Multi-turn Conversation and Prompts for Pharmacovigilance in Patient Assistance

Data Characteristics

Pharmacovigilance data in Patient Assistance Programs (PAPs) originates from patient self-reports, healthcare professional records, and pharmaceutical company collections. This data is primarily unstructured text, such as patient verbal accounts, phone call transcripts, and email exchanges. Data updates frequently, potentially daily or in real-time for adverse event reports. Document structures vary, including free-text descriptions, structured questionnaire responses, and medical examination reports. Common fields include patient ID, report date, drug name, adverse event description (symptoms, signs, severity), actions taken, and outcomes. Adverse event descriptions often mix medical terminology with non-professional patient language.

Constraints Imposed by These Characteristics on Multi-turn Conversation and Prompts

High-frequency updates and diverse data sources require the dialogue system to quickly learn and adapt to new information. The mix of unstructured text and medical terminology means prompt design must balance natural language understanding with specialized knowledge extraction. This prevents misinterpretations due to lexical ambiguity or missing information. In multi-turn conversations, the system must guide the patient to gradually provide key information from fragmented descriptions. This includes the adverse event timeline, symptom evolution, and medication history. Patient assistance scenarios demand high accuracy and safety; misleading responses can have severe consequences. Therefore, the dialogue system must strictly adhere to medical and ethical guidelines during information retrieval and generation. Maintaining conversation history and context is crucial for multi-turn dialogue coherence and completeness.

Configuration Settings

Three Common Mistakes

1. The conversation deviates from the topic or fails to link to previous medical descriptions after a few turns. This occurs because maxContext is set too low, preventing the system from effectively using historical dialogue information.
2. The system fails to retrieve relevant adverse event knowledge when the user mentions specific drug names or symptoms. This happens because the Similarity threshold (Similarity Threshold) is set too high, filtering out partially relevant text segments with slightly different phrasing.
3. In a multi-turn conversation, the user asks about drug side effects, and the system repeatedly provides the same response. This is due to improper context management across multiple AI Dialogue nodes in the workflow, where each node generates responses independently.

How to Verify Configuration

1. Simulate the complete process of a patient reporting an adverse event. Check if multi-turn dialogue can continuously guide the user to provide key information and gradually converge on a specific adverse event type.
2. Randomly select multiple patient reports containing complex medical terminology and non-professional descriptions. Test if the system can accurately identify key fields such as drugs, symptoms, and time.
3. Test the maxContext configuration through continuous questioning. Confirm that conversation content remains coherent within the specified number of turns and that context is not lost.
4. Compare the system's preliminary adverse event judgments with those of human experts. Verify how Recall count (Retrieval Count) and Similarity threshold (Similarity Threshold) affect result accuracy.

The values provided are common starting points. Measure them against your 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.