Data Characteristics in this Category
Contract Sales Organizations (CSOs) in pharmacovigilance primarily use real-world data, clinical trial data, and post-market adverse event reports from their pharmaceutical partners. This data is a mix of structured (e.g., database records, XML files) and unstructured formats (e.g., free-text medical reports, scanned handwritten doctor's notes). Data updates frequently, especially during new drug launches or severe adverse events, when report volumes can surge. Document structures are complex, often containing medical terminology, abbreviations, dosage units (e.g., mg, ml), time units (e.g., hours, days), and descriptions of individual patient variations. Fields are diverse, covering patient demographics, medication history, adverse event descriptions, diagnostic results, and treatment measures. Data from different sources may have varying field names or missing information.
Constraints Imposed by These Characteristics on "Multi-turn Conversations and Prompts"
The complexity of CSO pharmacovigilance data directly impacts the design of multi-turn conversations and prompts. High update frequency requires prompts to quickly adapt to new adverse event types or drug information to avoid obsolescence. Mixed data structures mean that multi-turn conversations must handle both structured queries (e.g., "query the incidence of adverse events for a specific drug within a certain period") and unstructured text analysis (e.g., "summarize patient medication adherence in this report"). The presence of medical terminology and abbreviations demands strong semantic understanding from prompts to accurately parse professional vocabulary from user input and maintain contextual consistency across multiple turns. Additionally, diverse fields and units require prompts to precisely specify information extraction scope and format. This ensures accurate extraction of critical information like dosages and times from raw data, preventing information bias due to unit confusion or missing fields.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8 turns | In pharmacovigilance, users often need to ask follow-up questions or clarify details multiple times. A medium-length context helps maintain coherence. |
Chunk size (Segment Length) | 500 characters | Key information density is high in medical reports. Shorter segment lengths improve recall precision. |
Recall count (Recall Count) | Top 10 entries (Top 10) | Given the complexity and potential relevance of medical reports, increasing the recall count improves coverage. |
Similarity threshold (Similarity Threshold) | 0.75 | Medical accuracy requirements are high. A higher similarity threshold reduces irrelevant or misleading information. |
Rerank result count (Rerank Return Count) | Top 5 entries (Top 5) | After high recall, reranking filters for the most relevant few results, improving user experience. |
SYSTEM_PROMPT | Include "As a pharmacovigilance assistant, focus on adverse event analysis." | Clearly defines the AI's role and task scope, enhancing professionalism and focus in responses. |
Three Common Mistakes
- Symptom: After a user inputs "cephalosporin allergy," the system fails to identify "cephalosporin" as a potential adverse reaction to an antibiotic. Instead, it returns irrelevant general drug information. Reason: The
SYSTEM_PROMPTor training data lacks a deep understanding of medical terminology and its classification system, leading to insufficient semantic association. - Symptom: In a conversation, a user asks "what reaction did a patient experience after taking 500mg of the drug?" The system fails to accurately extract the
500mgdosage information for a related query. Reason: The prompt, when processing text combining numbers and units, fails to effectively instruct the model to normalize units or extract them precisely. - Symptom: LaTeX formulas render correctly in the debugging preview but display as raw code when published to the application. Reason: The rendering engine or frontend component in the application's publishing environment does not support LaTeX format parsing, or the relevant configuration is not enabled.
How to Confirm Proper Configuration
- Conduct multi-turn simulated conversations covering common adverse event queries, drug dosage confirmations, and patient condition descriptions. Check the accuracy and coherence of responses.
- Randomly select several complex adverse event reports. Ask questions about key information within each report and verify if the system can accurately extract and summarize it.
- Test the system's ability to recognize medical terminology, abbreviations, and specific units. Ensure it correctly understands user intent across different phrasing.
- Check the published application's conversation interface to confirm that all special formats (e.g., LaTeX formulas, tables) are rendered and displayed correctly.
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.