Data Characteristics in this Domain
SMO (Site Management Organization) clinical trial pre-screening data primarily comes from patient medical records, examination reports, informed consent forms, and trial protocol documents. This data typically exists as unstructured text, semi-structured tables, and structured database records. Patient medical records and examination reports update at varying frequencies, from hours to weeks, depending on patient condition and trial requirements. Document structures are complex. For example, medical records include chief complaints, present illness, past medical history, physical examinations, and auxiliary examinations. Field and unit standardization varies significantly. Free-text descriptions, medical abbreviations, and laboratory results (e.g., mg/dL, mmol/L, IU/L) require unit conversion and standardization. Trial protocol documents are often hundreds of pages long, typically PDF or Word files, containing strict inclusion/exclusion criteria, visit schedules, and drug dosages.
Constraints from Data Characteristics on Multiturn Conversation and Prompts
SMO clinical trial pre-screening data characteristics impose significant constraints on multiturn conversation and prompt design. First, uncertain data update frequency requires the dialogue system to handle time-sensitive information and trigger data update or verification processes. Second, complex document structures and inconsistent field units require prompts to have robust entity recognition, relationship extraction, and unit conversion capabilities. This ensures accurate understanding of user intent and extraction of key information from unstructured data. For example, when a user asks, "Does the patient meet the inclusion criterion of platelet count greater than 100 G/L?", the system must identify the "platelet count" field in the medical record and handle different unit representations like G/L or *10^9/L. Furthermore, the large volume and specialized nature of trial protocol documents require the dialogue system to precisely locate relevant sections and understand complex medical terminology and logical judgments. This avoids pre-screening errors due to information overload or comprehension deviations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for this Value |
|---|---|---|
maxContext | 8 | Ensures coverage of sufficient historical turns in multiturn conversations to maintain contextual understanding of complex medical records and inclusion/exclusion criteria. |
Chunk size (Segment Length) | 500 characters (characters) | Accommodates the typically long paragraphs and high information density in medical records and trial protocols, ensuring each segment contains complete semantic information. |
Recall count (Recall Count) | 10 entries (items) | Increases the number of recalled items to improve the retrieval rate of relevant information, considering the complexity of medical documents and potential keyword diversity. |
Similarity threshold (Similarity Threshold) | 0.75 | Balances recall precision and recall rate in specialized domains, ensuring highly relevant results are returned and reducing misjudgments. |
Rerank result count (Reranked Return Count) | 5 entries (items) | After recalling multiple items, reranking focuses on the most relevant few, improving the accuracy of the final results presented to the user. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Processing large trial protocol documents and patient medical records may require longer parsing times. This avoids file processing interruptions due to timeouts. |
Three Common Mistakes
- AI response ends the conversation directly, preventing normal continuation of subsequent processes: This typically occurs due to a lack of clear next-step instructions or conditional judgments in the workflow design. The AI completes the current task but is not guided to the next stage.
- Conversations frequently time out: This can happen if the knowledge base recalls too much data, or if complex instructions in the prompt cause the model inference time to exceed the system's response limit.
- FastGPT calls for a regular conversation but involves the knowledge base: This usually happens when the prompt contains knowledge-base-related trigger words or implicit queries, leading the system to mistakenly believe knowledge base retrieval is needed.
How to Confirm Correct Configuration
- Select representative patient medical records. Conduct multiturn inquiries about inclusion/exclusion criteria. Verify consistency between the system's judgment and human judgment, and check which document snippets were cited.
- Upload an examination report containing complex medical terminology and multiple unit representations. Verify if the AI can accurately identify and convert values for all key fields.
- For a complete trial protocol document, test multiple complex logical queries regarding inclusion/exclusion criteria. Confirm the system correctly understands and provides justifications.
- Simulate conversations under poor network conditions or with large data volumes. Observe response times and adjust parameters like
PARSE_FILE_TIMEOUT_SECONDSto keep them within an acceptable range.
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.