Data Characteristics
Pharmacoeconomics research documents primarily include cost-benefit, cost-effectiveness, cost-minimization, and cost-utility analysis reports. Data sources are typically official reports from Health Technology Assessment (HTA) agencies worldwide, clinical trial data reports, real-world evidence (RWE) studies, and pharmaceutical companies' internal market access strategy documents. These documents have a relatively low update frequency, usually released with new drug launches, expanded indications, or national healthcare policy adjustments. The annual update volume is small. Document structure is highly standardized, typically including an abstract, background, methodology (model building, parameter selection, sensitivity analysis), results, discussion, and conclusions. Data fields involve drug prices, treatment costs, disease burden, Quality-Adjusted Life Years (QALYs), and Incremental Cost-Effectiveness Ratios (ICER). Units are mainly currency (USD, EUR, RMB), time (years, months), ratios (%), and utility values (QALY).
Constraints on Deployment and Upgrade
The standardized structure and specific field requirements of pharmacoeconomics documents necessitate a focus on the accuracy of parsing models and the rigor of field mapping during deployment. Due to the infrequent document updates, initial deployment involves a relatively large amount of data cleaning and preprocessing. Subsequent incremental updates require less effort. Documents contain numerous tables and charts, demanding high-quality PDF or image parsing capabilities, especially for extracting complex model parameters and sensitivity analysis results. The presence of specific terminology and units requires the model to have a high level of domain expertise in semantic understanding to avoid confusion. Deployment requires reserving sufficient storage space for raw documents and structured data, considering potential future report types. Upgrades primarily focus on the model's adaptability to new report structures, new fields, and improved parsing efficiency.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Pharmacoeconomics reports are often lengthy, containing complex charts and tables, requiring more parsing time. |
Chunk size (Segment Length) | 800–1200 characters | Ensures each segment contains sufficient contextual information to understand complex economic arguments. |
Overlap Length | 150 characters | Guarantees semantic coherence between segments, preventing critical information from being truncated. |
maxContext | 8192 | Provides an ample context window for in-depth analysis and reasoning when handling complex queries. |
Similarity threshold (Similarity Threshold) | 0.75 | Improves retrieval accuracy, reduces interference from irrelevant information, and matches specialized terminology. |
UPLOAD_FILE_MAX_SIZE | 100 MB | Reports may contain numerous embedded charts and high-resolution images, leading to large file sizes. |
Three Common Mistakes
- Issue: Extracted ICER or QALY values are empty or malformed. Reason: The parser fails to correctly identify the numerical format in specific tables or text sections of the report, leading to ineffective data cleaning.
- Issue: MongoDB connection fails, and logs show
authentication failed. Reason: TheMONGODB_URIconfiguration has incorrect database name, username, or password, or permission settings are wrong, preventing FastGPT from establishing a connection. - Issue: The text content extraction component in the workflow cannot retrieve data from references. Reason: The connection logic between the knowledge base references and the text content extraction component is misconfigured. The component expects an input format that does not match the actual received reference data format.
How to Verify Configuration
- Upload a typical pharmacoeconomics report. Check the structured parsing results and verify if key fields (e.g., ICER, QALY, cost) are accurately extracted and correctly formatted.
- Execute a workflow with complex queries. Verify if it can provide accurate and professional answers based on the report content, especially for questions involving model parameters and sensitivity analysis.
- Check system logs to ensure no persistent database connection errors or file parsing timeout warnings.
- Confirm through the FastGPT interface that the number and content of segments in the knowledge base meet expectations, with no large number of empty or invalid segments.
Note: 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.