Data Characteristics
Medical insurance claims data originates from hospital information systems (HIS) or medical insurance bureau settlement platforms. Data updates frequently, typically in daily or weekly batches. Raw documents have complex structures, including XML and JSON electronic medical record summaries, expense lists, and scanned paper invoices or expense details. Key fields include patient basic information, diagnosis codes (ICD-10), treatment item codes, drug codes, medical insurance payment categories, reimbursement ratios, and out-of-pocket amounts. Data often contains numerous abbreviations and aliases. Coding standards can vary slightly between different medical institutions.
Constraints on Deployment and Upgrade
High-frequency updates and complex data structures challenge FastGPT's data ingestion module. Stable data source connectors are necessary to handle batch data imports and incremental updates. The sensitive nature of medical insurance claims data requires the deployment environment to meet strict data security and privacy protection standards, such as encrypted data storage and access control. Diverse coding standards and abbreviations require FastGPT's knowledge base to have robust semantic understanding and entity recognition capabilities to ensure accurate pre-screening logic. Large volumes of unstructured documents, like scanned images, require OCR pre-processing to convert them into retrievable text. Furthermore, complex field relationships determine the depth of knowledge graph construction, impacting query recall precision.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1024 MB | Medical insurance claims data packages can be large; support for single large file uploads is necessary. |
maxContext | 1000–1500 characters | Medical insurance claim documents are information-dense, requiring a longer context window to capture key details. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing complex structured or semi-structured documents (e.g., XML, JSON) can be time-consuming. |
Chunk size | 800 characters | Ensures each segment contains sufficient medical insurance claim field information, preventing semantic fragmentation. |
Recall count | Top 8 entries | Clinical trial pre-screening has high relevance requirements; increasing recall helps capture potential associations. |
Similarity threshold | Calibrated by actual measurement | Medical insurance claim fields have many synonyms and coding differences; actual testing is needed to determine the balance point. |
Common Mistakes
- Knowledge base query results are empty because incremental synchronization strategies for data sources are not configured correctly. This leads to discrepancies between the knowledge base data and actual medical insurance claims data versions.
- Some critical fields (e.g., diagnosis codes) are incorrectly identified or missing in pre-screening results. This occurs when the knowledge base construction does not adequately handle coding standard differences and abbreviations from various medical institutions.
- Certain functions (e.g., multi-team collaboration) are unavailable after local deployment. This happens when the relevant permission management module is not correctly configured or enabled, leading to functional limitations.
Verification
- Import a batch of the latest medical insurance claims data. Check if the knowledge base can retrieve all new patient expense details and diagnosis information. Verify the accuracy of key fields.
- Perform a pre-screening query on a medical insurance claim document containing complex diagnosis codes and various drug names. Check if the system's pre-screening conclusion matches a human assessment. Verify if the recalled key paragraphs accurately include relevant codes and drug information.
- Simulate different roles (e.g., data administrator, clinician) logging into the system. Verify if their respective permission settings are effective. For example, check if a data administrator can manage data sources and if a clinician can only view pre-screening results.
Note: The values provided are common starting points. Measure them against your own samples for optimal performance.
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.