Data Characteristics
Telemedicine registration and declaration data originate from various sources. These include regulatory documents, clinical trial reports, ethics review approvals, medical device registration certificates, and software test reports. Documents typically exist as PDFs, Word files, or scanned images. Content structures are complex, containing large amounts of unstructured text, nested tables, charts, and images. Regulatory documents may be revised annually or new versions released irregularly. Internal clinical data and technical documentation iterate continuously with R&D progress. Fields and units involve medical terminology, measurement units (e.g., mg/dL, mmHg), specific codes (e.g., ICD-10, SNOMED CT), and regulatory numbers. Accuracy requirements are high.
Constraints Imposed by These Characteristics on Document Parsing and Chunking
The complexity of telemedicine registration and declaration data presents specific challenges for document parsing and chunking. Diverse and heterogeneous document formats require parsers with robust compatibility. Optical Character Recognition (OCR) accuracy is critical for scanned documents to prevent loss of key information. The presence of nested tables and charts means simple text chunking strategies are insufficient to maintain content integrity. Support for table structure recognition and chart description extraction is necessary. Frequent updates to regulatory documents necessitate regular incremental updates to the knowledge base. Chunking strategies must efficiently identify and update changed sections, avoiding duplicate storage and invalid indexing. Furthermore, recognizing medical terminology and specific codes demands advanced tokenization and entity extraction algorithms to ensure subsequent retrieval accuracy.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 800–1200 characters | Ensures individual chunks contain sufficient context while avoiding information overload. Balances the integrity of regulatory clauses with the detail of clinical reports. |
Chunk overlap | 50–100 characters | Preserves contextual continuity, especially when processing regulatory clauses spanning multiple pages or paragraphs. Helps maintain semantic coherence. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large clinical trial reports or scanned documents with numerous images and tables can be time-consuming. |
UPLOAD_FILE_MAX_SIZE | 500 MB | A single declaration file may contain multiple attachments or high-resolution images. This ensures large file uploads proceed without issues. |
EnabledOCR | Yes | A large volume of declaration materials are scanned documents. Enabling OCR is a prerequisite for obtaining text content and ensures no information is missed. |
Table Recognition Strategy | Structured | Declaration materials contain numerous tables. Structured recognition effectively extracts table content and converts it into a retrievable format. |
Common Pitfalls
- Parsing timeouts or partial document content missing. This occurs when
PARSE_FILE_TIMEOUT_SECONDSis set too low, failing to process oversized files or complex document structures. - Incomplete or chaotic table information in retrieval results. This happens when
Table Recognition Strategyis not enabled or incorrectly configured, leading to table content being treated as plain text during chunking. - Older versions of relevant regulatory clauses are recalled after a knowledge base update. This is due to a lack of effective management for document version numbers or update dates, resulting in coexistence of old and new knowledge.
How to Verify Configuration
- Upload a telemedicine registration and declaration document containing complex tables and scanned content. Check if the parsed chunks completely retain table structures and text from images.
- Retrieve specific regulatory clauses. Verify that the recalled chunks contain the latest version of the clause and that its context is coherent.
- Simulate parsing a large clinical report. Observe logs for timeout errors and check if parsing time falls within the
PARSE_FILE_TIMEOUT_SECONDSrange.
The values provided are common starting points. Measure them against your own samples to determine optimal settings.
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.