Data Characteristics
Data for clinical trial pre-screening in hospital operations primarily originates from internal hospital information systems. These include Electronic Health Records (EHR), Laboratory Information Management Systems (LIMS), Picture Archiving and Communication Systems (PACS), and Clinical Trial Management Systems (CTMS). Data updates frequently. Patient visit records and lab results are entered in real-time. Medication use and treatment plan adjustments also update accordingly. Document structures vary. EHRs contain unstructured physician progress notes, structured diagnostic codes (e.g., ICD-10), and examination reports. LIMS data mainly consists of structured lab indicators and results. PACS stores medical images in DICOM format. Fields include patient basic information, diagnoses, medications, treatment history, family history, and genetic test results. Units strictly follow medical standards, such as mmol/L, mg/dL, mm, and cm².
Constraints on Multi-turn Conversations and Prompts
The data characteristics of clinical trial pre-screening in hospital operations impose specific requirements on multi-turn conversations and prompt construction. First, the complexity of data sources means prompts must effectively integrate heterogeneous information from different systems. For example, they must extract key symptom descriptions from unstructured progress notes and link them with structured lab results. Second, the high update frequency requires the conversation system to reflect the patient's latest status promptly. Prompt design must consider how to guide users to query the most recent data, avoiding outdated information. The diversity of document structures means prompts must have strong information extraction capabilities. For instance, they must identify lesion characteristics from free-text descriptions in imaging reports and convert them into structured conditions for screening. Additionally, strict medical units and professional terminology require prompts to accurately parse and generate this information. This avoids screening errors due to unit confusion or misinterpretation of terms, such as distinguishing between blood pressure units mmHg and kPa.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8192 token | Ensures the system can carry multi-turn conversation history and key medical record summaries, maintaining conversational coherence. |
temperature | 0.3–0.5 | Tends to generate factual, low-divergence responses, reducing hallucinations and improving the accuracy of clinical screening. |
top_p | 0.8–0.9 | Moderately increases response diversity while maintaining response quality, avoiding overly rigid answers. |
prompt | Calibrate based on actual measurements | Must include clear screening objectives, descriptions of patient data sources, and standardization of professional terminology. |
recall_num | Top 10 | Balances recall efficiency with information completeness, ensuring coverage of potentially relevant patient data. |
similarity_threshold | 0.75 | Ensures the relevance of recalled data, reducing interference from irrelevant information in pre-screening results. |
Common Pitfalls
- Confusion of critical medical indicator units in conversations. For example, misinterpreting "g/L" in a complete blood count as "mg/L." This occurs when prompts do not explicitly specify unit parsing rules.
- The system repeatedly asks for patient information already mentioned earlier in the conversation, leading to a poor user experience. This happens when
maxContextis set too low, preventing effective memory of multi-turn conversation history. - Discrepancies exist between the model's pre-screening results and actual screening criteria. For example, a critical exclusion criterion might be missed. This occurs when the constraints on screening logic in the prompt are not specific enough, allowing the model to generate unconstrained responses.
Verification of Configuration
- Simulate multi-turn conversations. Verify if the system accurately understands and integrates various patient medical indicators, diagnoses, and treatment history.
- In practical operations, evaluate if the system can accurately identify cases that meet or do not meet enrollment criteria from simulated patient data, based on the screening conditions set in the prompt. Check its recall and precision.
- Observe the system's performance when handling medical professional terminology and units. Ensure its parsing and output of units like "mmol/L" and "mg/dL" comply with medical standards.
- Compare with manual screening results to validate the effectiveness of AI pre-screening results. Adjust
similarity_thresholdandpromptcontent based on differences.
The values provided 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.