Multiturn Conversation and Prompts for Pharmacovigilance in

Data Characteristics in This Category

Laboratory service data in pharmacovigilance primarily originates from clinical trial reports, real-world evidence (RWE) data, case report forms (CRF), medical literature, and regulatory guidelines and databases. Data update frequencies vary. Clinical trial data typically updates periodically during the trial cycle. RWE data may update continuously. Regulatory guidelines are released quarterly or annually. Document structures are predominantly semi-structured and unstructured, including PDF medical reports, Word document SOPs (Standard Operating Procedures), and case records containing tables, charts, and free text. Fields include patient demographics, medication history, adverse event (AE) descriptions, severity, outcome, causality assessment, laboratory test results (e.g., liver function indicators, kidney function indicators), and drug batch numbers. Units cover common medical measurement units such as mg, ml, μg/L, mmol/L.

Constraints Imposed by These Characteristics on Multiturn Conversations and Prompts

The diversity and update frequency of laboratory service data constrain the timeliness and accuracy of the knowledge base in multiturn conversations. Semi-structured and unstructured documents require efficient information extraction to accurately identify key adverse events, laboratory indicators, and associated drug information. This impacts prompt design, which must effectively guide the model to focus on specific entities. The specialized and ambiguous nature of medical terminology requires the dialogue system to possess context understanding and disambiguation capabilities, preventing information misjudgment due to terminology misunderstandings. For example, an elevated ALT statement may require multiple rounds of questioning, combined with the patient's medical history and medication use, to determine its clinical significance. Furthermore, the large volume of numerical laboratory indicators in the data requires the model to perform numerical comparisons and trend analysis. This demands that prompts effectively define numerical ranges and identify outliers.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`500-800 characters` (characters)	Adapts to medical text paragraph length, balancing contextual completeness and retrieval efficiency.
`Recall count` (Retrieval Count)	`8-12 entries` (items)	Ensures coverage of multi-source information, such as case reports, SOPs, and literature, preventing critical information omission.
`Similarity threshold` (Similarity Threshold)	`0.75-0.85`	Balances precise matching of professional terms with semantically similar clinical expressions.
`Rerank result count` (Reranked Return Count)	`4-6 entries` (items)	Filters out core information most relevant to the multiturn conversation context, improving response quality.
`maxContext`	`4096-8192 token`	Handles complex medical records and multiple reports' context, supporting long conversations and traceability.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Processes large PDF reports or batch-uploaded documents, preventing parsing timeouts.

Three Common Pitfalls

The dialogue system returns "401 No auth credentials found": This often occurs when integrating third-party medical databases or regulatory APIs, due to incorrect or expired API Key or authentication token configurations.
In multiturn conversations, follow-up questions about specific laboratory indicators fail to retrieve precise numerical values: This happens when numerical data is not structured during knowledge base construction, or prompts fail to effectively guide the model to extract numbers and units from unstructured text.
Incorrect or omitted adverse event causality assessment: This may be due to a Recall count (Retrieval Count) that is too low, causing critical medication history, past medical history, or comorbidity information to be missed by the model, affecting the comprehensiveness of the assessment.

How to Verify Proper Configuration

Test whether multiturn conversations accurately identify and follow up on the same adverse event described in different sources (e.g., case reports, literature). Verify that the model's understanding of event severity and outcome aligns with the original data.
Validate that the system, during multiturn interactions, can accurately extract and compare liver function indicators or kidney function indicators values at different time points, and can assess whether their trend is abnormal based on prompts.
Simulate user queries. Check if the model can integrate information from multiple documents (e.g., clinical trial reports and the latest regulatory guidelines) to comprehensively assess drug risks and provide corresponding recommendations. Verify that the assessment results are comprehensive and medically logical.

The values provided are common starting points and should be measured 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.