Data Characteristics
II-III phase clinical trial pharmacovigilance data originates from clinical trial protocols, case report forms (CRFs), safety databases, medical monitor reports, and investigator-submitted adverse event reports. This data exists as structured database entries, unstructured text reports (e.g., medical narratives, follow-up records), imaging reports, and laboratory results. Data updates frequently. Adverse event reports may be recorded in real-time during trials. Document structures vary, including standardized medical terminology, coding systems (e.g., MedDRA), and free-text descriptions. Fields include event occurrence time, severity, outcome, causality assessment, and actions taken. Units involve dosage (mg), frequency (times/day), and duration (days).
Constraints on Multi-turn Conversation and Prompts
The multi-source nature and high update frequency of II-III phase clinical pharmacovigilance data require multi-turn conversation systems to quickly integrate and retrieve the latest information. Unstructured text reports contain extensive medical terminology and ambiguous descriptions, challenging prompt semantic understanding and entity recognition capabilities. The conversation system needs to accurately identify key information such as patient symptoms, drug names, and event occurrence times. MedDRA coding necessitates that prompts map natural language descriptions to standardized codes, ensuring data consistency and comparability. Due to the high sensitivity of safety data, conversational interactions must ensure precise information acquisition, avoiding misinterpretation or omission of critical safety signals. This directly impacts prompt complexity and multi-turn conversation flow design to support rigorous risk assessment and decision-making.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 8 | Retains sufficient historical information in multi-turn conversations, covering common scenarios like symptom follow-up and medication history verification. |
Similarity threshold (Similarity Threshold) | 0.75 | Balances recall and accuracy, reduces false positives, and avoids missing critical similar adverse event reports. |
Recall count (Recall Count) | 10–15 | Controls the input length processed by the model, improving response speed while ensuring relevant information coverage. |
Chunk size (Segment Length) | 500 characters | Accommodates long descriptions in medical reports, ensuring semantic completeness and preventing truncation of key information. |
Rerank result count (Reranked Return Count) | 5 | Selects the most relevant document segments for the current conversation, reducing noise processing by the model and improving answer accuracy. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Addresses the need to parse large clinical report files, preventing parsing timeouts due to excessive file size. |
Common Pitfalls
- When a user asks for specific adverse event details in a multi-turn conversation, the system's reply is empty. This often occurs because the prompt fails to accurately extract key entities (e.g., adverse event name, drug name) from the user's query, leading to irrelevant knowledge base retrieval results.
- The conversation workflow's response time is significantly longer than during workflow debugging. This may be due to outdated knowledge base indexes in the production environment, leading to reduced retrieval efficiency, or increased model loading and inference times.
- The system fails to correctly identify date information provided by the user, resulting in incorrect timestamps when generating reports. This often happens because date format parsing rules in the prompt are undefined, failing to effectively use system-provided current date variables.
Verification
- For typical adverse event queries, simulate conversations to check if responses include all key safety data points and verify consistency with original reports.
- Review system logs to check the knowledge base recall count and similarity scores for each query, ensuring they fall within the expected range. This validates the recall mechanism.
- In multi-turn conversations, intentionally introduce various expressions for key information like dates and dosages. Check if the system accurately identifies and maps them to the correct fields or units, verifying prompt robustness.
The values provided are common starting points and should be measured against specific 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.