Data Characteristics in this Category
Health insurance access pre-screening data for clinical trials primarily originates from public documents like national and local health insurance catalogs, drug catalogs, and treatment item catalogs issued by medical insurance bureaus. Additional sources include clinical trial application materials submitted by pharmaceutical companies, medical literature, and drug inserts. Data update frequencies vary: health insurance catalogs typically update annually, some local policies may adjust quarterly, and clinical trial data generates in real-time with project progression. Document structures are complex, encompassing both structured table data (e.g., drug reimbursement scope, payment standards) and extensive unstructured text (e.g., clinical trial protocols, research reports, medical guidelines). Fields include generic drug names, dosages, specifications, indications, payment categories, restricted payment conditions, clinical staging, and adverse reactions. Units cover dosage units (mg, IU), time units (weeks, months), and cost units (CNY).
Constraints Imposed by these Characteristics on Model Integration and Configuration
The multi-source and heterogeneous nature of health insurance access data requires models with robust multimodal information processing capabilities to parse both structured tables and unstructured text. Annual or quarterly update frequencies necessitate support for incremental training and rapid deployment to adapt to policy changes. Complex document structures and vast text data demand high accuracy and efficiency in document parsing, especially for extracting key fields and understanding restrictive conditions. Field and unit specificities, such as multiple representations for drug specifications, require models to perform standardization and normalization during data preprocessing to prevent misjudgment due to inconsistent units. Furthermore, regional variations in health insurance policies mandate model configurations that support multi-regional parameter adjustments for precise pre-screening.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Health insurance policy documents are often large, requiring ample timeout for parsing. |
maxContext | 800–1200 characters | Clinical trial protocols and medical literature have high contextual relevance, necessitating a longer context window. |
Recall Count | Top 10 | Health insurance access pre-screening involves dense knowledge points, requiring more relevant items for higher coverage. |
Similarity Threshold | 0.75 | Health insurance policies demand high matching precision, ensuring recalled content is highly relevant to the query. |
Rerank Return Count | Top 5 | After reranking, reviewing a small number of the most relevant results improves decision-making efficiency. |
Model Version | DeepSeek-V3.1 | Adopting the latest version enhances performance for complex semantic understanding and reasoning requirements. |
Three Common Mistakes
- The model provider page lacks call logs, making it impossible to trace detailed model request processes and error messages, hindering problem identification.
- When invoking a text-to-image model in FastGPT, an empty or unexpected generation result may occur if
Model Output Formatis not correctly configured for image URL or Base64 encoding. - A reranking model is unusable within the model but functions correctly in knowledge base search tests because
Rerank Model IDis not correctly specified in the model configuration or the model deployment status is abnormal.
How to Verify Correct Configuration
- Submit a simulated health insurance access query containing drug name, indications, and payment standards. Check if the returned results include all relevant health insurance policy clauses.
- Upload a typical clinical trial protocol document. Observe the document parsing status and key information extraction success. Compare with the original document to verify the accuracy of
extracted fields. - Query specific restricted payment conditions. Verify if the model correctly identifies and excludes ineligible drugs or patients. Adjust
Similarity Thresholdto observe changes in recall results.
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.