Data Characteristics for This Category
Medical insurance settlement product data originates from various healthcare providers. This includes treatment records, expense lists, drug and consumable details, and policy documents and coding standards issued by medical insurance bureaus. Data updates frequently. Policy documents may release quarterly or annually, while expense lists and treatment records are real-time or near real-time. Document structures vary. This includes structured XML or JSON for expense details, semi-structured PDF for policy interpretations, and unstructured Word or image formats for medical records. Fields typically involve patient information, diagnosis codes (e.g., ICD-10), surgical codes, generic drug names, consumable codes, service item codes, settlement amounts, and reimbursement ratios. Units involve amounts (Yuan), quantities (pieces/boxes), and percentages (%), with high precision requirements.
Constraints Imposed by These Characteristics on "Workflow Orchestration"
High update frequency of medical insurance settlement data requires workflows with rapid response and dynamic adjustment capabilities to adapt to policy changes. Diverse document structures necessitate flexible data extraction and parsing components to handle different information formats. The precision of structured data requires strict rule-matching capabilities in data cleaning and validation steps to ensure accurate coding and amounts. Semi-structured and unstructured data demand stronger semantic understanding and information extraction capabilities. Furthermore, fields involving amount and ratio calculations impose precision and robustness requirements on the workflow's logical judgment and numerical calculation modules to prevent settlement errors due to calculation mistakes. Multi-source data convergence requires workflows to effectively integrate interfaces from different systems.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 3000 tokens | Balances understanding of complex policy documents, avoids information truncation. |
Chunk size | 500 characters | Adapts to the length of policy clauses and expense details, maintaining semantic integrity. |
Recall count | Top 8 entries | Increases recall rate for relevant policies and historical settlement cases, covering more matching possibilities. |
Similarity threshold | 0.75 | Ensures recalled policies or rules are highly relevant to the query, reducing misjudgments. |
PARSE_FILE_TIMEOUT_SECONDS | 180 seconds | Accommodates parsing time for large policy files or scanned documents, preventing timeout interruptions. |
Rerank result count | Top 3 entries | Focuses on the most relevant settlement suggestions or policy items, improving decision efficiency. |
Three Common Pitfalls
- Symptom: Medical insurance settlement results show numerous "no effective policy matched" prompts. Reason: During data preprocessing, coding or keyword extraction from medical insurance policy documents is inaccurate, leading to incorrect matching during the recall phase.
- Symptom: Workflow responds slowly or freezes when processing specific medical insurance service items. Reason: The
maxContextparameter is set too low. This requires multiple context switches or information completion when processing complex service items with many associated rules. - Symptom: Uploaded expense list files fail to have fields correctly identified. Reason: The workflow's document parser is not adapted for specific expense list formats (e.g., custom PDF templates from certain hospitals), or
PARSE_FILE_TIMEOUT_SECONDSis set too low.
How to Verify Configuration
- Select typical and representative medical insurance settlement scenarios, including normal settlement, special disease settlement, and out-of-area settlement, for end-to-end testing.
- Compare workflow processing results with manually calculated settlement amounts and reimbursement ratios. Verify if the difference is within an acceptable error range. The error threshold must be defined based on actual business needs.
- Check workflow logs to confirm execution time for critical steps such as data extraction, policy matching, and rule judgment. Ensure performance meets requirements. Performance metrics must be determined based on system load and user experience goals.
- Randomly select a percentage of settlement cases. Check if the cited policy clauses and rule bases are accurate. Verify the accuracy of recall and matching.
The values provided are common starting points. They 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.