Data Characteristics in this Category
Pharmacoeconomics research data comes from various sources. These typically include clinical trial data, real-world data (RWD), literature search results, cost-effectiveness analysis model parameters, and policy and regulatory documents. Data update frequencies vary. Clinical trial data usually stabilizes after study completion, while real-world data may update quarterly or annually. Document types cover research reports, statistical analysis plans, model specifications, drug inserts, medical insurance catalogs, and pricing documents. Formats are often PDF, Word, Excel, or CSV. Fields involve drug efficacy, adverse event rates, treatment costs, disease burden, and quality of life (QALYs, DALYs). Units include patient numbers, percentages, monetary units (e.g., USD, RMB), time units (years, months), and utility values. Some data also includes specific national or regional medical coding systems.
Constraints Imposed by these Characteristics on Workflow Orchestration
The diversity and complexity of pharmacoeconomics data impose specific requirements on workflow orchestration. First, research reports and policy documents in PDF and Word formats require efficient text extraction and structured processing to identify key parameters and conclusions. Second, cost and utility data in Excel and CSV formats need precise numerical parsing and unit conversion capabilities to ensure data consistency. Varying data update frequencies necessitate incremental update and version management mechanisms within the workflow. These mechanisms prevent redundant processing and track the impact of different data versions on analysis results. Furthermore, pharmacoeconomics models often involve complex multi-variable calculations and sensitivity analyses. This requires workflows to integrate external computational modules or support complex logical operations within AI processes. Accurate understanding of medical terminology and economic concepts also requires AI models with strong domain knowledge and the ability to process lengthy professional documents.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 4000–8000 Token | Pharmacoeconomics documents are often long, requiring a larger context window to accommodate more information. |
Chunk size (Segment Length) | 800 characters | Ensures each text segment contains sufficient semantic information, reducing the chance of critical information being truncated. |
Recall count (Recall Count) | Top 5–8 entries | Guarantees retrieval results cover multiple relevant data points and supporting evidence, improving accuracy. |
Similarity threshold (Similarity Threshold) | 0.75 | Filters out text snippets highly relevant to the query, avoiding interference from irrelevant information. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Addresses the need to upload large research reports and PDF files containing numerous charts. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles the parsing of complex PDF and Word documents, preventing processing failures due to timeouts. |
Three Common Mistakes
- The workflow does not start after document upload, or a message indicates an unsupported file type. This occurs because the file size exceeds the
UPLOAD_FILE_MAX_SIZElimit, or a specific file format not configured for the workflow is uploaded. - The AI conversation node returns incomplete or incorrect information when processing long texts, sometimes directly reporting
Input length exceeded. This happens when themaxContextparameter is set too low, causing the input content to exceed the AI model's processing limit. - The workflow completes, but cost data or utility values in the generated report are empty or clearly incorrect. This is due to specific fields in Excel or CSV files not being correctly identified or parsed, possibly because of inconsistent units or abnormal data formats.
How to Confirm Proper Configuration
- Upload various types (PDF, Word, Excel) and sizes of pharmacoeconomics reports. Confirm the workflow can parse and extract key information correctly.
- For a document containing complex economic models, use the AI conversation node to ask about key parameters and conclusions. Verify the accuracy and completeness of the answers.
- Test with Excel data containing specific monetary units and medical codes. Check if the workflow can correctly identify and process these fields.
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.