Model Integration and Configuration for Market Access

Data Characteristics

Market access data in biopharmaceuticals primarily originates from official sources. These include the National Medical Products Administration (NMPA), provincial and municipal medical insurance bureaus, and health commissions. Data types include regulations, policy documents, review guidelines, approval information, and medical insurance catalogs. Data updates frequently, especially during policy adjustments or peak new drug approval periods. Documents come in various formats: PDF for laws and regulations, Word for policy interpretations, and Excel for medical insurance payment standards or drug catalogs. These documents often have complex hierarchical structures, such as chapters, clauses, and annexes. They contain extensive specialized terminology, drug names, indications, payment scopes, reimbursement ratios, registration numbers, and manufacturers. Common units include currency (CNY), dates (year-month-day), and percentages (%).

Constraints Imposed by Data Characteristics on Model Integration and Configuration

The multi-source nature and high update frequency of market access data require flexible data source configuration and efficient synchronization mechanisms for timely information. Diverse document formats (PDF, Word, Excel) challenge file parsing capabilities. This necessitates support for multiple parsers to accurately extract both structured and unstructured information. Specialized terminology and complex fields, such as distinguishing between generic and brand drug names or specific descriptions of indications, demand refined entity recognition and relationship extraction during knowledge base construction. This prevents information confusion or omission. Numerical data, like medical insurance payment standards and reimbursement ratios, requires precise numerical understanding and calculation capabilities for accurate consultation responses. Furthermore, the iterative nature of regulations and policies requires effective version management and incremental updates in the knowledge base to reflect the latest policy changes.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Market access policy documents are often large, including charts and attachments.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDF or Word documents can be time-consuming; this prevents timeouts.
`Chunk size` (Segment Length)	`800–1200 characters`	Ensures completeness of policy clauses and avoids truncation of key information.
`Recall count` (Recall Count)	`Top 8 entries` (Top 8)	Market access queries typically require synthesizing information from multiple regulations or policies.
`Similarity threshold` (Similarity Threshold)	`0.78`	Policy texts require semantic precision; a higher threshold ensures recall relevance.
`Rerank result count` (Reranked Return Count)	`Top 5 entries` (Top 5)	After reranking, focus on the most relevant and authoritative policies or clauses.

Three Common Mistakes

Knowledge base query results deviate significantly from expectations, even returning irrelevant content. The model output cites irrelevant policy clauses. This occurs when Similarity threshold (Similarity Threshold) is set too low, leading to the recall of many irrelevant or generalized document segments.
Uploading large policy files results in a long wait or a "request error" message, with logs showing 504 Gateway Timeout. This happens when PARSE_FILE_TIMEOUT_SECONDS is set too low, or UPLOAD_FILE_MAX_SIZE restricts file uploads.
The model cannot accurately answer questions about medical insurance reimbursement ratios or specific drug registration numbers. The output lacks relevant numerical values or fields are empty. This is because the file parsing stage failed to correctly identify and extract numerical data from Excel tables or structured fields from PDFs.

How to Verify Configuration

Upload typical policy documents (e.g., the new National Medical Insurance Catalog, Drug Registration Administration Measures). Check if the files parse successfully and segment correctly, and if segment content maintains semantic integrity.
For specific drugs (e.g., an innovative drug), inquire about market access conditions, payment scope, and reimbursement ratios. Check if the model output accurately cites relevant clauses and data, and compare against original documents to confirm information accuracy.
Simulate a policy update scenario. Upload a new version of a policy document. Observe the knowledge base's ability to distinguish between old and new policies after the update. Use specific queries to verify if the model prioritizes citing the latest policy clauses.

The values provided are common starting points. Measure them against your 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.