Data Characteristics
Medical insurance settlement quality documents originate from policy regulations, operational guidelines, and settlement rules published by various levels of medical insurance bureaus. They also include internal implementation specifications, training materials, and FAQs from medical institutions. These documents update frequently, especially policy regulations, which typically adjust quarterly or annually, with more frequent changes during major reforms. Document structures are primarily unstructured text, such as PDF policy files and Word internal notices. They contain extensive technical terms, acronyms (e.g., DRG, DIP, CHS-DRG), and specific numerical or enumerated fields like cost codes, payment ratios, and settlement cycles. Text content often describes processes, conditional judgments, and exception handling. Settlement rules vary by region and medical institution level, introducing regional and hierarchical data characteristics.
Constraints on Multi-turn Conversations and Prompts
The frequent updates to medical insurance settlement documents require the knowledge base to quickly synchronize the latest information. This ensures the timeliness of knowledge points cited in multi-turn conversations. Unstructured text and numerous technical terms demand stronger semantic understanding and terminology recognition from the model. This prevents misinterpretations that could lead to incorrect settlement guidance. Regional and hierarchical differences mean the multi-turn conversation system must guide users to specify their region and medical institution level. This allows precise matching of relevant rules from the vast knowledge base. Furthermore, process descriptions and conditional judgments in the documents impose higher demands on prompt design. Prompts must ensure the model accurately judges context and provides corresponding operational steps or condition explanations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 500–800 characters | Medical insurance policy documents often have long paragraphs with multiple information points; this length helps maintain semantic completeness. |
Recall count (Retrieval Count) | Top 8–12 chunks | Medical insurance settlement rules are complex; retrieving too few chunks might not cover all relevant conditions. |
Similarity threshold (Similarity Threshold) | 0.78–0.85 | This avoids retrieving irrelevant content due to similar technical terms while ensuring highly relevant documents are retrieved. |
maxContext | 3000–4000 tokens | Multi-turn conversations about medical insurance settlement often involve multiple exchanges, requiring a longer context to maintain coherence. |
Rerank result count (Reranked Return Count) | Top 5 chunks | Retrieved documents still need fine-grained sorting to ensure the most relevant rules are presented to the user first. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Medical insurance policy files are often large, requiring a longer parsing time to avoid timeouts. |
Common Pitfalls
- Errors in key figures like settlement amounts or ratios during conversations. This might occur if the knowledge base has not updated with the latest policies or if the model fails to correctly identify all constraints when processing multi-condition rules.
- The system cannot provide clear answers or gives overly general responses when users ask about specific medical service insurance payments. This might be due to incomplete detailed medical insurance codes or payment catalogs for relevant service items in the knowledge base.
- The system does not respond to user-provided region or hospital level information in multi-turn conversations. This leads to medical insurance policies in replies not matching the user's actual situation. This might be because prompts do not effectively guide the model to identify and utilize these critical contextual details.
Validation Steps
- Select recently updated medical insurance policy documents. Ask questions about key clauses and verify if the answers accurately cite the latest content.
- Simulate medical insurance settlement questions from different regions and medical institution levels. Observe whether the system provides differentiated responses consistent with local policies based on the simulated scenarios.
- Conduct multi-turn conversation tests on common difficult questions in medical insurance settlement processes. Confirm if the system can consistently provide effective guidance in conversation depth and logical coherence, and identify key fields in user intent.
The values given 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.