Data Characteristics
Site Management Organizations (SMOs) handle clinical trial pre-screening data from various sources. These include electronic medical record (EMR) systems, Laboratory Information Management Systems (LIMS), and investigator-completed subject screening logs. The data contains patient demographics, diagnoses, medical history, medication records, biochemical indicators, and imaging reports.
Data update frequencies vary. Vital signs and lab results may update in real-time, while diagnoses and medical history are more stable. Document types include structured tables, semi-structured reports, and unstructured imaging descriptions. Fields and units are medically specific. For example, a complete blood count report shows WBC (white blood cell count) in 10^9/L and ALT (alanine aminotransferase) in U/L. Diagnosis codes often follow ICD-10 standards.
Constraints on Citation and Traceability
The diverse and specialized nature of SMO clinical trial pre-screening data imposes strict requirements on citation and traceability.
Unstructured text, such as imaging report descriptions, requires semantic integrity during chunking. Avoid truncating critical medical terms to maintain retrieval accuracy. Structured and semi-structured data require correct identification and association of field names and values. Medical indicator units must display with the citation to prevent misinterpretation.
Varying update frequencies necessitate distinguishing data versions or timestamps in the knowledge base to ensure citation timeliness. Patient privacy and medical data sensitivity require citations to precisely point to original files or records. This facilitates auditing and compliance reviews, preventing information leakage or misuse.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters | Balances semantic integrity of medical text with retrieval efficiency. |
Recall count (Recall Count) | Top 10–15 items | Ensures coverage of multi-source heterogeneous data, improving information recall. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Balances retrieval precision and recall breadth, reducing false positives. |
Rerank result count (Rerank Return Count) | Top 5 items | Focuses on the most relevant medical information, enhancing user experience. |
Citation Metadata Fields | source_file_id, timestamp, page_number | Ensures traceability to the original file, record update time, and page number. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles parsing large or complex medical reports, preventing timeout interruptions. |
Common Pitfalls
- Garbled citation numbers appear in AI dialogue output, reverting to quotes after output. This indicates text encoding or frontend rendering issues failing to handle special characters correctly.
- Citations are provided for questions not present in the knowledge base. This occurs when the
Similarity threshold(Similarity Threshold) is set too low, recalling and citing irrelevant content. - The dialogue request interface does not return a
citecitation ID. This happens if the interface configuration or data model does not include the citation ID field, or if the backend processing logic does not pass the citation ID.
Verification Steps
- For typical pre-screening scenarios, use queries of varying complexity. Check if the file ID, page number, and timestamp in the AI's response accurately correspond to the original document content.
- Simulate misleading questions or questions not in the knowledge base. Observe if the AI still provides citations. Adjust the
Similarity threshold(Similarity Threshold) and observe changes in citation recall. - Check if the API response includes
cite_idor other traceable citation identifiers. Attempt to trace back to the original data using these identifiers. - Upload and retrieve complex reports containing medical terminology and units. Verify that chunking maintains semantic integrity and that units display with the citation.
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.