Knowledge Base Retrieval and Recall for Pharmacoeconomics

Data Characteristics in This Category

Pharmacoeconomics regulatory submission documents typically include cost-benefit analyses, cost-effectiveness analyses, and cost-utility analyses. These reports draw data from diverse sources, such as clinical trial data, real-world evidence (RWE), national or regional medical insurance payment standards, drug procurement prices, disease epidemiology data, and patient quality of life assessment scales (e.g., EQ-5D). Data update frequencies vary; clinical data and payment standards might update annually, while epidemiological data have longer update cycles. Document structures are complex, often containing numerous charts, statistical results, sensitivity analyses, model construction details, and references. Fields include drug generic names, dosages, specifications, prices, treatment plans, efficacy indicators (e.g., QALY, LYG), adverse event rates, resource consumption, and healthcare service utilization. Units include monetary units (e.g., CNY, USD), time units (years, months), and quality-adjusted life years (QALYs).

Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall

The complex structure and multi-source nature of pharmacoeconomics data impose several constraints on knowledge base retrieval and recall. Extensive charts and statistical results mean that plain text segmentation cannot fully retain information, requiring enhanced parsing capabilities for non-text content. Inconsistent data update frequencies necessitate that the knowledge base supports granular document version management and incremental updates to ensure the timeliness of recalled information. The specificity of fields and units, such as QALYs and different monetary units, requires vector models to understand the semantics of these specialized terms and distinguish numerical contexts. Furthermore, reports often contain complex causal relationships and model assumptions. Simple keyword matching can lead to misinterpretation or missing information, requiring more advanced semantic understanding and contextual association capabilities to ensure recalled segments provide a complete chain of argumentation.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Pharmacoeconomics reports can be large, containing numerous charts and model data.
`Chunk size` (Chunk Length)	`800–1200 characters`	Ensures capture of complete argumentative paragraphs and contextual information.
`Overlap Length`	`150 characters`	Maintains contextual continuity between chunks.
`Recall count` (Recall Count)	`5–8 entries`	Balances the comprehensiveness of recall with interference from irrelevant information.
`Similarity threshold` (Similarity Threshold)	Calibrate by measurement	Requires adjustment based on the specific model and dataset to balance recall and precision.
`Rerank result count` (Reranked Return Count)	`3 entries`	Prioritizes the most relevant core information.

Three Common Mistakes

When uploading large PDF reports, the system displays a file size exceeds limit error because the UPLOAD_FILE_MAX_SIZE configuration is too small.
After multiple turns of conversation, follow-up questions about pharmacoeconomics model parameters result in inconsistent answers from the model. This occurs because knowledge base chunks are too short, leading to critical context being cut off.
Querying cost-effectiveness data for a specific drug returns a large amount of irrelevant clinical trial data, failing to accurately focus on economic evaluation content. This happens because the Similarity threshold (similarity threshold) is set too low or the vector model's understanding of specialized terms is insufficient.

How to Confirm Proper Configuration

Upload representative pharmacoeconomics reports. Check if files parse successfully and if complete content is viewable in the knowledge base preview.
Ask multi-turn questions about key conclusions, model assumptions, and sensitivity analysis results within the reports. Evaluate if recalled segments contain all necessary information for answering and check the logical coherence of the answers.
Input queries containing specialized terms (e.g., QALY, ICER). Check if recall results accurately focus on relevant passages. Compare recall accuracy and quantity across different Similarity threshold (similarity thresholds) to determine an appropriate range.
Simulate real-world submission preparation scenarios. Pose questions that require integrating information across documents. Observe if the system can effectively link data from different sources and provide comprehensive answers.

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.