Data Characteristics
Remote healthcare registration documents primarily include medical device registration certificates, service qualification licenses, clinical trial reports, user manuals, safety assessment reports, software validation files, and regulatory compliance statements. Data sources are diverse, involving the National Medical Products Administration (NMPA), provincial and municipal health commissions, industry associations, and third-party testing organizations. Document update frequencies vary; regulatory documents typically undergo annual revisions or new releases, while clinical data and safety reports may be supplemented based on product iterations or adverse event feedback.
Document structures are often formal reports in PDF format, detailed descriptions in Word format, statistical data in Excel format, and some image and video files. Core fields include Product Name, Registration Number, Scope of Application, Performance Indicators, Software Version, Test Report Number, Clinical Trial Approval Number, and Adverse Event Number. Units include mm, Hz, dB, milliseconds, and times/minute.
Constraints on Citation and Traceability
The strong regulatory constraints of remote healthcare declaration documents demand precise citation sources and complete traceability. Documents are often lengthy formal reports. Segmenting them too short can lead to semantic loss, while segmenting too long affects retrieval accuracy.
Regulatory updates require knowledge base version management to ensure citations use the latest effective text while retaining historical versions for comparison. Multiple institutional data sources require effective differentiation and access control for documents from different sources. This prevents cross-referencing or citing unofficial documents without legal effect.
Technical specifications contain numerous specialized terms and units of measurement. The model must accurately identify and associate these to avoid citation errors due to unit confusion. Furthermore, understanding charts and data in clinical trial and safety assessment reports requires combining them with text descriptions, which challenges the completeness of cited content.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Segment Length | 500–800 characters | Balances semantic completeness of long documents with retrieval granularity, reducing truncation risk. |
Segment Overlap Length | 50–100 characters | Ensures contextual continuity, preventing critical information from being split across segments. |
Recall Count | Top 8–12 | Covers multiple sources, improves relevance recall rate, and reduces omissions. |
Similarity Threshold | 0.78–0.85 | Excludes low-relevance content, enhances citation accuracy, and reduces miscitations. |
Rerank Return Count | Top 3–5 | Focuses on the most relevant content, optimizes final presentation, and improves readability. |
Enable Document Versioning | Yes | Ensures cited regulations and technical standards are the latest effective versions and supports historical traceability. |
Common Mistakes
- Symptom: The model's output citation source is empty or prompts "No relevant citation found." Reason: Knowledge base segment length is improperly set, leading to truncated or semantically incomplete key information, preventing retrieval matching.
- Symptom: Cited content has low relevance to the question, or includes information about other products or services. Reason: The
Similarity Thresholdis set too low, failing to effectively filter out irrelevant recall results. - Symptom: The system prompts
KnowledgeBaseError: Invalid document ID. Reason: Citation variables or knowledge base IDs were not updated after document updates or deletions, leading to pointers to non-existent resources.
Verification Steps
- For typical declaration questions, verify that the model's output citation sources point to the correct regulatory clauses, technical report sections, or clinical data tables.
- Simulate a regulatory update scenario. After uploading a new document version, check if the model's citations automatically switch to the latest version and if historical document citation records are retrievable.
- Randomly select specialized terms and units of measurement from multiple declaration documents. Test if the model can accurately identify and cite the original text containing these terms.
- Check if the citation source's file name and page number precisely correspond to the original document content. This ensures a complete and verifiable traceability chain.
Note: The values provided are common starting points and should be measured against specific 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.