Data Characteristics
Healthcare reimbursement registration and declaration documents typically include policy files, medical service catalogs, fee schedules, audit rules, expense details, settlement reports, and approval documents. These data primarily originate from official websites of national and provincial healthcare security administrations, information systems of medical institutions, and internal financial systems. Policy files and catalogs update frequently, usually quarterly or annually, with occasional ad-hoc updates for major policy changes. Expense details and settlement reports are often generated monthly or quarterly. Document structures are predominantly PDF, Word, and Excel. PDF is common for policy files and approval letters, while Excel is used for detailed data and reports. Fields and units for monetary values are typically precise to the cent. Quantity units vary, including "times," "person-times," "items," and "units," and strictly adhere to healthcare coding standards.
Constraints on Deployment and Upgrade
The update frequency and diverse data formats of healthcare reimbursement documents necessitate continuous integration and automation for FastGPT deployment. Large volumes of PDF documents require efficient text extraction and structured processing to ensure comprehensive knowledge base content. Numerical data and coded fields in Excel demand accurate recognition and indexing capabilities to prevent recall inaccuracies due to parsing errors. Furthermore, the regional and temporal nature of healthcare policies requires the knowledge base to support parallel maintenance of multiple versions and regions. Upgrades must allow for seamless switching or merging to avoid confusion between old rules and new policies. Dispersed data sources demand high stability and compatibility from data synchronization connectors to ensure uninterrupted data flow during deployment and upgrade.
Configuration Strategy
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 200 MB | Individual healthcare policy documents or large reports can be substantial. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | PDF file parsing is complex, especially when involving charts and tables. |
Chunk size | 800–1200 characters | Healthcare policy clauses are logically dense; context integrity must be maintained. |
Similarity threshold | 0.75 | Ensures high relevance between recalled healthcare clauses and the query intent. |
maxContext | 16 | Addresses complex logical relationships and multiple conditions in healthcare policy queries. |
Recall count | Top 10 entries | Covers potentially multiple relevant clauses in healthcare policies. |
Common Pitfalls
- Query results still include expired healthcare policy clauses after a knowledge base update. This occurs because the knowledge base index was not rebuilt promptly or old data was not effectively purged.
- Uploaded Excel settlement detail data fails to accurately match specific amounts or codes during searches. This manifests as empty field values or low match accuracy, due to the file parser failing to correctly identify data types for all numerical or coded columns.
- After upgrading the system to V4.12.3, custom plugins exhibit flickering or delayed display of results when handling download addresses. This occurs because the plugin code is not fully compatible with the new version's frontend rendering mechanism, or the handling of asynchronous operations has changed.
Verification Steps
- Upload the latest healthcare policy PDF file and verify that its text content is fully extracted and searchable.
- Upload an Excel settlement data file containing complex amounts and codes. Use precise queries to verify that specific amounts and codes accurately recall corresponding entries.
- Query key clauses of both new and old versions of healthcare policies in the knowledge base. Confirm that query results only include currently effective policy content and do not mix in outdated information.
- Simulate multiple users concurrently accessing and editing the knowledge base. Verify that the collaborative sharing mode functions correctly and data consistency is maintained.
These values 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.