Citation Sources and Traceability for Clinical Trial

Data Characteristics in This Category

Data for clinical trial pre-screening in hospital operations originates primarily from internal systems. These include Electronic Medical Records (EMR), Laboratory Information Systems (LIS), Picture Archiving and Communication Systems (PACS), and operational management systems. This data updates frequently. Patient visits, test results, and treatment plans are recorded in real-time or near real-time. Document structures typically combine structured data with unstructured text. Examples include diagnostic records, physician orders, examination reports, progress notes, and nursing records. Fields include basic patient information, diagnostic codes (e.g., ICD-10), laboratory indicators (e.g., complete blood count, liver and kidney function), imaging descriptions, and medication records (ATC codes). Units are highly standardized, for example, mmol/L, ng/mL. The data volume is large and involves multi-modal information. Unstructured text contains extensive clinical descriptive language and specialized terminology.

Constraints Imposed by These Characteristics on "Citation Sources and Traceability"

The real-time nature and high update frequency of hospital operational data require citation sources to have dynamic refresh capabilities. This ensures pre-screening results are based on the latest patient information. The heterogeneous data structure, especially the mix of structured and unstructured data, challenges the extraction and integration of cited content. It requires precise identification of key information and its association with original sources. Specialized terminology and abbreviations in documents increase the difficulty of semantic understanding. This impacts recall accuracy. Large amounts of sensitive patient information mean citation sources must strictly adhere to data security and privacy protection regulations during traceability. For example, cited content cannot directly expose patient identity information. Standardized units for laboratory indicators and medication records require unit consistency in citations. This avoids pre-screening judgment errors due to unit conversion mistakes.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for this Value
`Chunk size` (Chunk Size)	`800–1200 characters`	Accommodates the length of clinical progress notes and examination reports, ensuring contextual completeness.
`Recall count` (Recall Count)	`Top 5`	Balances recall efficiency and relevance, avoiding the introduction of excessive irrelevant information that could interfere with judgment.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Addresses the need for precise matching of clinical terminology, improving recall accuracy.
`Rerank result count` (Reranked Return Count)	`Top 3`	Performs a secondary selection based on initial recall, further enhancing relevance.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Handles potential time consumption for parsing large EMR documents, preventing timeout interruptions.
`MAX_CHUNKS_PER_FILE`	`2000`	Hospital operational data documents are often lengthy; this allows processing more chunks to cover comprehensive information.

Three Common Mistakes

When generating knowledge base Q&A, some files fail to generate Q&A pairs. The original text is directly used. This typically occurs because document content is too complex or formatted inconsistently. The parser fails to effectively identify and extract key information.
After configuring the model, test prompts show errors, but the model still runs in citations. This may stem from model service connectivity issues or abnormal model loading status. The test interface returns errors, but the actual invocation path differs.
In simple knowledge base applications, only one document can be retrieved at a time, limiting the number of citations. This is due to default configurations or application logic restricting the number of documents that can be cited in a single query. It fails to fully utilize multi-source knowledge bases.

How to Confirm Proper Configuration

Submit complex queries involving typical clinical scenarios. Check if the returned citation sources include multiple relevant documents. Verify their content matches the original data sources.
Randomly select cited snippets from pre-screening results. Manually cross-reference the snippet's location and context in the original EMR or examination report. Ensure the traceability path is clear and accurate.
Monitor system logs. Confirm that during high-concurrency queries, file parsing and vector retrieval tasks complete within the PARSE_FILE_TIMEOUT_SECONDS setting. Ensure no timeout errors occur.
Compare recall results across different Similarity threshold (similarity thresholds). Confirm that all potentially relevant patient information is effectively recalled while meeting clinical precision requirements.

Note: The values provided are common starting points. Measure against specific samples to determine optimal settings.

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.