Data Characteristics in this Domain
Real-World Evidence (RWE) registration and submission documents involve diverse data sources. These include Electronic Health Records (EHR), medical insurance claims databases, disease registries, patient-reported outcome (PRO) data, and wearable device data. This data typically exists as unstructured text, semi-structured tables, and structured database records. Update frequencies vary; some data, like EHRs, might update in real-time, while medical insurance claims data might update quarterly or annually in batches. Document structures are diverse. For example, Clinical Study Reports (CSRs) usually follow ICH E3 guidelines. RWE reports might include study protocols, statistical analysis plans (SAPs), and data analysis reports. Fields and units within these documents show significant variation in standardization, often using custom codes and abbreviations.
Constraints Imposed on Multi-turn Conversations and Prompts by these Characteristics
Data source diversity requires multi-turn conversation systems to have robust heterogeneous data processing capabilities. The system must extract key information from different data formats. Varying document update frequencies mean the conversation system needs to refresh its knowledge base regularly. This ensures access to the latest data and avoids generating submission documents based on outdated information. The prevalence of unstructured and semi-structured documents challenges prompt generalization. Prompts must effectively guide the model to understand context and accurately extract and summarize information. Non-standardized fields and units necessitate incorporating entity recognition and standardization modules into multi-turn conversations. These modules map natural language descriptions to predefined medical terms and measurement units, reducing ambiguity.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8192 token | RWE reports are lengthy. A longer conversation context maintains coherence. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Balances semantic completeness with segment retrieval efficiency, avoiding overly long or short text blocks. |
Recall count (Retrieval Count) | Top 10–15 entries (top 10–15 items) | Ensures coverage of sufficient relevant information snippets for complex queries. |
Similarity threshold (Similarity Threshold) | Calibrated by actual measurement, e.g., 0.75 | Balances retrieval precision and recall, reducing interference from irrelevant information. |
Rerank result count (Reranked Return Count) | Top 5 entries (top 5 items) | Optimizes the quality of information presented to the model, focusing on the most relevant evidence. |
ENABLE_IMAGE_PROCESSING | True (if supported) | Real-world data may contain charts. Image processing support aids understanding. |
Three Common Pitfalls
- Data gaps or inaccuracies appear in conversations. This might be due to an outdated knowledge base or prompts that fail to effectively guide the model to extract key data from unstructured text.
- The model frequently shows comprehension biases when processing specific medical terms or measurement units. This happens due to a lack of entity recognition and standardization configuration tailored for biomedical domain-specific vocabulary.
- The system cannot process user-uploaded image attachments during conversations. This prevents analysis of critical data within images. The cause is an unenabled or incorrectly configured image recognition function.
How to Verify Correct Configuration
- Conduct multi-turn conversation tests for typical submission document preparation scenarios. Check if the model accurately extracts key information like study objectives, methods, and main results. Compare these against original documents.
- Evaluate the model's performance in handling custom codes and abbreviations. Verify if it correctly identifies and maps them to standard medical terms. This can be done by cross-referencing against a glossary.
- Upload RWE reports containing charts. Verify if the system correctly identifies and parses key data points within the charts. This can be confirmed by comparing chart data with model-extracted results.
- Check if the conversation system responds quickly after data updates. Verify if query results reflect the latest data. This can be done by periodically updating simulated data and observing system responses.
The values provided are common starting points and should be measured against the reader's 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.