Data Characteristics
Hospital operations policy data originates from internal management systems, regulatory documents, operating manuals, and quality management files. This data has a relatively low update frequency, typically updated every few months to several years, coinciding with policy adjustments, process optimizations, or new technology introductions. Document formats are primarily PDF, Word, or scanned images, containing significant amounts of unstructured text, charts, and flowcharts. Data fields include department names, policy numbers, publication dates, revision histories, scope, specific operating procedures, responsible parties, and oversight mechanisms. Units are typically dates, department names, or operational sequences.
Constraints Imposed by Data Characteristics on Model Integration and Configuration
The low update frequency of hospital operations policies means less frequent model training and knowledge base refreshes. However, initial data cleaning and preprocessing require substantial effort. Diverse document formats necessitate robust document parsing capabilities, especially for unstructured text and embedded charts. Complex fields, such as policy numbers and revision histories, require precise entity recognition and relationship extraction to support structured queries. Information like operating procedures and responsible parties demands that the model understand process logic and causal relationships. The presence of scanned documents increases the difficulty of OCR recognition and post-processing, directly impacting text content accuracy.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 800–1200 characters | Balances policy completeness with model context processing capabilities. |
Overlap Length | 100 characters | Ensures semantic continuity between paragraphs, preventing key information from being cut off. |
Recall count | Top 8 | Covers sufficient relevant policy clauses, improving answer accuracy. |
Similarity threshold | 0.75–0.85 | Filters out irrelevant recall results, improving recall quality. |
maxContext | 4000 characters | Accommodates the paragraph length of most policy documents, reducing truncation. |
PARSE_FILE_TIMEOUT | 600 seconds | Handles the parsing time for large PDF or Word documents. |
Common Pitfalls
- Symptom: Channel link returns
undefined is not valid json. Reason: A node in the workflow outputs an unexpected format, causing subsequent nodes to fail parsing. - Symptom: Critical policy clauses are missing or inaccurate in model responses. Reason: Document parsing failed to correctly extract text from charts or scanned images.
- Symptom: System response is slow, especially during peak hours. Reason: The underlying API model's concurrent request limit is set too low, unable to support multiple simultaneous users.
Verification of Configuration
- Upload typical policy documents. Check if segmented content in the knowledge base is complete and without obvious semantic breaks.
- Ask questions about complex policy processes. Verify if the model can accurately identify key steps, responsible parties, and relevant clauses.
- Simulate multi-user concurrent queries. Observe if system response time is stable, without significant delays or errors.
- Check logs. Confirm that the document parsing process has no abnormal errors and that key fields are extracted correctly.
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.