Database and Operations for Structured Analysis of

Data Characteristics

Pharmacoeconomic research documents include Cost-Effectiveness Analysis (CEA), Cost-Utility Analysis (CUA), Cost-Minimization Analysis (CMA), and disease burden reports. Data sources are primarily drug registration applications from pharmaceutical companies, Health Technology Assessment (HTA) reports, clinical trial reports, and published academic papers. Documents are typically in PDF format, lengthy, and contain numerous charts, statistical data, economic model parameters, sensitivity analysis results, and multivariate regression tables. Key fields include drug cost, treatment effectiveness (QALYs, LYs), ICER (Incremental Cost-Effectiveness Ratio), discount rate, exchange rate, utility values, and probability distribution parameters. Data update frequency is relatively low, mainly occurring with new drug launches, expanded indications, or adjustments to medical insurance catalogs, typically annually or biennially.

Constraints on Database and Operations

The characteristics of pharmacoeconomic documents impose specific requirements on database and operations. Document complexity and numerous charts make traditional text parsing prone to losing critical information. This necessitates intelligent structured parsing capabilities to accurately extract core economic indicators like ICER and QALY, along with their confidence intervals. Documents are often large (tens of MB) and contain complex tables and nested structures. This requires high-concurrency processing for file uploads and parsing, and proper configuration of timeout mechanisms to prevent failures due to large files or lengthy parsing times. Key economic parameters such as discount rate and exchange rate are time-sensitive. The database must support version management and timestamps to ensure retrieval results are based on the latest or specific historical data. Finally, sensitive commercial data and intellectual property are involved. This demands high requirements for domestic database alternatives and data isolation capabilities, requiring consideration of compatibility with domestic databases or private deployment solutions.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	Pharmacoeconomic PDF documents often contain many charts and are large; large file uploads must be supported.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Complex document parsing takes a long time; avoid parsing failures due to timeouts.
`Chunk size`	`800–1200 characters`	Balances context completeness and retrieval efficiency, ensuring continuity of economic model descriptions.
`Recall count`	`10–15 entries`	Ensures coverage of highly relevant economic indicators and analysis results.
`Similarity threshold`	Calibrated based on actual measurements	Ensures retrieval accuracy and avoids interference from irrelevant segments.
`Rerank result count`	`5 entries`	Prioritizes displaying the most relevant core economic conclusions after reranking.

Common Mistakes

Uploading large PDF documents results in timeout of 360000ms exceeded: This indicates file parsing exceeded the system's default timeout limit. Increase the PARSE_FILE_TIMEOUT_SECONDS parameter.
Retrieval results show ICER or QALY as empty or missing: Structured document parsing failed to accurately identify or extract key values from tables. The parsing model's ability to recognize specific tables or figures may need optimization.
System response slows down or crashes during concurrent uploads or queries: The database connection pool is too small, or server resources are insufficient to handle high-concurrency requests. Adjust database configuration or scale up resources based on actual load.

Verification

Upload and successfully parse multiple typical pharmacoeconomic PDF documents. Check parsing logs for no timeouts or error messages.
For parsed documents, query fields like ICER, QALY, and discount rate using keywords. Verify the accuracy and completeness of the extracted content.
Use the system's API to simulate high-concurrency queries. Monitor database and server resource usage to ensure system stability under high load.

The 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.