Multi-Turn Conversations and Prompts for Structured Analysis

Data Characteristics in this Category

Pharmacoeconomics research uses diverse data sources: clinical trial reports, real-world evidence (RWE), healthcare resource utilization data, drug price databases, literature reviews, and expert interview records. Data update frequencies vary. Clinical guidelines and drug formularies may update annually, while RWE data accumulates continuously. Document formats include structured databases, semi-structured reports (e.g., PDF health technology assessment reports, economic model reports), and unstructured text (e.g., research protocols, meeting minutes). Fields and units are highly specialized. For example, the Incremental Cost-Effectiveness Ratio (ICER) is priced in "USD/Quality-Adjusted Life Year (QALY)." The "discount rate" is typically a percentage. Often, sensitivity analysis parameters, cost components, and effect indicators are involved.

Constraints Imposed by these Characteristics on Multi-Turn Conversations and Prompts

The specialized and data-intensive nature of pharmacoeconomics documents demands high accuracy for multi-turn conversations and precision for prompts. Documents contain numerous specialized terms and abbreviations. The model must accurately identify and understand context, avoiding ambiguity in multi-turn interactions. For example, extracting ICER values requires identifying the numerical value and associating it with the corresponding intervention, comparator, and utility unit. Varying data update frequencies mean the knowledge base needs version management capabilities to ensure conversations use the latest or specified data version. The presence of semi-structured documents requires more complex parsing logic for information extraction, potentially involving table and chart content recognition. Furthermore, specific field units and calculation logic (e.g., the impact of discount rates on future cost-effectiveness) require prompts to guide the model in correct numerical inference and interpretation.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`500–800 characters`	Pharmacoeconomics reports often contain dense information and complex logical chains. Shorter segments may break key information; longer segments introduce too much noise.
`Recall count` (Recall Count)	`8–12 items`	Ensures coverage of multiple relevant data points and argumentation fragments in complex queries, supporting multi-turn reasoning.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Domain-specific terms have high similarity. A higher threshold is needed to filter the most relevant items and avoid semantic generalization.
`maxContext`	`32000 tokens`	Pharmacoeconomics analysis often requires integrating information from multiple documents. A longer context window supports deeper multi-turn reasoning.
`Rerank result count` (Reranked Return Count)	`5 items`	Prioritizes the most relevant and reranked key information, improving user efficiency in obtaining core conclusions.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDF reports or documents with complex tables can be time-consuming. Sufficient time must be allocated.

Three Common Pitfalls

Symptom: The model provides inconsistent explanations for ICER values or QALYs in multi-turn conversations. Reason: The knowledge base contains different versions or calculation methods for the same concept, without effective differentiation or prioritization.
Symptom: After parsing an uploaded Excel file, the model cannot answer questions about the table data within it. Reason: The xlsx file parser configuration is insufficient. It fails to correctly identify and extract structured table data, resulting in content not being effectively indexed.
Symptom: AI response annotations in the conversation component trigger only after the stream response completes, leading to long user wait times. Reason: Under default configuration, content annotation generation depends on the complete response. Consider optimizing for parallel processing or pre-generation.

How to Confirm Proper Configuration

Select a report containing key economic indicators (e.g., ICER, QALY, cost composition). Ask multi-turn questions to check if the model can accurately extract and interpret values while maintaining contextual consistency.
Upload a pharmacoeconomics analysis PDF document with complex tables and charts. Ask about specific data points within the tables or charts. Confirm the model can correctly identify and cite relevant content.
For different versions of the same pharmacoeconomics assessment in the knowledge base, query specific years or version information. Verify the model retrieves the correct data based on version guidance.

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.