Multi-Turn Conversations and Prompts for Real-World Study

Real-World Study Data Characteristics

Real-World Study (RWS) R&D document data originates primarily from Electronic Health Record (EHR) systems, insurance claims databases, patient registries, wearable devices, and patient-reported outcomes (PROs). Data update frequencies vary: EHR data may update in real-time, while insurance claims typically update quarterly or annually. Document structures are diverse, including unstructured clinical notes, semi-structured lab reports, and structured diagnostic codes (e.g., ICD-10) and medication records. Clinical notes and progress reports contain extensive free text describing disease progression, treatment adjustments, and adverse events. Fields cover patient demographics, diagnoses, treatments, medications, lab results, and imaging reports. Units include measurements (mg, mL), time (days, weeks), and numerical values (mmol/L, U/L).

Constraints on Multi-Turn Conversations and Prompts

The diverse and unstructured nature of RWS data imposes specific requirements on multi-turn conversation and prompt design. First, extensive free text (e.g., medical records) demands robust entity recognition and relationship extraction capabilities. This extracts key information like disease names, drug dosages, treatment durations, and adverse reactions from complex narratives. Second, varying data update frequencies make knowledge base timeliness critical. The multi-turn conversation system must identify and present the latest available data version or guide users to query data within a specific timeframe. Third, semantic complexity in RWS document fields, such as multiple expressions for the same diagnosis, requires prompt design to consider synonyms and hierarchical relationships for accurate recall. Finally, multi-turn conversations need to handle uncertainty and ambiguous queries. For example, if a user provides only partial symptom descriptions, the system must guide them to provide additional information to precisely locate relevant document snippets.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`chunkSize`	`800–1200 characters`	Balances the completeness of clinical narratives in RWS documents with retrieval efficiency, avoiding excessive splitting that leads to semantic loss.
`overlapSize`	`100–200 characters`	Ensures contextual continuity between adjacent text blocks, aiding the understanding of complex medical concepts.
`recallTopK`	`top 5–8`	Balances recall rate with model processing load; RWS queries often require more comprehensive information.
`rerankTopN`	`top 3`	Improves the ranking of the most relevant document snippets from initial recall results.
`temperature`	`0.3–0.5`	Ensures model output stability and accuracy, preventing the generation of inaccurate or speculative content in the medical domain.
`maxContext`	`6000–8000 tokens`	Accommodates potentially long accumulated context in RWS multi-turn conversations, especially when tracing medical history or treatment plans.

Common Pitfalls

Conversations that return "no relevant information found" or irrelevant content often stem from prompts that inadequately guide the model to understand the complexity of medical terminology or synonym variations, leading to insufficient retrieval recall.
The AI conversation module in a workflow fails to display file links or user questions, manifesting as missing interface elements or empty data fields. This may occur if configuration items like fileLink or userQuestion are not correctly mapped or bound to front-end components in the workflow definition.
When querying a patient's medication records for a specific period, the system returns data for all time periods. This often happens because the date range limitation in the prompt is unclear, or the model fails to correctly parse the time constraint.

Configuration Verification

For typical RWS queries, such as "patient ID 123 liver function abnormality records," verify that the conversation results accurately cite liver function indicators and abnormal values from the document, and confirm that the cited document snippets are from the specified patient.
Use a series of queries containing medical synonyms and abbreviations, such as "hypertension" and "HTN." Check if the system consistently recalls relevant documents and observe if the recallTopK document snippets cover these variations.
Simulate multi-turn conversations, for example, first asking about a patient's diagnosis, then inquiring about treatment plans and adverse reactions. Check if the model maintains contextual coherence and extracts key information from historical conversations to guide subsequent queries, for instance, if maxContext supports long conversations.
Randomly select multiple RWS reports. Validate that document chunking maintains the completeness of clinical narratives under the specified chunkSize and overlapSize parameters, preventing critical information from being split across different segments.

The values given 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.