition | Medical insurance documents come in various formats, including PDF, Word, and HTML. Automatic recognition ensures compatibility with different sources and simplifies document processing. |
File Parsing Timeout (File Parsing Timeout) | 600 seconds (600 seconds) | Parsing large medical insurance policy documents can be time-consuming. Increasing the timeout prevents parsing failures due to large files or network latency. |
Common Mistakes
- The workflow stops midway. The AI has responded, but subsequent processes are not triggered: This occurs when the large model generates a response, but the workflow lacks clear conditions or instructions to continue with subsequent judgment or extraction nodes.
- Custom global variables cannot be configured in the conditional judgment node: This issue typically arises from incorrect
typesettings for global variables, preventing the judgment node from correctly recognizing their values for logical comparison. - Inaccurate medical insurance amount calculations: The main reason is that numerical fields extracted from documents are not converted to the correct data type or precisely calculated within the workflow before being sent directly to the large model.
How to Verify Configuration
- Test whether the workflow correctly identifies and executes to the final response node for medical insurance settlement questions of varying complexity.
- Select medical insurance policy clauses containing specific amounts and percentages. Verify if the workflow accurately extracts and calculates the expected results after execution.
- Use queries that trigger conditions like out-of-area medical treatment or special diseases. Check if the workflow's conditional branches execute along the expected paths.
- Simulate a policy update scenario. Upload a new version of a medical insurance document. Observe if the workflow updates the knowledge base promptly and provides answers based on the new policy.
The values provided 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.