Data Characteristics in This Domain
Pharmacoeconomics research data primarily comes from clinical trial reports, real-world evidence (RWE) databases, health insurance payment standards, centralized drug procurement results, and guidelines and reports from various Health Technology Assessment (HTA) agencies. Document update frequencies vary. Clinical trial data typically releases after study completion. Health insurance payment standards and procurement results adjust annually or semi-annually. Document structures usually include an introduction, methodology, results, discussion, and conclusion. The methodology section details model construction, parameter selection, and sensitivity analysis. Fields involve drug costs (in CNY or USD), efficacy indicators (e.g., QALY, DALY), and various probabilities (e.g., disease incidence, adverse event rates). Units and measurement methods are rigorous and diverse.
Constraints Imposed by These Characteristics on Workflow Orchestration
Pharmacoeconomics document data characteristics impose specific requirements on workflow orchestration. First, diverse data sources mean the workflow must support importing and processing multi-source heterogeneous documents, including structured data tables and unstructured text. Second, varying update frequencies require flexible knowledge base update strategies, such as periodic automatic synchronization for health insurance payment standards. Rigorous fields and units in documents demand highly precise entity recognition and numerical parsing during knowledge extraction and information comparison. This prevents critical decision deviations due to unit confusion or misread values. Furthermore, numerous model parameters and sensitivity analysis results mean the workflow must identify and link logical relationships between different parameters. This supports constructing complex reasoning chains, ensuring the quality document's logical integrity and data accuracy.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters | Pharmacoeconomics documents have strong contextual relevance; longer segments maintain semantic integrity. |
Recall count (Recall Count) | Top 5–8 items | Ensures the model retrieves sufficient relevant evidence for complex reasoning, preventing information omission. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Balances recall accuracy and completeness; a higher threshold reduces interference from irrelevant information. |
Rerank result count (Reranked Return Count) | 3–5 items | Focuses on the most relevant key information, reduces model processing burden, and improves response speed. |
maxContext | 4000–6000 tokens | Accommodates the length and information density of pharmacoeconomics research reports, providing ample context. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Provides sufficient parsing time for large clinical trial reports or complex model documents. |
Three Common Pitfalls
- In multi-step workflows, subsequent steps fail to reference knowledge base retrieval results from preceding steps. This usually occurs due to incorrect global or local variable scope configuration, preventing proper variable passing.
- A knowledge base retrieval node sets a
datasetidglobal variable, but it does not take effect during execution. This often results from variable name typos, the variable not initializing at workflow start, or the variable being unexpectedly overwritten at a specific node. - Workflow execution times out, displaying
Workflow execution timed out. This can happen when parsing large documents or performing complex chained reasoning, and a single node's processing time exceeds the system's maximum allowed time.
How to Confirm Correct Configuration
- Test with various pharmacoeconomics document types (e.g., cost-effectiveness analysis reports, budget impact analysis reports). Observe if knowledge base recall content is accurate and comprehensive, especially for extracting key parameters and conclusions.
- At each critical workflow node, check variable values in the output logs. Confirm that global and local variable passing and assignment align with expectations, especially for key parameters like
datasetid. - For scenarios involving multi-step reasoning, verify that the final generated quality document or response's data references, logical deductions, and conclusions are highly consistent with the original document content. Check for factual errors or omissions.
The values given 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.