Data Characteristics in This Category
Data for Real-World Evidence (RWE) regulations primarily originates from hospital electronic health records (EHRs), insurance claims databases, patient registries, wearable devices, and guidelines published by medical journals and regulatory bodies. Update frequencies vary, from real-time (for some EHR data) to quarterly or annually (for insurance claims data). Documents typically exist as unstructured text (e.g., clinical notes, medical reports), semi-structured tables (e.g., patient follow-up forms, adverse drug reaction reports), and structured data (e.g., ICD-10 diagnostic codes, lab results). Fields cover patient demographics, diagnoses, treatment plans, medication records, follow-up outcomes, and adverse events. This data often includes extensive medical terminology, abbreviations, and mixed units (e.g., mg, g, mmol/L, U/L).
Constraints Imposed by These Characteristics on Multi-Turn Conversations and Prompts
The diversity and complexity of RWE regulatory data impose specific requirements on the accuracy of multi-turn conversations and prompt construction. Medical terminology and abbreviations in unstructured text demand strong semantic understanding from the model to avoid misinterpretation due to lexical ambiguity. Dispersed information from multiple data sources requires finer control over context in multi-turn conversations to ensure effective integration of information from different origins. Varying update frequencies mean that prompts might need to specify a time range to retrieve the latest or specific historical information. The presence of different unit systems requires prompts to guide the model in unit conversion or explicitly state the desired unit, preventing numerical confusion. In multi-turn conversations, users may progressively refine query conditions, which requires prompts to adapt flexibly and guide the model in logical reasoning and information filtering.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 6 turns | Balances multi-turn query coherence with avoiding irrelevant information from overly long contexts. |
Chunk size (Chunk Size) | 800–1200 characters | Accommodates the paragraph length of medical texts, ensuring each chunk contains sufficient semantic information. |
Recall count (Recall Count) | 10–15 items | Increases the probability of recalling relevant document snippets, addressing the diversity of medical terminology and synonyms. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Balances relevance with noise avoidance, filtering highly relevant regulatory texts. |
Rerank result count (Reranked Return Count) | 5 items | Further refines recall results, focusing on the regulatory clauses or explanations most relevant to the user's question. |
temperature | 0.3–0.5 | Reduces the randomness of model-generated content, ensuring answers are based on original regulatory text and improving accuracy. |
Common Pitfalls
- Users receive garbled or irrelevant content after asking a question. This might be due to incompatible document encoding formats or chunking strategies that truncate key information.
- The model forgets previous turns' context in multi-turn conversations, leading to disjointed answers. This usually happens when the
maxContextparameter is set too low, failing to retain enough historical conversation information. - When querying specific medical indicators, the model returns inconsistent numerical units. This can stem from diverse unit representations in the regulatory documents and prompts failing to explicitly specify the required units.
How to Confirm Proper Configuration
- Perform stress testing on multi-turn conversations. Observe if the model maintains contextual coherence over 5 or more consecutive turns and correctly responds to progressively refined queries.
- Select regulatory documents containing unstructured clinical notes and structured lab reports. Ask diverse questions to check if the model can accurately extract and integrate information from different formats.
- Query medical indicators involving different unit systems. Verify if the model's returned values and units align with the original regulatory text or user expectations.
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.