Data Characteristics
Pharmacoeconomics data primarily comes from policy documents published by national healthcare security administrations and health commissions, drug centralized procurement results, clinical trial reports, and various pharmacoeconomics evaluation guidelines. These documents update annually or are revised irregularly, such as adjustments to the "National Basic Medical Insurance, Work-Related Injury Insurance, and Maternity Insurance Drug Catalog." Document formats are often official PDF files, draft guidelines in Word, or drug price lists in Excel. Files frequently contain numerous tables, charts, and complex legal and regulatory clauses. Key fields include generic drug name, dosage form, specification, medical insurance payment standard, indications, reimbursement ratio, evaluation methodology, and sensitivity analysis results. Units involve monetary amounts (Yuan), quantities (boxes/tablets), time (years/months), and percentages (%). Multilingual abbreviations are common.
Constraints on "Source and Traceability"
The formal nature of pharmacoeconomics policies and guidelines demands accurate citation of sources, requiring direct links to original documents. Most documents are complex PDFs. This requires the knowledge base to have robust PDF parsing capabilities. It must accurately extract text and tabular data while preserving original layout information to prevent information loss or misalignment. The uncertain update frequency means the knowledge base needs flexible manual updates and incremental synchronization to capture policy changes promptly. Multilingual abbreviations and specialized terminology necessitate advanced semantic understanding and query expansion capabilities. The strictness of regulatory Q&A requires answers to provide clear original text snippets with traceability to specific clauses or tables, ensuring verifiable information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 3000 tokens | Accommodates complex policy text length, ensuring sufficient context for understanding. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Balances semantic completeness and search efficiency, avoiding redundant information in long paragraphs. |
Recall count (Retrieval Count) | Top 5 entries (top 5) | Improves retrieval accuracy, reduces irrelevant information interference, and focuses on key policy content. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Ensures semantic relevance of retrieved content, filtering out ambiguous matches. |
Original File Link | Enabled (enabled) | Forces the inclusion of direct access paths to original policy documents in answers. |
Knowledge base searchCitation limit (Knowledge Base Search Reference Limit) | 10000 characters (characters) | Ensures sufficiently long policy text snippets can be referenced within the workflow. |
Common Pitfalls
- Answers fail to provide direct links to original policy documents, preventing users from verifying information sources. This occurs if the
Original File Linkoption in the knowledge base configuration is not enabled or if the URL is not correctly extracted during file parsing. - AI answers show discrepancies in critical figures like drug reimbursement ratios or payment standards, conflicting with the original policy text. This happens if the knowledge base segmentation strategy splits table data or if the parser fails to correctly identify numerical fields within table structures.
- When a user queries "medical insurance payment standard for a certain drug," the system's retrieved snippet does not fully match the user's intent, instead providing descriptions of other aspects of the drug. This occurs if the semantic similarity threshold is set too low, leading to the retrieval of semantically weakly related document fragments.
Verification Steps
- Select several typical pharmacoeconomics policy questions. Verify that answers accurately cite key clauses and figures from the original text and that links correctly navigate to the corresponding sections of the original document.
- Upload policy files containing complex tables. Check if the knowledge base's parsed content fully retains table structures and data, focusing on fields like drug names, prices, and reimbursement ratios.
- Simulate a policy update scenario. After updating parts of a policy document, observe if the knowledge base synchronizes updates promptly and if questions regarding the new policy yield answers based on the latest information.
- Test queries containing specialized terminology and abbreviations. Ensure the system correctly understands the query intent and retrieves high-quality citation snippets from relevant documents. Compare the
similarityscore of the retrieved snippets with the original text.
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.