Workflow Orchestration for Clinical Trial Pre-screening in

Data Characteristics in this Category

Medical affairs data for clinical trial pre-screening originates from global clinical trial registries (e.g., ClinicalTrials.gov, WHO ICTRP), internal research reports, medical literature databases (e.g., PubMed, Embase), and pharmaceutical companies' internal drug development pipelines. Data update frequencies vary. Clinical trial registry information typically updates quarterly or monthly, while internal reports and literature generate in real-time based on research progress. Document types are diverse. They include structured trial protocol summaries, unstructured Investigator's Brochures (IB), sample Case Report Forms (CRF), and semi-structured medical journal articles. Fields and units are highly specialized. Examples include dosage units (mg/kg, IU), time points (weeks, months, years), biomarker values (ng/mL, U/L), and disease staging (TNM staging). Data often contains numerous abbreviations, specialized terminology, and complex clinical pathway descriptions.

Constraints Imposed by These Characteristics on "Workflow Orchestration"

The diversity of medical affairs data challenges workflow input processing. Workflows require flexibility to handle structured, semi-structured, and unstructured data. The high density of specialized terminology and abbreviations demands that language models within the workflow possess strong medical domain understanding, potentially requiring customized dictionaries. Inconsistent data update frequencies mean the workflow's ingestion stage needs to support various trigger mechanisms, such as scheduled tasks and event-driven triggers. Complex logic involved in clinical trial pre-screening, such as patient inclusion/exclusion criteria comparison, drug interaction analysis, and multi-dimensional risk assessment, requires workflow orchestration to support complex conditional branching, parallel processing, and sub-workflow calls. Furthermore, high requirements for data accuracy and traceability necessitate clear logging for every operation within the workflow, ensuring result traceability.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for this Value
`maxContext`	`2048`	Medical documents have a large amount of contextual information. Ensure the model can cover key information.
`Recall count` (Recall Count)	`Top 10 entries` (Top 10)	Pre-screening logic is complex. Increasing recall improves coverage of relevant information and reduces the risk of omissions.
`Similarity threshold` (Similarity Threshold)	`0.75`	Clinical terminology requires high precision. A threshold that is too low may introduce irrelevant information, while one that is too high may lead to omissions.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (600 seconds)	Parsing large Investigator's Brochures or multiple literature pieces can take a long time.
`Chunk size` (Segment Length)	`800–1200 characters` (800–1200 characters)	Balances semantic completeness with model processing efficiency. Avoids losing critical context after splitting long texts.
`Rerank result count` (Rerank Return Count)	`Top 5 entries` (Top 5)	Refines results based on initial recall, improving the precision and relevance of the final output.

Three Common Pitfalls

Workflow execution timeout, with logs showing Execution Timeout. This usually happens when processing large PDF documents or complex knowledge base queries, where a single node's processing time exceeds the preset timeout parameter.
Omission of critical patient inclusion/exclusion criteria in pre-screening results. The returned summary or judgment is incomplete. This occurs because the Recall count (Recall Count) is set too low, or the knowledge base segmentation strategy is inappropriate, preventing the model from acquiring relevant information.
Incorrect triggering of target workflows or parameter passing errors during multi-workflow calls. Logs show Workflow Not Found or Parameter Mismatch. This happens when the workflowId or parameter mapping rules for sub-workflows are not correctly configured in the main workflow.

How to Confirm Proper Configuration

Select a test document containing typical complex medical terminology and clinical pathways. Run the workflow. Check if the output pre-screening results accurately identify all inclusion/exclusion criteria and correctly summarize key information.
Simulate data update events, such as adding new clinical trial data to the knowledge base. Observe if the workflow triggers correctly and updates its internal index or judgment logic, ensuring the ingestion module functions properly.
In the workflow orchestration interface, check if all conditional branches and parallel nodes execute according to the expected logic. Pay particular attention to judgments involving multiple combined conditions, such as patient age and disease stage.
Review workflow execution logs. Confirm that all critical steps, such as document parsing, knowledge recall, and model inference, complete within the set timeout period and without error codes such as 400 Bad Request or 500 Internal Server Error.

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.