Data Characteristics for This Category
Medical insurance access pharmacovigilance data primarily originates from the National Medical Insurance Drug List, provincial and municipal supplementary medical insurance lists, centralized drug procurement results, adverse drug reaction (ADR) monitoring reports published by the National Medical Products Administration (NMPA), and pharmacoeconomic evaluation reports submitted by pharmaceutical companies. Data update frequencies vary; the medical insurance drug list typically adjusts annually, while ADR reports are continuously released. Document structures are complex. The medical insurance drug list mainly consists of structured tables, including fields such as generic drug name, dosage form, specifications, payment standards, and restricted payment scope. ADR reports are largely unstructured text, covering patient basic information, medication details, ADR occurrence processes, and outcomes. Pharmacoeconomic reports are usually in PDF or Word format, containing numerous charts and specialized terminology.
Constraints from These Characteristics on "Tool Calling and Plugins"
The annual update cycle of medical insurance access data requires tool calling to incorporate version management capabilities when comparing old and new directories, and to identify differences between versions. The unstructured nature of ADR reports makes text parsing and entity recognition critical, demanding robust natural language processing capabilities. The complexity of drug payment scopes and restrictions places high demands on the tool's rule engine and logical judgment to accurately assess medical insurance payment conditions. Furthermore, specialized charts and data in pharmacoeconomic reports necessitate multimodal processing capabilities to effectively extract key information, such as the incremental cost-effectiveness ratio (ICER) and quality-adjusted life years (QALY). The diversity of data sources also means the tool must support various interface protocols and data formats.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
maxContext | 8192 tokens | To handle longer medical insurance policy texts and adverse reaction reports, ensuring context completeness. |
model_id | anthropic.claude-v3-sonnet | Balances complex text understanding capabilities with response speed, suitable for medical insurance policy and report analysis. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | To accommodate parsing time for large pharmacoeconomic reports or multiple adverse reaction reports. |
Recall count (Recall Count) | 20 items | To improve the accuracy of recalling relevant information from vast medical insurance directories and adverse reaction knowledge bases. |
Similarity threshold (Similarity Threshold) | 0.75 | For precise matching of medical insurance payment conditions, drug indications, and adverse reaction events. |
Multimodal System Plugin | Enable image recognition | To parse charts in pharmacoeconomic reports and extract key data and conclusions. |
Three Common Pitfalls
- API call returns
aiPointsNotEnough: This usually indicates insufficient model call quota. Check the account's AI points balance. - Incorrect medical insurance payment condition judgment: This occurs when plugin rules do not fully account for detailed clauses in the medical insurance directory's restricted payment scope, such as age, disease stage, or specific examination results.
- Incomplete entity extraction from adverse reaction reports: This may happen if the model or plugin lacks sufficient recognition capability for medical terminology and synonyms when processing unstructured text, leading to key information omissions.
How to Verify Correct Configuration
- For a specific drug in the medical insurance directory, input its generic name and indications. Verify that the tool accurately returns the payment standards and restrictions for that drug across different medical insurance directories, and compare with official directories.
- Submit a pharmacoeconomic report containing multiple charts. Check if the multimodal system plugin successfully identifies and extracts key data points from the charts, such as costs, effects, and ICER values.
- Input a typical adverse reaction monitoring report. Observe if the tool accurately identifies and labels core entities like drug name, adverse reaction event, occurrence time, and patient characteristics. Compare the results with manual annotations.
- For complex logic within medical insurance access rules, design queries with various condition combinations. Verify that the tool's decision chain executes correctly and provides expected judgment results.
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.