Data Characteristics
Medical insurance claims data originates from various medical insurance institutions' settlement platforms. It typically exists as structured or semi-structured electronic documents (e.g., XML, JSON, CSV). Data updates frequently, usually through daily or weekly batch processing, with some core settlement data updating in real-time. Document content includes patient basic information, visit records, diagnosis codes (e.g., ICD-10), treatment plans, medication lists (ATC codes), medical service item codes (e.g., CPT/HCPCS), expense details, and medical insurance payment ratios. Field names and units adhere to national or local medical insurance bureau standards. For example, currency units are "RMB Yuan," and quantity units are "times," "boxes," "milligrams," etc., with strict data validation rules.
Constraints Imposed by Data Characteristics on "Reference and Traceability"
The structured nature of medical insurance claims data allows for more precise targeting of specific fields during knowledge base retrieval, reducing the need for fuzzy matching of unstructured text. Frequent updates require the knowledge base to support rapid incremental updates and version management to ensure the timeliness of referenced data. Standardized fields and units mean that references must strictly maintain their original format to prevent traceability issues due to unit conversion or field misinterpretation. The abundance of coding information (ICD-10, ATC, CPT/HCPCS) requires the knowledge base to correctly parse and associate these codes with their descriptions, improving readability. Detailed expense breakdowns necessitate fine-grained referencing, requiring traceability to specific service items or medications.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
Chunk size (Chunk Size) | 200-300 characters | Medical insurance claims data is typically itemized; shorter chunks help maintain the integrity and semantic coherence of individual records. |
Recall count (Recall Count) | Top 10 | Clinical trial pre-screening involves multi-dimensional information; increasing the recall count can improve relevance coverage. |
Similarity threshold (Similarity Threshold) | 0.75-0.85 | Medical insurance data is highly structured, allowing for a higher threshold to ensure retrieval precision. |
maxContext | 2000-3000 tokens | Ensures sufficient capacity to accommodate detailed information from multiple medical insurance claim records for comprehensive model judgment. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses potential time consumption when parsing large batches of medical insurance claims data files, preventing processing failures due to timeouts. |
Citation source display format (Reference Source Display Format) | Field Name: Value (Source File ID) | Clearly displays specific field content and provides a file ID for tracing back to the original data document. |
Common Pitfalls
- Reference numbers in output appear garbled or malformed: This occurs when text encoding is inconsistent between the knowledge base or large language model, leading to incorrect parsing of special characters.
- Slow knowledge base query speed, impacting response time: This is due to unoptimized knowledge base indexing strategies, or inefficient index rebuilding/updating mechanisms when processing large volumes of frequently updated medical insurance data.
- Inability to correctly reference data after calling the knowledge base in a workflow: This results from incorrect knowledge base connector configuration, or when the data format returned by the knowledge base in the tool call module does not match expectations, leading to subsequent parsing failures.
Verification Steps
- Perform end-to-end testing. Input typical clinical trial pre-screening questions and verify that the referenced medical insurance claims data in the output matches the original document content. Confirm that the source file ID is traceable.
- Monitor knowledge base logs and check the response time for each knowledge base query, ensuring it falls within an acceptable range. If queries are too slow, investigate index status and query optimization strategies.
- Examine the field values referenced in the large language model's output. Compare them against the original medical insurance claims data to confirm that numerical values, units, and codes match exactly.
- Simulate incremental updates of medical insurance claims data. Verify that new data can be correctly retrieved and referenced after the knowledge base update, and that references to older data remain correct.
The values provided above 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.