Data Characteristics
Data for DTP pharmacies in clinical trial pre-screening comes from three main sources: patient Electronic Health Records (EHR), pharmacy sales and medication records, and clinical trial protocol details from recruitment platforms. Patient EHRs typically contain diagnostic information, medical history, allergy history, and medication status. This data is relatively standardized but may include unstructured text. Pharmacy sales records are primarily structured, detailing medication names, dosages, and purchase times. These records update frequently, daily or weekly. Clinical trial protocol documents are often in PDF or Word format, containing inclusion/exclusion criteria, study drug information, and trial duration. These documents feature complex fields and specialized terminology, with units like dosage (mg, g) and duration (days, weeks, months). Updates occur monthly or quarterly, depending on trial progress.
Constraints from Data Characteristics on Multi-turn Conversation and Prompt Engineering
The multi-source and complex nature of DTP pharmacy data imposes specific requirements on multi-turn conversation and prompt design. Unstructured text in EHRs makes accurate extraction of key medical history a challenge in conversations, requiring more refined entity recognition and relation extraction prompts. The real-time nature of pharmacy sales records demands that the conversation system quickly query and integrate the latest medication data. Prompts must explicitly specify the query time range and medication types. The specialized and diverse nature of clinical trial protocol documents means that when matching patients to trials, prompts must understand and convert complex inclusion/exclusion criteria. For example, converting "ECOG score ≤ 1" into patient-understandable language and guiding the patient to provide relevant information. Furthermore, differing update frequencies across data sources require differentiated handling in data synchronization and information prompting within the multi-turn conversation mechanism, to avoid providing outdated or inaccurate trial information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for this Value |
|---|---|---|
maxContext | 8 turns | Balances memory capacity and computational cost to handle complex medical history inquiries. |
Chunk size | 500–800 characters | Adapts to the density of specialized terminology in clinical trial protocol documents, improving recall accuracy. |
Recall count | 7 items | Covers multiple potentially matching clinical trials, ensuring comprehensiveness. |
Similarity threshold | 0.75 | Balances recall and precision, filtering out irrelevant trial information. |
Rerank result count | 3 items | Prioritizes the most relevant trials, reducing user reading burden. |
max_tokens | 2048 | Ensures generated answers can fully include key trial information and explanations. |
Three Common Mistakes
- When asking about a patient's medical history or medication status in a conversation, the system fails to accurately understand the patient's natural language description, leading to mismatched trial recommendations. This is due to insufficient understanding of medical terminology and entity extraction capabilities in the prompts.
- When calling an external tool to query the latest medication sales data, the tool call fails or returns an empty result, making it impossible to determine the patient's current medication status. This is due to incorrect parameter mapping in the tool configuration or improper handling of API interface errors.
- The user repeatedly asks the same question or provides the same information in the conversation, and the system fails to identify duplicate content, leading to inefficient dialogue. This is due to ineffective use of historical dialogue or
maxContextbeing set too low to maintain sufficient memory.
How to Verify Configuration
- Simulate various patient conversation paths to check if the system can accurately identify and extract patient symptoms, diagnoses, and medication information, and convert them into structured data usable for matching clinical trials.
- Verify whether the system can correctly call external tools in multi-turn conversations, such as querying a patient's historical medication records or the latest clinical trial recruitment information, and check the accuracy and completeness of the returned results.
- Evaluate whether the system, when faced with ambiguous or incomplete information, can prompt the patient for more detailed necessary information, ultimately providing clinical trial pre-screening recommendations that align with DTP pharmacy business scenarios.
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.