Model Access and Configuration for Pharmacoeconomics

Data Characteristics

Pharmacoeconomics data comes from cost-effectiveness analysis reports, Health Technology Assessment (HTA) documents, real-world evidence (RWE) studies, and clinical trial results. This data combines structured tables (e.g., cost components, efficacy indicators, QALY values) and unstructured text (e.g., research methods, sensitivity analysis discussions). Data updates are infrequent, typically quarterly or annually, coinciding with new drug approvals, guideline updates, or major study publications. Documents are complex, containing specialized terminology, statistical symbols, and specific units (e.g., USD, EUR, QALY, DALY, ICER). Field names may have multiple abbreviations or synonyms; for example, "Incremental Cost-Effectiveness Ratio" might be abbreviated as ICER.

Constraints on Model Access and Configuration

The mixed structured and unstructured nature of pharmacoeconomics data requires models to effectively identify and extract key numerical and text information during preprocessing. Low update frequency means model training and index building do not need to be frequent, but each update requires data integrity and consistency. Unique units and complex terminology demand strong semantic understanding from text vectorization models, requiring significant domain knowledge. The presence of field name abbreviations and synonyms requires models to perform effective semantic expansion during retrieval and matching to avoid missing relevant information due to terminology mismatches. Data source diversity also increases the complexity of data cleaning and standardization, affecting the quality of model input data.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`800–1200 characters`	Ensures individual text segments contain enough context to understand pharmacoeconomics concepts and prevents truncation of critical information.
`Recall count` (Recall Count)	`15–20 items`	Pharmacoeconomics analysis often involves multi-dimensional data and arguments; increasing recall helps cover a more comprehensive background.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements	Balancing recall and precision based on specific datasets and model performance, avoiding omissions or introducing too much noise.
`Rerank result count` (Rerank Return Count)	`5–8 items`	Reduces redundant information presented to the user while maintaining relevance, focusing on core findings.
`Index Model` (Indexing Model)	Domain-specific or fine-tuned model	Better understanding of pharmacoeconomics specific terminology, concepts, and data structures, improving vectorization quality.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Provides sufficient file parsing time when processing large HTA reports or multi-page PDF documents, preventing timeout failures.

Common Pitfalls

File Parsing Timeout (File parsing timeout) errors occur when uploading large HTA reports or multi-page PDF documents because PARSE_FILE_TIMEOUT_SECONDS is not set to a sufficiently long processing time.
Retrieval results contain many irrelevant reports or paragraphs. This may happen if the Similarity threshold (similarity threshold) is set too low, leading to the recall of semantically distant content.
The model fails to provide detailed numerical values when asked about a specific drug's cost-effectiveness analysis. This may be because the vector model used lacks effective understanding of pharmacoeconomics-specific terms and units, preventing correct extraction and indexing of key data.

Verification Steps

Randomly select 5 typical pharmacoeconomics reports, upload them, and check the indexing status to confirm all files are successfully parsed and indexed.
Formulate 10 queries about specific drugs, treatment plans, and economic indicators (e.g., ICER values) from the reports. Check if the returned results contain relevant numerical values and conclusions, and assess their accuracy.
Compare the consistency of recall results when the model processes queries containing abbreviations (e.g., QALY) and full terms (e.g., "Quality-Adjusted Life Year") to confirm the model's semantic expansion capability.
Check log output to ensure no significant data import failed or vectorization error messages appear during data updates or index rebuilding.

The values given are common starting points and should be measured 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.