Data Characteristics
Medical insurance access policy data primarily originates from official documents published by national and local medical insurance bureaus. These include the National Medical Insurance Drug Catalog, the National Basic Medical Insurance, Work Injury Insurance, and Maternity Insurance Drug Catalog Adjustment Work Plan, and provincial/municipal medical insurance reimbursement policy details. Documents are typically in PDF or DOCX formats, with varying degrees of structure. They contain both structured table data and extensive unstructured text descriptions. Data updates frequently, usually with 1-2 national catalog adjustments annually. Local policies may update irregularly based on actual conditions. Fields within documents include drug name, dosage form, specification, medical insurance payment standard, payment scope, and restricted payment conditions. Units include currency (Yuan), quantity (tablets, injections, boxes), and percentages. Some policy clauses also contain complex logical judgments and exception clauses.
Constraints Imposed by Data Characteristics on Workflow Orchestration
The official nature and update frequency of medical insurance access policy data require high reliability and periodic data synchronization capabilities in workflow design. Diverse document formats and varying structural complexity necessitate integrating multiple document parsing tools into the workflow. Specifically, complex logical judgments and restrictive conditions in unstructured text demand advanced AI understanding and reasoning capabilities. This requires refining knowledge chunking strategies and recall enhancement mechanisms within the workflow. The diversity of fields and units, along with calculations involving specific numerical ranges and percentages, means the workflow must incorporate accurate entity recognition and numerical processing modules to ensure precise question-answering results. Furthermore, regional policy differences require the workflow to support multi-dimensional filtering and contextual association to provide targeted medical insurance access consultations.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 4000 token | Medical insurance policy texts are generally long; this ensures coverage of key policy clauses. |
Chunk size | 800–1200 characters | Balances semantic completeness with recall efficiency, preventing context loss from over-segmentation. |
Recall count | Top 10 entries | Medical insurance access questions often require cross-validation from multiple policies; increasing recall improves coverage. |
Similarity threshold | 0.78 | For precise matching of policy texts, a higher threshold filters irrelevant information. Calibrate based on actual measurements. |
tool_code_timeout | 600 seconds | Addresses potentially long-running operations from complex medical insurance calculation logic or external system calls. |
LLM_MODEL_NAME | gpt-4o | Requires high-performance language models for complex policy understanding and logical reasoning. |
Common Pitfalls
- Tool call modules do not execute as expected; logs show
tool not foundortool_response_empty. This typically occurs when thenameordescriptionin the tool definition does not strictly match the reference name in the workflow, or when requiredvariablesare not passed correctly. - AI dialogue nodes return results missing critical values or restrictive conditions, such as medical insurance payment ratios or reimbursement scopes for specific diseases. This often results from overly granular knowledge base segmentation, where a single segment fails to provide complete logical context, or from a
Similarity thresholdthat is too high, filtering out low-similarity segments containing crucial information. - Parallel AI dialogue nodes within the workflow produce inconsistent or conflicting final output results. The symptom is significant discrepancies in
output variableresults. This happens when parallel nodes lack clear coordination mechanisms or shared context, leading them to reason based on incomplete inputs.
How to Verify Configuration
- Select typical medical insurance access questions, such as "What is the medical insurance payment scope and reimbursement ratio for a certain drug in a specific province?" Execute the workflow and check if the final answer includes all key information, such as drug name, payment standard, payment conditions, reimbursement ratio, and applicable population.
- Given the high update frequency of medical insurance policies, manually upload the latest policy documents. Verify if the workflow can accurately parse and integrate new information, for example, by querying updated drug catalogs or payment standards.
- Construct queries involving complex logical judgments, such as "Are there special reimbursement regulations for children using a certain medical insurance drug?" Examine the workflow's reasoning process and results to ensure it correctly handles conditional branches and exception clauses, and that the reasoning path aligns with expectations.
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.