Pharmacoeconomics and Pharmacovigilance: Deployment and

Data Characteristics

Pharmacoeconomic data originates from real-world evidence (RWE) studies, clinical trials, medical claims databases, drug registration databases, and regulatory adverse event reporting systems. Data update frequencies vary. Some clinical trial data releases are periodic, while adverse drug reaction (ADR) data may update in real-time or daily. Document structures are diverse, including structured database records, semi-structured medical reports (e.g., HL7 CDA), and unstructured academic papers and regulatory documents. Fields and units are highly specific. For example, cost data may be in USD/year or EUR/course, utility data in QALY (Quality-Adjusted Life Year) or LYG (Life Year Gained). Drug names use International Nonproprietary Names (INN) or brand names, involving critical medical fields like dosage, frequency, and route.

Constraints on Deployment and Upgrade

The complexity of pharmacoeconomic data sources requires FastGPT to configure multi-source data connectors during deployment to integrate data streams from different systems. Varying update frequencies, especially for real-time ADR data, necessitate an incremental update mechanism for the knowledge base. This mechanism must support high-frequency, small-batch data ingestion to avoid full rebuilds. Diverse document structures demand robust document parsers capable of handling various formats like structured CSV/JSON, semi-structured XML/CDA, and unstructured PDF/DOCX, while accurately extracting key information. The specificity of fields and units requires precise entity recognition and relationship extraction for medical and economic concepts during knowledge base construction. Examples include identifying treatment cost and QALY value and ensuring correct association of values with units. This directly impacts the accuracy of subsequent RAG retrieval and the professionalism of generated answers.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Pharmacoeconomic reports and clinical trial documents are often large, requiring support for a high single-file upload limit.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDFs or complex XML files can take a long time. This prevents interruptions due to timeouts.
`Chunk size` (Segment Length)	`800–1200 characters`	Ensures each segment contains sufficient context to understand economic models or clinical study details without being excessively long.
`Recall count` (Recall Count)	`Top 10 entries` (Top 10)	Pharmacoeconomic questions often require synthesizing multiple pieces of evidence. Increasing recall quantity can improve relevance.
`Similarity threshold` (Similarity Threshold)	`0.75`	Pharmacovigilance data demands high accuracy. A higher threshold can reduce irrelevant recall.
`Rerank result count` (Reranked Return Count)	`5 entries` (5 items)	Pharmacoeconomic evaluations typically require comparing the most relevant studies or reports.

Common Pitfalls

Symptom: The model's answers to QALY or ICER-related questions lack numerical values or calculation logic. Cause: The document parser failed to correctly identify and extract key numerical fields and units from semi-structured reports.
Symptom: After a knowledge base update, newly entered adverse drug reaction data does not appear in search results. Cause: The incremental update mechanism is not configured correctly, or scheduled tasks are not executing as expected, preventing real-time data synchronization.
Symptom: After local deployment, accessing the FastGPT login interface shows a Database connection error. Cause: Database configurations (e.g., DB_HOST, DB_PORT) are incorrect, or the database service is not running.

Verification Steps

Import a test set containing pharmacoeconomic evaluation reports and ADR data. Verify that the knowledge base correctly identifies and stores key entities such as QALY值, 治疗成本, and Drug Name.
Simulate a new batch upload of ADR data. Observe the knowledge base update logs and verify that the latest data is retrievable.
Test file uploads using pharmacoeconomic documents in different formats (e.g., PDF, XML, CSV) to confirm the parser handles them without errors.
Pose complex calculation questions (e.g., cost-benefit analysis) related to pharmacoeconomics to FastGPT. Check if the answers cite numerical values and methodologies from relevant documents.

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.