Data Characteristics in This Category
Pharmacoeconomics data comes from clinical trial reports, real-world evidence (RWE) studies, medical insurance reimbursement policies, drug price databases, and Health Technology Assessment (HTA) agency reports. Data update frequencies vary. Clinical trial data typically releases with study progress. Drug prices and medical insurance policies may adjust quarterly or annually. Document formats are diverse. They include structured database records, unstructured research papers, PDF reports, and government documents. Key fields include drug name, indication, efficacy metrics (e.g., QALY, LYG), costs (procurement, management, complication treatment), utility values, and related sensitivity analysis parameters. Cost data typically uses local currency units. Inflation and discount rates require consideration.
Constraints from These Characteristics on "Deployment and Upgrade"
Pharmacoeconomics data complexity and varied update frequencies require flexible data ingestion pipeline configuration during FastGPT deployment. This pipeline must adapt to multiple document formats and data sources. Unstructured documents need enhanced text parsing capabilities to ensure accurate key field extraction. Currency units and time value in cost data mean knowledge base construction must consider semantic understanding and unit conversion for numerical data. High-frequency updates for medical insurance policies and drug prices demand robust incremental updates and version management for the knowledge base. This avoids outdated data interfering with retrieval results. During upgrades, the model's understanding of these specific fields must remain compatible. It must also handle minor changes in data structure or field definitions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Pharmacoeconomics reports often contain many charts and appendices, leading to large file sizes. |
maxContext | 3000 characters | Ensures capture of the complete context for complex economic models and arguments. |
Chunk size | 800–1200 characters | Balances context completeness and retrieval efficiency, preventing critical arguments from being split. |
Similarity threshold | 0.75 | Pharmacoeconomics concepts are rigorous; a higher threshold ensures retrieval result precision. |
Rerank result count | Top 5 entries | Prioritizes a small number of high-quality results validated by the reranking model, improving accuracy. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing large PDF reports and complex tables requires longer parsing times. |
Three Common Mistakes
- Retrieval results show a large amount of outdated or incorrect data after a knowledge base update. This usually happens when the incremental update mechanism is misconfigured, leading to mixed new and old data or old data not being effectively marked.
- Retrieval recall for specific pharmacoeconomics terms significantly decreases after a FastGPT version upgrade. The reason might be a change in the embedding vectors for domain-specific vocabulary in the new model version, which is incompatible with the existing knowledge base's vector space.
- Chinese content appears as garbled characters when importing CSV-formatted drug price or cost-effectiveness data. This usually occurs because the CSV file's encoding format (e.g.,
GBK) does not match FastGPT's defaultUTF-8encoding.
How to Confirm Correct Configuration
- Upload typical pharmacoeconomics reports (PDF, Word formats). Check if text content parses completely and if key fields (e.g., cost, utility value) extract correctly.
- For medical insurance policies or drug price data, perform time-constrained queries. Verify the timeliness and accuracy of retrieval results.
- Use queries containing specific economic terms (e.g., "incremental cost-effectiveness ratio," "discount rate"). Check if relevant documents are included in the recall results and evaluate their relevance.
- Import cost data containing various currency units and numerical values. Verify the system's ability to recognize and process numerical information and its units.
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.