Data Characteristics in this Category
Pharmacoeconomics data primarily originates from clinical trial reports, real-world evidence (RWE) databases, healthcare cost databases, drug pricing policy documents, and research reports published by health technology assessment (HTA) agencies. Data update frequencies vary. Clinical trial data typically generates once after project completion, while RWE data may update quarterly or annually. Document structures are diverse, including PDF reports, raw data in Excel or CSV formats, and query results from various databases. Fields cover costs (e.g., drug acquisition costs, hospitalization fees, outpatient fees), effects (e.g., Quality-Adjusted Life Years QALY, survival time, disease progression time), and resource utilization (e.g., physician visits, hospital days). Units include currency (USD, EUR, CNY), time (years, months, days), and quantity (units, times). Data often includes confidence intervals and sensitivity analysis results.
Constraints Imposed by these Characteristics on Tool Calling and Plugins
The complexity and diversity of pharmacoeconomics data sources require tool calling to have robust file parsing and multi-source data integration capabilities. For example, processing PDF table data in HTA reports necessitates OCR technology and table structure recognition plugins. The statistical characteristics of cost and effect data (e.g., distribution types, confidence intervals) dictate the need for specific statistical analysis tools and economic model plugins during data processing and model construction, rather than simple arithmetic averaging. Inconsistent data update frequencies mean different refresh strategies are necessary for various data sources when building the knowledge base. Furthermore, discrepancies in currency and time units within the data require plugins to correctly identify and unify dimensions during data extraction and transformation, preventing calculation errors due to unit confusion.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Pharmacoeconomics reports often contain numerous charts and complex text, requiring longer parsing times. |
MAX_TOKENS_PER_CHUNK | 800–1200 characters | Ensures each chunk contains sufficient context while preventing individual chunks from becoming too long, which could impact recall accuracy. |
SIMILARITY_THRESHOLD | 0.78 | Balances recall relevance with recall quantity, striking a balance between precision and coverage. |
EMBEDDING_MODEL | text-embedding-ada-002 | Widely used for medical texts, demonstrating good semantic understanding capabilities. |
EXTERNAL_TOOL_CONFIG | Calibrated based on actual measurements, e.g., Python_Stats_Plugin | For specific statistical analyses or economic models, configuration is necessary based on the tool's API and functionality. |
CURRENCY_CONVERSION_RATE | By months Update | Ensures cost data accuracy in cross-country comparisons or long-term analyses. |
Three Common Pitfalls
- After calling an external statistical plugin, statistical indicator fields in the returned results are empty. This occurs because the plugin's output format does not match expectations, leading to incorrect field parsing.
- The AI model cites outdated or incorrect cost data in its responses. This happens when the automatic refresh task for the relevant database is misconfigured, causing the knowledge base data to not update promptly.
- During economic model simulations, the AI fails to identify and call the correct decision tree or Markov model plugin. This is due to overly broad tool descriptions or calling conditions, leading to ambiguous model selection.
How to Confirm Proper Configuration
- Select a pharmacoeconomics report containing complex tables and multi-unit data. After parsing with tool calling, verify that the extracted key cost and effect data are complete and that units are correct.
- Manually trigger an update process for an RWE database in the knowledge base. Check if the updated data matches the original database and confirm that relevant statistical plugins can correctly process the new data.
- Design a query that requires specific statistical analysis or economic modeling. Observe if the AI accurately identifies and calls the corresponding external plugin, then perform an initial verification of the plugin's returned results.
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.