Model Access and Configuration for Pharmacoeconomics

Data Characteristics in this Category

Pharmacoeconomics regulatory submission documents involve diverse data types. These primarily originate from clinical trial reports, real-world study data, cost-effectiveness analysis models, systematic reviews, and meta-analysis reports. Data updates are infrequent, typically occurring with new drug development progress or guideline releases. Document structures primarily consist of structured tabular data (e.g., cost data, efficacy data, utility values), semi-structured reports (e.g., methodology descriptions, sensitivity analysis reports), and unstructured text (e.g., literature reviews, expert opinions). Fields and units are highly specialized. For example, cost data may involve currency units like USD or EUR. Efficacy data may involve specific medical economics indicators such as QALY (Quality-Adjusted Life Year) and ICER (Incremental Cost-Effectiveness Ratio), along with various clinical outcome indicators (e.g., OS, PFS).

Constraints Imposed by These Characteristics on "Model Access and Configuration"

The specialized and diverse nature of pharmacoeconomics data imposes specific requirements on model access and configuration. First, large volumes of structured tabular data require precise parsing. Traditional text segmentation methods may be insufficient to capture internal logical relationships within tables, necessitating enhanced table parsing capabilities. Second, the multiple specialized units and indicators within the data mean the model must avoid unit confusion or indicator misuse when understanding and generating content. This requires sufficient exposure to relevant knowledge during model training or fine-tuning. Third, the mixture of semi-structured and unstructured content in documents increases the complexity of information extraction. The model needs to extract key information from different formats. Finally, because data updates are infrequent but highly specialized, the requirement for model knowledge timeliness is relatively relaxed. However, the demand for depth and accuracy of specialized knowledge is extremely high. This directly influences the choice of knowledge base construction and model recall strategies.

Configuration Settings

Configuration Item	Recommended Value	Rationale for Recommendation
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Pharmacoeconomics reports often contain numerous charts and detailed data, resulting in large file sizes.
`Chunk size`	`800 characters`	Balances tabular and text content, maintains contextual coherence, and prevents critical information from being truncated.
`Recall count`	`10 entries`	Ensures the model retrieves sufficient relevant context from a highly specialized knowledge base.
`Similarity threshold`	`0.75`	Improves recall accuracy and reduces interference from irrelevant information, especially in specialized domains.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing large or complex format documents (e.g., PDFs with embedded objects) can be time-consuming.
`maxContext`	`3000 Tokens`	Pharmacoeconomics analysis typically requires a longer context to understand logical chains and data relationships.

Three Common Pitfalls

The model returns "cannot provide a valid response" or incomplete information extraction when parsing Excel or PDF documents containing complex nested tables. This occurs because the default document parser inadequately supports complex table structures and fails to correctly identify row and column associations.
The model's responses show confusion between currency units or medical economics indicators, such as incorrectly using QALY as the unit for ICER. This happens because the distinction between related concepts in the knowledge base is insufficient, or the model lacks adequate contextual guidance when understanding specialized terminology.
A locally deployed model fails to respond to requests after addition, with logs showing Connection refused or API Key invalid. This may be due to the model service not starting correctly, or FastGPT's OPENAI_API_KEY or OPENAI_BASE_URL configurations not matching the local model interface.

How to Verify Correct Configuration

Upload a PDF document containing a complex cost-effectiveness analysis table. Ask a question about the ICER value of a specific drug. Check the accuracy and completeness of the returned result.
Query the knowledge base using different currency units (e.g., USD, EUR) and medical economics indicators (e.g., QALY, LYG). Verify whether the model can correctly distinguish and use these specialized terms.
Use endpoints like /ping or /health to check the availability of the locally deployed model service. Confirm that OPENAI_API_KEY and OPENAI_BASE_URL configurations match the actual service interface.
Test the parsing speed of pharmacoeconomics report documents of varying lengths and complexities. Ensure processing completes and data is successfully ingested within PARSE_FILE_TIMEOUT_SECONDS.

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.