Data Characteristics
Medical insurance access quality documentation includes drug/device registration approvals, medical insurance catalog negotiation documents, pharmacoeconomic evaluation reports, clinical trial reports, real-world study data, post-market risk management plans, and various policy interpretations. Data sources are diverse, covering official documents from the National Healthcare Security Administration, National Medical Products Administration, and National Health Commission, internal R&D and marketing materials from pharmaceutical companies, and analysis reports from third-party professional organizations. Document update frequencies vary; policy documents may be released or revised annually, while drug clinical data is continuously generated throughout the R&D cycle. Document structures are diverse, including structured table data (e.g., pharmacoeconomic model parameter tables, medical insurance payment standards) and large amounts of unstructured text (e.g., clinical study protocols, results analysis). Fields and units are specialized, often involving generic drug names, brand names, indications, dosage and administration, medical insurance coverage, reimbursement ratios, patient populations, clinical endpoints (e.g., PFS, OS, ORR), cost-effectiveness ratios (e.g., ICER), and corresponding currency units, time units, and medical measurement units.
Constraints on Multi-turn Conversations and Prompts
The complexity of medical insurance access documents imposes specific requirements on multi-turn conversation and prompt design. First, diverse document sources and varying update frequencies necessitate flexible document import and version management in the knowledge base to ensure conversations are based on the latest authoritative data. Second, the prevalence of unstructured text and specialized terminology requires prompt design to focus on extracting key information and understanding concepts, avoiding misinterpretations due to semantic ambiguity. For example, the same drug may have different reimbursement conditions in different medical insurance catalogs, requiring the conversation to accurately identify the user's query context. Furthermore, the long decision chain involved in medical insurance access requires conversations to connect information from multiple documents for logical reasoning and cross-validation. For instance, evaluating the medical insurance access potential of a new drug requires considering its clinical efficacy, pharmacoeconomic data, and current medical insurance policies simultaneously. Finally, the specialized nature of fields and units means prompts must guide the model to focus on the accuracy of numerical values and units, preventing calculation errors or unit confusion in scenarios like cost-effectiveness analysis.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
chunkSize | 800–1200 characters | Medical insurance documents are dense with specialized terminology; maintaining contextual completeness helps model understanding. |
overlapRate | 0.1 | Reduces information redundancy while ensuring semantic continuity between chunks. |
maxContextTokens | 4096 tokens | Medical insurance access decisions often require long conversation histories and multi-document information, ensuring the model can handle complex queries. |
recallCount | top 8–12 items | Comprehensively considers medical insurance policies, clinical data, and economic reports to ensure comprehensive information recall. |
similarityThreshold | 0.75 | Medical insurance access demands high information accuracy; increasing the threshold reduces interference from irrelevant or low-quality information. |
reRankCount | top 5 items | Further refines recall results, prioritizing document segments most relevant to core medical insurance access issues. |
Common Mistakes
- Vague answers regarding drug reimbursement ratios or applicable populations in conversations. This occurs because the knowledge base chunking strategy fails to effectively retain critical qualifying conditions, preventing the model from accurate extraction.
- When users ask about a drug's medical insurance policy in a specific province, the model provides national policies. This happens because the knowledge base lacks regional tagging or metadata management for documents, leading to a lack of geographic specificity in retrieval results.
- When processing pharmacoeconomic reports containing large amounts of tabular data, the model fails to correctly identify and calculate cost-effectiveness ratios. This is due to insufficient structured extraction capabilities for complex tables in the file parser, or prompts that do not explicitly require the model to perform numerical operations.
Verification of Configuration
- Test multi-turn conversations for typical medical insurance access consulting scenarios involving various document types (e.g., policy documents, clinical reports, economic evaluations) to verify the model's ability to maintain context.
- Randomly select medical insurance access-related questions for specific drugs or devices from the knowledge base. Compare model answers with original document information to check the accuracy of key fields (e.g., indications, reimbursement conditions, payment standards).
- Simulate user queries containing specialized terminology and complex logic. Check if the model correctly understands and provides relevant document segments. Evaluate conversation response speed to ensure it is within
PARSE_FILE_TIMEOUT_SECONDS.
The values provided are common starting points. They 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.