Pharmacoeconomics Pharmacovigilance: Multi-turn

Data Characteristics

Pharmacoeconomic data originates from clinical trial reports, real-world evidence (RWE) studies, health insurance catalogs, drug procurement data, and adverse drug reaction (ADR) monitoring reports. Data update frequencies vary. Clinical trial data typically publishes after study completion, while ADR data may update in real-time or quarterly. Document structures are diverse. Examples include structured database records (e.g., ICD-10 codes, ATC classifications), semi-structured research reports (PDF, Word documents containing cost-effectiveness analyses, drug utilization evaluations), and unstructured free text (patient medical records, healthcare provider notes). Fields and units are highly specialized. Cost data, for instance, may involve multiple currency units like USD, EUR, or RMB. Health outcome indicators, such as Quality-Adjusted Life Years (QALY) or specific disease incidence rates, require strict differentiation.

Constraints on Multi-turn Conversations and Prompts

The diversity of pharmacoeconomic data challenges multi-turn conversation accuracy. First, multiple currencies and units require the model to perform unit conversion and dimension matching. Failure to do so can lead to incorrect cost-effectiveness calculations. Second, the mix of structured and unstructured data requires the RAG system to effectively extract key information from various document formats. For example, extracting a drug's annual treatment cost from a research report while linking its incidence rate from an ADR database. Different update frequencies necessitate knowledge base version management to ensure the conversation references the latest data. Medical terminology and abbreviations in free text require the model to possess domain-specific semantic understanding to avoid misinterpreting user intent.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`maxContext`	`2000 characters`	Pharmacoeconomic analysis often requires multi-dimensional data support. Maintain a sufficiently long context to understand complex cost-effectiveness models.
`Chunk Size`	`500 characters`	Ensure individual knowledge chunks contain enough context to understand cost, efficacy, and ADR associations.
`Recall Count`	`Top 8`	Pharmacoeconomic data is highly interconnected. Increasing the recall count improves the probability of retrieving relevant facts.
`Similarity Threshold`	`0.75`	Increase the threshold to ensure retrieved knowledge is highly relevant to pharmacoeconomic professional queries, reducing interference from generalized information.
`Rerank Count`	`Top 3`	Rerank the recalled knowledge to further improve the ranking of the most relevant information, focusing on core data.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing large clinical trial reports or ADR database files requires longer parsing times.

Common Pitfalls

Cost calculation errors or unit confusion appear in conversations. This occurs because the model fails to correctly identify currency or measurement units from different data sources.
After a user uploads an xlsx file containing pharmacoeconomic data, the AI cannot engage in conversation. This happens because the UPLOAD_FILE_MAX_SIZE parameter is too small, causing file upload failure, or PARSE_FILE_TIMEOUT_SECONDS is insufficient to process large tables.
After multi-turn conversations, the model cannot link to specific drugs or ADR events mentioned in earlier dialogue. This occurs because maxContext is set too short, leading to key information truncation.

Verification Steps

Upload a pharmacoeconomic research report PDF containing multiple currencies and health outcome indicators. Ask questions about the cost-effectiveness analysis results. Check if the model accurately identifies and provides results.
Simulate a user asking about a specific drug's ADR incidence rate and related treatment costs. Verify if the data returned by the model aligns with the latest version of the ADR monitoring report and health insurance payment standards.
Conduct a long conversation test. Gradually delve into a drug's long-term economic impact and safety data. Observe if the model maintains contextual coherence and continues to provide relevant information.

Note: The values provided are common starting points. Measure them against specific 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.