Data Characteristics for this Category
Medical insurance settlement registration and declaration documents originate from various sources. These typically include policy documents from the National Healthcare Security Administration, detailed implementation rules from local medical insurance departments, medical service price catalogs, medical insurance payment standards for drugs and consumables, and internal hospital settlement statements and expense details. This data updates frequently. Policy documents and payment standards, in particular, may be adjusted quarterly or annually, or even revised temporarily due to unforeseen events. Document structures are complex, encompassing original policy texts in PDF format and detailed data in Excel or CSV. There are numerous specialized fields, such as medical insurance payment codes, service item names, payment ratios, limited payment conditions, cost units (e.g., "yuan/time," "yuan/day"), and various Diagnosis Related Groups (DRG) or Diagnosis Intervention Packet (DIP) weighting coefficients.
Constraints Imposed by these Characteristics on Multi-Turn Conversations and Prompts
The multi-source nature and high update frequency of medical insurance settlement data require the knowledge base to perform frequent and efficient document synchronization and index rebuilding. This ensures the timeliness and accuracy of conversation content. Complex document structures and specialized fields mean that knowledge base chunking requires more refined strategies. This prevents critical information from being truncated or context lost, which would affect subsequent question-answering accuracy. For example, a limited payment condition may span multiple paragraphs, requiring the model to integrate information across multiple turns. Multi-turn conversations need the ability to identify and track core entities like medical insurance payment codes and service items. This ensures accurate association even if users use different expressions in different turns. Prompt design must guide the model to prioritize information extraction from the latest data and handle user queries about historical or future policy directions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8 | Ensures the model can cover typical medical insurance policy query scenarios while balancing computational cost. |
Chunk size (Chunk Size) | 500–800 characters | Medical insurance policy texts often have long paragraphs containing multiple conditions and explanations. This length helps preserve complete semantic meaning. |
Recall count (Recall Count) | 8 | Medical insurance settlement questions often involve cross-referencing multiple policy regulations. Increasing recall helps ensure comprehensive coverage. |
Similarity threshold (Similarity Threshold) | 0.78 | Medical insurance terminology and policy descriptions are precise. A higher threshold reduces interference from irrelevant or ambiguous information. |
Rerank result count (Reranked Return Count) | 5 | Based on a high recall count, reranking selects the most relevant entries, reducing the model's processing burden. |
prompt | Calibrate by actual measurement | Must include instructions guiding the model to focus on key information such as medical insurance codes, payment conditions, and effective dates. |
Common Mistakes
- The conversation displays a "payment standard not found" message. This occurs when knowledge base document chunks are too granular, splitting a single payment code or limited condition, preventing the model from acquiring complete context.
- When a user asks about regional differences in medical insurance payments for a service item, the model's response is limited to a single region. This happens when the prompt does not explicitly request retrieval of multi-regional policies, or regional information is not effectively associated within the knowledge base.
- User-uploaded medical insurance settlement statement attachments are not effectively parsed, preventing the conversation from using the attachment content. This is because the document parsing module is not configured to recognize and extract tables from specific formats (e.g., scanned PDFs).
How to Verify Configuration
- Select recently updated medical insurance policy documents. Ask questions about key payment items and limited conditions within them. Check if the model's response accurately cites the latest regulations.
- Simulate a user asking multiple rounds of questions about a medical insurance service item, including payment standards, scope of application, and reimbursement ratios. Observe if the model maintains contextual consistency and progressively provides detailed answers.
- Attempt to pose questions containing vague terminology or abbreviations. Check if the model can correctly identify and associate them with standard medical insurance vocabulary in the knowledge base, and provide reasonable responses.
The values provided are common starting points and should be measured against the reader's 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.