Knowledge Base Retrieval for Medical Insurance Access and

Data Characteristics

Medical insurance access and pharmacovigilance data originates from documents published by the National Healthcare Security Administration and provincial medical security departments. It also includes drug centralized procurement announcements, medical insurance negotiation results, drug inserts, and clinical guidelines. Data updates are typically quarterly or annually. Non-periodic updates occur with major policy changes or negotiation results. Document structures vary: policy texts are often PDFs, negotiation agreements are Word documents, and drug lists and reimbursement scopes are Excel files. Key fields include generic drug name, brand name, medical insurance payment standard, payment scope, restrictions, indications, adverse reaction monitoring requirements, and clinical usage pathways. Units include currency (Yuan), quantity (boxes/syringes), time (years/months), and percentages (%).

Constraints on Knowledge Base Retrieval and Recall

The diverse document structures of medical insurance access data require robust document parsing capabilities, especially for structured extraction of tables and complex paragraphs from PDFs. The relatively fixed update frequency necessitates an efficient incremental update mechanism to ensure information timeliness. Policy documents often contain legal and medical terminology. This demands a vector model with high-precision semantic understanding to avoid ambiguity. Numerical information, such as payment standards and restrictions, requires support for exact matching and range queries during retrieval (e.g., querying drugs within a specific price range). Adverse reaction monitoring requirements are typically free-text descriptions. The model must accurately extract key information points from long texts and link them to specific drugs.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size`	`800–1200 characters`	Medical insurance policy texts have long paragraphs with multiple conditions; this length ensures semantic completeness.
`Overlap Length`	`100–200 characters`	Maintains contextual continuity between segments and handles key information spanning multiple paragraphs.
`Recall count`	`Top 8–12 entries`	Policy documents are complex; recalling more relevant segments covers all potential associations.
`Similarity threshold`	Calibrate by testing	Balances recall and precision, optimized for medical insurance terminology.
`Rerank result count`	`Top 5 entries`	Further filters results to improve precision and relevance.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing large PDF and Excel files may require longer parsing times.

Common Pitfalls

Symptom: Retrieval results lack key medical insurance payment scopes or restrictions. Reason: Chunk size is set too small, truncating text containing complete payment condition descriptions and affecting semantic integrity.
Symptom: embedding rate limit exceeded error during knowledge base vectorization. Reason: The number of concurrently processed documents exceeds the request rate limit of the Embedding service provider.
Symptom: Uploaded Excel drug lists fail to parse, unable to extract table data. Reason: The file parser has insufficient support for complex merged cells or special character encodings.

Validation Steps

Select typical medical insurance access policy documents. Input queries related to payment standards, restrictions, and adverse reaction monitoring. Verify that the returned results contain all necessary information.
Test uploading Excel drug list files of varying complexity. Check if key fields like generic drug name, payment price, and reimbursement scope are correctly extracted.
Perform incremental update tests on the knowledge base. Verify that newly published medical insurance negotiation result files are quickly indexed and retrievable.
Monitor knowledge base retrieval response times. Ensure acceptable latency under concurrent query pressure.

The values provided are common starting points. Measure them 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.